Need code for FECG extraction

Thanks & Regards
Ms Bhoker

hi gust;

Need code for FECG extraction, using DWT or another methods

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matlab codes for fetal ecg extraction

Fetal electrocardiogram (FECG) extraction has a vital role in medical diagnosis during pregnancy. In this
paper, a method for removal of background noise and artifacts from FECG signals using adaptive filters is proposed.
The proposed method uses adaptive noise cancellation and digital filters for FECG extraction. Proposed FECG
extraction algorithm is implemented in MATLAB using simulink models. Simulation results shows that fetal heart rate
can be extracted by counting the peaks of R-R interval from the extracted FECG noise free signal.


% Mother & Fetus hearbeat detection example - Med2011
% By Igal
%% Init
% clear all; close all;
Fs = 4e3;
Time = 40;
NumSamp = Time * Fs;
load Hd;


%% Mom's Heartbeat
% In this example, we shall simulate the shapes of the electrocardiogram
% for both the mother and fetus. The following commands create an
% electrocardiogram signal that a mother's heart might produce assuming
% a 4000 Hz sampling rate. The heart rate for this signal is approximately
% 89 beats per minute, and the peak voltage of the signal is 3.5 millivolts.
x1 = 3.5*ecg(2700).'; % gen synth ECG signal
y1 = sgolayfilt(kron(ones(1,ceil(NumSamp/2700)+1),x1),0,21); % repeat for NumSamp length and smooth
n = 1:Time*Fs';
del = round(2700*rand(1)); % pick a random offset
mhb = y1(n + del)'; %construct the ecg signal from some offset
t = 1/Fs:1/Fs:Time';
subplot(3,3,1); plot(t,mhb);
axis([0 2 -4 4]);
grid;
xlabel('Time [sec]');
ylabel('Voltage [mV]');
title('Maternal Heartbeat Signal');

%% Fetus Heartbeat
% The heart of a fetus beats noticeably faster than that of its mother,
% with rates ranging from 120 to 160 beats per minute. The amplitude of the
% fetal electrocardiogram is also much weaker than that of the maternal
% electrocardiogram. The following series of commands creates an electrocardiogram
% signal corresponding to a heart rate of 139 beats per minute and a peak voltage
% of 0.25 millivolts.
x2 = 0.25*ecg(1725);
y2 = sgolayfilt(kron(ones(1,ceil(NumSamp/1725)+1),x2),0,17);
del = round(1725*rand(1));
fhb = y2(n + del)';
subplot(3,3,2); plot(t,fhb,'m');
axis([0 2 -0.5 0.5]);
grid;
xlabel('Time [sec]');
ylabel('Voltage [mV]');
title('Fetal Heartbeat Signal');

%% The measured signal
% The measured fetal electrocardiogram signal from the abdomen of the mother is
% usually dominated by the maternal heartbeat signal that propagates from the
% chest cavity to the abdomen. We shall describe this propagation path as a linear
% FIR filter with 10 randomized coefficients. In addition, we shall add a small
% amount of uncorrelated Gaussian noise to simulate any broadband noise sources
% within the measurement. Can you determine the fetal heartbeat rate by looking
% at this measured signal?
Wopt = [0 1.0 -0.5 -0.8 1.0 -0.1 0.2 -0.3 0.6 0.1];
%Wopt = rand(1,10);
d = filter(Wopt,1,mhb) + fhb + 0.02*randn(size(mhb));
subplot(3,3,3); plot(t,d,'r');
axis([0 2 -4 4]);
%axis tight;
grid;
xlabel('Time [sec]');
ylabel('Voltage [mV]');
title('Measured Signal');

%% Measured Mom's heartbeat
% The maternal electrocardiogram signal is obtained from the chest of the mother.
% The goal of the adaptive noise canceller in this task is to adaptively remove the
% maternal heartbeat signal from the fetal electrocardiogram signal. The canceller
% needs a reference signal generated from a maternal electrocardiogram to perform this
% task. Just like the fetal electrocardiogram signal, the maternal electrocardiogram
% signal will contain some additive broadband noise.
x = mhb + 0.02*randn(size(mhb));
subplot(3,3,4); plot(t,x);
axis([0 2 -4 4]);
grid;
xlabel('Time [sec]');
ylabel('Voltage [mV]');
title('Reference Signal');

%% Applying the adaptive filter
% The adaptive noise canceller can use almost any adaptive procedure to perform its task.
% For simplicity, we shall use the least-mean-square (LMS) adaptive filter with 15
% coefficients and a step size of 0.00007. With these settings, the adaptive noise canceller
% converges reasonably well after a few seconds of adaptation--certainly a reasonable
% period to wait given this particular diagnostic application.

h = adaptfilt.lms(15, 0.001);
[y,e] = filter(h,x,d);

% [y,e] = FECG_detector(x,d);

subplot(3,3,5); plot(t,d,'c',t,e,'r');
%axis([0 7.0 -4 4]);
grid;
xlabel('Time [sec]');
ylabel('Voltage [mV]');
title('Convergence of Adaptive Noise Canceller');
legend('Measured Signal','Error Signal');

%% Recovering the fetus' hearbeat
% The output signal y(n) of the adaptive filter contains the estimated maternal
% heartbeat signal, which is not the ultimate signal of interest. What remains in the
% error signal e(n) after the system has converged is an estimate of the fetal heartbeat
% signal along with residual measurement noise.
subplot(3,3,6); plot(t,e,'r'); hold on; plot(t,fhb,'b');
axis([Time-4 Time -0.5 0.5]);
grid on;
xlabel('Time [sec]');
ylabel('Voltage [mV]');
title('Steady-State Error Signal');
legend('Calc Fetus','Ref Fetus ECG');

%% Counting the peaks to detect the heart rate
% The idea is to clean up the signal, and then set some dynamic threshold, so that any signal
% crossing the threshold is considered a peak. The peaks can be counted per time window.
%[num,den] = fir1(100,100/2000);
filt_e = filter(Hd,e);
subplot(3,3,7); plot(t,fhb,'r'); hold on; plot(t,filt_e,'b');
axis([Time-4 Time -0.5 0.5]);
grid on;
xlabel('Time [sec]');
ylabel('Voltage [mV]');
title('Filtered signal');
legend('Ref Fetus','Filtered Fetus');
thresh = 4*mean(abs(filt_e))*ones(size(filt_e));
peak_e = (filt_e >= thresh);
edge_e = (diff([0; peak_e]) >0);
subplot(3,3,8); plot(t,filt_e,'c'); hold on; plot(t,thresh,'r'); plot(t,peak_e,'b');
xlabel('Time [sec]');
ylabel('Voltage [mV]');
title('Peak detection');
legend('Filtered fetus','Dyna thresh','Peak marker', 'Location','SouthEast');
axis([Time-4 Time -0.5 0.5]);
subplot(3,3,9); plot(t,filt_e,'r'); hold on; plot(t,edge_e,'b'); plot(0,0,'w');
fetus_calc = round((60/length(edge_e(16001:end))*Fs)* sum(edge_e(16001:end)));
fetus_bpm = ['Fetus Heart Rate =' mat2str(fetus_calc)];
xlabel('Time [sec]');
ylabel('Voltage [mV]');
title('Reconstructed fetus signal');
legend('Fetus Sig','Edge marker',fetus_bpm, 'Location','SouthEast');
axis([Time-4 Time -0.5 0.5]);