function [fetal_QRSAnn_est,QT_Interval] = physionet2013(tm,ECG)
% Template algorithm for Physionet/CinC competition 2013. This function can
% be used for events 1 and 2. Participants are free to modify any
% components of the code. However the function prototype must stay the
% same:
%
% [fetal_QRSAnn_est,QT_Interval] = physionet2013(tm,ECG) where the inputs and outputs are specified
% below.
%
% inputs:
% ECG: 4x60000 (4 channels and 1min of signal at 1000Hz) matrix of
% abdominal ECG channels.
% tm : Nx1 vector of time in milliseconds
% output:
% FQRS: FQRS markers in seconds. Each marker indicates the position of one
% of the FQRS detected by the algorithm.
% QT_Interval: 1x1 estimated fetal QT duration (enter NaN or 0 if you do wish to calculate)
%
%
% Author: Joachim Behar - IPMG Oxford (joachim.behar@eng.ox.ac.uk)
% Last updated: March 3, 2013 Ikaro Silva
% April 15, 2013 Joachim Behar
% April 28, 2013 Akshay Dhawan
try
% ---- check size of ECG ----
if size(ECG,2)>size(ECG,1)
ECG = ECG';
end
fs = 1000; % sampling frequency
N = size(ECG,2); % number of abdominal channels
% ---- preprocessing ----
[FilteredECG] = preprocessing(ECG,fs);
MQRS = PeakDetectionAkshay(FilteredECG(:,1),500);
% ---- MECG cancellation ----
for i=1:N % run algorithm for each channel
FECG(:,i) = MECGcancellationAkshay(MQRS,FilteredECG(:,i)',fs,20);
end
FQRS=PanTompkinsAkshay(FECG);
for i=1:4
std_FQRS(i)=std(diff(FQRS{i}));
mean_FQRS(i)=1000/mean(diff(FQRS{i}))*60;
end
[~,ind]=sort(std_FQRS,1,'ascend');
for i=1:4
if (mean_FQRS(ind(i))>80 && mean_FQRS(ind(i))<200)
break;
end
end
fetal_QRSAnn_est = FQRS{i};
QT_Interval = 0;
catch ex
%if something goes wrong then use a random interval
fetal_QRSAnn_est = sort(randi(60000,[1,140]),'ascend');
QT_Interval = 0;
end
end
function [FilteredECG] = preprocessing(ECG,fs)
% ---- preprocess the data ----
FilteredECG = ECG;
end
function residual = MECGcancellation(peaks,ECG,fs,nbCycles)
% MECG cancellation algorithm inspired from [1].
%
% inputs:
% fs: sampling frequency
% nbCycles: number of cycles on which to build the mean MECG template
% ECG: matrix of abdominal ECG channels.
% peaks: MQRS markers in seconds. Each marker corresponds to the
% position of a MQRS.
%
% output:
% residual: residual containing the FECG.
%
% Author: Joachim Behar - IPMG Oxford (joachim.behar@eng.ox.ac.uk)
% Last updated: 03_02_2013
%
% [1] Martens S et al. A robust fetal ECG detection method for
% abdominal recordings. Physiol. Meas. (2007) 28(4) 373�388
% ---- constants ----
r = nbCycles;
ECG_last_r_cycles = zeros(0.7*fs,r);
Pstart = 0.25*fs-1;
Tstop = 0.45*fs;
N = length(peaks); % number of MECG QRS
ECG_temp = zeros(1,length(ECG));
% ---- ECG template ----
for i=1:r
peak_nb = peaks(i+1); % +1 to unsure full cycles
ECG_last_r_cycles(:,i) = ECG(peak_nb-Pstart:peak_nb+Tstop)';
end
ECG_mean = mean(ECG_last_r_cycles,2);
% ---- MECG cancellation ----
for i=1:N
if peaks(i)>Pstart && length(ECG)-peaks(i)>Tstop
M = zeros (0.7*fs,3);
M(1:0.2*fs,1) = ECG_mean(1:Pstart-0.05*fs+1);
M(0.2*fs+1:0.3*fs,2) = ECG_mean(Pstart-0.05*fs+2:Pstart+0.05*fs+1);
M(0.3*fs+1:end,3) = ECG_mean(Pstart+2+0.05*fs:Pstart+1+Tstop);
a = (M'*M)\M'*ECG(peaks(i)-Pstart:peaks(i)+Tstop)';
ECG_temp(peaks(i)-Pstart:peaks(i)+Tstop) = a(1)*M(:,1)'+a(2)*M(:,2)'+a(3)*M(:,3)';
end
end
% compute residual
residual = ECG - ECG_temp;
end
function residual = MECGcancellationAkshay(peaks,ECG,fs,nbCycles)
% MECG cancellation algorithm inspired from [1].
