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FeatureFinder 2.4.1

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FeatureFinder 2.4.1

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20 Aug 2012 (Updated )

A user-friendly tool for signal filtering and feature extraction. Well-suited to large data sets!

HRV_SDNN.m
function nFeature=HRV_SDNN(nData,Fs,nBL_Range,nTarg_Range)

% Feature description
if nargin==0
    nFeature=['The heart rate variability is calculated for the target ',...
        'window as the standard deviation of the distances between ',...
        'adjacent positive peaks. NOTE: Data requires adequate ',...
        'filtering for peak detection to be effective. Use ',...
        '''TargetMeanHR'' to check.'];               
    return
end

nData=nData(nTarg_Range);

% Set each pt to -1, 0, or 1 depending on movement
nMove=diff(nData);
nMove(nMove>0)=1;
nMove(nMove<0)=-1;        

% Find sharp peaks
iPeaks=[0;diff(nMove)==-2;0];

% Find peaks of width 2
W=2;
nWidth2=[zeros(W-1,1);nMove((W+1):end)-nMove(1:end-W);zeros(W-1,1)];
iPeaks=iPeaks|[nMove==0&nWidth2==-2;0];

% Find peaks of width 3-8
for j=3:8
    for i=1:length(nMove)-j    
        if all(nMove(i:i+j)==[1;zeros(j-1,1);-1])
            iPeaks(i+1)=1;
        end
    end
end

% Filter out those peaks with negative amplitudes
iPeaks=find(iPeaks);
iPeaks(nData(iPeaks)<=0)=[];

% Determine timing of peaks
nPeakTimes=(iPeaks-1)/Fs;

% Calculate SDNN
nSDNN=std(diff(nPeakTimes));
nFeature=nSDNN;

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