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80 lines
2.5 KiB
80 lines
2.5 KiB
function [SE] = funcSampleEntropy(DataIn, m, r)




%% Description


% Calculate the sample entropy as described in


% Richman JS, Moorman JR (2000)


% "Physiological timeseries analysis using approximate entropy and sample entropy"


% American Journal of Physiology. Heart and Circulatory Physiology [2000, 278(6):H203949]


%


% The sample entropy is calculated as the natural logarithm of the


% probability that two samples of length m that are within a distance of r,


% remain within a distance of r when adding one additional sample. Note


% that distance is considered as the maximum of the distances for the


% individual dimensions 1 to m, and that the input data is normalised.


%


% Input:


% DataIn: a onedimensional time series


% m: the dimension of the vectors to be used. The vectors consist of m


% consecutive samples


% r: the maximum distance between two samples to qualify as a


% mathch, relative to the std of DataIn


%


% Output:


% SE: the calculated sample entropy


%




%% Copyright


% COPYRIGHT (c) 2012 Sietse Rispens, VU University Amsterdam


%


% This program is free software: you can redistribute it and/or modify


% it under the terms of the GNU General Public License as published by


% the Free Software Foundation, either version 3 of the License, or


% (at your option) any later version.


%


% This program is distributed in the hope that it will be useful,


% but WITHOUT ANY WARRANTY; without even the implied warranty of


% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the


% GNU General Public License for more details.


%


% You should have received a copy of the GNU General Public License


% along with this program. If not, see <http://www.gnu.org/licenses/>.




%% Author


% Sietse Rispens




%% History


% 7 May 2012, version 1.0




%% Check input


if size(DataIn,1) ~= 1 && size(DataIn,2) ~= 1


error('DataIn must be a vector');


end


DataIn = DataIn(:)/std(DataIn(:));


N = size(DataIn,1);


if Nm <= 0


error('m must be smaller than the length of the time series DataIn');


end




%% Create the vectors Xm to be compared


Xm = zeros(Nm,m);


for i = 1:m,


Xm(:,i) = DataIn(i:end1m+i,1);


end




%% Count the numbers of matches for Xm and Xmplusone


CountXm = 0;


CountXmplusone = 0;


XmDist = nan(size(Xm));


for i = 1:Nm,


for j=1:m,


XmDist(:,j)=abs(Xm(:,j)Xm(i,j));


end


IdXmi = find(max(XmDist,[],2)<=r);


CountXm = CountXm + length(IdXmi)  1;


CountXmplusone = CountXmplusone + sum(abs(DataIn(IdXmi+m)DataIn(i+m))<=r)  1;


end




%% Return sample entropy


SE = log(CountXmplusone/CountXm);