%
% inputs:
% fs: sampling frequency
% nbCycles: number of cycles on which to build the mean MECG template
% ECG: matrix of abdominal ECG channels.
% peaks: MQRS markers in seconds. Each marker corresponds to the
% position of a MQRS.
%
% output:
% residual: residual containing the FECG.
%
% Author: Joachim Behar - IPMG Oxford (joachim.behar@eng.ox.ac.uk)
% Last updated: 03_02_2013
%
% [1] Martens S et al. A robust fetal ECG detection method for
% abdominal recordings. Physiol. Meas. (2007) 28(4) 373�388
%
% Akshay Dhawan added in an adaptive mean feature
% ---- constants ----
r = nbCycles;
ECG_last_r_cycles = zeros(0.7*fs,r);
Pstart = 0.25*fs-1;
Tstop = 0.45*fs;
N = length(peaks); % number of MECG QRS
ECG_temp = zeros(1,length(ECG));
% ---- ECG template ----
for i=1:r
peak_nb = peaks(i+1); % +1 to unsure full cycles
ECG_last_r_cycles(:,i) = ECG(peak_nb-Pstart:peak_nb+Tstop)';
end
ECG_mean = mean(ECG_last_r_cycles,2);
% ---- MECG cancellation ----
for i=1:N
if peaks(i)>Pstart && length(ECG)-peaks(i)>Tstop
if (i<=10)
range=i:i+19;
elseif (i>10 && i<(N-10))
range=i-9:i+10;
else
range=i-19:i;
end
ECG_last_r_cycles=zeros(0.7*fs,length(range));
for j=1:length(range)
try
peak_nb = peaks(range(j)); % +1 to unsure full cycles
ECG_last_r_cycles(:,j) = ECG(peak_nb-Pstart:peak_nb+Tstop)';
catch ex
ECG_last_r_cycles(:,j) = NaN;
end
end
ECG_mean = nanmean(ECG_last_r_cycles,2);
M = zeros (0.7*fs,3);
M(1:0.2*fs,1) = ECG_mean(1:Pstart-0.05*fs+1);
M(0.2*fs+1:0.3*fs,2) = ECG_mean(Pstart-0.05*fs+2:Pstart+0.05*fs+1);
M(0.3*fs+1:end,3) = ECG_mean(Pstart+2+0.05*fs:Pstart+1+Tstop);
a = (M'*M)\M'*ECG(peaks(i)-Pstart:peaks(i)+Tstop)';
ECG_temp(peaks(i)-Pstart:peaks(i)+Tstop) = a(1)*M(:,1)'+a(2)*M(:,2)'+a(3)*M(:,3)';
end
end
% compute residual
residual = ECG - ECG_temp;
end
function [SelectedResidual,ChannelNb] = ChannelSelectionOrCombination(FECG)
% This function is used to select one of the four abdominal channels
% that are available or to combine information from these channels
% (e.g. using PCA) before FQRS detection
ChannelNb = 1;
SelectedResidual = FECG(:,ChannelNb); % channel 1 is arbitrarily selected here
end
function FECG = ResidualPostProcessing(FECG)
% if postprocessing is performed on the residuals.
end
function peaks = PeakDetection(x,ff,varargin)
%
% peaks = PeakDetection(x,f,flag),
% R-peak detector based on max search
%
% inputs:
% x: vector of input data
% f: approximate ECG beat-rate in Hertz, normalized by the sampling frequency
% flag: search for positive (flag=1) or negative (flag=0) peaks. By default
% the maximum absolute value of the signal, determines the peak sign.
%
% output:
% peaks: vector of R-peak impulse train
%
% Notes:
% - The R-peaks are found from a peak search in windows of length N; where
% N corresponds to the R-peak period calculated from the given f. R-peaks
% with periods smaller than N/2 or greater than N are not detected.
% - The signal baseline wander is recommended to be removed before the
% R-peak detection
%
%
% Open Source ECG Toolbox, version 1.0, November 2006
% Released under the GNU General Public License
% Copyright (C) 2006 Reza Sameni
% Sharif University of Technology, Tehran, Iran -- GIPSA-Lab, INPG, Grenoble, France
% reza.sameni@gmail.com
% Last modified 03_02_2013: Joachim Behar, IPMG Oxford.
% 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.
