diff --git a/HRV_Output/Annotation/FOE0213.hea b/HRV_Output/Annotation/FOE0213.hea new file mode 100644 index 0000000..48aec88 --- /dev/null +++ b/HRV_Output/Annotation/FOE0213.hea @@ -0,0 +1,3 @@ +HRV_Output\Annotation\FOE0213 1 500 17181500 +HRV_Output\Annotation\FOE0213.jqrs 16+24 1/mV 12 +#Creator: HRV_toolbox write_hea.m \ No newline at end of file diff --git a/HRV_Output/Annotation/FOE0213.jqrs b/HRV_Output/Annotation/FOE0213.jqrs new file mode 100644 index 0000000..55f48c3 Binary files /dev/null and b/HRV_Output/Annotation/FOE0213.jqrs differ diff --git a/HRV_Output/Annotation/FOE0213.sqijs b/HRV_Output/Annotation/FOE0213.sqijs new file mode 100644 index 0000000..8e5a3c2 Binary files /dev/null and b/HRV_Output/Annotation/FOE0213.sqijs differ diff --git a/HRV_Output/Annotation/FOE0213.sqijw b/HRV_Output/Annotation/FOE0213.sqijw new file mode 100644 index 0000000..b85e57b Binary files /dev/null and b/HRV_Output/Annotation/FOE0213.sqijw differ diff --git a/HRV_Output/Annotation/FOE0213.sqrs b/HRV_Output/Annotation/FOE0213.sqrs new file mode 100644 index 0000000..d136d7c Binary files /dev/null and b/HRV_Output/Annotation/FOE0213.sqrs differ diff --git a/HRV_Output/Annotation/FOE0213.wqrs b/HRV_Output/Annotation/FOE0213.wqrs new file mode 100644 index 0000000..dcf1d25 Binary files /dev/null and b/HRV_Output/Annotation/FOE0213.wqrs differ diff --git a/HRV_Output/Annotation/FOK0204.hea b/HRV_Output/Annotation/FOK0204.hea new file mode 100644 index 0000000..a6ca638 --- /dev/null +++ b/HRV_Output/Annotation/FOK0204.hea @@ -0,0 +1,3 @@ +HRV_Output\Annotation\FOK0204 1 500 32582000 +HRV_Output\Annotation\FOK0204.jqrs 16+24 1/mV 12 +#Creator: HRV_toolbox write_hea.m \ No newline at end of file diff --git a/HRV_Output/Annotation/FOK0204.jqrs b/HRV_Output/Annotation/FOK0204.jqrs new file mode 100644 index 0000000..15f431f Binary files /dev/null and b/HRV_Output/Annotation/FOK0204.jqrs differ diff --git a/HRV_Output/Annotation/FOK0204.sqijs b/HRV_Output/Annotation/FOK0204.sqijs new file mode 100644 index 0000000..cb039eb Binary files /dev/null and b/HRV_Output/Annotation/FOK0204.sqijs differ diff --git a/HRV_Output/Annotation/FOK0204.sqijw b/HRV_Output/Annotation/FOK0204.sqijw new file mode 100644 index 0000000..c241195 Binary files /dev/null and b/HRV_Output/Annotation/FOK0204.sqijw differ diff --git a/HRV_Output/Annotation/FOK0204.sqrs b/HRV_Output/Annotation/FOK0204.sqrs new file mode 100644 index 0000000..a667a58 Binary files /dev/null and b/HRV_Output/Annotation/FOK0204.sqrs differ diff --git a/HRV_Output/Annotation/FOK0204.wqrs b/HRV_Output/Annotation/FOK0204.wqrs new file mode 100644 index 0000000..ac0151c Binary files /dev/null and b/HRV_Output/Annotation/FOK0204.wqrs differ diff --git a/HRV_Output/Annotation/FOK0207.hea b/HRV_Output/Annotation/FOK0207.hea new file mode 100644 index 0000000..c7f13ff --- /dev/null +++ b/HRV_Output/Annotation/FOK0207.hea @@ -0,0 +1,3 @@ +HRV_Output\Annotation\FOK0207 1 500 16280000 +HRV_Output\Annotation\FOK0207.jqrs 16+24 1/mV 12 +#Creator: HRV_toolbox write_hea.m \ No newline at end of file diff --git a/HRV_Output/Annotation/FOK0207.jqrs b/HRV_Output/Annotation/FOK0207.jqrs new file mode 100644 index 0000000..6c557d4 Binary files /dev/null and b/HRV_Output/Annotation/FOK0207.jqrs differ diff --git a/HRV_Output/Annotation/FOK0207.sqijs b/HRV_Output/Annotation/FOK0207.sqijs new file mode 100644 index 0000000..d5835ee Binary files /dev/null and b/HRV_Output/Annotation/FOK0207.sqijs differ diff --git a/HRV_Output/Annotation/FOK0207.sqijw b/HRV_Output/Annotation/FOK0207.sqijw new file mode 100644 index 0000000..8fa6172 Binary files /dev/null and b/HRV_Output/Annotation/FOK0207.sqijw differ diff --git a/HRV_Output/Annotation/FOK0207.sqrs b/HRV_Output/Annotation/FOK0207.sqrs new file mode 100644 index 0000000..60ed67d Binary files /dev/null and b/HRV_Output/Annotation/FOK0207.sqrs differ diff --git a/HRV_Output/Annotation/FOK0207.wqrs b/HRV_Output/Annotation/FOK0207.wqrs new file mode 100644 index 0000000..d0333f4 Binary files /dev/null and b/HRV_Output/Annotation/FOK0207.wqrs differ diff --git a/HRV_Output/ParametersTable20220802_ActigraphyECG.csv b/HRV_Output/ParametersTable20220802_ActigraphyECG.csv new file mode 100644 index 0000000..9e80d47 --- /dev/null +++ b/HRV_Output/ParametersTable20220802_ActigraphyECG.csv @@ -0,0 +1,100 @@ +Tab1,Tab2 +Fs,NaN +writedata,HRV Output +data confidence level,1 +windowlength,300 +increment,30 +numsegs,5 +RejectionThreshold,0.2 +MissingDataThreshold,0.15 +rawsig,0 +debug,0 +sqi LowQualityThreshold,0.9 +sqi windowlength,10 +sqi increment,1 +sqi TimeThreshold,0.1 +sqi margin,2 +preprocess figures,0 +preprocess gaplimit,2 +preprocess per limit,0.2 +preprocess forward gap,3 +preprocess method outliers,rem +preprocess lowerphysiolim,0.375 +preprocess upperphysiolim,2 +preprocess method unphysio,rem +preprocess threshold1,0.9 +preprocess minlength,30 +af on,1 +af windowlength,30 +af increment,30 +PVC qrsth,0.1 +timedomain on,1 +timedomain dataoutput,0 +timedomain alpha,50 +timedomain win tol,0.15 +ulf, 0-0.0033 +vlf,0.0033- 0.04 +lf, 0.04- 0.15 +hf, 0.15- 0.4 +freq on,1 +freq limits,[] +freq zero mean,1 +freq method,lomb +freq plot on,0 +freq debug sine,0 +freq debug freq,0.15 +freq debug weight,0.03 +freq normalize lomb,0 +freq burg poles,15 +freq resampling freq,7 +freq resample interp method,cub +freq resampled burg poles,100 +sd on,1 +sd segmentlength,300 +prsa on,1 +prsa win length,30 +prsa thresh per,20 +prsa plot results,0 +prsa scale,2 +prsa min anch,20 +PeakDetect REF PERIOD,0.25 +PeakDetect THRES,0.6 +PeakDetect fid vec,[] +PeakDetect SIGN FORCE,[] +PeakDetect debug,0 +PeakDetect ecgType,MECG +PeakDetect windows,15 +MSE on,1 +MSE windowlength,[] +MSE increment,[] +MSE RadiusOfSimilarity,0.15 +MSE patternLength,2 +MSE maxCoarseGrainings,20 +MSE method,fir +MSE moment,mean +MSE constant r,1 +Entropy on,1 +Entropy RadiusOfSimilarity,0.15 +Entropy patternLength,2 +DFA on,1 +DFA windowlength,[] +DFA increment,[] +DFA minBoxSize,4 +DFA maxBoxSize,[] +DFA midBoxSize,16 +poincare on,1 +HRT on,1 +HRT BeatsBefore,2 +HRT BeatsAfter,16 +HRT GraphOn,0 +HRT windowlength,24 +HRT increment,24 +HRT filterMethod,mean5before +gen figs,0 +save figs,0 +output format,csv +output separate,1 +output num win,[] +output ann format,binary +time,20220802 +filename,20220802 ActigraphyECG diff --git a/HRV_Output/ParametersTable20220802_ActigraphyECG.tex b/HRV_Output/ParametersTable20220802_ActigraphyECG.tex new file mode 100644 index 0000000..6c9d1ea --- /dev/null +++ b/HRV_Output/ParametersTable20220802_ActigraphyECG.tex @@ -0,0 +1,102 @@ +\begin{tabular}{|l|l|} +\hline +Fs&NaN\\\hline +writedata&HRV Output\\\hline +data confidence level&1\\\hline +windowlength&300\\\hline +increment&30\\\hline +numsegs&5\\\hline +RejectionThreshold&0.2\\\hline +MissingDataThreshold&0.15\\\hline +rawsig&0\\\hline +debug&0\\\hline +sqi LowQualityThreshold&0.9\\\hline +sqi windowlength&10\\\hline +sqi increment&1\\\hline +sqi TimeThreshold&0.1\\\hline +sqi margin&2\\\hline +preprocess figures&0\\\hline +preprocess gaplimit&2\\\hline +preprocess per limit&0.2\\\hline +preprocess forward gap&3\\\hline +preprocess method outliers&rem\\\hline +preprocess lowerphysiolim&0.375\\\hline +preprocess upperphysiolim&2\\\hline +preprocess method unphysio&rem\\\hline +preprocess threshold1&0.9\\\hline +preprocess minlength&30\\\hline +af on&1\\\hline +af windowlength&30\\\hline +af increment&30\\\hline +PVC qrsth&0.1\\\hline +timedomain on&1\\\hline +timedomain dataoutput&0\\\hline +timedomain alpha&50\\\hline +timedomain win tol&0.15\\\hline +ulf& 0-0.0033\\\hline +vlf&0.0033- 0.04\\\hline +lf& 0.04- 0.15\\\hline +hf& 0.15- 0.4\\\hline +freq on&1\\\hline +freq limits&[]\\\hline +freq zero mean&1\\\hline +freq method&lomb\\\hline +freq plot on&0\\\hline +freq debug sine&0\\\hline +freq debug freq&0.15\\\hline +freq debug weight&0.03\\\hline +freq normalize lomb&0\\\hline +freq burg poles&15\\\hline +freq resampling freq&7\\\hline +freq resample interp method&cub\\\hline +freq resampled burg poles&100\\\hline +sd on&1\\\hline +sd segmentlength&300\\\hline +prsa on&1\\\hline +prsa win length&30\\\hline +prsa thresh per&20\\\hline +prsa plot results&0\\\hline +prsa scale&2\\\hline +prsa min anch&20\\\hline +PeakDetect REF PERIOD&0.25\\\hline +PeakDetect THRES&0.6\\\hline +PeakDetect fid vec&[]\\\hline +PeakDetect SIGN FORCE&[]\\\hline +PeakDetect debug&0\\\hline +PeakDetect ecgType&MECG\\\hline +PeakDetect windows&15\\\hline +MSE on&1\\\hline +MSE windowlength&[]\\\hline +MSE increment&[]\\\hline +MSE RadiusOfSimilarity&0.15\\\hline +MSE patternLength&2\\\hline +MSE maxCoarseGrainings&20\\\hline +MSE method&fir\\\hline +MSE moment&mean\\\hline +MSE constant r&1\\\hline +Entropy on&1\\\hline +Entropy RadiusOfSimilarity&0.15\\\hline +Entropy patternLength&2\\\hline +DFA on&1\\\hline +DFA windowlength&[]\\\hline +DFA increment&[]\\\hline +DFA minBoxSize&4\\\hline +DFA maxBoxSize&[]\\\hline +DFA midBoxSize&16\\\hline +poincare on&1\\\hline +HRT on&1\\\hline +HRT BeatsBefore&2\\\hline +HRT BeatsAfter&16\\\hline +HRT GraphOn&0\\\hline +HRT windowlength&24\\\hline +HRT increment&24\\\hline +HRT filterMethod&mean5before\\\hline +gen figs&0\\\hline +save figs&0\\\hline +output format&csv\\\hline +output separate&1\\\hline +output num win&[]\\\hline +output ann format&binary\\\hline +time&20220802\\\hline +filename&20220802 ActigraphyECG\\\hline +\end{tabular} diff --git a/HRV_Output/ParametersTable20220817_ActigraphyECG.csv b/HRV_Output/ParametersTable20220817_ActigraphyECG.csv new file mode 100644 index 0000000..bedb632 --- /dev/null +++ b/HRV_Output/ParametersTable20220817_ActigraphyECG.csv @@ -0,0 +1,100 @@ +Tab1,Tab2 +Fs,NaN +writedata,HRV Output +data confidence level,1 +windowlength,300 +increment,30 +numsegs,5 +RejectionThreshold,0.2 +MissingDataThreshold,0.15 +rawsig,0 +debug,0 +sqi LowQualityThreshold,0.9 +sqi windowlength,10 +sqi increment,1 +sqi TimeThreshold,0.1 +sqi margin,2 +preprocess figures,0 +preprocess gaplimit,2 +preprocess per limit,0.2 +preprocess forward gap,3 +preprocess method outliers,rem +preprocess lowerphysiolim,0.375 +preprocess upperphysiolim,2 +preprocess method unphysio,rem +preprocess threshold1,0.9 +preprocess minlength,30 +af on,1 +af windowlength,30 +af increment,30 +PVC qrsth,0.1 +timedomain on,1 +timedomain dataoutput,0 +timedomain alpha,50 +timedomain win tol,0.15 +ulf, 0-0.0033 +vlf,0.0033- 0.04 +lf, 0.04- 0.15 +hf, 0.15- 0.4 +freq on,1 +freq limits,[] +freq zero mean,1 +freq method,lomb +freq plot on,0 +freq debug sine,0 +freq debug freq,0.15 +freq debug weight,0.03 +freq normalize lomb,0 +freq burg poles,15 +freq resampling freq,7 +freq resample interp method,cub +freq resampled burg poles,100 +sd on,1 +sd segmentlength,300 +prsa on,1 +prsa win length,30 +prsa thresh per,20 +prsa plot results,0 +prsa scale,2 +prsa min anch,20 +PeakDetect REF PERIOD,0.25 +PeakDetect THRES,0.6 +PeakDetect fid vec,[] +PeakDetect SIGN FORCE,[] +PeakDetect debug,0 +PeakDetect ecgType,MECG +PeakDetect windows,15 +MSE on,1 +MSE windowlength,[] +MSE increment,[] +MSE RadiusOfSimilarity,0.15 +MSE patternLength,2 +MSE maxCoarseGrainings,20 +MSE method,fir +MSE moment,mean +MSE constant r,1 +Entropy on,1 +Entropy RadiusOfSimilarity,0.15 +Entropy patternLength,2 +DFA on,1 +DFA windowlength,[] +DFA increment,[] +DFA minBoxSize,4 +DFA maxBoxSize,[] +DFA midBoxSize,16 +poincare on,1 +HRT on,1 +HRT BeatsBefore,2 +HRT BeatsAfter,16 +HRT GraphOn,0 +HRT windowlength,24 +HRT increment,24 +HRT filterMethod,mean5before +gen figs,0 +save figs,0 +output format,csv +output separate,1 +output num win,[] +output ann format,binary +time,20220817 +filename,20220817 ActigraphyECG diff --git a/HRV_Output/ParametersTable20220817_ActigraphyECG.tex b/HRV_Output/ParametersTable20220817_ActigraphyECG.tex new file mode 100644 index 0000000..1798d1e --- /dev/null +++ b/HRV_Output/ParametersTable20220817_ActigraphyECG.tex @@ -0,0 +1,102 @@ +\begin{tabular}{|l|l|} +\hline +Fs&NaN\\\hline +writedata&HRV Output\\\hline +data confidence level&1\\\hline +windowlength&300\\\hline +increment&30\\\hline +numsegs&5\\\hline +RejectionThreshold&0.2\\\hline +MissingDataThreshold&0.15\\\hline +rawsig&0\\\hline +debug&0\\\hline +sqi LowQualityThreshold&0.9\\\hline +sqi windowlength&10\\\hline +sqi increment&1\\\hline +sqi TimeThreshold&0.1\\\hline +sqi margin&2\\\hline +preprocess figures&0\\\hline +preprocess gaplimit&2\\\hline +preprocess per limit&0.2\\\hline +preprocess forward gap&3\\\hline +preprocess method outliers&rem\\\hline +preprocess lowerphysiolim&0.375\\\hline +preprocess upperphysiolim&2\\\hline +preprocess method unphysio&rem\\\hline +preprocess threshold1&0.9\\\hline +preprocess minlength&30\\\hline +af on&1\\\hline +af windowlength&30\\\hline +af increment&30\\\hline +PVC qrsth&0.1\\\hline +timedomain on&1\\\hline +timedomain dataoutput&0\\\hline +timedomain alpha&50\\\hline +timedomain win tol&0.15\\\hline +ulf& 0-0.0033\\\hline +vlf&0.0033- 0.04\\\hline +lf& 0.04- 0.15\\\hline +hf& 0.15- 0.4\\\hline +freq on&1\\\hline +freq limits&[]\\\hline +freq zero mean&1\\\hline +freq method&lomb\\\hline +freq plot on&0\\\hline +freq debug sine&0\\\hline +freq debug freq&0.15\\\hline +freq debug weight&0.03\\\hline +freq normalize lomb&0\\\hline +freq burg poles&15\\\hline +freq resampling freq&7\\\hline +freq resample interp method&cub\\\hline +freq resampled burg poles&100\\\hline +sd on&1\\\hline +sd segmentlength&300\\\hline +prsa on&1\\\hline +prsa win length&30\\\hline +prsa thresh per&20\\\hline +prsa plot results&0\\\hline +prsa scale&2\\\hline +prsa min anch&20\\\hline +PeakDetect REF PERIOD&0.25\\\hline +PeakDetect THRES&0.6\\\hline +PeakDetect fid vec&[]\\\hline +PeakDetect SIGN FORCE&[]\\\hline +PeakDetect debug&0\\\hline +PeakDetect ecgType&MECG\\\hline +PeakDetect windows&15\\\hline +MSE on&1\\\hline +MSE windowlength&[]\\\hline +MSE increment&[]\\\hline +MSE RadiusOfSimilarity&0.15\\\hline +MSE patternLength&2\\\hline +MSE maxCoarseGrainings&20\\\hline +MSE method&fir\\\hline +MSE moment&mean\\\hline +MSE constant r&1\\\hline +Entropy on&1\\\hline +Entropy RadiusOfSimilarity&0.15\\\hline +Entropy patternLength&2\\\hline +DFA on&1\\\hline +DFA windowlength&[]\\\hline +DFA increment&[]\\\hline +DFA minBoxSize&4\\\hline +DFA maxBoxSize&[]\\\hline +DFA midBoxSize&16\\\hline +poincare on&1\\\hline +HRT on&1\\\hline +HRT BeatsBefore&2\\\hline +HRT BeatsAfter&16\\\hline +HRT GraphOn&0\\\hline +HRT windowlength&24\\\hline +HRT increment&24\\\hline +HRT filterMethod&mean5before\\\hline +gen figs&0\\\hline +save figs&0\\\hline +output format&csv\\\hline +output separate&1\\\hline +output num win&[]\\\hline +output ann format&binary\\\hline +time&20220817\\\hline +filename&20220817 ActigraphyECG\\\hline +\end{tabular} diff --git a/HRV_Output/ParametersTable20220823_ActigraphyECG.csv b/HRV_Output/ParametersTable20220823_ActigraphyECG.csv new file mode 100644 index 0000000..4695c14 --- /dev/null +++ b/HRV_Output/ParametersTable20220823_ActigraphyECG.csv @@ -0,0 +1,100 @@ +Tab1,Tab2 +Fs,NaN +writedata,HRV Output +data confidence level,1 +windowlength,300 +increment,30 +numsegs,5 +RejectionThreshold,0.2 +MissingDataThreshold,0.15 +rawsig,0 +debug,0 +sqi LowQualityThreshold,0.9 +sqi windowlength,10 +sqi increment,1 +sqi TimeThreshold,0.1 +sqi margin,2 +preprocess figures,0 +preprocess gaplimit,2 +preprocess per limit,0.2 +preprocess forward gap,3 +preprocess method outliers,rem +preprocess lowerphysiolim,0.375 +preprocess upperphysiolim,2 +preprocess method unphysio,rem +preprocess threshold1,0.9 +preprocess minlength,30 +af on,1 +af windowlength,30 +af increment,30 +PVC qrsth,0.1 +timedomain on,1 +timedomain dataoutput,0 +timedomain alpha,50 +timedomain win tol,0.15 +ulf, 0-0.0033 +vlf,0.0033- 0.04 +lf, 0.04- 0.15 +hf, 0.15- 0.4 +freq on,1 +freq limits,[] +freq zero mean,1 +freq method,lomb +freq plot on,0 +freq debug sine,0 +freq debug freq,0.15 +freq debug weight,0.03 +freq normalize lomb,0 +freq burg poles,15 +freq resampling freq,7 +freq resample interp method,cub +freq resampled burg poles,100 +sd on,1 +sd segmentlength,300 +prsa on,1 +prsa win length,30 +prsa thresh per,20 +prsa plot results,0 +prsa scale,2 +prsa min anch,20 +PeakDetect REF PERIOD,0.25 +PeakDetect THRES,0.6 +PeakDetect fid vec,[] +PeakDetect SIGN FORCE,[] +PeakDetect debug,0 +PeakDetect ecgType,MECG +PeakDetect windows,15 +MSE on,1 +MSE windowlength,[] +MSE increment,[] +MSE RadiusOfSimilarity,0.15 +MSE patternLength,2 +MSE maxCoarseGrainings,20 +MSE method,fir +MSE moment,mean +MSE constant r,1 +Entropy on,1 +Entropy RadiusOfSimilarity,0.15 +Entropy patternLength,2 +DFA on,1 +DFA windowlength,[] +DFA increment,[] +DFA minBoxSize,4 +DFA maxBoxSize,[] +DFA midBoxSize,16 +poincare on,1 +HRT on,1 +HRT BeatsBefore,2 +HRT BeatsAfter,16 +HRT GraphOn,0 +HRT windowlength,24 +HRT increment,24 +HRT filterMethod,mean5before +gen figs,0 +save figs,0 +output format,csv +output separate,1 +output num win,[] +output ann format,binary +time,20220823 +filename,20220823 ActigraphyECG diff --git a/HRV_Output/ParametersTable20220823_ActigraphyECG.tex b/HRV_Output/ParametersTable20220823_ActigraphyECG.tex new file mode 100644 index 0000000..4e23663 --- /dev/null +++ b/HRV_Output/ParametersTable20220823_ActigraphyECG.tex @@ -0,0 +1,102 @@ +\begin{tabular}{|l|l|} +\hline +Fs&NaN\\\hline +writedata&HRV Output\\\hline +data confidence level&1\\\hline +windowlength&300\\\hline +increment&30\\\hline +numsegs&5\\\hline +RejectionThreshold&0.2\\\hline +MissingDataThreshold&0.15\\\hline +rawsig&0\\\hline +debug&0\\\hline +sqi LowQualityThreshold&0.9\\\hline +sqi windowlength&10\\\hline +sqi increment&1\\\hline +sqi TimeThreshold&0.1\\\hline +sqi margin&2\\\hline +preprocess figures&0\\\hline +preprocess gaplimit&2\\\hline +preprocess per limit&0.2\\\hline +preprocess forward gap&3\\\hline +preprocess method outliers&rem\\\hline +preprocess lowerphysiolim&0.375\\\hline +preprocess upperphysiolim&2\\\hline +preprocess method unphysio&rem\\\hline +preprocess threshold1&0.9\\\hline +preprocess minlength&30\\\hline +af on&1\\\hline +af windowlength&30\\\hline +af increment&30\\\hline +PVC qrsth&0.1\\\hline +timedomain on&1\\\hline +timedomain dataoutput&0\\\hline +timedomain alpha&50\\\hline +timedomain win tol&0.15\\\hline +ulf& 0-0.0033\\\hline +vlf&0.0033- 0.04\\\hline +lf& 0.04- 0.15\\\hline +hf& 0.15- 0.4\\\hline +freq on&1\\\hline +freq limits&[]\\\hline +freq zero mean&1\\\hline +freq method&lomb\\\hline +freq plot on&0\\\hline +freq debug sine&0\\\hline +freq debug freq&0.15\\\hline +freq debug weight&0.03\\\hline +freq normalize lomb&0\\\hline +freq burg poles&15\\\hline +freq resampling freq&7\\\hline +freq resample interp method&cub\\\hline +freq resampled burg poles&100\\\hline +sd on&1\\\hline +sd segmentlength&300\\\hline +prsa on&1\\\hline +prsa win length&30\\\hline +prsa thresh per&20\\\hline +prsa plot results&0\\\hline +prsa scale&2\\\hline +prsa min anch&20\\\hline +PeakDetect REF PERIOD&0.25\\\hline +PeakDetect THRES&0.6\\\hline +PeakDetect fid vec&[]\\\hline +PeakDetect