% Function for calculating multiscale entropy
% input: signal
% m: match point(s)
% r: matching tolerance
% factor: number of scale factor
% sampenc is available at http://people.ece.cornell.edu/land/PROJECTS/Complexity/sampenc.m
% 
% Multi-scale sample Entropy (Mscale-En) is an indicator of gait predictability. Multi-scale entropy takes the
% complexity of a system into account by calculating the
% predictability of a signal over time scales with increasing
% length. A ‘coarse-graining’ process is applied to the acceleration signals; non-overlapping windows of data
% points with an increasing length τ are constructed, with
% τ representing the time scale with a tolerance of r (in the
% present study τ = 7 and r = 0.2). A complete predictable
% signal will adopt a Mscale-En value of 0 [29].

function e = msentropy(input,m,r,factor)

y=input;
y=y-mean(y);
y=y/std(y);

for i=1:factor
   s=coarsegraining(y,i); % dont have this function yet. 
   sampe=sampenc(s,m+1,r);
   e(i)=sampe(m+1);   
end
e=e';