N = length(x);
peaks = zeros(1,N);
th = .5;
rng = floor(th/ff);
if(nargin==3),
flag = varargin{1};
else
flag = abs(max(x))>abs(min(x));
end
if(flag)
for j = 1:N,
% index = max(j-rng,1):min(j+rng,N);
if(j>rng && j<N-rng)
index = j-rng:j+rng;
elseif(j>rng)
index = N-2*rng:N;
else
index = 1:2*rng;
end
if(max(x(index))==x(j))
peaks(j) = 1;
end
end
else
for j = 1:N,
% index = max(j-rng,1):min(j+rng,N);
if(j>rng && j<N-rng)
index = j-rng:j+rng;
elseif(j>rng)
index = N-2*rng:N;
else
index = 1:2*rng;
end
if(min(x(index))==x(j))
peaks(j) = 1;
end
end
end
% remove fake peaks
I = find(peaks);
d = diff(I);
% z = find(d<rng);
peaks(I(d<rng))=0;
peaks = find(peaks);
end
function peaks = PeakDetectionAkshay(x,ff,varargin)
%
% peaks = PeakDetection(x,f,flag),
% R-peak detector based on max search
%
% inputs:
% x: vector of input data
% f: approximate window length that would contain one ECG
% flag: search for positive (flag=1) or negative (flag=0) peaks. By default
% the maximum absolute value of the signal, determines the peak sign.
%
% output:
% peaks: vector of R-peak impulse train
%
% Notes:
% - The R-peaks are found from a peak search in windows of length N; where
% N corresponds to the R-peak period calculated from the given f. R-peaks
% with periods smaller than N/2 or greater than N are not detected.
% - The signal baseline wander is recommended to be removed before the
% R-peak detection
%
%
% Akshay Dhawan
max_ecgs=zeros(1,1);
counter=1;
N=length(x);
%subtract the mean and highpass to get rid of any low freq components
x=x-mean(x);
d = fdesign.highpass('N,F3dB',4,1.5,1000);
hd = design(d,'butter');
x=filtfilt(hd.sosMatrix,hd.ScaleValues,x);
%turn it into a maxima problem, not a minima problem
flag = abs(max(x))>abs(min(x));
if (~flag)
x=-1*x;
end
%get threshold using maternal peaks
for i=1:ff:(N-ff)
max_ecgs(counter)=max(x(i:(i+ff)));
counter=counter+1;
end
thres=0.6*mean(max_ecgs);
refractory=300; %300ms = 300 samples (BPM ~ 180) very conservative approach
[~,peaks]=findpeaks(x, 'MINPEAKHEIGHT', thres, 'MINPEAKDISTANCE', refractory );
%if #peaks seems wrong, then try again with inverted signal
if (length(peaks)<40 || length(peaks)>200)
x=-1*x;
%get threshold using maternal peaks
for i=1:ff:(N-ff)
max_ecgs(counter)=max(x(i:(i+ff)));
counter=counter+1;
end
thres=0.6*mean(max_ecgs);
refractory=300; %300ms = 300 samples (BPM ~ 180) very conservative approach
[~,peaks]=findpeaks(x, 'MINPEAKHEIGHT', thres, 'MINPEAKDISTANCE', refractory );
end
end
function peaks=PanTompkinsAkshay(FECG)
%Apply algorithm to each signal and see which has the highest probability
%of correctness
%integration window
N=100;
fs=1000;
window=(1/N).*ones(1,N);
%Butterworth filters, 15-25 Hz bandpass
hlow=fdesign.lowpass('N,F3dB',12,25,fs);
dlow=design(hlow,'butter');
hhigh=fdesign.highpass('N,F3dB',12,15,fs);
dhigh=design(hhigh,'butter');
filtered=FECG;
%Apply algorithm
for i=1:size(FECG,2)
filtered(:,i)=filtfilt(dhigh.sosMatrix,dhigh.ScaleValues,filtfilt(dlow.sosMatrix,dlow.ScaleValues,FECG(:,i)));
filtered(:,i)=conv(filtered(:,i).^2,window,'same');
max_ecgs=zeros(1,1);
counter=1;
ff=500; %approx one QRS per window
%get threshold using QRS peaks
for j=1:ff:(N-ff)
max_ecgs(counter)=max(filtered(j:(j+ff),i));
counter=counter+1;
end
thres=0.6*mean(max_ecgs);
refractory=250; %250ms = 250 samples (BPM ~ 240) very conservative approach
[~,peaks{i}]=findpeaks(filtered(:,i), 'MINPEAKHEIGHT', thres, 'MINPEAKDISTANCE', refractory );
end
end