80 lines
2.5 KiB
Matlab
80 lines
2.5 KiB
Matlab
function [SE] = funcSampleEntropy(DataIn, m, r)
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%% Description
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% Calculate the sample entropy as described in
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% Richman JS, Moorman JR (2000)
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% "Physiological time-series analysis using approximate entropy and sample entropy"
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% American Journal of Physiology. Heart and Circulatory Physiology [2000, 278(6):H2039-49]
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%
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% The sample entropy is calculated as the natural logarithm of the
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% probability that two samples of length m that are within a distance of r,
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% remain within a distance of r when adding one additional sample. Note
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% that distance is considered as the maximum of the distances for the
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% individual dimensions 1 to m, and that the input data is normalised.
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%
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% Input:
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% DataIn: a one-dimensional time series
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% m: the dimension of the vectors to be used. The vectors consist of m
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% consecutive samples
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% r: the maximum distance between two samples to qualify as a
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% mathch, relative to the std of DataIn
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%
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% Output:
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% SE: the calculated sample entropy
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%
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%% Copyright
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% COPYRIGHT (c) 2012 Sietse Rispens, VU University Amsterdam
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%
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% This program is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% This program is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with this program. If not, see <http://www.gnu.org/licenses/>.
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%% Author
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% Sietse Rispens
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%% History
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% 7 May 2012, version 1.0
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%% Check input
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if size(DataIn,1) ~= 1 && size(DataIn,2) ~= 1
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error('DataIn must be a vector');
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end
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DataIn = DataIn(:)/std(DataIn(:));
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N = size(DataIn,1);
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if N-m <= 0
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error('m must be smaller than the length of the time series DataIn');
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end
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%% Create the vectors Xm to be compared
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Xm = zeros(N-m,m);
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for i = 1:m,
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Xm(:,i) = DataIn(i:end-1-m+i,1);
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end
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%% Count the numbers of matches for Xm and Xmplusone
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CountXm = 0;
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CountXmplusone = 0;
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XmDist = nan(size(Xm));
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for i = 1:N-m,
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for j=1:m,
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XmDist(:,j)=abs(Xm(:,j)-Xm(i,j));
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end
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IdXmi = find(max(XmDist,[],2)<=r);
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CountXm = CountXm + length(IdXmi) - 1;
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CountXmplusone = CountXmplusone + sum(abs(DataIn(IdXmi+m)-DataIn(i+m))<=r) - 1;
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end
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%% Return sample entropy
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SE = -log(CountXmplusone/CountXm);
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