SIGN FORCE&[]\\\hline +PeakDetect debug&0\\\hline +PeakDetect ecgType&MECG\\\hline +PeakDetect windows&15\\\hline +MSE on&1\\\hline +MSE windowlength&[]\\\hline +MSE increment&[]\\\hline +MSE RadiusOfSimilarity&0.15\\\hline +MSE patternLength&2\\\hline +MSE maxCoarseGrainings&20\\\hline +MSE method&fir\\\hline +MSE moment&mean\\\hline +MSE constant r&1\\\hline +Entropy on&1\\\hline +Entropy RadiusOfSimilarity&0.15\\\hline +Entropy patternLength&2\\\hline +DFA on&1\\\hline +DFA windowlength&[]\\\hline +DFA increment&[]\\\hline +DFA minBoxSize&4\\\hline +DFA maxBoxSize&[]\\\hline +DFA midBoxSize&16\\\hline +poincare on&1\\\hline +HRT on&1\\\hline +HRT BeatsBefore&2\\\hline +HRT BeatsAfter&16\\\hline +HRT GraphOn&0\\\hline +HRT windowlength&24\\\hline +HRT increment&24\\\hline +HRT filterMethod&mean5before\\\hline +gen figs&0\\\hline +save figs&0\\\hline +output format&csv\\\hline +output separate&1\\\hline +output num win&[]\\\hline +output ann format&binary\\\hline +time&20220823\\\hline +filename&20220823 ActigraphyECG\\\hline +\end{tabular} diff --git a/HRV_Output/ParametersTable20220824_ActigraphyECG.csv b/HRV_Output/ParametersTable20220824_ActigraphyECG.csv new file mode 100644 index 0000000..fd81db2 --- /dev/null +++ b/HRV_Output/ParametersTable20220824_ActigraphyECG.csv @@ -0,0 +1,100 @@ +Tab1,Tab2 +Fs,NaN +writedata,HRV Output +data confidence level,1 +windowlength,300 +increment,30 +numsegs,5 +RejectionThreshold,0.2 +MissingDataThreshold,0.15 +rawsig,0 +debug,0 +sqi LowQualityThreshold,0.9 +sqi windowlength,10 +sqi increment,1 +sqi TimeThreshold,0.1 +sqi margin,2 +preprocess figures,0 +preprocess gaplimit,2 +preprocess per limit,0.2 +preprocess forward gap,3 +preprocess method outliers,rem +preprocess lowerphysiolim,0.375 +preprocess upperphysiolim,2 +preprocess method unphysio,rem +preprocess threshold1,0.9 +preprocess minlength,30 +af on,1 +af windowlength,30 +af increment,30 +PVC qrsth,0.1 +timedomain on,1 +timedomain dataoutput,0 +timedomain alpha,50 +timedomain win tol,0.15 +ulf, 0-0.0033 +vlf,0.0033- 0.04 +lf, 0.04- 0.15 +hf, 0.15- 0.4 +freq on,1 +freq limits,[] +freq zero mean,1 +freq method,lomb +freq plot on,0 +freq debug sine,0 +freq debug freq,0.15 +freq debug weight,0.03 +freq normalize lomb,0 +freq burg poles,15 +freq resampling freq,7 +freq resample interp method,cub +freq resampled burg poles,100 +sd on,1 +sd segmentlength,300 +prsa on,1 +prsa win length,30 +prsa thresh per,20 +prsa plot results,0 +prsa scale,2 +prsa min anch,20 +PeakDetect REF PERIOD,0.25 +PeakDetect THRES,0.6 +PeakDetect fid vec,[] +PeakDetect SIGN FORCE,[] +PeakDetect debug,0 +PeakDetect ecgType,MECG +PeakDetect windows,15 +MSE on,1 +MSE windowlength,[] +MSE increment,[] +MSE RadiusOfSimilarity,0.15 +MSE patternLength,2 +MSE maxCoarseGrainings,20 +MSE method,fir +MSE moment,mean +MSE constant r,1 +Entropy on,1 +Entropy RadiusOfSimilarity,0.15 +Entropy patternLength,2 +DFA on,1 +DFA windowlength,[] +DFA increment,[] +DFA minBoxSize,4 +DFA maxBoxSize,[] +DFA midBoxSize,16 +poincare on,1 +HRT on,1 +HRT BeatsBefore,2 +HRT BeatsAfter,16 +HRT GraphOn,0 +HRT windowlength,24 +HRT increment,24 +HRT filterMethod,mean5before +gen figs,0 +save figs,0 +output format,csv +output separate,1 +output num win,[] +output ann format,binary +time,20220824 +filename,20220824 ActigraphyECG diff --git a/HRV_Output/ParametersTable20220824_ActigraphyECG.tex b/HRV_Output/ParametersTable20220824_ActigraphyECG.tex new file mode 100644 index 0000000..8c6dc4f --- /dev/null +++ b/HRV_Output/ParametersTable20220824_ActigraphyECG.tex @@ -0,0 +1,102 @@ +\begin{tabular}{|l|l|} +\hline +Fs&NaN\\\hline +writedata&HRV Output\\\hline +data confidence level&1\\\hline +windowlength&300\\\hline +increment&30\\\hline +numsegs&5\\\hline +RejectionThreshold&0.2\\\hline +MissingDataThreshold&0.15\\\hline +rawsig&0\\\hline +debug&0\\\hline +sqi LowQualityThreshold&0.9\\\hline +sqi windowlength&10\\\hline +sqi increment&1\\\hline +sqi TimeThreshold&0.1\\\hline +sqi margin&2\\\hline +preprocess figures&0\\\hline +preprocess gaplimit&2\\\hline +preprocess per limit&0.2\\\hline +preprocess forward gap&3\\\hline +preprocess method outliers&rem\\\hline +preprocess lowerphysiolim&0.375\\\hline +preprocess upperphysiolim&2\\\hline +preprocess method unphysio&rem\\\hline +preprocess threshold1&0.9\\\hline +preprocess minlength&30\\\hline +af on&1\\\hline +af windowlength&30\\\hline +af increment&30\\\hline +PVC qrsth&0.1\\\hline +timedomain on&1\\\hline +timedomain dataoutput&0\\\hline +timedomain alpha&50\\\hline +timedomain win tol&0.15\\\hline +ulf& 0-0.0033\\\hline +vlf&0.0033- 0.04\\\hline +lf& 0.04- 0.15\\\hline +hf& 0.15- 0.4\\\hline +freq on&1\\\hline +freq limits&[]\\\hline +freq zero mean&1\\\hline +freq method&lomb\\\hline +freq plot on&0\\\hline +freq debug sine&0\\\hline +freq debug freq&0.15\\\hline +freq debug weight&0.03\\\hline +freq normalize lomb&0\\\hline +freq burg poles&15\\\hline +freq resampling freq&7\\\hline +freq resample interp method&cub\\\hline +freq resampled burg poles&100\\\hline +sd on&1\\\hline +sd segmentlength&300\\\hline +prsa on&1\\\hline +prsa win length&30\\\hline +prsa thresh per&20\\\hline +prsa plot results&0\\\hline +prsa scale&2\\\hline +prsa min anch&20\\\hline +PeakDetect REF PERIOD&0.25\\\hline +PeakDetect THRES&0.6\\\hline +PeakDetect fid vec&[]\\\hline +PeakDetect SIGN FORCE&[]\\\hline +PeakDetect debug&0\\\hline +PeakDetect ecgType&MECG\\\hline +PeakDetect windows&15\\\hline +MSE on&1\\\hline +MSE windowlength&[]\\\hline +MSE increment&[]\\\hline +MSE RadiusOfSimilarity&0.15\\\hline +MSE patternLength&2\\\hline +MSE maxCoarseGrainings&20\\\hline +MSE method&fir\\\hline +MSE moment&mean\\\hline +MSE constant r&1\\\hline +Entropy on&1\\\hline +Entropy RadiusOfSimilarity&0.15\\\hline +Entropy patternLength&2\\\hline +DFA on&1\\\hline +DFA windowlength&[]\\\hline +DFA increment&[]\\\hline +DFA minBoxSize&4\\\hline +DFA maxBoxSize&[]\\\hline +DFA midBoxSize&16\\\hline +poincare on&1\\\hline +HRT on&1\\\hline +HRT BeatsBefore&2\\\hline +HRT BeatsAfter&16\\\hline +HRT GraphOn&0\\\hline +HRT windowlength&24\\\hline +HRT increment&24\\\hline +HRT filterMethod&mean5before\\\hline +gen figs&0\\\hline +save figs&0\\\hline +output format&csv\\\hline +output separate&1\\\hline +output num win&[]\\\hline +output ann format&binary\\\hline +time&20220824\\\hline +filename&20220824 ActigraphyECG\\\hline +\end{tabular} diff --git a/createBecomeFeedback.m b/createBecomeFeedback.m new file mode 100644 index 0000000..bac14e4 --- /dev/null +++ b/createBecomeFeedback.m @@ -0,0 +1,102 @@ +%---------------------------- +% SETUP +%---------------------------- + +clear +close all + +subName = 'FOK0208'; % change subject id here +userName = 'dorothee_poehlchen'; % change your name here + +gitdir = fullfile('C:\Users\',userName,'Documents\GitHub\becomeFeedback'); +basedir = 'O:\Psychophys\Dreem'; +diarydir = fullfile(basedir, 'Actigraphy\Raw\Sleep Diaries'); +actidir = fullfile(basedir, 'Actigraphy\Raw\Clean Acti Data'); +ecgdir = fullfile(basedir, 'Actigraphy\Raw\ECG_RR'); +dreemdir = fullfile(basedir,'data\BeCOME' ); +outdir = fullfile(basedir,'data\BeCOME_feedback' ); +fundir = fullfile(gitdir, 'functions'); +libsdir = fullfile(gitdir, 'libs'); + +addpath(genpath(fundir)); +addpath(genpath(libsdir)); + + +%---------------------------- +% SETUP +%---------------------------- + +timeStep = minutes(10); % downsample data from 30s to 10m timesteps +missingDataThreshold = 6; % missing value if 6 timeSteps at least are 0 +timeCenteredMean = hours(24); % moving mean of 24 hours +sleepBoutThreshold = 3; % equals to 30 min +percTreshold = 15; % everyoriginal datapoint < 15% of moving mean = sleep +sleepGapLength = 3; % correlation gap (finds correlation peak) +centeredBoutMean = hours(4); % last step from correl data to only 1 onset and offset per night --> 4 hour moving mean +meanTreshold = 0.4; % for the moving mean on the binary sleep bouts to smooth them +color.sleepSmoothed = [240/255,128/255,128/255]; %salmon color = night sleep episodes +color.acti = [112/255,128/255,144/255]; % gray = actigraphy raw data +color.sleepCorr = [255/255,215/255,0/255]; % gold = sleep determined by correlation filter --> showing sleep fragmentation +color.ecg = [1/255,0/255,1/255]; %sea green = ecg bpm data + +HRVparams = InitializeHRVparams('ActigraphyECG'); % HRV paraeters for calculation %%% +HRVparams.Fs = 500; % sampling frequency +HRVparams.windowlength = 300; % Default: 300, seconds +HRVparams.increment = 60; % 60 second increments +HRVparams.freq.method = 'fft'; % Default: 'lomb' + +%---------------------------- +% Readout actigraphy and ecg +%---------------------------- + +[diarySleepTable, acti, ecgData] = loadSubjectData(diarydir, actidir, ecgdir, subName); + +if (isempty(ecgData.rr)) && (~isempty(ecgData.rawData)) + ecgData = convertToRR (ecgData, HRVparams, subName,ecgdir); % Convert ECG waveform in rr intervals (if its a new data set) + HRVMetrics = calcHRVvariables (ecgData.rr,ecgData.t,HRVparams,subName,ecgData.hdr,timeStep); +elseif (~isempty(ecgData.rr)) + HRVMetrics = calcHRVvariables (ecgData.rr,ecgData.t,HRVparams,subName,ecgData.hdr,timeStep); +else + HRVMetrics = []; +end + + +%---------------------------- +% Calculate sleep bouts from actigraphy +%---------------------------- + +calcSleepFromActi + + +%---------------------------- +% Plot actigraphy and ecg figures +%---------------------------- + +[figures, ecgFig, sleepECGAll] = ReportFigures(sleepECG,cleanedActi,HRVMetrics); +doublePlot = doubleAreaLinePlot(sleepECGAll); + + + +%---------------------------- +% Get dreem data +%---------------------------- + +% [dreemHypno, dreemReport] = loadSubjectDataDreemSD(dreemdir, subName); % if there is no dreem data COMMENT THIS LINE OUT! +% [allinonePhysSleep] = getPhysiologyDataDreem (dreemReport); +% nightData = synchronize (dreemHypno,allinonePhysSleep,'first','previous'); +% figuresNight = dreemNightFigures(nightData); + + + + +%---------------------------- +% Create the final report +%---------------------------- +createBecomeReport + + +%---------------------------- +% Save report and data +%---------------------------- +mkdir(fullfile(outdir,subName)) +save(fullfile(outdir,subName,[subName,'rpt']),'rpt') diff --git a/functions/+bouts/calcSleepBouts.m b/functions/+bouts/calcSleepBouts.m new file mode 100644 index 0000000..2383478 --- /dev/null +++ b/functions/+bouts/calcSleepBouts.m @@ -0,0 +1,22 @@ +function sleepBouts = calcSleepBouts(actiSleep) +% Define all Onsets and Offsets in the Smoothed Bouts (including naps) +actisleeponstmp = find(diff(actiSleep.(1)) == 1)+1; +actisleepoffstmp = find(diff(actiSleep.(1)) == -1); +actisleeponstmpTime = actiSleep.Time(actisleeponstmp); +actisleepoffstmpTime = actiSleep.Time(actisleepoffstmp); + +for i = 1:length(actisleeponstmp) + indSleepOff = find(actisleepoffstmpTime > actisleeponstmpTime(i),1,'first'); + BoutOn = actisleeponstmpTime(i); + BoutOff = actisleepoffstmpTime(indSleepOff); + if isempty(BoutOff) + BoutOff = NaT; BoutOff.TimeZone = 'Europe/Berlin'; + BoutOn = NaT; BoutOn.TimeZone = 'Europe/Berlin'; + end + sleepBoutstmp(i).ActiSleepOns = BoutOn; + sleepBoutstmp(i).ActiSleepOffs = BoutOff; + sleepBoutstmp(i).MedTime = BoutOn + (BoutOff-BoutOn)/2; +end +sleepBouts = struct2table(sleepBoutstmp); +sleepBouts(isnat(sleepBouts.ActiSleepOns),:) = []; +end \ No newline at end of file diff --git a/functions/+bouts/concatSleepBoutsByDist.m b/functions/+bouts/concatSleepBoutsByDist.m new file mode 100644 index 0000000..f7fefeb --- /dev/null +++ b/functions/+bouts/concatSleepBoutsByDist.m @@ -0,0 +1,7 @@ +function [outputArg1,outputArg2] = concatSleepBoutsByDist(inputArg1,inputArg2) +%CONCATSLEEPBOUTSBYDIST Summary of this function goes here +% Detailed explanation goes here +outputArg1 = inputArg1; +outputArg2 = inputArg2; +end + diff --git a/functions/+bouts/joinActiWithDiarySleep.m b/functions/+bouts/joinActiWithDiarySleep.m new file mode 100644 index 0000000..999455c --- /dev/null +++ b/functions/+bouts/joinActiWithDiarySleep.m @@ -0,0 +1,5 @@ +function sleepBouts = joinActiWithDiarySleep(actiTable,diarySleep) +%% create a table that combines subjective sleep diary information and the actigraphy readouts +diarySleep = diarySleep(~isnat(diarySleep.DayNoon),:); +sleepBouts = outerjoin(actiTable,diarySleep,'MergeKeys',true,'Type','left'); +end \ No newline at end of file diff --git a/functions/+bouts/pickOneBoutPerDay.m b/functions/+bouts/pickOneBoutPerDay.m new file mode 100644 index 0000000..25ba76c --- /dev/null +++ b/functions/+bouts/pickOneBoutPerDay.m @@ -0,0 +1,37 @@ +function actisleepbouts = pickOneBoutPerDay(sleepBouts) +% this makes sure that someone who only slept shortly during lunch but not +% during the night does not have "nightsleep" +% --> excludes people without nocturnal sleep + +startdate = noon(sleepBouts.ActiSleepOns(1)); +enddate = noon(sleepBouts.ActiSleepOns(end)); +numdays = days(enddate - startdate)+1; % add 1 or not? + +% define onsets and offsets per period of interest +% find 3PM of the day before they went to bed +% create a sleep reference time, +% then, the closest sleep bout is chosen for this day + +daytmp = 1; +for daycount = 1:ceil(numdays) + try + dayNoon(daycount) = startdate+caldays(daycount-1); + TimeBoutSleepRef = startdate + hours(15) + days(daytmp-1); + [TimeDiff,IndTimeDiff] = min(abs((sleepBouts.MedTime)-(TimeBoutSleepRef))); + + if TimeDiff > duration(10,00,00) + actisleepons_times(daycount,1) = NaT; actisleepons_times.TimeZone = 'Europe/Berlin'; + actisleepoffs_times(daycount,1)= NaT; actisleepoffs_times.TimeZone = 'Europe/Berlin'; + else + actisleepons_times(daycount,1) = sleepBouts.ActiSleepOns(IndTimeDiff); + actisleepoffs_times(daycount,1) = sleepBouts.ActiSleepOffs(IndTimeDiff); + end + catch + disp('there are no sleep bouts for this subject') + end + daytmp = daytmp + 1; +end + +Weekday = weekday(dayNoon'); +actisleepbouts = table(dayNoon', actisleepons_times,actisleepoffs_times,Weekday, 'VariableNames', {'DayNoon', 'ActiSleepOns','ActiSleepOffs','ActiWeekday'}); +end \ No newline at end of file diff --git a/functions/+dreem/importDreemHypno.m b/functions/+dreem/importDreemHypno.m new file mode 100644 index 0000000..2d401ab --- /dev/null +++ b/functions/+dreem/importDreemHypno.m @@ -0,0 +1,54 @@ +function [hypnogram, header]= importDreemHypno(filename) +%IMPORTFILE Import data from a text file +% B52036092430FB145927B481110C4HYPNOGRAM = IMPORTFILE(FILENAME) reads +% data from text file FILENAME for the default selection. Returns the +% data as a table. +% +% B52036092430FB145927B481110C4HYPNOGRAM = IMPORTFILE(FILE, DATALINES) +% reads data for the specified row interval(s) of text file FILENAME. +% Specify DATALINES as a positive scalar integer or a N-by-2 array of +% positive scalar integers for dis-contiguous row intervals. +% +% Example: +% b52036092430fb145927b481110c4hypnogram = importfile("/Users/blaskovichborbala/Documents/GitHub/Smartwatch-Dreem/DreemHypno/446b5203-6092-430f-b145-927b481110c4_hypnogram.txt", [19, Inf]); +% +% See also READTABLE. +% +% Auto-generated by MATLAB on 29-Apr-2020 12:14:40 + +%% Input handling + +% If dataLines is not specified, define defaults +header = importDreemHypnoHeader(filename); +dataLines = [19, Inf]; + +%% Setup the Import Options and import the data +opts = delimitedTextImportOptions("NumVariables", 4); + +% Specify range and delimiter +opts.DataLines = dataLines; +opts.Delimiter = "\t"; + +% Specify column names and types +opts.VariableNames = ["SleepStage", "TimeNight", "Event", "Durations"]; +opts.VariableTypes = ["string", "datetime", "string", "double"]; + +opts.SelectedVariableNames = ["SleepStage", "TimeNight", "Durations"]; + +% Specify file level properties +opts.ExtraColumnsRule = "ignore"; +opts.EmptyLineRule = "read"; + +% Specify variable properties +%get the day here +opts = setvaropts(opts, "TimeNight", "InputFormat", "HH:mm:ss"); + + +%, {'SLEEP-S0','SLEEP-REM','SLEEP-S1','SLEEP-S2', 'SLEEP-S3', 'SLEEP-MT'} +%specify categorical to ordinal +% Import the data +hypnogram = readtable(filename, opts); + +hypnogram.SleepStage = categorical(hypnogram.SleepStage, {'SLEEP-MT', 'SLEEP-S3','SLEEP-S2','SLEEP-S1','SLEEP-REM','SLEEP-S0'}, 'Ordinal', true); +hypnogram.StageNum = double(hypnogram.SleepStage); +end \ No newline at end of file diff --git a/functions/+dreem/importDreemHypnoHeader.m b/functions/+dreem/importDreemHypnoHeader.m new file mode 100644 index 0000000..85887e3 --- /dev/null +++ b/functions/+dreem/importDreemHypnoHeader.m @@ -0,0 +1,55 @@ +function res = importDreemHypnoHeader(filename) +%IMPORTFILE Import data from a text file +% B52036092430FB145927B481110C4HYPNOGRAMHEADER = IMPORTFILE(FILENAME) +% reads data from text file FILENAME for the default selection. +% Returns the data as a string array. +% +% B52036092430FB145927B481110C4HYPNOGRAMHEADER = IMPORTFILE(FILE, +% DATALINES) reads data for the specified row interval(s) of text file +% FILENAME. Specify DATALINES as a positive scalar integer or a N-by-2 +% array of positive scalar integers for dis-contiguous row intervals. +% +% Example: +% b52036092430fb145927b481110c4hypnogramheader = importfile("/Users/blaskovichborbala/Documents/GitHub/Smartwatch-Dreem/DreemHypno/446b5203-6092-430f-b145-927b481110c4_hypnogram_header.txt", [2, Inf]); +% +% See also READTABLE. +% +% Auto-generated by MATLAB on 29-Apr-2020 12:36:38 + +%% Input handling + +% If dataLines is not specified, define defaults + + dataLines = [2, 16]; + + +%% Setup the Import Options and import the data +opts = delimitedTextImportOptions("NumVariables", 1); + +% Specify range and delimiter +opts.DataLines = dataLines; + +% Specify column names and types +opts.VariableNames = "DreemHypnogramExport"; +opts.VariableTypes = "string"; + +% Specify file level properties +opts.ExtraColumnsRule = "ignore"; +opts.EmptyLineRule = "read"; + +% Specify variable properties +opts = setvaropts(opts, "DreemHypnogramExport", "WhitespaceRule", "preserve"); +opts = setvaropts(opts, "DreemHypnogramExport", "EmptyFieldRule", "auto"); + +% Import the data +hypnoHeader = readmatrix(filename, opts); + +% Transform the data to get out the date +res = struct(); +res.patientID = extractAfter(hypnoHeader(2),": "); +res.recordingDate = datetime(extractAfter(hypnoHeader(3),":"), "InputFormat", "MM/dd/yy"); +res.scorerName = extractAfter(hypnoHeader(14),": "); +res.scorerDate = datetime(extractAfter(hypnoHeader(15),": "), "InputFormat", "MM/dd/yy - HH:mm:ss", 'TimeZone', 'Europe/Berlin'); + + +end \ No newline at end of file diff --git a/functions/+sleepreadouts/circentrain.m b/functions/+sleepreadouts/circentrain.m new file mode 100644 index 0000000..ded959e --- /dev/null +++ b/functions/+sleepreadouts/circentrain.m @@ -0,0 +1,11 @@ +function circEntr = circentrain(sleepReadouts) +% split groups by weekends and weekdays +sleepReadouts.weekend = categorical(sleepReadouts.Weekend); +G = findgroups(sleepReadouts.Weekend); +weekday_weekend_diff = splitapply(@nanmean,sleepReadouts.MidTimeFromDayNoon,G); + +% calculate circadian entrainment +circEntr = repmat(std(sleepReadouts.MidTimeFromDayNoon,'omitnan'),height(sleepReadouts),1); +circEntr = round(minutes(circEntr)); % what is the standard deviation of mid-sleep? in mins +end + diff --git a/functions/+sleepreadouts/nightvars.m b/functions/+sleepreadouts/nightvars.m new file mode 100644 index 0000000..5b36809 --- /dev/null +++ b/functions/+sleepreadouts/nightvars.m @@ -0,0 +1,73 @@ +function sleepReadouts = nightvars(sleepReadouts,sleepBinary, sleepActidata) +% use sleepBouts to calculate further sleep parameters +% 1. step: create timeranges for each sleep period +% 2. iterate over timeranges and calc readout for each + +for i = 1:size(sleepReadouts,1) + sleep_trs = timerange(sleepReadouts.ActiSleepOns(i),sleepReadouts.ActiSleepOffs(i)); + if i == 1 % if is the first day of recording compute day time range from Noon to sleep onset + wake_trs = timerange(sleepReadouts.DayNoon(1),sleepReadouts.ActiSleepOns(1)); + else % else compute day time range from sleep off until next onset + wake_trs = timerange(sleepReadouts.ActiSleepOffs(i-1), sleepReadouts.ActiSleepOns(i)); + end + tmpData = sleepBinary.