function [peaks,mn,r] = PeakDetection3(ref,fs,h,th,fmax,goodint)
% My changes to this code: adding the intmax input which checks if any
% interval of the input signal has anomalies. If yes, that part is not counted
% when calculating the th*max(r) margin.
% peaks = PeakDetection3(x,fs,h,th,fmax),
% R-peak detector based on a matched filter
%
% inputs:
% x: vector of input data
% fs: sampling rate
% h: template waveform
% th: detection threshold
% fmax: maximum expected frequency of the R-peaks
%
% output:
% peaks: vector of R-peak impulse train
%
%
% Open Source ECG Toolbox, version 2.0, March 2008
% Released under the GNU General Public License
% Copyright (C) 2008 Reza Sameni
% Sharif University of Technology, Tehran, Iran -- GIPSA-Lab, INPG, Grenoble, France
% reza.sameni@gmail.com
% 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 2 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.
if nargin==6
nblocks=length(goodint);
else
goodint=1;
nblocks=1;
end
N = length(ref);
blocksize=N/nblocks;
goodind=zeros(1,N);
for i=1:nblocks
goodind((i-1)*blocksize+1:i*blocksize)=goodint(i)*ones(1,blocksize);
end
L = length(h);
h = h(end:-1:1);
w = floor(L/2);
r = filter(h,1,[ref zeros(1,w-1)]);
r = r(w:N+w-1);
r(r<th*max(r(logical(goodind)))) = 0;
peaks = zeros(1,length(ref));
if(nargout==1)
wlen2 = round(fs/fmax);
I = find(r>0);
for i = 1:length(I),
ind = max(1,I(i)-wlen2):min(N,I(i)+wlen2);
if (max(r(ind))==r(I(i))),
peaks(I(i)) = 1;
end
end
else
wlen2 = round(fs/fmax);
I = find(r>0);
seg = zeros(length(I),L);
for i = 1:length(I),
ind = max(1,I(i)-wlen2):min(N,I(i)+wlen2);
if (max(r(ind))==r(I(i))),
peaks(I(i)) = 1;
sg = ref(max(1,I(i)-w+1):min(N,I(i)+w));
seg(i,:) = [sg zeros(1,L-length(sg))];
end
end
mn0 = mean(seg,1);
% Robust weighted averaging
noise = seg - mn0(ones(size(seg,1),1),:);
vr = var(noise,[],2);
sm = sum(1./vr);
weight = 1./(vr*sm);
mn = weight'*seg;
mn = sqrt(sum(h.^2))*mn /sqrt(sum(mn.^2));
end