--- a
+++ b/featurebased-approach/subfunctions/lib/Fast Approximate Entropy/ApEn.m
@@ -0,0 +1,82 @@
+function apen = ApEn( dim, r, data, tau )
+%ApEn
+%   dim : embedded dimension
+%   r : tolerance (typically 0.2 * std)
+%   data : time-series data
+%   tau : delay time for downsampling
+
+%   Changes in version 1
+%       Ver 0 had a minor error in the final step of calculating ApEn
+%       because it took logarithm after summation of phi's.
+%       In Ver 1, I restored the definition according to original paper's
+%       definition, to be consistent with most of the work in the
+%       literature. Note that this definition won't work for Sample
+%       Entropy which doesn't count self-matching case, because the count 
+%       can be zero and logarithm can fail.
+%
+%       A new parameter tau is added in the input argument list, so the users
+%       can apply ApEn on downsampled data by skipping by tau. 
+%---------------------------------------------------------------------
+% coded by Kijoon Lee,  kjlee@ntu.edu.sg
+% Ver 0 : Aug 4th, 2011
+% Ver 1 : Mar 21st, 2012
+%---------------------------------------------------------------------
+%
+%
+%Copyright (c) 2012, Kijoon Lee
+%All rights reserved.
+%
+%Redistribution and use in source and binary forms, with or without
+%modification, are permitted provided that the following conditions are
+%met:
+%
+%    * Redistributions of source code must retain the above copyright
+%      notice, this list of conditions and the following disclaimer.
+%    * Redistributions in binary form must reproduce the above copyright
+%      notice, this list of conditions and the following disclaimer in
+%      the documentation and/or other materials provided with the distribution
+%
+%THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+%AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+%IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+%ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+%LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+%CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+%SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+%INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+%CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+%ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+%POSSIBILITY OF SUCH DAMAGE.
+%
+%
+
+if nargin < 4, tau = 1; end
+if tau > 1, data = downsample(data, tau); end
+    
+N = length(data);
+result = zeros(1,2);
+
+for j = 1:2
+    m = dim+j-1;
+    phi = zeros(1,N-m+1);
+    dataMat = zeros(m,N-m+1);
+    
+    % setting up data matrix
+    for i = 1:m
+        dataMat(i,:) = data(i:N-m+i);
+    end
+    
+    % counting similar patterns using distance calculation
+    for i = 1:N-m+1
+        tempMat = abs(dataMat - repmat(dataMat(:,i),1,N-m+1));
+        boolMat = any( (tempMat > r),1);
+        phi(i) = sum(~boolMat)/(N-m+1);
+    end
+    
+    % summing over the counts
+    result(j) = sum(log(phi))/(N-m+1);
+end
+
+apen = result(1)-result(2);
+
+end
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