(1)(sleep_trs); + tmpDayData = sleepBinary.(1)(wake_trs); % Sleep binary for day wake period + tmpActi = sleepActidata.(1)(sleep_trs); + tmpDayActi = sleepActidata.(1)(wake_trs); % actigraphy for day wake period + tmpTime = sleepBinary.Time(sleep_trs); + tmpTimeDay = sleepBinary.Time(wake_trs); + + TST(i,1) = sum(tmpData == 1)*10; % total sleep time in 10 minutes + sleep_midtime(i,1) = median(tmpTime); + MidTimeFromDayNoon(i) = sleep_midtime(i,1) - sleepReadouts.DayNoon(i); + % STST(i)= hours(abs(sleepReadouts.DiarySleepOns(i)-sleepReadouts.DiarySleepOffs(i))); + + if isempty(tmpData) || tmpData(1) == 0 + idx_first_sleep = find(diff(tmpData) == 1,1,'first')+1; % to only look at real awakenings during the night, cut off wake periods that are before the actual sleep onset and sleep offset in the sleep binary + else + idx_first_sleep = 1; + end + + + if isempty(tmpData) || tmpData(end) == 0 + idx_last_sleep = find(diff(tmpData) == -1,1,'last')-1; + else + idx_last_sleep = length(tmpData); + end + + tmpWakeData = tmpData(idx_first_sleep:idx_last_sleep); + awake_number(i,1) = sum(diff(tmpWakeData)== -1); + waso(i,1) = sum(tmpWakeData == 0)*10; % in minutes + meanAwake_length(i,1) = waso(i,1)/(awake_number(i,1)); + sleep_movement(i,1) = nansum(tmpActi(tmpData == 1))/TST(i); + wake_movement(i,1) = nansum(tmpActi(tmpData == 0))/waso(i); + daywake_movement(i,1) = nanmean(tmpDayActi(tmpDayData == 0)); + + if sleepReadouts.ActiWeekday(i) == 6 || sleepReadouts.ActiWeekday(i) == 7 % weekend = days 6,7 + weekend(i,1) = 1; + else + weekend(i,1) = 0; + end +end + +% create sleepReadouts +sleepReadouts.TST = TST; +sleepReadouts.SleepMidtime = sleep_midtime; +sleepReadouts.SleepMove = sleep_movement; +sleepReadouts.WakeNum = awake_number; +sleepReadouts.Weekend = weekend; +sleepReadouts.MidTimeFromDayNoon = MidTimeFromDayNoon'; +sleepReadouts.DayMove= daywake_movement; +% sleepReadouts.STST=STST'; + +for i = 1:length(awake_number) + if awake_number(i) == 0 + sleepReadouts.Waso(i)= 0; + sleepReadouts.MeanWakeLength(i) = 0; + sleepReadouts.WakeMove(i) = 0; + else + sleepReadouts.Waso(i)= waso(i); + sleepReadouts.MeanWakeLength(i) = meanAwake_length(i); + sleepReadouts.WakeMove(i) = wake_movement(i); + end +end \ No newline at end of file diff --git a/functions/+sleepreadouts/socjetlag.m b/functions/+sleepreadouts/socjetlag.m new file mode 100644 index 0000000..1eefc46 --- /dev/null +++ b/functions/+sleepreadouts/socjetlag.m @@ -0,0 +1,26 @@ +function socJetl = socjetlag(sleepReadouts) +% split groups by weekends and weekdays +sleepReadouts.weekend = categorical(sleepReadouts.Weekend); +G = findgroups(sleepReadouts.Weekend); +weekday_weekend_diff = splitapply(@nanmean,sleepReadouts.MidTimeFromDayNoon,G); + +% calculate social jetlag (first day of the week is sunday, e.g. --> +% weekday = 3 = Tuesday ) +% social jetlag can only be calculated with a minimum of 2 weekend days and +% 2 weekdays +if isempty(abs(diff(weekday_weekend_diff))) + socJetl = NaT(1,height(sleepReadouts))'; +elseif (length(G(G == 1)) >= 2) || (length(G(G == 2)) >= 2) + socJetl = repmat(abs(diff(weekday_weekend_diff)),height(sleepReadouts),1); +else + socJetl = NaN(1,height(sleepReadouts))'; +end + +try + socJetl = round(minutes(socJetl)); % how many hours do weekday and weekend mid-sleeptime differ? (absolute difference in mins) +catch + disp('issue with calculation of socJetl, possibly only weekdays or only weekends?') + socJetl = NaN(1,height(sleepReadouts))'; +end + + diff --git a/functions/+stats/anova.m b/functions/+stats/anova.m new file mode 100644 index 0000000..0ae3960 --- /dev/null +++ b/functions/+stats/anova.m @@ -0,0 +1,27 @@ +function res = anova(values, group) + % mes is not working with categoricals + if(iscategorical(group)) + group = double(group); + end + + [p, anovaTbl] = anova1(values, group, 'off'); + + anovaTbl(cellfun(@isempty, anovaTbl)) = {nan}; + res = cell2table(anovaTbl(2:end, 2:end)); + res.Properties.VariableNames = matlab.lang.makeValidName(anovaTbl(1, 2:end)); + res.Properties.RowNames = matlab.lang.makeValidName(anovaTbl(2:end, 1)); + + assert(p == res.Prob_F(1), 'different p values'); + + % order for mes1way + [~, order] = sort(group); + group = group(order); + values = values(order); + + res.eta2 = nan(3,1); + res.eta2Ci = nan(3,2); + + eta2 = mes1way(values,'eta2','group',group,'nBoot',10000); + res.eta2(1) = eta2.eta2; + res.eta2Ci(1,:) = eta2.eta2Ci; +end \ No newline at end of file diff --git a/functions/+stats/binomTest.m b/functions/+stats/binomTest.m new file mode 100644 index 0000000..bac5fa6 --- /dev/null +++ b/functions/+stats/binomTest.m @@ -0,0 +1,243 @@ +function [pout, frequencies] = binomTest(values, p, Sided) + frequencies = cell2table(tabulate(values)); + frequencies.Properties.VariableNames = {'Value', 'Count', 'Percent'}; + s=sum(values); + n=numel(values); + pout = myBinomTest(s,n,p,Sided); + +function pout=myBinomTest(s,n,p,Sided) +%function pout=binomTest(s,n,p,Sided) +% +% Performs a binomial test of the number of successes given a total number +% of outcomes and a probability of success. Can be one or two-sided. +% +% Inputs: +% s- (Scalar or Array) The observed numebr of successful outcomes +% n- (Scalar or Array) The total number of outcomes (successful or not) +% p- (Scalar or Array) The proposed probability of a successful outcome +% Sided- (String) can be 'one', 'two' (the default), or 'two, equal +% counts'. A value of 'one' will perform a one-sided test to +% determine if the observed number of successes are either +% significantly greater than or less than the expected number +% of successes, depending on whether s is greater than or less +% than the observed number of successes. 'Two' will use the +% method of small p-values (see reference below) to perform a +% two-tailed test to calculate the probability of observing +% any equally unlikely or more unlikely value greater than or +% less than the expected number of successes (ie with the +% same cdf value of the distribution. 'Two, equal counts' +% will perform a two-sided test that the that the actual +% number of success is different from the expected number of +% successes in any direction. +% +% Outputs: +% pout- The probability of observing the resulting value of s or +% another value more extreme (the precise meaning of which +% depends on the value of Sided) given n total outcomes with +% a probability of success of p. +% +% s, n and p can be scalars or arrays of the same size. The +% dimensions and size of pout will match that of these inputs. +% +% For example, the signtest is a special case of this where the value of p +% is equal to 0.5 (and a 'success' is dfeined by whether or not a given +% sample is of a particular sign.), but the binomial test and this code is +% more general allowing the value of p to be any value between 0 and 1. +% +% The results when Sided='two' and when Sided='two, equal counts' are +% identical only when p=0.5, but are different otherwise. For more +% description, see the second reference below. +% +% References: +% http://en.wikipedia.org/wiki/Binomial_test +% http://www.graphpad.com/guides/prism/6/statistics/index.htm?stat_binomial.htm +% +% by Matthew Nelson July 21st, 2009 +% +% Last Updated by Matthew Nelson May 23, 2015 +% matthew.nelson.neuro@gmail.com + + +if nargin<4 || isempty(Sided); Sided='two'; end +if nargin<3 || isempty(p); p=0.5; end + +s=floor(s); +[s,n,p]= EqArrayAndScalars(s,n,p); + +E=p.*n; + +GreaterInds=s>=E; +pout=zeros(size(GreaterInds)); + +Prec=1e-14; % there are some rounding errors in matlab's binopdf, such that we need to specify a level of tolerance when using the 'two' test + +switch lower(Sided) + case {'two','two, equal counts'} + if all(p)==0.5 && strcmpi(Sided,'two'); + % to avoid the rounding problems mentioned above, use the equal counts method which is is theoretically identical in this special case and is not susceptible to this rounding error + Sided='two, equal counts'; + end + + dE=pout; + + %note that matlab's binocdf(s,n,p) gives the prob. of getting up to AND INCLUDING s # of successes... + %Calc pout for GreaterInds first + if any(GreaterInds) + pout(GreaterInds)=1-binocdf( s(GreaterInds)-1,n(GreaterInds),p(GreaterInds)); %start with the prob of getting >= s # of successes + + %now figure the difference from the expected value, and figure the prob of getting lower than that difference from the expected value # of successes + dE(GreaterInds)=s(GreaterInds)-E(GreaterInds); + + if strcmpi(Sided,'two, equal counts') + s2= floor(E(GreaterInds)-dE(GreaterInds)); + + % if s2<0 we add nothing because a negative number of sucesses is impossible + if s2>=0 + pout(GreaterInds)=pout(GreaterInds)+ binocdf(s2,n(GreaterInds),p(GreaterInds)); %the binonmial is a discrete dist. ... so it's value over non-integer args has no meaning... this flooring of E-dE actually doesn't affect the outcome (the result is the same if the floor was removed) but it's included here as a reminder of the discrete nature of the binomial + end + + %If the expected value is exactly equaled, the above code would have added the probability at that discrete value twice, so we need to adjust (in this case, pout will always = 1 anyways) + EqInds=dE==0; + if any(EqInds) + pout(EqInds)=pout(EqInds)- binopdf( E(EqInds),n(EqInds),p(EqInds) ); + end + else + Inds=find(GreaterInds); + + % find the first value on the other side of the expected value with probability less than or equal to the probability that we found... + targy=binopdf(s(GreaterInds),n(GreaterInds),p(GreaterInds)); + + % start by guessing a constant dE, and adjusting from there + s2=max(floor(E(GreaterInds)-dE(GreaterInds)),0); %the binonmial is a discrete dist. ... so it's value over non-integer args has no meaning... this flooring of E-dE actually doesn't affect the outcome (the result is the same if the floor was removed) but it's included here as a reminder of the discrete nature of the binomial + + y=binopdf(s2,n(GreaterInds),p(GreaterInds)); + for ii=1:length(Inds) + SkipPAdd=false; + if y(ii) <= targy(ii) + % search forward until we find the correct limit + while y(ii) <= targy(ii) && s2(ii) targy(ii) && s2(ii) Prec && s2(ii)targy(ii) % bc of rounding error again, avoid this line + if (y(ii) - targy(ii)) > Prec % in this case s2 is at 0, and the prob stil wasn't low enough so we need to add nothing new to pout + SkipPAdd=true; + end + end + + if ~SkipPAdd + % adding the lesser-than tail here + pout(Inds(ii))=pout(Inds(ii))+ binocdf(s2(ii),n(Inds(ii)),p(Inds(ii))); + end + end + end + end + + %Calc pout for LesserInds second + if any(~GreaterInds) + pout(~GreaterInds)=binocdf(s(~GreaterInds),n(~GreaterInds),p(~GreaterInds)); %start with the prob of getting <= s # of successes + + %now figure the difference from the expected value, and figure the prob of getting greater than that difference from the expected value # of successes + dE(~GreaterInds)=E(~GreaterInds)-s(~GreaterInds); + + if strcmpi(Sided,'two, equal counts') + s2=ceil(E(~GreaterInds)+dE(~GreaterInds)); + + if s2<=n(~GreaterInds) + pout(~GreaterInds)=pout(~GreaterInds) + 1-binocdf(s2-1,n(~GreaterInds),p(~GreaterInds)); + end + else + Inds=find(~GreaterInds); + + % find the first value on the other side of the expected value with probability less than or equal to the probability that we found... + targy=binopdf(s(~GreaterInds),n(~GreaterInds),p(~GreaterInds)); + + % start by guessing a constant dE, and adjusting from there + s2=min(ceil(E(~GreaterInds)+dE(~GreaterInds)),n(~GreaterInds)); %the binonmial is a discrete dist. ... so it's value over non-integer args has no meaning... this flooring of E-dE actually doesn't affect the outcome (the result is the same if the floor was removed) but it's included here as a reminder of the discrete nature of the binomial + + y=binopdf(s2,n(~GreaterInds),p(~GreaterInds)); + for ii=1:length(Inds) + SkipPAdd=false; + if y(ii) <= targy(ii) + % search backward until we find the correct limit + while y(ii) <= targy(ii) && s2(ii)>E(Inds(ii)) + s2(ii)=s2(ii)-1; + y(ii)=binopdf(s2(ii),n(Inds(ii)),p(Inds(ii))); + end + s2(ii)=s2(ii)+1; % because the last iteration would have crossed the boundary, and we want the first s2 with a binopdf <= targy + else + %while y(ii) > targy(ii) && s2(ii) Prec && s2(ii)targy(ii) % bc of rounding error again, avoid this line + if (y(ii) - targy(ii)) > Prec % in this case s2 is at n, and the prob stil wasn't low enough so we need to add nothing new to pout + SkipPAdd=true; + end + end + + if ~SkipPAdd + % adding the greater-than tail here + pout(Inds(ii))=pout(Inds(ii))+ 1-binocdf(s2(ii)-1,n(Inds(ii)),p(Inds(ii))); + end + end + + end + end + case 'one' %one-sided + if any(GreaterInds) + pout(GreaterInds)=1-binocdf(s(GreaterInds)-1,n(GreaterInds),p(GreaterInds)); %just report the prob of getting >= s # of successes + end + if any(~GreaterInds) + pout(~GreaterInds)=binocdf(s(~GreaterInds),n(~GreaterInds),p(~GreaterInds)); %just report the prob of getting <= s # of successes + end + otherwise + error(['In myBinomTest, Sided variable is: ' Sided '. Unkown sided value.']) +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function varargout=EqArrayAndScalars(varargin) +%function varargout=EqArrayAndScalars(varargin) +% +% This will compare a collection of inputs that must be either scalars or +% arrays of the same size. If there is at least one array input, all scalar +% inputs will be replicated to be the array of that same size. If there are +% two or more array inputs that have different sizes, this will return an +% error. +% +% created by Matthew Nelson on April 13th, 2010 +% matthew.j.nelson.vumail@gmail.com + +d=zeros(nargin,1); +for ia=1:nargin + d(ia)=ndims(varargin{ia}); +end +maxnd=max(d); + +s=ones(nargin,maxnd); + +for ia=1:nargin + s(ia,1:d(ia))=size(varargin{ia}); +end +maxs=max(s); + +varargout=cell(nargin,1); +for ia=1:nargin + if ~all(s(ia,:)==maxs) + if ~all(s(ia,:)==1) + error(['Varargin{' num2str(ia) '} needs to be a scalar or equal to the array size of other array inputs.']) + else + varargout{ia}=repmat(varargin{ia},maxs); + end + else + varargout{ia}=varargin{ia}; + end +end diff --git a/functions/+stats/kruskalWallis.m b/functions/+stats/kruskalWallis.m new file mode 100644 index 0000000..e3855e9 --- /dev/null +++ b/functions/+stats/kruskalWallis.m @@ -0,0 +1,24 @@ +function res = kruskalWallis(values, group) + % The Kruskal Wallis test is the non parametric alternative to the One Way ANOVA. + % Non parametric means that the test doesn’t assume your data comes from a + % particular distribution. + % see: https://www.statisticshowto.datasciencecentral.com/kruskal-wallis/ + + % mes is not working with categoricals + if(iscategorical(group)) + group = double(group); + end + + [p, tbl] = kruskalwallis(values, group, 'off'); + + tbl(cellfun(@isempty, tbl)) = {nan}; + res = cell2table(tbl(2:end, 2:end)); + res.Properties.VariableNames = matlab.lang.makeValidName(tbl(1, 2:end)); + res.Properties.RowNames = matlab.lang.makeValidName(tbl(2:end, 1)); + + assert(res.Prob_Chi_sq(1) == p, 'different p values'); + % TODO: find effectsize + + % TODO: Group comparisons + +end \ No newline at end of file diff --git a/functions/+stats/mannWhitneyUtest.m b/functions/+stats/mannWhitneyUtest.m new file mode 100644 index 0000000..dfbcb0c --- /dev/null +++ b/functions/+stats/mannWhitneyUtest.m @@ -0,0 +1,16 @@ +function res = mannWhitneyUtest(dataA, dataB) + % The Mann-Whitney U test is the nonparametric equivalent of the two sample t-test. + % While the t-test makes an assumption about the distribution of a population + % (i.e. that the sample came from a t-distributed population), the Mann Whitney U Test + % makes no such assumption. + % see: https://www.statisticshowto.datasciencecentral.com/mann-whitney-u-test/ + % + % alternate null hypothesis: both samples have the same median + + + [res.p,res.h] = ranksum(dataA, dataB); + + res.cohensU1 = mes(dataA,dataB,'U1','nBoot',10000); + res.cohensU3 = mes(dataA,dataB,'U3','nBoot',10000); +end + diff --git a/functions/+stats/multipleTests.m b/functions/+stats/multipleTests.m new file mode 100644 index 0000000..02d1af7 --- /dev/null +++ b/functions/+stats/multipleTests.m @@ -0,0 +1,7 @@ +function res = multipleTests(data, groups,testFun) + [I, N] = findgroups(groups); + res = util.struct2flattable(splitapply(@(x) struct('count', numel(x), 'test', testFun(x)), data, I)); + res.group = N; + res = movevars(res, 'group', 'Before', 'count'); +end + diff --git a/functions/+stats/rmanova.m b/functions/+stats/rmanova.m new file mode 100644 index 0000000..72d0b81 --- /dev/null +++ b/functions/+stats/rmanova.m @@ -0,0 +1,38 @@ +function res = rmanova(data,subjectColumn,valueColumn,within,between) + + withinComb = data.(within{1}); + for i=2:numel(within) + withinComb = withinComb.*categorical(data.