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    Metadata details:
    filename: testChannelSelection7.m
    path of file: ./repos/OSET/matlab/tests/ecg
    
    the code of the file until where you have to start completion: % Channel selection based on different methods (modified version 2.0)
% The channel selection method tries to rank the components according to
% their similarity to the maternal and fetal mixtures. Therefore, it
% should give the noisy channels the lowest rank.
% Notes:
% 1- The first objective of this routine is not to reject the maternal
% signals, since they will be later rejected by the spatial filtering
% methods.
% 2- The ranking does not need to be perfect, as long as the bad channels
% roughly go to the end of the list and the good ones come to the
% beginnning. This guarantees that we can safely reject the last n% of the
% channels without loosing fetal ECG information
% 3
    
function has been designed based on wavelet decomposition using Coiflet mother wavelets)
ind1  = ChannelIndex1(x);

% finding the channels that have energy deviations within short windows (test of nonstationarity over short windows)
% (since a good ECG channel should have high energy deviations in windows with a fractional length of the average heart beat)
ind2 = ChannelIndex2(x,.2*fs);

% finding the channels that have energy deviations within long windows (test of stationarity over long windows)
% (since a good ECG channel should have low energy deviations in long windows)
ind3 = 1./ChannelIndex2(x,3*fs);

% finding nongaussian channels using negentropy estimation of gaussianity
ind4 = ChannelIndex4(x);

% channel evaluation based on zero-crossings; a clean signal should not have too many zero-crossings
ind5 = 1./ChannelIndex5(x);

% channel evaluation based on number of peaks within windows with a length of the average fetal heart rate
ind6 = ChannelIndex6(x,2.0/fs,.1);

% channel evaluation based on number of level crossings of signal energy
% % % [ind7 yy] = ChannelIndex7(x,.2*fs,.2);

%//////////////////////////////////////////////////////////////////////////
% channel selection based on 'weighted voting' among the different extracted measures

rank0 = ones(L1,1);
rank0(ind0>30) = L1;   % hard thresholding

[temp, I] = sort(ind1,'descend