This GIT respository contains all files needed for an adequate analysis of the gait (6MWT) accelerometer data.
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function [SE] = funcSampleEntropy(DataIn, m, r)
%% Description
% Calculate the sample entropy as described in
% Richman JS, Moorman JR (2000)
% "Physiological time-series analysis using approximate entropy and sample entropy"
% American Journal of Physiology. Heart and Circulatory Physiology [2000, 278(6):H2039-49]
%
% 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 one-dimensional 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 N-m <= 0
error('m must be smaller than the length of the time series DataIn');
end
%% Create the vectors Xm to be compared
Xm = zeros(N-m,m);
for i = 1:m,
Xm(:,i) = DataIn(i:end-1-m+i,1);
end
%% Count the numbers of matches for Xm and Xmplusone
CountXm = 0;
CountXmplusone = 0;
XmDist = nan(size(Xm));
for i = 1:N-m,
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);