(within{i})); + end + + withinComb=removecats(withinComb); + + catNames = categories(withinComb); + withinColumnNames = strrep(catNames, ' ', '_'); + withinColumnNames = strrep(withinColumnNames, '+', 'p'); + withinColumnNames = strrep(withinColumnNames, '-', 'm'); + withinComb = renamecats(withinComb, catNames, withinColumnNames); + + data.withinComb = withinComb; + udata = unstack(data, valueColumn, 'withinComb', 'GroupingVariables',{subjectColumn}, 'ConstantVariables', between); + + withinDesign = cell2table(split(catNames, ' '), 'VariableNames', within); + + betweenComb = char(join(between,'*')); + if(isempty(betweenComb)) + betweenComb = '1'; + end + + % remove nans + % udata = udata(~any(isnan(udata(:,3:end).Variables),2),:) + + rm = fitrm(udata,[char(join(withinColumnNames,',')), ' ~ ', betweenComb], 'WithinDesign', withinDesign); + + res = ranova(rm, 'WithinModel', char(join(within, '*'))); + + % partial eta square + %res.partialEta2=res.SumSq./(res.SumSq+res.SumSq(end)); + + % generalized eta square + %res.generalEta2=res.SumSq./(res.SumSq+res.SumSq(end)+betweenRes.SumSq(end)); +end \ No newline at end of file diff --git a/functions/+stats/tableCorrelation.m b/functions/+stats/tableCorrelation.m new file mode 100644 index 0000000..095d832 --- /dev/null +++ b/functions/+stats/tableCorrelation.m @@ -0,0 +1,40 @@ +function [coef, p]= tableCorrelation(A,B,Type) + if(~exist('Type','var')) + Type = 'Pearson'; + end + + varTypes = varfun(@class, B, 'OutputFormat', 'cell'); + doubleVars = strcmp(varTypes, 'double'); + tableVars = strcmp(varTypes, 'table'); + Bdoubles = B(:,doubleVars); + + % Correlations for doubles + [R,P] = corr(A.Variables,Bdoubles.Variables, 'Type',Type); + + coef = array2table(R); + p = array2table(P); + + coef.Properties.VariableNames = Bdoubles.Properties.VariableNames; + p.Properties.VariableNames = Bdoubles.Properties.VariableNames; + + coef.Properties.RowNames = A.Properties.VariableNames; + p.Properties.RowNames = A.Properties.VariableNames; + + coef.Properties.Description = ['Correlation-Type: ',Type]; + + % recursively add correlations for tables + for tableVar=B.Properties.VariableNames(tableVars) + tableVar=tableVar{1}; + [tableCoef, tableP]= stats.tableCorrelation(A,B.(tableVar),Type); + + assert (all(strcmp(coef.Properties.RowNames, tableCoef.Properties.RowNames)),'row order does not fit'); + assert (all(strcmp(coef.Properties.RowNames, tableP.Properties.RowNames)),'row order does not fit'); + + tableCoef.Properties.RowNames = {}; + tableP.Properties.RowNames = {}; + + coef.(tableVar) = tableCoef; + p.(tableVar) = tableP; + end +end + diff --git a/functions/+stats/transitionMatrix.m b/functions/+stats/transitionMatrix.m new file mode 100644 index 0000000..462fe03 --- /dev/null +++ b/functions/+stats/transitionMatrix.m @@ -0,0 +1,9 @@ +function transition = transitionMatrix(data) + numCluster = max(data); + TM = zeros(numCluster, numCluster); + for i=2:numel(data) + TM(data(i-1), data(i)) = TM(data(i-1), data(i))+1; + end + transition = round(TM./repmat(sum(TM,2),1,numCluster)*100); +end + diff --git a/functions/+stats/ttestDependent.m b/functions/+stats/ttestDependent.m new file mode 100644 index 0000000..155afb8 --- /dev/null +++ b/functions/+stats/ttestDependent.m @@ -0,0 +1,11 @@ +function res = ttestDependent(dataA, dataB) + [h,p,ci,res] = ttest(dataA, dataB); + res.h=h; + res.p=p; + res.ci=ci; + + mres=mes(dataA, dataB, 'mdbysd', 'isDep', 1); + res.mdbysd=mres.mdbysd; + res.mdbysdCi=mres.mdbysdCi; +end + diff --git a/functions/+stats/ttestIndependent.m b/functions/+stats/ttestIndependent.m new file mode 100644 index 0000000..e334ed2 --- /dev/null +++ b/functions/+stats/ttestIndependent.m @@ -0,0 +1,10 @@ +function res = ttestIndependent(dataA, dataB) + [h,p,ci,res] = ttest2(dataA, dataB); + res.h=h; + res.p=p; + res.ci=ci; + + mres=mes(dataA, dataB, 'glassdelta', 'nBoot', 10000); + res.glassdelta=mres.glassdelta; + res.glassdeltaCi=mres.glassdeltaCi; +end diff --git a/functions/+stats/ttestOneSample.m b/functions/+stats/ttestOneSample.m new file mode 100644 index 0000000..8aaa18b --- /dev/null +++ b/functions/+stats/ttestOneSample.m @@ -0,0 +1,16 @@ +function res = ttestOneSample(data, refValue) + if(~exist('refValue', 'var')) + refValue = 0; + end + + [h,p,ci,stats]=ttest(data,refValue); + res.h=h; + res.p=p; + res.ci=ci; + + mres=mes(data,refValue,'g1','nBoot',10000); + res.g1=mres.g1; + res.g1Ci=mres.g1Ci; + +end + diff --git a/functions/+util/chunkTimetableByRanges.m b/functions/+util/chunkTimetableByRanges.m new file mode 100644 index 0000000..e6eaadb --- /dev/null +++ b/functions/+util/chunkTimetableByRanges.m @@ -0,0 +1,13 @@ +function result = chunkTimetableByRanges(data, startTimes, endTimes) + result = cell(size(startTimes,1), width(data)); + + for row=1:size(startTimes,1) + epochData = data(timerange(startTimes(row), endTimes(row)),:); + + for col=1:width(data) + result(row,col) = {epochData.(col)'}; + end + end + result = cell2table(result, 'VariableNames', data.Properties.VariableNames); +end + diff --git a/functions/+util/crossjoin.m b/functions/+util/crossjoin.m new file mode 100644 index 0000000..8d723b3 --- /dev/null +++ b/functions/+util/crossjoin.m @@ -0,0 +1,8 @@ +function res = crossjoin(A,B) + +As = repmat(A,height(B),1); +Bs=reshape(repmat(B.Variables', height(A),1), 1, [])'; +Bs = table(Bs, 'VariableNames', B.Properties.VariableNames);; +res = [As, Bs]; +end + diff --git a/functions/+util/stats.m b/functions/+util/stats.m new file mode 100644 index 0000000..1494fca --- /dev/null +++ b/functions/+util/stats.m @@ -0,0 +1,12 @@ +function res = stats( v ) + res.count = length(v); + res.nans = sum(isnan(v)); + res.mean = nanmean(v); + res.median = nanmedian(v); + res.std = nanstd(v); + res.min = min(v); + res.max = max(v); + res.quantile25 = quantile(v, 0.25); + res.quantile75 = quantile(v, 0.75); +end + diff --git a/functions/+util/struct2flattable.m b/functions/+util/struct2flattable.m new file mode 100644 index 0000000..1e37b41 --- /dev/null +++ b/functions/+util/struct2flattable.m @@ -0,0 +1,19 @@ +function res = struct2flattable(istruct) + res = struct2table(istruct); + + vtypes = varfun(@class,res,'OutputFormat','cell'); + sidxs = find(strcmp(vtypes, 'struct')); + + structstable = table(); + for sidx=sidxs + stable = util.struct2flattable(res(:,sidx).Variables); + stable.Properties.VariableNames = strcat([res.Properties.VariableNames{sidx}, '_'], stable.Properties.VariableNames); + + structstable = [structstable, stable]; + end + cols = 1:width(res); + cols(sidxs) = []; + + res = [res(:, cols), structstable]; +end + diff --git a/functions/FreqDomHRV.m b/functions/FreqDomHRV.m new file mode 100644 index 0000000..ebe448f --- /dev/null +++ b/functions/FreqDomHRV.m @@ -0,0 +1,10 @@ +function [FreqMetrics] = FreqDomHRV (NN,tNN,sqi,HRVparams,tWin) + % 4. Frequency domain metrics (LF HF TotPow) + if HRVparams.freq.on + error_flag = 'Frequency Domain Analysis failure'; + FreqMetrics = EvalFrequencyDomainHRVstats(NN,tNN,sqi,HRVparams,tWin); + + end + +end + diff --git a/functions/PreprocessingRRintervals.m b/functions/PreprocessingRRintervals.m new file mode 100644 index 0000000..03daa1d --- /dev/null +++ b/functions/PreprocessingRRintervals.m @@ -0,0 +1,42 @@ +function [NN,tNN,tWin,sqi,flagged_beats] = PreprocessingRRintervals (rr,t,HRVparams,subID,varargin) +%% to set parameters and variables %% +if nargin < 4 + error('Wrong number of input arguments') +end +if nargin < 5 + subID = '0000'; +end +if nargin < 6 + ann = []; +end +if nargin < 7 + sqi = []; %even if I read in sqi file it just changes it to 0 for some reason +end + + +if length(varargin) == 1 || length(varargin) == 3 + error('Incomplete Signal-Type pair') +elseif length(varargin) == 2 + extraSigType = varargin(2); + extraSig = varargin{1}; +elseif length(varargin) == 4 + extraSigType = [varargin(2) varargin(4)]; + extraSig = [varargin{1} varargin{3}]; +end + +if isa(subID,'cell'); subID = string(subID); end + + + % 1. Preprocess Data, AF detection, create Windows Indexes + [NN, tNN, tWin] = PreparDataForHRVAnlysis(rr,t,ann,sqi,HRVparams,subID); + + + +function [NN, tNN, tWin, flagged_beats] = PreparDataForHRVAnlysis(rr,t,annotations,sqi,HRVparams,subjectID) + + % Exclude undesiderable data from RR series (i.e., arrhytmia, low SQI, ectopy, artefact, noise) + [NN, tNN, flagged_beats] = RRIntervalPreprocess(rr,t,annotations, HRVparams); + tWin = CreateWindowRRintervals(tNN, NN, HRVparams); % Create Windows for Time and Frequency domain + +end +end \ No newline at end of file diff --git a/functions/ReportFigEcg.m b/functions/ReportFigEcg.m new file mode 100644 index 0000000..aa799de --- /dev/null +++ b/functions/ReportFigEcg.m @@ -0,0 +1,19 @@ +function g = ReportFigEcg(data) + +%calculate bpm % +data.Bpm = 60000./data.NNmean; + +%create plot with only ecg data% +pdates = posixtime(data.Time); % need to change the format for gramm (cant handle datetime) + +%make the figure +xticks = linspace(pdates(1), pdates(end), 5); %to set x axis +g = gramm('x', pdates, 'y', data.Bpm); +g.set_names('x', 'Uhrzeit','y','Puls (Bpm)'); +g.geom_line(); +g.set_color_options('map',[0.5,0,0.5]); +% g.set_color_options('map','brewer1', 'n_color', 1, 'n_lightness', 1) +g.axe_property('XTick', xticks, 'XTickLabel', string(datetime(xticks, 'ConvertFrom', 'posixtime', 'Format', 'HH:mm'))); +g.stat_smooth('geom','area'); +end + diff --git a/functions/ReportFigures.m b/functions/ReportFigures.m new file mode 100644 index 0000000..55d8aa8 --- /dev/null +++ b/functions/ReportFigures.m @@ -0,0 +1,164 @@ +function [figures, ecgFig, sleepEcgAll] = ReportFigures(sleepECG,cleanedActi,HRVMetrics) + +if (isempty(sleepECG))&&(isempty(cleanedActi)) + sleepEcgAll = []; +elseif(isempty(sleepECG)) %when we dont have both acti and ECG data + cleanedActi.NNmean = nan(height(cleanedActi),1); + sleepEcgAll = cleanedActi; + % sleepEcgAll.Properties.DimensionNames{'time'} = 'Time'; +elseif (sum(sleepECG.NNmean == 0)) + + sleepEcgAll = outerjoin(sleepECG, cleanedActi); + sleepEcgAll.NNmean = nan(height(cleanedActi),1); + try + sleepEcgAll.Properties.DimensionNames{'Time'} = 'time'; + catch + sleepEcgAll.Properties.DimensionNames{'time'} = 'time'; + end +else + sleepEcgAll = outerjoin(sleepECG, cleanedActi); % create one table with all the data intended to present on the figure + try + sleepEcgAll.Properties.DimensionNames{'Time'} = 'time'; + catch + sleepEcgAll.Properties.DimensionNames{'time'} = 'time'; + end +end + + +%to make a new variable with the day number +if (~isempty(cleanedActi)) + firstNoon = dateshift(sleepEcgAll.time(1), 'end', 'day')-hours(12); % Find the first 21:00 (noon) in the dataset + sleepEcgAll.DayCount = caldays(between(firstNoon,sleepEcgAll.time))+1; %create a new variable showing nr of days + + sleepEcgAll.HoursOfDay = hours(timeofday(sleepEcgAll.time)); %create new variable with time info changed into double (for gramm) + sleepEcgAll.NoonStartHoursOfDay = mod(sleepEcgAll.HoursOfDay-12,24); %create a variable with relative time starting from noon as 0 + + %create the two figures (g1 only ECG data and g2 dayly report data) + xTicks = hours(0:2:24); % to be able to set the x axis back to the 'real time' + xTicksNoonStart = hours(mod(hours(xTicks)+12,24)); + xTicksNoonStart.Format = 'hh:mm'; + numDays = max(sleepEcgAll.DayCount); % to set the length of the loop (subploting every day separately) + + sleepEcgAll.Bpm = 60000./sleepEcgAll.NNmean; + + %% start drawing actigraphy figure %% + daysPerFig = 7; + numFigures = ceil(numDays/daysPerFig); + + for currentFigIndex = 1:numFigures + firstDay = 1+(currentFigIndex-1)*daysPerFig; + lastDay = min(firstDay+daysPerFig-1,numDays); + daysInFig = lastDay-firstDay+1; + %to make a new variable with the day number + + firstNoon = dateshift(sleepEcgAll.time(1), 'end', 'day')-hours(12); % Find the first 21:00 (noon) in the dataset + sleepEcgAll.DayCount = caldays(between(firstNoon,sleepEcgAll.time))+1; %create a new variable showing nr of days + % [sleepEcgAll.MonthsCount,sleepEcgAll.DayCount,sleepEcgAll.TimeCount] = split(calendarDuration(between(firstNoon,sleepEcgAll.time)),{'months','days','time'}); + % sleepEcgAll.DayCount=sleepEcgAll.DayCount+1; + % sleepEcgAll.MonthsCount=sleepEcgAll.MonthsCount+1; + + sleepEcgAll.HoursOfDay = hours(timeofday(sleepEcgAll.time)); %create new variable with time info changed into double (for gramm) + sleepEcgAll.NoonStartHoursOfDay = mod(sleepEcgAll.HoursOfDay-12,24); %create a variable with relative time starting from noon as 0 + + %create the two figures (g1 only ECG data and g2 dayly report data) + xTicks = hours(0:2:24); % to be able to set the x axis back to the 'real time' + xTicksNoonStart = hours(mod(hours(xTicks)+12,24)); + xTicksNoonStart.Format = 'hh:mm'; + % numMonths = max(sleepEcgAll.MonthsCount); + numDays = max(sleepEcgAll.DayCount); % to set the length of the loop (subploting every day separately) + + figName = sprintf('Fig%01d', currentFigIndex); + figures(currentFigIndex) = figure('Name', figName, 'Position',[1,41,1280,1500], 'color', 'w' );%[400, 500, 2000, 7*400] + + moveSum = rescale(sleepEcgAll.movesum)*1500; + + for day =firstDay:lastDay + sp(day) = subplot(7,1,day-firstDay+1); + hold on + % plot(sleepEcgAll.Time(sleepEcgAll.DayCount ==day), sleepEcgAll.movesum(sleepEcgAll.DayCount ==day)); + area(sleepEcgAll.time(sleepEcgAll.DayCount ==day),moveSum(sleepEcgAll.DayCount ==day)); + hold on + plot(sleepEcgAll.time(sleepEcgAll.DayCount ==day), sleepEcgAll.withCorrelationFilter(sleepEcgAll.DayCount ==day)*500, 'LineWidth', 2); + plot(sleepEcgAll.time(sleepEcgAll.DayCount ==day), sleepEcgAll.withSmoothedBouts(sleepEcgAll.DayCount ==day)*700, 'LineWidth', 3); + + ylabel('Aktivität'); + ylim([0, 1500]); + + if sum(~isnan(sleepEcgAll.NNmean(sleepEcgAll.DayCount ==day))) > 0 + yyaxis right; + plot(sleepEcgAll.time(sleepEcgAll.DayCount ==day), sleepEcgAll.Bpm(sleepEcgAll.DayCount ==day), 'LineWidth', 2); + ylim([0, 150]); + ylabel('BPM'); + end + end + legendPos = sp(1).Position; + legendPos(1) = legendPos(1)+0.1; % x-pos (left lower corner) + legendPos(2) = legendPos(2)+legendPos(4)+0.02109; % y-pos (left lower corner) + legendPos(3) = 0.01; % legend width + legendPos(4) = 0.01; % legend height + legend('Aktivität','Schlafsegmente','Schlafperiode','Bpm', 'Location', legendPos); + end +else + figures = figure(); +end + +if isempty(HRVMetrics) + HRVMetrics.NNmean = 0; +end +if (~isempty(HRVMetrics)) ||sum(HRVMetrics.NNmean)~= 0 + + %% start drawing actigraphy figure %% + % for monthCount = 1:numMonths % everything here is for that one participant (months count!!!!!!! + daysPerFig = 7; + numFigures = ceil(numDays/daysPerFig); + + for currentFigIndex = 1:numFigures + firstDay = 1+(currentFigIndex-1)*daysPerFig; + lastDay = min(firstDay+daysPerFig-1,numDays); + daysInFig = lastDay-firstDay+1; + + figName = sprintf('Fig%01d', currentFigIndex); + figures(currentFigIndex) = figure('Name', figName, 'Position',[1,41,1280,1500], 'color', 'w' );%[400, 500, 2000, 7*400] + + moveSum = rescale(sleepEcgAll.movesum)*1500; + + for day =firstDay:lastDay + sp(day) = subplot(7,1,day-firstDay+1); + hold on + % plot(sleepEcgAll.Time(sleepEcgAll.DayCount ==day), sleepEcgAll.movesum(sleepEcgAll.DayCount ==day)); + area(sleepEcgAll.time(sleepEcgAll.DayCount==day),moveSum(sleepEcgAll.DayCount==day)); + hold on + plot(sleepEcgAll.time(sleepEcgAll.DayCount==day), sleepEcgAll.withCorrelationFilter(sleepEcgAll.DayCount==day)*500, 'LineWidth', 2); + plot(sleepEcgAll.time(sleepEcgAll.DayCount==day), sleepEcgAll.withSmoothedBouts(sleepEcgAll.DayCount==day)*700, 'LineWidth', 3); + + ylabel('Aktivität'); + ylim([0, 1500]); + + if sum(~isnan(sleepEcgAll.NNmean(sleepEcgAll.DayCount==day))) > 0 + yyaxis right; + plot(sleepEcgAll.time(sleepEcgAll.DayCount==day), sleepEcgAll.Bpm(sleepEcgAll.DayCount==day), 'LineWidth', 2); + ylim([0, 150]); + ylabel('BPM'); + end + end + legendPos = sp(1).Position; + legendPos(1) = legendPos(1)+0.1; % x-pos (left lower corner) + legendPos(2) = legendPos(2)+legendPos(4)+0.02109; % y-pos (left lower corner) + legendPos(3) = 0.01; % legend width + legendPos(4) = 0.01; % legend height + legend('Aktivität','Schlafsegmente','Schlafperiode','Bpm', 'Location', legendPos); + end + + + if sum(sleepECG.NNmean)~= 0 + ecgFig = figure('Position', [150, 200, 400, 250],'color','w'); + g1 = ReportFigEcg (HRVMetrics); + g1.draw(); + + else + ecgFig = figure(); + end + + +end + diff --git a/functions/TimeDomHRV.m b/functions/TimeDomHRV.m new file mode 100644 index 0000000..944234e --- /dev/null +++ b/functions/TimeDomHRV.m @@ -0,0 +1,10 @@ +function [TimeMetrics] = TimeDomHRV (NN,tNN,sqi,HRVparams,tWin) + % 3. Calculate time domain HRV metrics - Using HRV Toolbox for PhysioNet + % Cardiovascular Signal Toolbox Toolbox Functions + if HRVparams.timedomain.on + error_flag = 'Time Domain Analysis failure'; + TimeMetrics = EvalTimeDomainHRVstats(NN,tNN,sqi,HRVparams,tWin); + + end +end + diff --git a/functions/actigraphyPreparation.m b/functions/actigraphyPreparation.m new file mode 100644 index 0000000..1b7417f --- /dev/null +++ b/functions/actigraphyPreparation.m @@ -0,0 +1,29 @@ +function cleanedActi = actigraphyPreparation(actigraphyData, timeStep, missingDataThreshold) +% actigraphyData is a timetable with two columns: +% movement: double +% light: double + +% retime timeStep, take only the movement data +cleanedActi = retime(actigraphyData(:, 'movements'),'regular','sum','TimeStep',timeStep); +cleanedActi.Properties.VariableNames{1} = 'movesum'; + + +% quality Check +%if the whole recording starts with 0 change it to NaN until the first +%numbers +if cleanedActi.movesum(1) == 0 + [ii, jj]=find(~cleanedActi.movesum); + lastZeroIndex = find((diff(ii)>1),1, 'first'); + cleanedActi.movesum (1:lastZeroIndex) = nan; +end +% find parts, where the actimeter has not been worn and change them to NAN +% at least consecutive 60 minutes (6 epochs)of 0 activity +% between two not 0-s and within 6 epochs will be changed ito a nan and +% count as the same phase. gapsum = calculates the sum of the changed +% data (to know how much was changed) +[cleanedActi.movesum, ~] = excludeArtifact(cleanedActi.movesum, missingDataThreshold); + + +% cleanData: timetable with one column: +% movesum +end \ No newline at end of file diff --git a/functions/calcHRVvariables.m b/functions/calcHRVvariables.m new file mode 100644 index 0000000..f4ba14b --- /dev/null +++ b/functions/calcHRVvariables.m @@ -0,0 +1,31 @@ +function [HRVMetrics,TimeMetrics] = calcHRVvariables (rr,t,HRVparams,subName,hdr,timeStep) + +%preprocess RR intervals, clean the data based on the parameters given in +%the HRVparameters constant variables +[NN,tNN,tWin,sqi] = PreprocessingRRintervals (rr,t,HRVparams,subName); + +%calculate all time domain metrics for the whole recording (function taken +%partially from physioNet-Cardiovascular Toolbox +[TimeMetrics] = TimeDomHRV (NN,tNN,sqi,HRVparams,tWin); %because of the windowing we have a value for every minuite calculated with a 5 min moving window + +%calculate all frequency domain metrics for the whole recording (function taken +%partially from physioNet-Cardiovascular Toolbox +[FreqMetrics] = FreqDomHRV (NN,tNN,sqi,HRVparams,tWin); + +%organize all the variables into one matrix +HRVout = [cell2mat(struct2cell(TimeMetrics))' cell2mat(struct2cell(FreqMetrics))']; + +%organize the variable names into a cell array +HRVtitle = [fieldnames(TimeMetrics)' fieldnames(FreqMetrics)']; + +%organize the data into a timetable +t0 = datetime([hdr.startdate, ' ', hdr.starttime], 'InputFormat', 'dd.MM.yy HH.mm.ss','TimeZone','Europe/Berlin'); +TT_HRVMetrics = array2timetable (HRVout,'TimeStep',minutes(1),'StartTime',t0); +TT_HRVMetrics.Properties.VariableNames = HRVtitle; + + +% restrict to NNmean. think about if meaning through the other measures +% is right or wrong +HRVMetrics = retime(TT_HRVMetrics(:, 'NNmean'),'regular','mean','TimeStep',timeStep); + +end \ No newline at end of file diff --git a/functions/calcSleepFromActi.m b/functions/calcSleepFromActi.m new file mode 100644 index 0000000..bd49f1e --- /dev/null +++ b/functions/calcSleepFromActi.m @@ -0,0 +1,52 @@ +% get sleep periods estimated from actigraphy +if(~isempty(acti)) && (~isempty(HRVMetrics)) + cleanedActi = actigraphyPreparation(acti, timeStep, missingDataThreshold); + sleep = sleepDetection(cleanedActi,timeCenteredMean,sleepBoutThreshold,percTreshold,sleepGapLength,centeredBoutMean,meanTreshold); + + % synchronize actigraphy and ecg data + sleepECG = synchronize(sleep,HRVMetrics); + % calculate trend in the whole night ECG only durng sleep periods + sleepECG.SleepNNmean = sleepECG.NNmean; + sleepECG.SleepNNmean (sleepECG.withCorrelationFilter ==0) = NaN; + + % get sleep readout calculated ONLY actigraphy and NN_mean + sleepBoutsActiAll = bouts.calcSleepBouts(sleep(:,{'withSmoothedBouts'})); % could also be changed to 'withSmoothedBouts' to use for only Actigraphy data--> gives you one sleep period/day + sleepBoutsEcg = util.chunkTimetableByRanges(sleepECG(:,{'SleepNNmean'}), sleepBoutsActiAll.ActiSleepOns, sleepBoutsActiAll.ActiSleepOffs); % getting the NN_mean values /every sleep bout to analyze cycles + sleepBoutsActiAll = [sleepBoutsActiAll, sleepBoutsEcg]; %add it to the main table + sleepBoutsActiAll = [sleepBoutsActiAll, struct2table(cellfun(@util.stats, sleepBoutsActiAll.SleepNNmean))]; %calculate descriptive statistics and add it to the main table + + % to calculate sleep for every day only with actigraphy + sleepBoutsActiOnePerDay = bouts.pickOneBoutPerDay(sleepBoutsActiAll); + + nightVars = sleepreadouts.nightvars(sleepBoutsActiOnePerDay,sleep(:, {'withCorrelationFilter'}), cleanedActi); + SocJet = sleepreadouts.socjetlag(nightVars); + CircEntr = sleepreadouts.circentrain(nightVars); + extsReadouts = table(SocJet, CircEntr); + extsReadouts.Properties.VariableDescriptions = {'Social Jetlag', 'Circadian Entrainment'}; + sleepReadouts = [nightVars, extsReadouts]; + if isnat(sleepReadouts.ActiSleepOns(end)) + sleepReadouts(end,:) = []; + end +elseif (~isempty(acti)) && (isempty(HRVMetrics)) %in case we dont have ECG data + cleanedActi = actigraphyPreparation(acti, timeStep, missingDataThreshold); + sleep = sleepDetection(cleanedActi,timeCenteredMean,sleepBoutThreshold,percTreshold,sleepGapLength,centeredBoutMean,meanTreshold); + % get sleep readout calculated ONLY actigraphy and NN_mean + sleepBoutsActiAll = bouts.calcSleepBouts(sleep(:,{'withSmoothedBouts'})); % could also be changed to 'withSmoothedBouts' to use for only Actigraphy data--> gives you one sleep period/day + sleepECG = sleep; + sleepECG.NNmean = zeros(height(sleepECG),1); + + % to calculate sleep for every day only with actigraphy + sleepBoutsActiOnePerDay = bouts.pickOneBoutPerDay(sleepBoutsActiAll); + + nightVars = sleepreadouts.nightvars(sleepBoutsActiOnePerDay,sleep(:, {'withCorrelationFilter'}), cleanedActi); + SocJet = sleepreadouts.socjetlag(nightVars); + CircEntr = sleepreadouts.circentrain(nightVars); + extsReadouts = table(SocJet, CircEntr); + extsReadouts.Properties.VariableDescriptions = {'Social Jetlag', 'Circadian Entrainment'}; + sleepReadouts = [nightVars, extsReadouts]; + if isnat(sleepReadouts.ActiSleepOns(end)) + sleepReadouts(end,:) = []; + end +else + sleepBoutsActiAll = []; +end \ No newline at end of file diff --git a/functions/convertToRR.m b/functions/convertToRR.m new file mode 100644 index 0000000..1f4be38 --- /dev/null +++ b/functions/convertToRR.m @@ -0,0 +1,17 @@ +function ecgData = convertToRR (ecgData, HRVparams, subName,dir) +%transform the raw edf into RR intervals +[t, rr, jqrs_ann, SQIvalue , tSQI] = ConvertRawDataToRRIntervals(ecgData.rawData, HRVparams, subName); +sqi = [tSQI', SQIvalue']; + +%Save the RR intervals into a separate mat file +filename_RRintervals = [subName]; +save(fullfile(dir, filename_RRintervals),'jqrs_ann','rr','t'); + +%Add the new variables to the ecg table for the next function to calculate +%with + ecgData.rr = rr; + ecgData.t = t; + + +end + diff --git a/functions/createBecomeReport.m b/functions/createBecomeReport.m new file mode 100644 index 0000000..c27f5fd --- /dev/null +++ b/functions/createBecomeReport.m @@ -0,0 +1,253 @@ +import mlreportgen.report.*; +import mlreportgen.dom.*; +import mlreportgen.utils.*; + +rpt = Report([outdir,'\' subName],'pdf'); +section = Section(); + +%% +plot1 = Image("C:\Users\dorothee_poehlchen\Documents\GitHub\becomeFeedback\functions\firstpage.png"); +% plot1.Width = "4in"; +% plot1.Height = "4in"; +append(section,plot1); + + + +%% SLEEP GOOD TO KNOW +p0a = Paragraph('Allgemeine Information: Schlaf'); +p0a.Style = {Bold,LineSpacing(2.5)}; +p0a.Underline = 'single'; +p0a.FontSize = '14pt'; +add(section, p0a); + +p0 = Paragraph('Schlaf ist ein Zustand der äußeren Ruhe bei Menschen und Tieren. Dabei unterscheiden sich viele Lebenszeichen von denen des Wachzustands. Puls, Atemfrequenz und Blutdruck sinken im sogenannten NREM-Schlaf ab und die Gehirnaktivität verändert sich. Der NREM-Schlaf kann weiter in 3 Unterphasen unterteilt werden, die die Vertiefung des Schlafes (N1 0) + if(zeroCount > nullThreshold) + artiFree(i-(1:zeroCount))= nan; + nullSum = nullSum+zeroCount; + end + zeroCount = 0; + end + else + zeroCount = zeroCount+1; + end +end + +if(zeroCount > nullThreshold) + artiFree((end-zeroCount+1):end)= nan; + nullSum = nullSum+zeroCount; +end +end + diff --git a/functions/excludeNotSleep.m b/functions/excludeNotSleep.m new file mode 100644 index 0000000..d02e1b8 --- /dev/null +++ b/functions/excludeNotSleep.m @@ -0,0 +1,26 @@ +function [sleep, nullSum] = excludeNotSleep(sleep, nullThreshold) +% everything between two 0 s within at least 30 minutes will be changed into +% ones and exclude from the analysis +% nullSum: how many nulls were changed; notZeroCount: counting the notzeros in +% the data +% So to make it clear; if between two zeroes you have less than three ones +% (e.g., one or two) turn those ones into zeros (it is not sleep). + +nullSum = 0; +ZeroCount = 0; +for i = 1:length(sleep) + if (sleep(i) == 0) + if (ZeroCount > 0) + if(ZeroCount < nullThreshold) + sleep(i-(1:ZeroCount))= 0; + nullSum = nullSum+ZeroCount; + end + ZeroCount = 0; + end + else + ZeroCount = ZeroCount+1; + end + end +end + + diff --git a/functions/firstpage.png b/functions/firstpage.png new file mode 100644 index 0000000..a4b436a Binary files /dev/null and b/functions/firstpage.png differ diff --git a/functions/importPhilipsActi.m b/functions/importPhilipsActi.m new file mode 100644 index 0000000..d5b519b --- /dev/null +++ b/functions/importPhilipsActi.m @@ -0,0 +1,48 @@ +function actiTable = importPhilipsActi(filename) +%IMPORTFILE Import data from a text file +% FOE0140NEW = IMPORTFILE(FILENAME) reads data from text file FILENAME +% for the default selection. Returns the data as a table. +% +% FOE0140NEW = IMPORTFILE(FILE, DATALINES) reads data for the specified +% row interval(s) of text file FILENAME. Specify DATALINES as a +% positive scalar integer or a N-by-2 array of positive scalar integers +% for dis-contiguous row intervals. +% +% Example: +% FOE0140new = importfile("D:\git\BeCOME-Actigraphy\generated\ActiNew\FOE0140_new.csv", [29, 40177]); +% +% See also READTABLE. +% +% Auto-generated by MATLAB on 08-Oct-2020 14:28:30 + +%% Input handling + +% If dataLines is not specified, define defaults +if nargin < 2 + dataLines = [29, Inf]; +end + +%% Setup the Import Options and import the data +opts = delimitedTextImportOptions("NumVariables", 12, "Encoding", "UTF-8"); + +% Specify range and delimiter +opts.DataLines = dataLines; +opts.Delimiter = ","; + +% Specify column names and types +opts.VariableNames = ["Zeile", "Datum", "Zeit", "StatusNichtamHandgelenk", "Aktivitt", "Markierung", "WeiesLicht", "RotesLicht", "GrnesLicht", "BlauesLicht", "SchlafWach", "Intervallstatus"]; +opts.VariableTypes = ["double", "datetime", "datetime", "double", "double", "double", "double", "double", "double", "double", "double", "categorical"]; + +% Specify file level properties +opts.ExtraColumnsRule = "ignore"; +opts.EmptyLineRule = "read"; + +% Specify variable properties +opts = setvaropts(opts, "Intervallstatus", "EmptyFieldRule", "auto"); +opts = setvaropts(opts, "Datum", "InputFormat", "dd.MM.yyyy"); +opts = setvaropts(opts, "Zeit", "InputFormat", "HH:mm:ss"); + +% Import the data +actiTable = readtable(filename, opts); + +end \ No newline at end of file diff --git a/functions/loadSubjectData.m b/functions/loadSubjectData.m new file mode 100644 index 0000000..f9ac099 --- /dev/null +++ b/functions/loadSubjectData.m @@ -0,0 +1,59 @@ +function [diarySleepTable, acti, ecgData] = loadSubjectData(diarydir, actidir, ecgdir, subName) +subNameOld = sprintf('%s_30s',subName); +subNameNew = sprintf('%s_new',subName); +actifile = fullfile(actidir, [subName, '.csv']); +actifile_new = fullfile(actidir, [subNameNew, '.csv']); +actifile_old = fullfile(actidir, [subNameOld, '.xls']); +diaryfile = fullfile(diarydir, [subName, '_subsleep.xlsx']); +ecgFile = fullfile(ecgdir, [subName, '.EDF']); +ecgFileRR = fullfile(ecgdir, [subName, '.mat']); + +% preprocess the diary file +if(exist(diaryfile, 'file')) + diarySleepRaw = readSleepDiary(diaryfile); + diarySleepTable = convertSleepDiary(diarySleepRaw); +else + warning('%s: diary is missing\n', subName); +end + + +% preprocess the actifile, convert time zone, cut/clean data +if(exist(actifile, 'file')) + acti = readActigraphy(actifile); +elseif(exist(actifile_old, 'file')) + acti = readOldActigraphy(actifile_old); +elseif (exist(actifile_new, 'file')) + acti = readActigraphyNew(actifile_new); +else + acti = []; + warning('%s: acti is missing\n', subName); +end + +% preprocess ECG file +if(exist(ecgFileRR, 'file')) & (exist(ecgFile, 'file')) + ecgData = load (ecgFileRR); + ecgData.hdr = edfread(ecgFile); + ecgData.rawData = []; %read in the edf file only the hdr +elseif (exist(ecgFile, 'file')) + [hdr,record] = edfread(ecgFile);%read in the edf file and get the raw data out (if you dont have RR files) + ecgData.rawData = record(1,:); + ecgData.hdr = hdr; + ecgData.rr = []; +else + ecgData.rawData = []; + ecgData.rr = []; + warning('%s: ECG is missing\n', subName); +end + + + +% if there are diaries and actigraphy sets make sure dates are aligned +if(exist(diaryfile, 'file')) && (~isempty(acti)) + diarySleepTable = alignDates(diarySleepTable,acti,subName); +else + diarySleepTable = []; +end + + +end + diff --git a/functions/loadSubjectDataDreemSD.m b/functions/loadSubjectDataDreemSD.m new file mode 100644 index 0000000..f252f26 --- /dev/null +++ b/functions/loadSubjectDataDreemSD.m @@ -0,0 +1,105 @@ +function [allinoneHypno, allinoneReport] = loadSubjectDataDreemSD (dreemdir, subName) +%% read in files/day +allFiles = dir(dreemdir); +allFiles = allFiles(~[allFiles.isdir]); +allFiles = struct2table(allFiles); +participantFiles = allFiles(startsWith(convertCharsToStrings(allFiles.name),subName),:); + +minFileSize = 100000000; %exclude the files where we just start a recording for showing participants how dreem works + +allinoneHypno = timetable(); +allinoneReport = table(); + +for record = 1:height(participantFiles) + fileName = sprintf(participantFiles.name{record}); + if (~isempty(participantFiles))&& participantFiles.bytes(record)> minFileSize + + + if length(fileName) == 20 + fileNr = str2double(sprintf(fileName(8:9))); + elseif length(fileName) == 19 + fileNr = str2double(sprintf(fileName(8))); + end + %read in the hypno file for the given recording + TThypno = util.readHypnoFromH5 (fullfile(dreemdir, fileName)); + TThypno.DayCount = repmat(fileNr, height(TThypno),1); + allinoneHypno = vertcat(allinoneHypno,TThypno); + + %read in the report file for the given recording + reportRaw = util.readReportFromH5(fullfile(dreemdir, fileName)); + reportRaw.duration_n1 = sum(TThypno.hypnoConverted == 3)*seconds(30); + reportRaw.duration_n1.Format = 'hh:mm:ss'; + reportRaw.duration_n2 = sum(TThypno.hypnoConverted == 2)*seconds(30); + reportRaw.duration_n2.Format = 'hh:mm:ss'; + reportRaw.duration_n3 = sum(TThypno.hypnoConverted == 1)*seconds(30); + reportRaw.duration_n3.Format = 'hh:mm:ss'; + reportRaw.duration_rem = sum(TThypno.hypnoConverted == 4)*seconds(30); + reportRaw.duration_rem.Format = 'hh:mm:ss'; + try + allinoneReport = vertcat(allinoneReport,reportRaw); + + catch + if isempty(reportRaw.acc_confidence{1,1}) + reportRaw.acc_confidence = NaN; + reportRaw.ppg_confidence = NaN; + + end + allinoneReport = vertcat(allinoneReport,reportRaw); + end + elseif (~isempty(participantFiles))&& participantFiles.bytes(record)< minFileSize + warning('%s: Record is too short,not a night recording.', fileName) + + else + warning('%s: hypno and report are missing from day %d\n', subName, record); + end +end + +%organizing the tables into a nice format +if (~isempty(allinoneReport) && ~isempty(allinoneHypno)) + allinoneHypno = sortrows(allinoneHypno); + + columnNamesToDelete = {'deep_sleep_duration','dreemnogram','sleep_score_confidence','stim_impact','smart_alarm_enable_time',... + 'max_alarm_ring_time','real_alarm_ring_time','sleep_onset_score','favorite_position','diff_heart_rate','alarm_ring_label','alarm_score',... + 'dreem_boost','activation_vector','activation_sleep_onset','activation_stims','activation_alarm','sip_package','quality_over_night',... + 'alarm_package','sub_sleep_scores','restlessness','ratio_favorite_position','favorite_position_sleep_onset','ratio_favorite_position_sleep_onset',... + 'positiongram','brainogram','metrigram_downsampling','last_asleep_label',... + 'complete','timezone','algo_version','reason_for_stop','is_nap','sip_used','sip_duration','alarm_type','alarm_label','alarm_ring_sample','alarm_ring_enable_time',... + 'alarm_ring_max_time','alarm_ring_start_time','alarm_ring_duration','real_version','root_version','smart_alarm','recovery','config_template_id',... + 'config_template_version','emergency_alarm','corruption_over_night',... + 'record_type','first_n3_duration','radar_scores','radar_metrics','acc_confidence','ppg_confidence','facc_confidence','fppg_confidence',... + 'eeg_frontal_confidence','eeg_occipital_confidence','n3_confidence','sleep_onset_confidence','relaxogram','heartrategram_confidence',... + 'respirogram_confidence','positiongram_confidence','on_head_confidence','sleep_efficiency_cbt','record_score','avg_current',... + 'nb_stimuli_control_in_onset','nb_stimuli_control_after_onset','quality_sleep_efficiency','quality_sleep_onset_duration','quality_awakenings_duration',... + 'quality_awakenings','nb_relax_metric_thresholds','relax_metric_thresholds','quality_sleep_schedule_cbt','quality_sleep_schedule',... + 'relax_metric_delta_comp','time_before_first_relax_state','relax_score','max_relax_state','relax_states_durations','microphone',... + 'record_uuid','fabric_serial','fabric_type','fabric_date','distractions','stop_time','duration','wake_up_durations'}; + + allinoneReport (:, columnNamesToDelete) = []; + + %change it into real time + allinoneReport.sleep_start_time = datetime(allinoneReport.sleep_start_time, 'ConvertFrom', 'posixtime', 'TimeZone', 'UTC'); + allinoneReport.sleep_start_time.TimeZone = 'local'; + allinoneReport.sleep_stop_time = datetime(allinoneReport.sleep_stop_time, 'ConvertFrom', 'posixtime', 'TimeZone', 'UTC'); + allinoneReport.sleep_stop_time.TimeZone = 'local'; + % new doro + allinoneReport.sleep_onset_time = datetime(allinoneReport.sleep_onset_time, 'ConvertFrom', 'posixtime', 'TimeZone', 'UTC'); + allinoneReport.sleep_onset_time.TimeZone = 'local'; + allinoneReport.start_time = datetime(allinoneReport.start_time, 'ConvertFrom', 'posixtime', 'TimeZone', 'UTC'); + allinoneReport.start_time.TimeZone = 'local'; + + %change durations into durations + allinoneReport.sleep_duration = seconds (allinoneReport.sleep_duration); + allinoneReport.sleep_duration.Format = 'hh:mm:ss'; + allinoneReport.sleep_onset_duration = seconds (allinoneReport.sleep_onset_duration); + allinoneReport.sleep_onset_duration.Format = 'hh:mm:ss'; + allinoneReport.waso = seconds (allinoneReport.waso); + allinoneReport.waso.Format = 'hh:mm:ss'; + + + + % test: + while numel(find(diff(allinoneHypno.Time)<= 0)) > 0 + doubleIdx = find(diff(allinoneHypno.Time)<= 0); + allinoneHypno(doubleIdx,:) = []; + end +end \ No newline at end of file diff --git a/functions/noon.m b/functions/noon.m new file mode 100644 index 0000000..9d71c82 --- /dev/null +++ b/functions/noon.m @@ -0,0 +1,20 @@ +function time = noon(dates) + +for i = 1:length(dates) + if (hour(dates(i))>= 0) && (hour(dates(i))< 12) + time(i) = dates(i) - caldays(1); + time.Hour = 12; + time.Minute = 00; + time.Second = 00; + + %caldiff(diarySleep.dayNoon(i)-diarySleep.sleepOnset(i)) + else + time(i) = dates(i); + time.Hour = 12; + time.Minute = 00; + time.Second = 00; + end +end + +time = time'; +end \ No newline at end of file diff --git a/functions/readActigraphyNew.m b/functions/readActigraphyNew.m new file mode 100644 index 0000000..e667e59 --- /dev/null +++ b/functions/readActigraphyNew.m @@ -0,0 +1,43 @@ +function data = readActigraphyNew(filename) + +data = importPhilipsActi(filename); % read actigraphy table +data.Datum.Format = 'dd.MM.uuuu HH:mm'; +data.Zeit.Format = 'dd.MM.uuuu HH:mm'; +data.Properties.VariableNames{'Aktivitt'} = 'movements'; +data.timeMerged = data.Datum + timeofday(data.Zeit); +data.timeMerged = datetime(data.timeMerged, 'TimeZone', 'UTC'); + +data = table2timetable(data, 'RowTimes',data.timeMerged); +data.Properties.DimensionNames{'Time'} = 'time'; +data.time.TimeZone = 'Europe/Berlin'; + +% firstNanRow = find(isnat(data.time)); +% % if ~isempty(firstNanRow) +% data = data(1:(firstNanRow-1),:); +% else +% data = data(1:end,:); +% end + +% extract date information only from actigraphy +[y, m, d] = ymd(data.time); +dates = datetime([y m d]); +dates.TimeZone = 'Europe/Berlin'; + +% if recording starts before 12 AM, cut off data before +firstday = dates(1); +idx_firstday = find(dates == firstday); +data.time(idx_firstday); +l = length(idx_firstday); + +for i = 1:(l-1) + if hour(data.time(idx_firstday(1)+i)) >= 12 + idx_12 = idx_firstday(1)+i; + break + end +end + +if ~exist('idx_12') + idx_12 = height(data); +end +end + diff --git a/functions/sleepDetection.m b/functions/sleepDetection.m new file mode 100644 index 0000000..79c7263 --- /dev/null +++ b/functions/sleepDetection.m @@ -0,0 +1,183 @@ +function sleepData = sleepDetection(sleepData,timeCenteredMean,sleepBoutThreshold,percTreshold,sleepGapLength,centeredBoutMean,meanTreshold) +% actiData: timetable with one column: +% movesum: double +% classifying potential sleep +% 'bins with activity counts below 15% of the 24h centered moving +% average are classified as sleep' +% 24h centered moving average on the cleanedActi data + +M = movmean(sleepData.movesum,timeCenteredMean,'omitnan','SamplePoints',sleepData.time); +tmp = [sleepData.movesum,M]; + +% Compare movesum and movemean in each row. See if cleanedActi.movesum(i) is +% less than 15% of M(i). if yes than its sleep if no than its not. +sleep = []; +for i = 1:length(tmp) + perc = tmp(i,1)*100/tmp(i,2); + if perc < percTreshold + sleep(i) = 1; + else + sleep(i) = 0; + end +end + +% remove last sleep, if there is no offset +if(sleep(end) == 1) + k = 0; + while(sleep(end-k) == 1) + sleep(end-k) = 0; + k = k+1; + end +end + +% Filtering procedure: Bout length filter on sleep criteria, only call it sleep if > 30 min (sleepBoutThreshold) +[sleepLengthFilter, ~] = excludeNotSleep(sleep', sleepBoutThreshold); + +% --> until here: binary sleep variable of min 30 min sleep epochs +% --> now: find the problematic sleep offset! +% correlation procedure : Pearsons correlations coefficient on generated binary test +% series matrix to determine sleep offsets generate the Time series matrix +Ts = ones(72,72); % 12 hour mock matrix, each colum is one example of how sleep could "be present" +for i = 2: size (Ts) + Ts(i,1:(i-1)) =0; +end + + +% cut up the binary sleep column into sleep periods (from onset +12h periods) +% make an onset index for all 1 following 0 and followed by 1 (sleep periods onsets) +for i = 2:(length(sleepLengthFilter)-1) % until length - 1 in case there is a 1 followed by a 1 at the end of recording + if sleepLengthFilter(i) ==1 && sleepLengthFilter(i-1) ==0 && sleepLengthFilter(i+1) ==1 + sleepOns(i) = 1; + else + sleepOns(i) = 0; + end +end + +sleepOns = [sleepOns';0]; % add a final 0 because we subtracted 1 from length in step above +sleep = sleep'; + +% take the sleepOns data apart into sleep sections according to the +% sleepOns onsets (sum(sleepOns) = how many small sleep periods are +% in the data +sleep_periods = zeros(72,sum(sleepOns)); % 72 cause its 12 hours +count = 1; + +for i = 1:length(sleepOns) + if sleepOns(i)==1 && i+71< length(sleepOns) + sleep_periods(:,count) = sleep(i:i+71); + count = count+1; + elseif sleepOns(i)==1 && i+71> length(sleepOns) + sleep_periods(1:length(sleep(i:end)),count) = sleep(i:end); + count = count+1; + end +end + +% correlate each row (sleep period) with each column of the Test +% serie matrix and save all the rho-s in a variable for each sleep +% period +for i = 1:size(sleep_periods,2) + for j = 1:size(Ts,2) + sleep_rho(j,i) = corr(sleep_periods(:,i),Ts(:,j)); + end +end + +% create a sleep offset criteria and varible +% whenever it finds a rho number which is bigger than the one +% before it and bigger than the next 3 following it stops in +% that column and goes to the next one +sleepOffs = zeros(size(sleep_rho)); +for i = 1:size(sleep_rho,2) + for j = 2:(size(sleep_rho,1)-1) % have to correct for the filtering size--> the first one should not be a sleep offset and the last three has to be included in the filter, so the loop have to be smaller than that (72-3) + % if (sleep_rho(j,i) >= sleep_rho(j-1,i)) && (sleep_rho(j,i)>sleep_rho(j+1,i)) && sleep_rho(j,i)>sleep_rho(j+2,i) && sleep_rho(j,i)>sleep_rho(j+3,i) && sleep_rho(j,i)>sleep_rho(j+4,i) + if (sleep_rho(j,i) >= sleep_rho(j-1,i)) && (sleep_rho(j,i)>max(sleep_rho((j+1):min(j+sleepGapLength, length(sleepOffs)),i))) + sleepOffs(1:j,i)= 1; + break + end + end +end + +% ################## integration into sleep table ################# +sleepOnsets = find(sleepOns == 1); +assert(length(sleepOnsets) == size(sleepOffs, 2)); + +tillSleep = zeros(length(sleepOns),1); +for i=1:length(sleepOnsets) + onsetIdx = sleepOnsets(i); + sleepLength = find(sleepOffs(:,i) == 0, 1)-1; + + if(sleepLength > 0) + offsetIdx = onsetIdx+sleepLength-1; + tillSleep(onsetIdx:offsetIdx) = 1; + end +end + +sleepData = timetable(sleepData.time,sleepLengthFilter,tillSleep); +sleepData.Properties.VariableNames{1} = 'withLengthFilter'; +sleepData.Properties.VariableNames{2} = 'withCorrelationFilter'; + +% ###################################################### --> still too +% fragmented for our purpose (on point definition of sleep ons and offs +% instead of the awakenings during the night +SM = movmean(sleepData.withCorrelationFilter,centeredBoutMean,'omitnan','SamplePoints',sleepData.Time); + +sleepMeanBouts = []; +for i = 1:length(SM) + if SM(i) < meanTreshold + sleepData.sleepMeanBouts(i,1) = 0; + else + sleepData.sleepMeanBouts(i,1) = 1; + end +end + +% quality Check +%if the whole recording starts with 1 change it to 0 until the first +%numbers because it cant start with sleep!! This is an artifact in the data +if sleepData.sleepMeanBouts(1) == 1 + [ii, jj]=find(~sleepData.sleepMeanBouts); + sleepData.sleepMeanBouts (1:ii(1)-1) = 0; +end +%if the whole recording ends with 1 change it to 0 until the first +%numbers because it cant end with sleep!! This is an artifact in the data +if sleepData.sleepMeanBouts(end) == 1 + [ii, jj]=find(~sleepData.sleepMeanBouts); + sleepData.sleepMeanBouts (ii(end)+1:end) = 0; +end + +%figure, plot([sleepData.withCorrelationFilter, SM, sleepData.sleepMeanBouts*2]) +sleepData.Properties.VariableNames{3} = 'withSmoothedBouts'; + +% correct offset (comparing withCorrelationFilter and withSmoothedBouts) +% sometimes, smoothing overerstimates the offset, +% so if it does not coincide with the correlation filter value, take the last 1 from the correlation filter +offs = find(diff(sleepData.withSmoothedBouts)==-1); +for off = offs' + if(sleepData.withCorrelationFilter(off)== 1) + corrOff = find(sleepData.withCorrelationFilter(off:end)== 0,1)+ off-2; + sleepData.withSmoothedBouts(off:corrOff) = 1; + else + corrOff = find(sleepData.withCorrelationFilter(1:off)==1,1,'last'); + sleepData.withSmoothedBouts((corrOff+1):off) = 0; + end +end + +% correct onsets (comparing withCorrelationFilter and withSmoothedBouts) +ons = find(diff(sleepData.withSmoothedBouts)==1)+1; +for on = ons' + if(sleepData.withCorrelationFilter(on)==1) + corrOn = find(sleepData.withCorrelationFilter(1:on)==0,1,'last')+1; + sleepData.withSmoothedBouts(corrOn:on) = 1; + else + corrOn = find(sleepData.withCorrelationFilter(on:end)==1,1)+on-1; + sleepData.withSmoothedBouts(on:(corrOn-1)) = 0; + end +end + +% check whether step above worked +smoothedOns = find(diff(sleepData.withSmoothedBouts)==1)+1; +corrOns = find(diff(sleepData.withCorrelationFilter)==1)+1; +assert(isempty(setdiff(smoothedOns,corrOns)), 'artificial onset in smoothedSleep'); + +smoothedOffs = find(diff(sleepData.withSmoothedBouts)==-1)+1; +corrOffs = find(diff(sleepData.withCorrelationFilter)==-1)+1; +assert(isempty(setdiff(smoothedOffs,corrOffs)), 'artificial offset in smoothedSleep'); +end \ No newline at end of file diff --git a/libs/.DS_Store b/libs/.DS_Store new file mode 100644 index 0000000..6443372 Binary files /dev/null and b/libs/.DS_Store differ diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/.github/ISSUE_TEMPLATE/bug_report.md b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 0000000..3d9ef7a --- /dev/null +++ b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,31 @@ +--- +name: Bug report +about: Create a report to help us improve +title: '' +labels: '' +assignees: '' + +--- + +**Describe the bug** +A clear and concise description of what the bug is. + +**To Reproduce** +Steps to reproduce the behavior: +1. Go to '...' +2. Click on '....' +3. Scroll down to '....' +4. See error + +**Expected behavior** +A clear and concise description of what you expected to happen. + +**Screenshots** +If applicable, add screenshots to help explain your problem. + +**Desktop (please complete the following information):** + - OS: [e.g. iOS] + - Matlab version and installed toolboxes + +**Additional context** +Add any other context about the problem here. diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/.DS_Store b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/.DS_Store new file mode 100644 index 0000000..28799d6 Binary files /dev/null and b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/.DS_Store differ diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/Basic_Demo.m b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/Basic_Demo.m new file mode 100644 index 0000000..db10a64 --- /dev/null +++ b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/Basic_Demo.m @@ -0,0 +1,73 @@ +% OVERVIEW: +% This basic demo will allow you to load an ECG file in matlab +% compatible wfdb format, detect the locations of the R peaks, +% perform signal quality (SQI) analysis and plot the results. +% +% OUTPUT: +% A figure with the loaded ECG signal and detected peaks will be +% generated +% +% DEPENDENCIES & LIBRARIES: +% https://github.com/cliffordlab/PhysioNet-Cardiovascular-Signal-Toolbox +% REFERENCE: +% Vest et al. "An Open Source Benchmarked HRV Toolbox for Cardiovascular +% Waveform and Interval Analysis" Physiological Measurement (In Press), 2018. +% REPO: +% https://github.com/cliffordlab/PhysioNet-Cardiovascular-Signal-Toolbox +% ORIGINAL SOURCE AND AUTHORS: +% Giulia Da Poian +% COPYRIGHT (C) 2018 +% LICENSE: +% This software is offered freely and without warranty under +% the GNU (v3 or later) public license. See license file for +% more information +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +clear all +clc + +run(['..' filesep 'startup.m']) + +% Where are the data, in this demo they are located in a subfolder +InputFolder = [pwd filesep 'TestData' filesep 'mitdb-Arrhythmia']; % path to the folder where you data are located +SigName = '200m'; + +% load the ecg signa using load (it loads a variable called val) +load([InputFolder filesep SigName]); +% the signal has two channels, from now on we will use just one +ecg = val(1,:); +% Get sampling frequency Fs from header file +sigInfo = readheader([InputFolder filesep SigName '.hea']); +Fs = sigInfo.freq; +% time vector for visualization (in seconds) +tm = 0:1/Fs:(length(ecg)-1)/Fs; + +% plot the signal +figure(1) +plot(tm,ecg); +xlabel('[s]'); +ylabel('[mV]') + + +% Detection of the R-peaks using the jqrs.m function included in the +% toolbox, requires to set initialization parameters calling the +% InitializeHRVparams.m function + +HRVparams = InitializeHRVparams('Demo'); +% set the exact sampling frequency usign the one from the loaded signal +HRVparams.Fs = Fs; +% call the function that perform peak detection +r_peaks = jqrs(ecg,HRVparams); + +% plot the detected r_peaks on the top of the ecg signal +figure(1) +hold on; +plot(r_peaks./Fs, ecg(r_peaks),'o'); +legend('ecg signal', 'detected R peaks') + + + + + + + diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/DemoAnnotatedData.m b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/DemoAnnotatedData.m new file mode 100644 index 0000000..74895e4 --- /dev/null +++ b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/DemoAnnotatedData.m @@ -0,0 +1,111 @@ +% OVERVIEW: +% This is demo for the HRV PhysioNet Cardiovascular Signal Toolbox +% using RR intervals with annotations. +% Provided data are a subset from the MIT Physionet +% NSR dataset, which contains long-term ECG recordings of subjects +% in normal sinus rhythm. +% It shows how to automaticly import multiple files from a folder, +% perfrom the HRV analysis on each of them and then store the results +% in .csv format. +% It uses the default parameters in the configuration file using +% 'demo_NSR' option : InitializeHRVparams('demo_NSR'). +% +% OUTPUT: +% HRV Metrics exported to .cvs files +% +% DEPENDENCIES & LIBRARIES: +% https://github.com/cliffordlab/PhysioNet-Cardiovascular-Signal-Toolbox +% REFERENCE: +% Vest et al. "An Open Source Benchmarked HRV Toolbox for Cardiovascular +% Waveform and Interval Analysis" Physiological Measurement (In Press), 2018. +% REPO: +% https://github.com/cliffordlab/PhysioNet-Cardiovascular-Signal-Toolbox +% ORIGINAL SOURCE AND AUTHORS: +% Giulia Da Poian +% COPYRIGHT (C) 2018 +% LICENSE: +% This software is offered freely and without warranty under +% the GNU (v3 or later) public license. See license file for +% more information +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +clear; clc; close all; + +run(['..' filesep 'startup.m']) + +% Remove old files generated by this demo +OldFolder = [pwd,filesep, 'OutputData', filesep, 'ResultsNSR']; +if exist(OldFolder, 'dir') + rmdir(OldFolder, 's'); + fprintf('Old Demo Folder deleted \n \n'); +end + + +% Initialize settings for demo +HRVparams = InitializeHRVparams('demo_NSR'); +HRVparams.MSE.on = 0; % No MSE analysis for this demo +HRVparams.DFA.on = 0; % No DFA analysis for this demo +HRVparams.HRT.on = 0; % No HRT analysis for this demo +HRVparams.output.separate = 0; % For this demo write all the results in one file + + +% Check for a list of files to be analyzed in current directory in .dat format +[subjectIDs,filesTBA] = GenerateListOfFilesTBA(HRVparams.ext, HRVparams.readdata,[]); + +% Prepare for parallel loop by eliminating variables +clear nummatchingfiles x i filename flag match +numsub = length(subjectIDs); +notAnalyzed = 0; + +% NOTE: This loop can be run in parallel by changing the loop to a parfor +% loop. +for i_patient = 1:numsub + + thisPatient = strcat(HRVparams.readdata, filesep, subjectIDs(i_patient)); + try + + % 1. Import Patient Data + RRwindowStartIndices = []; + tNN = []; + NN = []; + [samples,annotations] = read_ann(thisPatient{1},HRVparams.ext); + rr = diff(samples)./HRVparams.Fs; + t = cumsum(rr); + + % Demo keeps only the first 2h + + rr = rr(t<60*60*2); + t = t(t<60*60*2); + + % 2. Perform HRV analysis on the RR intervals + [results, resFilenameHRV] = Main_HRV_Analysis(rr,t,'RRIntervals',HRVparams,subjectIDs(i_patient),annotations); + currentFile = [HRVparams.writedata filesep resFilenameHRV.HRV '.csv']; + + fprintf('\n'); + catch + + results = NaN; + col_titles = {'NaN'}; + currentFile = ''; + notAnalyzed = 1; + fprintf('Error on subject %s \n', char(subjectIDs(i_patient))); + + end + +end + + +% 3. Compare generated output file with the reference one + +referenceFile = ['ReferenceOutput' filesep 'NSR_HRV_allwindows_allpatients.csv']; +testHRV = CompareOutput(currentFile,referenceFile); + +if testHRV + fprintf('\n ** DemoAnnotatedData: TEST SUCCEEDED ** \n ') +elseif notAnalyzed == 0 + fprintf('\n ** DemoAnnotatedData: TEST FAILED ** \n') + fprintf('Error: generated output does not match reference \n') +elseif notAnalyzed == 1 + fprintf('\n** DemoAnnotatedData: TEST FAILED ** \n') + fprintf('Error: analysis not performed \n'); +end diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/DemoHRT.m b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/DemoHRT.m new file mode 100644 index 0000000..f0596a2 --- /dev/null +++ b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/DemoHRT.m @@ -0,0 +1,77 @@ +% OVERVIEW: +% Demo for Heart Rate Turbulence (HRT) analysis using the Physionet +% Cardiovascular Signal Toolbox +% Provided data are from the MIT Physionet Arrhytmia Database +% (https://www.physionet.org/physiobank/database/mitdb/) +% +% OUTPUT: +% HRV Metrics exported to .cvs files +% +% DEPENDENCIES & LIBRARIES: +% https://github.com/cliffordlab/PhysioNet-Cardiovascular-Signal-Toolbox +% REFERENCE: +% Vest et al. "An Open Source Benchmarked HRV Toolbox for Cardiovascular +% Waveform and Interval Analysis" Physiological Measurement (In Press), 2018. +% REPO: +% https://github.com/cliffordlab/PhysioNet-Cardiovascular-Signal-Toolbox +% ORIGINAL SOURCE AND AUTHORS: +% Giulia Da Poian +% COPYRIGHT (C) 2018 +% LICENSE: +% This software is offered freely and without warranty under +% the GNU (v3 or later) public license. See license file for +% more information +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +clear; clc; close all; + +run(['..' filesep 'startup.m']) + +% Remove old files generated by this demo +OldFolder = [pwd, filesep, 'OutputData', filesep, 'ResultsHRT']; +if exist(OldFolder, 'dir') + rmdir(OldFolder, 's'); + fprintf('Old Demo Folder deleted \n'); +end + + +% Initialize settings for demo +HRVparams = InitializeHRVparams('demoHRT'); +HRVparams.af.on = 0; % No AF analysis for this demo +HRVparams.MSE.on = 0; % No MSE analysis for this demo +HRVparams.DFA.on = 0; % No DFA analysis for this demo +HRVparams.timedomain.on= 0 ; % No Time Domain analysis for this demo +HRVparams.freq.on= 0 ; % No Frequency Domain analysis for this demo +HRVparams.Entropy.on = 0 ; % No Frequency Domain analysis for this demo +HRVparams.poincare.on = 0 ; % No Frequency Domain analysis for this demo +HRVparams.prsa.on = 0 ; % No Frequency Domain analysis for this demo +HRVparams.HRT.GraphOn = 1; % Plot results + + +% Load qrs and annotations from file +[qrs,ann] = read_ann(['TestData', filesep, 'Physionet_nsr2db' ... + filesep 'nsr004'],'ecg'); + +% Remove non-rhythm annotations +qrs(ann=='+' | ann=='~' | ann=='|' | ann=='x') = []; +ann(ann=='+' | ann=='~' | ann=='|' | ann=='x') = []; + + +RRInts = diff(qrs./HRVparams.Fs); % in seconds +tRRInts = qrs(2:end)./HRVparams.Fs; +Labels = ann(2:end); + +% Perform HRT analysis +[results, resFilename] = Main_HRV_Analysis(RRInts,tRRInts,'RRIntervals',... + HRVparams,'nsr004',Labels); + +% 3. Compare generated output file with the reference one +currentFile = [HRVparams.writedata filesep resFilename.HRT '.csv']; +referenceFile = ['ReferenceOutput' filesep 'HRTtest.csv']; +testHRV = CompareOutput(currentFile,referenceFile); + +if testHRV + fprintf('** Heart Rate Turbulence Demo: TEST SUCCEEDED ** \n ') +else + fprintf('** Heart Rate Turbulence Demo: TEST FAILED ** \n') +end + diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/DemoMultiScaleEntropy.m b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/DemoMultiScaleEntropy.m new file mode 100644 index 0000000..9eb3fcf --- /dev/null +++ b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/DemoMultiScaleEntropy.m @@ -0,0 +1,92 @@ +% OVERVIEW: +% Compute the MultiScale Entropy using RR intervals generated by +% RRgen +% OUTPUT: +% HRV Metrics exported to .cvs files +% +% DEPENDENCIES & LIBRARIES: +% https://github.com/cliffordlab/PhysioNet-Cardiovascular-Signal-Toolbox +% REFERENCE: +% Vest et al. "An Open Source Benchmarked HRV Toolbox for Cardiovascular +% Waveform and Interval Analysis" Physiological Measurement (In Press), 2018. +% REPO: +% https://github.com/cliffordlab/PhysioNet-Cardiovascular-Signal-Toolbox +% ORIGINAL SOURCE AND AUTHORS: +% Giulia Da Poian +% COPYRIGHT (C) 2018 +% LICENSE: +% This software is offered freely and without warranty under +% the GNU (v3 or later) public license. See license file for +% more information +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +clear all; clc; close all; + +run(['..' filesep 'startup.m']) + +% Remove old files generated by this demo +OldFolder = [pwd, filesep, 'OutputData', filesep, 'ResultsNSR']; +if exist(OldFolder, 'dir') + rmdir(OldFolder, 's'); + fprintf('Old Demo Folder deleted \n'); +end + + +% Initialize settings for demo +HRVparams = InitializeHRVparams('demo_RRgen'); +HRVparams.HRT.on = 0; % No HRT analysis for this demo +HRVparams.DFA.on = 0; % No DFA analysis for this demo + +% 1. Generate Data using RRgen + +rr = rrgen(HRVparams.demo.length,HRVparams.demo.pe,HRVparams.demo.pn,HRVparams.demo.seed); +t = cumsum(rr); + +% 2. Preprocess RR Data - Using HRV Toolbox +% Remove noise, Remove ectopy, Don't detrend (yet) +[NN, tNN, fbeats] = RRIntervalPreprocess(rr,t,[], HRVparams); + +% 3. Calculate the Multiscale Entropy + +fprintf('Computing MSE...this may take a few minutes...\n') +fprintf('Parameters used to calculate SempEntropy: m=%i r=%.2f \n', HRVparams.MSE.patternLength, HRVparams.MSE.RadiusOfSimilarity); +WindIdxs = CreateWindowRRintervals(tNN, NN, HRVparams,'mse'); +mse = EvalMSE(NN,tNN,[],HRVparams,WindIdxs); +fprintf('MSE completed!\n') + +% 4. Plot +fig = figure(); +ax = axes('Parent',fig, 'Position',[0.15 0.15 0.7 0.7]); +plot(mse,'LineWidth',2); +xlabel('Scale Factor'); +ylabel('H_{s}'); +title('Multiscale Entropy') +set(ax,'FontSize',18); +grid on + + +% 5. Save Results +Scales = 1:HRVparams.MSE.maxCoarseGrainings; +results = [Scales' mse]; +for i=1:length(WindIdxs); W{i}=strcat('t_', num2str(WindIdxs(i)));end +col_titles = {'Scales' W{:}}; + +% Generates Output - Never comment out +resFilenameMSE = SaveHRVoutput('RRgenData',[],results,... + col_titles, 'MSE', HRVparams, tNN, NN); + + + +% 6 Compare generated output file with the reference one + +currentFile = [HRVparams.writedata filesep resFilenameMSE '.csv']; +referenceFile = ['ReferenceOutput' filesep 'MSEDemo.csv']; +testHRV = CompareOutput(currentFile,referenceFile); + +if testHRV + fprintf('** DemoMultiScaleEntropy: TEST SUCCEEDED ** \n ') + fprintf('A file named %s.csv \n has been saved in %s \n', ... + resFilenameMSE, HRVparams.writedata); +else + fprintf('** DemoMultiScaleEntropy: TEST FAILED ** \n') +end diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/DemoPVC.m b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/DemoPVC.m new file mode 100644 index 0000000..b321934 --- /dev/null +++ b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/DemoPVC.m @@ -0,0 +1,81 @@ +% OVERVIEW: +% This is demo for the PVC detector included in the HRV PhysioNet +% Cardiovascular Signal Toolbox +% +% Provided data are a subset from the MIT Physionet Arrhythmia +% dataset +% It uses the default parameters in the configuration file using +% 'demo_PVC' option : InitializeHRVparams('demoPVC'). +% +% OUTPUT: +% HRV Metrics exported to .cvs files +% +% DEPENDENCIES & LIBRARIES: +% https://github.com/cliffordlab/PhysioNet-Cardiovascular-Signal-Toolbox +% REFERENCE: +% Vest et al. "An Open Source Benchmarked HRV Toolbox for Cardiovascular +% Waveform and Interval Analysis" Physiological Measurement (In Press), 2018. +% REPO: +% https://github.com/cliffordlab/PhysioNet-Cardiovascular-Signal-Toolbox +% ORIGINAL SOURCE AND AUTHORS: +% Giulia Da Poian +% COPYRIGHT (C) 2018 +% LICENSE: +% This software is offered freely and without warranty under +% the GNU (v3 or later) public license. See license file for +% more information +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +clear; clc; close all; + +run(['..' filesep 'startup.m']) + +% Remove old files generated by this demo +OldFolder = ['OutputData' filesep 'Results_PVC']; +if exist(OldFolder, 'dir') + rmdir(OldFolder, 's'); + fprintf('Old Demo Folder deleted \n \n'); +end + +sigName = '105'; + +% Initialize settings for demo +HRVparams = InitializeHRVparams('demoPVC'); + +% load mat file, and use gain and baseline information from hea file + +sig = load([HRVparams.readdata filesep sigName 'm']); +siginfo = readheader([HRVparams.readdata filesep sigName 'm.hea']); +% use first 5 minutes of the first channel +lead = 1; +ecg = (sig.val(lead,1:HRVparams.Fs*300)-siginfo.adczero(lead))./siginfo.gain(lead); + +PVCs = PVC_detect(ecg',sigName,HRVparams); + +figure(1) +plot(ecg,'LineWidth',1) +xlabel('Samples (@360 Hz)','FontSize',16) +ylabel('mV','FontSize',16) +hold on +plot(PVCs, ecg(PVCs), 'o','MarkerSize',8); + +% load annotations to compare PVCs from manually annotated by experts + +[pos,type] = read_ann([HRVparams.readdata filesep sigName],'atr'); +annotatedPVCs = pos(type=='V' & pos <= HRVparams.Fs*300); + +plot(annotatedPVCs, ecg(annotatedPVCs), 'x','MarkerSize',8); +legend({'ECG signal', 'Detected PVCs', 'Annotations PVCs'},'FontSize',16) + + +% Compare generated output file with the reference one +currentFile = [HRVparams.writedata filesep 'Annotation' filesep '105.pvc']; +referenceFile = ['ReferenceOutput' filesep '105.pvc']; +testHRV = CompareOutput(currentFile,referenceFile); + +if testHRV + fprintf('\n ** Demo PVC: TEST SUCCEEDED ** \n ') +else + fprintf('\n ** Demo PVC: TEST FAILED ** \n') + fprintf('Error: generated output does not match reference \n') +end diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/DemoRawDataAF.m b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/DemoRawDataAF.m new file mode 100644 index 0000000..c7b7287 --- /dev/null +++ b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/DemoRawDataAF.m @@ -0,0 +1,85 @@ +% OVERVIEW: +% This demonstration analyzes a segment of 5-minutes 'raw' data +% with known atrial fibrillation to show the operation of the +% AF detection algorithm. +% OUTPUT: +% HRV Metrics exported to .cvs files +% +% DEPENDENCIES & LIBRARIES: +% https://github.com/cliffordlab/PhysioNet-Cardiovascular-Signal-Toolbox +% REFERENCE: +% Vest et al. "An Open Source Benchmarked HRV Toolbox for Cardiovascular +% Waveform and Interval Analysis" Physiological Measurement (In Press), 2018. +% REPO: +% https://github.com/cliffordlab/PhysioNet-Cardiovascular-Signal-Toolbox +% ORIGINAL SOURCE AND AUTHORS: +% Giulia Da Poian +% COPYRIGHT (C) 2018 +% LICENSE: +% This software is offered freely and without warranty under +% the GNU (v3 or later) public license. See license file for +% more information +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +clear; clc; close all; + +run(['..' filesep 'startup.m']) + +% Remove old files generated by this demo +OldFolder = [pwd,filesep, 'OutputData', filesep, 'ResultsAFData']; +if exist(OldFolder, 'dir') + rmdir(OldFolder, 's'); + fprintf('Old Demo Folder deleted \n'); +end + + +HRVparams = InitializeHRVparams('demoAF'); % include the project name +HRVparams.poincare.on = 0; % Poincare analysis off for this demo +HRVparams.DFA.on = 0; % DFA analysis off for this demo +HRVparams.MSE.on = 0; % MSE analysis off for this demo +HRVparams.HRT.on = 0; % HRT analysis off for this demo + + +[subjectIDs,filesTBA] = GenerateListOfFilesTBA(HRVparams.ext,... + HRVparams.readdata,0); +idx = find(strcmp(subjectIDs,'TestAFdata')); +i_patient = idx; + +% 1. Load Raw Patient Data + +load(filesTBA{i_patient}); +% 2. Analyze data using HRV PhysioNet Cardiovascular Signal Toolbox +[results, resFilename] = Main_HRV_Analysis(signal(:,1),[],'ECGWaveform',... + HRVparams,subjectIDs(i_patient)); + + +% 3. Compare generated output file with the reference one + +currentFile = strcat(HRVparams.writedata, filesep, resFilename.HRV, '.csv'); +referenceFile = strcat('ReferenceOutput', filesep, 'AFDemo.csv'); +testHRV = CompareOutput(currentFile,referenceFile); + +% 3. Load QRS annotation saved by Main_HRV_Analysis +annotName = strcat(HRVparams.writedata, filesep, 'Annotation',filesep,... + subjectIDs(i_patient)); +jqrs_ann = read_ann( annotName{1} , 'jqrs'); +wqrs_ann = read_ann( annotName{1} , 'wqrs'); + +% For demo pourpose recompute bsqi +[sqijw, StartIdxSQIwindows] = bsqi(jqrs_ann,wqrs_ann,HRVparams); + +HRVparams.gen_figs = 1; +% Plot detected beats +if HRVparams.gen_figs + Plot_SignalDetection_SQI(time, signal(:,1), jqrs_ann, sqijw,'ECG') +end + + +if testHRV + fprintf('** DemoRawDataAF: TEST SUCCEEDED ** \n ') + fprintf('A file named %s.csv \n has been saved in %s \n', ... + resFilename.HRV, HRVparams.writedata); +else + fprintf('** DemoRawDataAF: TEST FAILED ** \n') +end + + diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/DemoRawDataICU.m b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/DemoRawDataICU.m new file mode 100644 index 0000000..e64c016 --- /dev/null +++ b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/DemoRawDataICU.m @@ -0,0 +1,114 @@ +% OVERVIEW: +% This demonstration analyzes a segment of data collected in the +% intensive care unit (ICU) which contains ECG, ABP, and PPG signals +% +% OUTPUT: +% HRV Metrics exported to .cvs files +% +% DEPENDENCIES & LIBRARIES: +% https://github.com/cliffordlab/PhysioNet-Cardiovascular-Signal-Toolbox +% REFERENCE: +% Vest et al. "An Open Source Benchmarked HRV Toolbox for Cardiovascular +% Waveform and Interval Analysis" Physiological Measurement (In Press), 2018. +% REPO: +% https://github.com/cliffordlab/PhysioNet-Cardiovascular-Signal-Toolbox +% ORIGINAL SOURCE AND AUTHORS: +% Giulia Da Poian +% COPYRIGHT (C) 2018 +% LICENSE: +% This software is offered freely and without warranty under +% the GNU (v3 or later) public license. See license file for +% more information +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +clear; clc; close all; + +run(['..' filesep 'startup.m']) + +% Remove old files generated by this demo +OldFolder = [pwd,filesep, 'OutputData', filesep, 'ResultsICU']; +if exist(OldFolder, 'dir') + rmdir(OldFolder, 's'); + fprintf('Old Demo Folder deleted \n'); +end + +HRVparams = InitializeHRVparams('demoICU'); % include the project name +HRVparams.poincare.on = 0; % Pinocare analysis off for this demo +HRVparams.DFA.on = 0; % DFA analysis off for this demo +HRVparams.MSE.on = 0; % MSE analysis off for this demo +HRVparams.HRT.on = 0; % HRT analysis off for this demo + + +[subjectIDs,filesTBA] = GenerateListOfFilesTBA(HRVparams.ext,HRVparams.readdata,0); + +idx = find(strcmp(subjectIDs,'TestICUdata')); + +i_patient = idx; + +% 1. Load Raw Patient Data (ECG Waveform) +load(filesTBA{i_patient}); + +% 2. Analyze data using HRV PhysioNet Cardiovascular Signal Toolbox +[~, resFilename] = Main_HRV_Analysis(signal(:,1),[],'ECGWaveform',... + HRVparams,subjectIDs(i_patient),[],[],signal(:,5),'PPG',signal(:,3),'ABP'); + +% 3. Load annotations ans SQI for plot +AnnFile = strcat(HRVparams.writedata, filesep, 'Annotation', filesep, ... + subjectIDs{i_patient}); +jqrs_ann = read_ann(AnnFile,'jqrs'); +[~,~,sqijw] = read_ann(AnnFile,'sqijw'); % SQI is x100 to be stored + +ppg_ann = read_ann(AnnFile,'ppg'); +qppg(signal(:,5),HRVparams.Fs); +[~,ppgsqi,ppgNumSqi] = read_ann(AnnFile,'sqippg'); + +abpann = read_ann(AnnFile,'abpm'); +features = abpfeature(signal(:,3), abpann, HRVparams.Fs); +[BeatQ, goodbeats] = jSQI(features, abpann, signal(:,3)); + +% 5. Plotting +HRVparams.gen_figs = 1; +if HRVparams.gen_figs + % ECG + Plot_SignalDetection_SQI(time, signal(:,1), jqrs_ann, sqijw'./100,'ECG') + + % ABP + Plot_SignalDetection_SQI(time, signal(:,3), abpann, double(~BeatQ(:,1))','ABP') + + % RESP + figure(88) + plot(time,signal(:,6)); + title('Respiration') + + % PLETH + Plot_SignalDetection_SQI(time, signal(:,5), ppg_ann, ppgNumSqi'./100,'PPG') + +end + +% 6. Pulse Transit Time +ptt = pulsetransit(jqrs_ann, abpann); + +% 7. Plot BP vs PTT +syst = features(1:length(ptt),2); +if HRVparams.gen_figs + figure(22) + plot(syst,ptt(:,3)./HRVparams.Fs,'o'); + xlabel('BP (mmHg)'); ylabel('PTT (s)'); + title('Pulse Transit Time - BP vs PTT (ABP - QRS)') + +end + + +% 8. Compare generated output file with the reference one + +currentFile = strcat(HRVparams.writedata, filesep, resFilename.HRV, '.csv'); +referenceFile = strcat('ReferenceOutput', filesep, 'ICU_HRV_allwindows.csv'); +testHRV = CompareOutput(currentFile,referenceFile); + +if testHRV + fprintf('** DemoRawDataICU: TEST SUCCEEDED ** \n ') + fprintf('A file named %s.csv \n has been saved in %s \n', ... + resFilename.HRV, HRVparams.writedata); +else + fprintf('** DemoRawDataICU: TEST FAILED ** \n') +end \ No newline at end of file diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/DemoStandardizedData.m b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/DemoStandardizedData.m new file mode 100644 index 0000000..4f66956 --- /dev/null +++ b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/DemoStandardizedData.m @@ -0,0 +1,69 @@ +% OVERVIEW: +% This function demonstrates the function of the synthetic RR interval +% generator RRGEN and the calculation of HRV metrics +% INPUT: +% No input necessary. RRGEN data is generated within this script +% OUTPUT: +% HRV Metrics exported to .cvs files +% +% DEPENDENCIES & LIBRARIES: +% https://github.com/cliffordlab/PhysioNet-Cardiovascular-Signal-Toolbox +% REFERENCE: +% Vest et al. "An Open Source Benchmarked HRV Toolbox for Cardiovascular +% Waveform and Interval Analysis" Physiological Measurement (In Press), 2018. +% REPO: +% https://github.com/cliffordlab/PhysioNet-Cardiovascular-Signal-Toolbox +% ORIGINAL SOURCE AND AUTHORS: +% Giulia Da Poian +% COPYRIGHT (C) 2018 +% LICENSE: +% This software is offered freely and without warranty under +% the GNU (v3 or later) public license. See license file for +% more information +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +clear; clc; close all; + +run(['..' filesep 'startup.m']) + +% Remove old files generated by this demo +OldFolder = [pwd, filesep, 'OutputData', filesep, 'ResultsRRgen']; +if exist(OldFolder, 'dir') + rmdir(OldFolder, 's'); + fprintf('Old Demo Folder deleted \n'); +end + + +% Initialize settings for demo +HRVparams = InitializeHRVparams('demo_RRgen'); +HRVparams.MSE.on = 0; % No MSE analysis for this demo +HRVparams.DFA.on = 0; % No DFA analysis for this demo +HRVparams.HRT.on = 0; % No HRT analysis for this demo + + + +% 1. Generate Data + +rr = rrgen(HRVparams.demo.length,HRVparams.demo.pe,HRVparams.demo.pn,... + HRVparams.demo.seed); +t = cumsum(rr); + +% 2. Analyze RR Data - Using HRV Toolbox Main function + +[results, resFilename] = Main_HRV_Analysis(rr,t,'RRIntervals',HRVparams,... + 'rrgenData',[]); + + +% 3 Compare generated output file with the reference one + +currentFile = [HRVparams.writedata filesep resFilename.HRV '.csv']; +referenceFile = ['ReferenceOutput' filesep 'StandardizedData_HRV_allwindows.csv']; +testHRV = CompareOutput(currentFile,referenceFile); + +if testHRV + fprintf('** DemoStandardizedData: TEST SUCCEEDED ** \n ') + fprintf('A file named %s.csv \n has been saved in %s \n', ... + resFilename.HRV, HRVparams.writedata); +else + fprintf('** DemoStandardizedData: TEST FAILED ** \n') +end diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/ReferenceOutput/.DS_Store b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/ReferenceOutput/.DS_Store new file mode 100644 index 0000000..5008ddf Binary files /dev/null and b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/ReferenceOutput/.DS_Store differ diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/ReferenceOutput/105.pvc b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/ReferenceOutput/105.pvc new file mode 100644 index 0000000..980d6b7 Binary files /dev/null and b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/ReferenceOutput/105.pvc differ diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/ReferenceOutput/AFDemo.csv b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/ReferenceOutput/AFDemo.csv new file mode 100644 index 0000000..0edc72d --- /dev/null +++ b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/ReferenceOutput/AFDemo.csv @@ -0,0 +1,2 @@ +patID,t_start,t_end,NNmean,NNmedian,NNmode,NNvariance,NNskew,NNkurt,NNiqr,SDNN,RMSSD,pnn50,btsdet,avgsqi,tdflag,ulf,vlf,lf,hf,lfhf,ttlpwr,fdflag,ac,dc,SampEn,ApEn +TestAFdata,,,,,,,,,,,,,,,3,,,,,,,3,,,, diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/ReferenceOutput/HRTtest.csv b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/ReferenceOutput/HRTtest.csv new file mode 100644 index 0000000..26423b7 --- /dev/null +++ b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/ReferenceOutput/HRTtest.csv @@ -0,0 +1,2 @@ +t_win,TO,TS,nPVCs +0,-5.87307661089698,12.5976562500001,16 diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/ReferenceOutput/ICU_HRV_allwindows.csv b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/ReferenceOutput/ICU_HRV_allwindows.csv new file mode 100644 index 0000000..70cf00f --- /dev/null +++ b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/ReferenceOutput/ICU_HRV_allwindows.csv @@ -0,0 +1,2 @@ +patID,t_start,t_end,NNmean,NNmedian,NNmode,NNvariance,NNskew,NNkurt,NNiqr,SDNN,RMSSD,pnn50,btsdet,avgsqi,tdflag,ulf,vlf,lf,hf,lfhf,ttlpwr,fdflag,ac,dc,SampEn,ApEn 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a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/ReferenceOutput/NSR_HRV_allwindows_allpatients.csv b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/ReferenceOutput/NSR_HRV_allwindows_allpatients.csv new file mode 100644 index 0000000..001641a --- /dev/null +++ b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/ReferenceOutput/NSR_HRV_allwindows_allpatients.csv @@ -0,0 +1,694 @@ +patID,t_start,t_end,NNmean,NNmedian,NNmode,NNvariance,NNskew,NNkurt,NNiqr,SDNN,RMSSD,pnn50,btsdet,avgsqi,tdflag,ulf,vlf,lf,hf,lfhf,ttlpwr,fdflag,ac,dc,SD1,SD2,SD1SD2,SampEn,ApEn +nsr001,0,300,674.9797,680,728,3234.3955,-0.11512,2.66232,88,56.8717,20.1209,0.0045249,443,1,5,769.43842,2618.4264,1416.4409,121.2459,11.68,4925.5516,5,-7.677,7.7279,14.2423,79.15775,0.17992,0.97911,0.89819 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/dev/null +++ b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/TestData/AF.info @@ -0,0 +1,10 @@ +Source: record ltafdb/74 Start: [20:33:33.000 18/07/2003] +val has 2 rows (signals) and 38400 columns (samples/signal) +Duration: 5:00 +Sampling frequency: 128 Hz Sampling interval: 0.0078125 sec +Row Signal Gain Base Units +1 ECG 0 0 mV +2 ECG 0 0 mV + +To convert from raw units to the physical units shown +above, subtract 'base' and divide by 'gain'. diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/TestData/Physionet_AFdb/03665.qrs b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/TestData/Physionet_AFdb/03665.qrs new file mode 100644 index 0000000..706a51f --- /dev/null +++ b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/TestData/Physionet_AFdb/03665.qrs @@ -0,0 +1,29 @@ +=¹º¶¸¹³¸·´k'⻶¸·¹¶³k)æm3Ï»º·º·³¹´¹j ß¼¹¶¹·³¸µ¸k(Ø»º¶»m- 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b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/TestData/TestICUdata.mat new file mode 100644 index 0000000..81c68af Binary files /dev/null and b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/TestData/TestICUdata.mat differ diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/TestData/mitdb-Arrhythmia/105.atr b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/TestData/mitdb-Arrhythmia/105.atr new file mode 100644 index 0000000..41f913d Binary files /dev/null and b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/TestData/mitdb-Arrhythmia/105.atr differ diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/TestData/mitdb-Arrhythmia/105.dat b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/TestData/mitdb-Arrhythmia/105.dat new file mode 100644 index 0000000..1a5cb33 Binary files /dev/null and b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/TestData/mitdb-Arrhythmia/105.dat differ diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/TestData/mitdb-Arrhythmia/105.hea b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/TestData/mitdb-Arrhythmia/105.hea new file mode 100644 index 0000000..40e0616 --- /dev/null +++ b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/TestData/mitdb-Arrhythmia/105.hea @@ -0,0 +1,7 @@ +105 2 360 650000 +105.dat 212 200 11 1024 935 -24679 0 MLII +105.dat 212 200 11 1024 1076 -15565 0 V1 +# 73 F 1624 1629 x1 +# Digoxin, Nitropaste, Pronestyl +# The PVCs are uniform. The predominant feature of this tape is +# high-grade noise and artifact. diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/TestData/mitdb-Arrhythmia/105m.hea b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/TestData/mitdb-Arrhythmia/105m.hea new file mode 100644 index 0000000..920f583 --- /dev/null +++ b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/TestData/mitdb-Arrhythmia/105m.hea @@ -0,0 +1,9 @@ +105m 2 360 650000 +105m.mat 16+24 200/mV 11 1024 935 -24679 0 MLII +105m.mat 16+24 200/mV 11 1024 1076 -15565 0 V1 +# 73 F 1624 1629 x1 +# Digoxin, Nitropaste, Pronestyl +# The PVCs are uniform. The predominant feature of this tape is +# high-grade noise and artifact. +#Creator: wfdb2mat +#Source: record 105 diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/TestData/mitdb-Arrhythmia/105m.mat b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/TestData/mitdb-Arrhythmia/105m.mat new file mode 100644 index 0000000..7d746d5 Binary files /dev/null and b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/TestData/mitdb-Arrhythmia/105m.mat differ diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/TestData/mitdb-Arrhythmia/200m.hea b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/TestData/mitdb-Arrhythmia/200m.hea new file mode 100644 index 0000000..aea5749 --- /dev/null +++ b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/TestData/mitdb-Arrhythmia/200m.hea @@ -0,0 +1,10 @@ +200m 2 360 650000 +200m.mat 16+24 200/mV 11 1024 1094 12838 0 MLII +200m.mat 16+24 200/mV 11 1024 1045 -25533 0 V1 +# 64 M 1953 3655 x1 +# Digoxin, Quinidine +# The PVCs are multiform. There are occasional bursts of high-frequency +# noise in the upper channel, and severe noise and artifact in the lower +# channel. +#Creator: wfdb2mat +#Source: record mitdb/200 diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/TestData/mitdb-Arrhythmia/200m.mat b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/TestData/mitdb-Arrhythmia/200m.mat new file mode 100644 index 0000000..1bc202a Binary files /dev/null and b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Demos/TestData/mitdb-Arrhythmia/200m.mat differ diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Documents/.DS_Store b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Documents/.DS_Store new file mode 100644 index 0000000..b5ebc23 Binary files /dev/null and b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Documents/.DS_Store differ diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Documents/An Open Source Benchmarked Toolbox for Cardiovascular Waveform and Interval Analysis.pdf b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Documents/An Open Source Benchmarked Toolbox for Cardiovascular Waveform and Interval Analysis.pdf new file mode 100644 index 0000000..d80094c Binary files /dev/null and b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Documents/An Open Source Benchmarked Toolbox for Cardiovascular Waveform and Interval Analysis.pdf differ diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Documents/CHANGELOG.md b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Documents/CHANGELOG.md new file mode 100644 index 0000000..00762a0 --- /dev/null +++ b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Documents/CHANGELOG.md @@ -0,0 +1,19 @@ +# Changelog +All notable changes to this project will be documented in this file. + +## 1.0.1 - 2018-09-17 + +- Added minimum number of anchors required to compute AC and DC in PRSA method + +- Added t_end in the .csv HRV metrics summary output file + +- Modified windows creation for MSE and DFA to deal with hours instead of seconds + +- Fixed incorrect x1000 in sd1/sd2 measure + +## 1.0.0 - 2018-01-05 +### Added +- Public version of the Physioney HRV Toolkit for Matlab + +[Unreleased]: https://github.com/cliffordlab/Physionet-HRV-toolbox-for-MATLAB/edit/master/compare/v1.0.0...HEAD + diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Documents/FAQ.md b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Documents/FAQ.md new file mode 100644 index 0000000..fd0565f --- /dev/null +++ b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Documents/FAQ.md @@ -0,0 +1,15 @@ +# Key Contributions: + +You might ask, why 'another' HRV toolbox? The following is a list of key contributions this toolbox and +accompanying publication makes to the field, and why you might want to use this in preference to other +toolboxes and software out there. + +* A full suite of waveform processing tools, for end-to-end processing +* Detailed explanations of preprocessing and parameter choices to identify where divergences in methods can occur, and to provide standardization in the field +* Benchmarked against other open source HRV tools to identify when they disagree with each other +* Benchmarked against PhysioNet's C code for compatibility, and hence can be used as a prototyping platform before switching to C for large scalable tasks or embedded systems +* A mostly comprehensive collection of standard and more recent HRV tools that go beyond other toolboxes +* Fully scriptable with no libraries outside Matlab required for reading data and annotations +* Fully WFDB-compatible +* Compatibility with commercial software often used by clinicians (e.g. Kubios) +* Open source and versioned on Github so the community may build upon it. diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Documents/LICENSE b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Documents/LICENSE new file mode 100644 index 0000000..9cecc1d --- /dev/null +++ b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Documents/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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Welcome to the WFDB App Toolbox user's group! Please see our FAQ prior to posting here.


If you are posting a questions or asking for help regarding an issue with the WFDB toolbox. PLEASE
INCLUDE the following in your message (otherwise your question maybe or will simply be ignored):

1. The exact commands that you have entered in MATLAB
2. The exact error messages that you received,  or a detailed description of what is wrong with your output.
3. Your Toolbox configuration details, which can be obtained by running:
    [~,config]=wfdbloadlib
    At the MATLAB/Octave commande prompt. Please paste the output of this command along with your post.
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1 post 5 views
Jan 18
WFDB Toolbox installation path contain white spaces (1)
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1 post 29 views
12/22/17
Reading EEG Signal using WFDB (1)
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1 post 28 views
12/21/17
wfdb2mat does not generate .mat file (4)
By Hong Zu Li
4 posts 80 views
12/21/17
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4 posts 27 views
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4 posts 120 views
12/12/17
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1 post 49 views
11/30/17
Reference to non-existent field 'WFDB_CUSTOMLIB'. (1)
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1 post 29 views
11/27/17
Cannot run wfdbdemo (1)
By mali...@gmail.com
1 post 21 views
11/22/17
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By k.vis...@gmail.com
1 post 15 views
11/14/17
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2 posts 137 views
11/7/17
Read file -- error (1)
By mario.i...@gmail.com
1 post 23 views
11/6/17
ã€æ•°äº¿å¨±ä¹ã€‘稳如泰山ã€708227】 ã€ä¸»ç®¡ 直属 招商 总代 开户】 数亿娱ä¹éš†é‡ç™»åœº 新濠集团旗下 实力雄厚 信誉优秀 您ä¸èƒ½é”™è¿‡ (1)
By winwin...@gmail.com
1 post 3 views
11/1/17
ã€æ•°äº¿å¨±ä¹ã€‘ã€ç›´å±ž 主管 招商 总代 开户】ã€Q1192431153】ã€åŠ æˆ‘备注88】 (4)
By winwin...@gmail.com
4 posts 17 views
11/1/17
Unexpected error using ECGPUWAVE (2)
By Manish Kaul
2 posts 16 views
9/22/17
\ No newline at end of file diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Documents/PhysioNet-Cardiovascular-Signal-Toolbox/HRV Toolbox for MATLAB_files/accessible-superfish.js b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Documents/PhysioNet-Cardiovascular-Signal-Toolbox/HRV Toolbox for MATLAB_files/accessible-superfish.js new file mode 100644 index 0000000..29c7005 --- /dev/null +++ b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Documents/PhysioNet-Cardiovascular-Signal-Toolbox/HRV Toolbox for MATLAB_files/accessible-superfish.js @@ -0,0 +1,230 @@ +/* makeAccessible + arguments: options, or container, name, and options + ** Note: container should be the top "ul" element in the list structure + + This function adds aria tree markup to the superfish menu structure, as well as keyboard navigation. + Options: + - open: called when a node is opened with node as argument; + - close: called when a node is closed, with node as an argument + - beforeOpen: called before open with node as an argument + ** note: if open and close functions are not supplied, this function will have no effect; the open and close functions should generally show / hide nodes, respectively. +*/ + + +function makeAccessible ($container, name) { +var options = {open: function(){}, close: function(){}}; +if (arguments.length == 1) { +options = $.extend (options, arguments[0]); +} else if (arguments.length == 2) { +options = $.extend (options, {$container: $container, name: name}); +} else if (arguments.length == 3) { +options = $.extend (options, {$container: $container, name: name}, arguments[2]); +} // if + +var activeDescendant_id = options.name + "activeDescendant"; +//debug ("makeAccessible:", options); + +return addKeyboardNavigation (addAria (options.$container)); + +function addAria ($container) { +/* focus management is done via aria-activedescendant rather than a roving tabindex. See the following for an explanation: +Keyboard-navigable JavaScript widgets - Accessibility | MDN +https://developer.mozilla.org/en-US/docs/Web/Accessibility/Keyboard-navigable_JavaScript_widgets +*/ + + +// remove all implicit keyboard focus handlers (i.e. links and buttons should not be tabbable here since we're using aria-activedescendant to manage focus) +$("a, button", $container).attr ("tabindex", "-1"); +//debug ("- implicit keyboard handling removed"); + +// "ul" requires role="group" +var $ul = $("ul", $container).addBack().attr ("role", "group"); + +// "li" are tree nodes and require role="treeitem" +$("li", $ul).attr ({"role": "treeitem"}); + +// add aria-expanded to nodes only if they are not leaf nodes +$("[role=treeitem]", $ul).has("[role=group]") +.attr ("aria-expanded", "false"); + +// unhide the top-level nodes and tell the container that the first node should have focus +$ul.first().find("[role=treeitem]").first() +//.show () +.attr ({"id": activeDescendant_id}); + +// replace role="group" with role="tree" on the first group and cause the tree to look for our currently active node +$ul.first() +.attr({ +"role": "tree", +"tabindex": "0", +"aria-activedescendant": activeDescendant_id +}).focus(); + +return $container; +} // addAria + +function addKeyboardNavigation ($container) { + +// add keyboard handler +$container.on ("keydown", keyboardHandler); +return $container; + +function keyboardHandler (e) { +var key = e.which || e.keyCode; +var $newNode = null; +var $currentNode = getCurrentNode(); + +if (key >= 35 && key <= 40) { +//debug ("key: " + key); +$newNode = navigate (getCurrentNode(), key); + +if (isValidNode($newNode)) { +//debugNode ($newNode, "navigate: "); +if ($newNode !== $currentNode && options.leaveNode && options.leaveNode instanceof Function) options.leaveNode ($currentNode, $newNode); +setCurrentNode ($newNode); +} // if +return false; +} // if + +// key not handled above, so let it keep its default action +return true; +} // keyboardHandler + + +// this function defines the actual keyboard behavior seen +// add code to "open()" and "close()" functions to integrate with current implementation +function navigate ($start, key) { +//debugNode ($start, "navigate: "); +if (! $start || $start.length == 0) return null; + +switch (key) { +case 38: return previous (); // upArrow moves to previous sibling +case 40: return next(); // downArrow moves to next sibling + +// leftArrow moves up a level and closes +case 37: +if (options.beforeClose && options.beforeClose instanceof Function) options.beforeClose($start); +$start = up(); + close(); + return $start; + +// rightArrow opens and moves down a level +case 39: if (! isOpened()) open (); +$start = down(); +if (options.afterOpen && options.afterOpen instanceof Function) options.afterOpen ($start); +return $start; + +default: return null; +} // switch + +function isOpened () { +return $start && $start.length == 1 && $start.attr("aria-expanded") == "true"; +} // isOpened + +function open () { +if ($start && $start.length == 1 && $start.is("[aria-expanded=false]")) { +$start.attr ("aria-expanded", "true"); +if (options.open && options.open instanceof Function) options.open ($start); +} // if +} // open + +function close () { +if ($start && $start.length == 1 && $start.is("[aria-expanded=true]")) { +$start.attr ("aria-expanded", "false"); +if (options.close && options.close instanceof Function) options.close($start); +} // if +} // close + +function next () { +return $start.next ("[role=treeitem]"); +} // next + +function previous () { +return $start.prev("[role=treeitem]"); +} // previous + +function up () { +return $start.parent().closest("[role=treeitem]"); +} // up + +function down () { +return $start.find("[role=treeitem]:first"); +} // down + +} // navigate + + +function getCurrentNode () { +var $node; +if (! activeDescendant_id) { +alert ("active descendant not defined"); +return null; +} // if + +$node = $("#" + activeDescendant_id); +return (isValidNode($node))? +$node : null; +} // getCurrentNode + +function setCurrentNode ($newNode) { +var $node = getCurrentNode (); +if ( +isValidNode ($newNode) +&& isValidNode ($node)) { + +$node.removeAttr ("id"); +$newNode.attr ({"id": activeDescendant_id}); + +$container.removeAttr ("aria-activedescendant") +.attr ("aria-activedescendant", activeDescendant_id); + +if (options.currentNode && options.currentNode instanceof Function) options.currentNode ($newNode); + +return $newNode; +} // if valid + +return null; +} // setCurrentNode + +function isValidNode ($node) { +return ($node && $node.length == 1); +} // isValidNode + + + +function debugNode ($node, label) { +//return; +var info = "(invalid)"; + +if (isValidNode($node)) info = $node[0].nodeName + $node.find("a:first").text(); +if (label) debug (label, info); +return info; +} // debugNode + +} // addKeyboardNavigation + +} // makeAccessible + +/*function debug (text) { +//return; +var text = $.map ($.makeArray (arguments), function (arg) { + var type = typeof(arg); + if (type === "array" || type == "object") return JSON.stringify(arg) + "\n"; + else return arg; +}).join (" "); + +if ($("#debug").length > 0) { +if (! text) { +$("#debug").html (""); +} else { + $("#debug").append (document.createTextNode(text), "
\n"); +} // if + +} else { +console.error (text); +} // if +} // debug +*/ + +//alert ("makeAccessible loaded"); + diff --git a/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Documents/PhysioNet-Cardiovascular-Signal-Toolbox/HRV Toolbox for MATLAB_files/analytics.js b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Documents/PhysioNet-Cardiovascular-Signal-Toolbox/HRV Toolbox for MATLAB_files/analytics.js new file mode 100644 index 0000000..cecf13b --- /dev/null +++ b/libs/PhysioNet-Cardiovascular-Signal-Toolbox-master/Documents/PhysioNet-Cardiovascular-Signal-Toolbox/HRV Toolbox for MATLAB_files/analytics.js @@ -0,0 +1,59 @@ +(function(){var $c=function(a){this.w=a||[]};$c.prototype.set=function(a){this.w[a]=!0};$c.prototype.encode=function(){for(var a=[],b=0;b\x3c/script>')):(c=M.createElement("script"), +c.type="text/javascript",c.async=!0,c.src=a,d&&(c.onload=d),b&&(c.id=b),a=M.getElementsByTagName("script")[0],a.parentNode.insertBefore(c,a)))},Ud=function(){return"https:"==M.location.protocol},be=function(a,b){return E(M.location[b?"href":"search"],a)},E=function(a,b){return(a=a.match("(?:&|#|\\?)"+K(b).replace(/([.*+?^=!:${}()|\[\]\/\\])/g,"\\$1")+"=([^&#]*)"))&&2==a.length?a[1]:""},xa=function(){var a=""+M.location.hostname;return 0==a.indexOf("www.")?a.substring(4):a},de=function(a,b){var c= +a.indexOf(b);if(5==c||6==c)if(a=a.charAt(c+b.length),"/"==a||"?"==a||""==a||":"==a)return!0;return!1},ya=function(a,b){var c=M.referrer;if(/^(https?|android-app):\/\//i.test(c)){if(a)return c;a="//"+M.location.hostname;if(!de(c,a))return b&&(b=a.replace(/\./g,"-")+".cdn.ampproject.org",de(c,b))?void 0:c}},za=function(a,b){if(1==b.length&&null!=b[0]&&"object"===typeof b[0])return b[0];for(var c={},d=Math.min(a.length+1,b.length),e=0;e=b.length)wc(a,b,c);else if(8192>=b.length)x(a,b,c)||wd(a,b,c)||wc(a,b,c);else throw ge("len",b.length),new Da(b.length);},pe=function(a,b,c,d){d=d||ua;wd(a+"?"+b,"",d,c)},wc=function(a,b,c){var d=ta(a+"?"+b);d.onload=d.onerror=function(){d.onload=null;d.onerror=null;c()}},wd=function(a,b,c,d){var e=O.XMLHttpRequest;if(!e)return!1; 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Moody , 12 July 2011 + +Further modified for PhysioNet by K. Pierce , 3 July 2015 +*****/ +var crumbsep = " > "; +var precrumb = ""; +var postcrumb = ""; +var sectionsep = "/"; +var rootpath = "/"; // Use "/" for root of domain. +var rootname = ""; + +var ucfirst = 0; // if set to 1, makes "directory" default to "Directory" +var showpage = ""; // this contains what to show as the current page in the crumb. 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The handlerOut function is only + * called after a matching handlerIn. + * + * hoverIntent r7 // 2013.03.11 // jQuery 1.9.1+ + * http://cherne.net/brian/resources/jquery.hoverIntent.html + * + * You may use hoverIntent under the terms of the MIT license. Basically that + * means you are free to use hoverIntent as long as this header is left intact. + * Copyright 2007, 2013 Brian Cherne + * + * // basic usage ... just like .hover() + * .hoverIntent( handlerIn, handlerOut ) + * .hoverIntent( handlerInOut ) + * + * // basic usage ... with event delegation! + * .hoverIntent( handlerIn, handlerOut, selector ) + * .hoverIntent( handlerInOut, selector ) + * + * // using a basic configuration object + * .hoverIntent( config ) + * + * @param handlerIn function OR configuration object + * @param handlerOut function OR selector for delegation OR undefined + * @param selector selector OR undefined + * @author Brian Cherne + **/ +(function($) { + $.fn.hoverIntent = function(handlerIn,handlerOut,selector) { + + // default configuration values + var cfg = { + interval: 100, + sensitivity: 7, + timeout: 0 + }; + + if ( typeof handlerIn === "object" ) { + cfg = $.extend(cfg, handlerIn ); + } else if ($.isFunction(handlerOut)) { + cfg = $.extend(cfg, { 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