Puspendu RoyECG analysis by using Matlab
Introduction
•Electrocardiogram (ECG or EKG) is a diagnostic tool that measures and records the electrical activity of the heart in exquisite detail.
•various conditions can be measured over a period of time by using electrodes placed on skin.
•These electrodes detects small electrical charges on our skin that originate due to the process of depolarization and polarization after every heartbeat
•ECG tracing of a normal heartbeat consists of a P-wave, a QRS -complex and a T-wave.
•
•The electrical signal begins in the sinoatrial node which is the P-wave.
•
•After the signal leaves the AV node it travels along a pathway called the bundle of His where this signals is known as the QRS-wave.
•
•The ventricles then recover to their normal electrical state which is known as the T-wave.
METHODOLOGY
1 R-peaks Detection
For R-peaks Detection we need to follow the given procedure:
Step 1: Remove low frequency components
•Change to fourier domain using the fft command (fast fourier transform)
•Remove the low frequencies found
•Back to time-domain using fft command
Step 2: Find local maxima using filter
Step 3: Remove small values, and store significant ones.
Step 4: Repeat the 2 and 3 steps.
2 Calculating Heart Beat
•Large peaks in the ECG signal represents QRS complex which is obtained when the heart beats.
•Therefore, the number of QRS complex gives us the number of times heart beats.
•Heart Beat rate in (beats/second) can be calculated by the formula.
•Rate=60*sampling rate / R-R interval
•
Ex:- 60(1min)*4(peaks)/2.5 seconds = 90
(where R-R interval = distance between first and last peak / length between two peaks)
ABNORMALITIES AND CONDITIONS OF HEART
qNormal heart beat range: 60-100 bpm
qHeart beat< 60-100bpm Bradycardia
qHeart beat > 60-100 bpm Tachycardia
qIf cycles not evenly placed Arrhythmia
If P-R interval > 0.2 secondsBlockage of AV node
Matlab CODE
% ECGDEMO ECG PROCESSING DEMONSTRATION - R-PEAKS DETECTION
%
% NOTE: Surya modified the code by adding Heart Rate calculation and
% some additional comments. Contact: p.surya1994@gmail.com
%
%
% This file is a part of a package that contains 5 files:
%
% 1. ecgdemo.m - (this file) main script file;
% 2. ecgdemowinmax.m - window filter script file;
% 3. ecgdemodata1.mat - first ecg data sample;
% 4. ecgdemodata2.mat - second ecg data sample;
% 5. readme.txt - description.
%
% The package downloaded from http://www.librow.com
% To contact the author of the sample write to Sergey Chernenko:
% S.Chernenko@librow.com
%
% To run the demo put
%
% ecgdemo.m;
% ecgdemowinmax.m;
% ecgdemodata1.mat;
% ecgdemodata2.mat
%
% in MatLab's "work" directory, run MatLab and type in
%
% >> ecgdemo
%
% The code is property of LIBROW
% You can use it on your own
% When utilizing credit LIBROW site
% We are processing two data samples to demonstrate two different situations
for demo = 1:2:3
% Clear our variables
clear ecg samplingrate corrected filtered1 peaks1 filtered2 peaks2 fresult
% Load data sample
switch(demo)
case 1,
plotname = 'Sample 1';
load ecgdemodata1;
case 3,
plotname = 'Sample 2';
load ecgdemodata2;
end
% Remove lower frequencies
fresult=fft(ecg);
fresult(1 : round(length(fresult)*5/samplingrate))=0;
fresult(end - round(length(fresult)*5/samplingrate) : end)=0;
corrected=real(ifft(fresult));
% Filter - first pass
WinSize = floor(samplingrate * 571 / 1000);%window size is 571
if rem(WinSize,2)==0
WinSize = WinSize+1;
end
filtered1=ecgdemowinmax(corrected, WinSize);
% Scale ecg
peaks1=filtered1/(max(filtered1)/8);
% Filter by threshold filter
for data = 1:1:length(peaks1)
if peaks1(data) < 4
peaks1(data) = 0;
else
peaks1(data)=1;
end
end
positions=find(peaks1);
distance=positions(2)-positions(1);
% Returns minimum distance between two peaks
for data=1:1:length(positions)-1
if positions(data+1)-positions(data)<distance
distance=positions(data+1)-positions(data);
end
end
% Optimize filter window size
QRdistance=floor(0.04*samplingrate);
if rem(QRdistance,2)==0
QRdistance=QRdistance+1;
end
WinSize=2*distance-QRdistance;
% Filter - second pass
filtered2=ecgdemowinmax(corrected, WinSize);
peaks2=filtered2;
for data=1:1:length(peaks2)
if peaks2(data)<4
peaks2(data)=0;
else
peaks2(data)=1;
end
end
%% This part of the code between the double comments is added by Surya Penmetsa
positions2=find(peaks2);
distanceBetweenFirstAndLastPeaks = positions2(length(positions2))-positions2(1);
averageDistanceBetweenPeaks = distanceBetweenFirstAndLastPeaks/length(positions2);
averageHeartRate = 60 * samplingrate/averageDistanceBetweenPeaks;
disp('Average Heart Rate = ');
disp(averageHeartRate);
% The code written by Surya Penmetsa Ends here.
%%
% Create figure - stages of processing
figure(demo); set(demo, 'Name', strcat(plotname, ' - Processing Stages'));
% Original input ECG data
subplot(3, 2, 1); plot((ecg-min(ecg))/(max(ecg)-min(ecg)));
title('\bf1. Original ECG'); ylim([-0.2 1.2]);
% ECG with removed low-frequency component
subplot(3, 2, 2); plot((corrected-min(corrected))/(max(corrected)-min(corrected)));
title('\bf2. FFT Filtered ECG'); ylim([-0.2 1.2]);
% Filtered ECG (1-st pass) - filter has default window size
subplot(3, 2, 3); stem((filtered1-min(filtered1))/(max(filtered1)-min(filtered1)));
title('\bf3. Filtered ECG - 1^{st} Pass'); ylim([0 1.4]);
% Detected peaks in filtered ECG
subplot(3, 2, 4); stem(peaks1);
title('\bf4. Detected Peaks'); ylim([0 1.4]);
% Filtered ECG (2-d pass) - now filter has optimized window size
subplot(3, 2, 5); stem((filtered2-min(filtered2))/(max(filtered2)-min(filtered2)));
title('\bf5. Filtered ECG - 2^d Pass'); ylim([0 1.4]);
% Detected peaks - final result
subplot(3, 2, 6); stem(peaks2);
title('\bf6. Detected Peaks - Finally'); ylim([0 1.4]);
% Create figure - result
figure(demo+1); set(demo+1, 'Name', strcat(plotname, ' - Result'));
% Plotting ECG in green
plot((ecg-min(ecg))/(max(ecg)-min(ecg)), '-g'); title('\bf Comparative ECG R-Peak Detection Plot');
% Show peaks in the same picture
hold on
% Stemming peaks in dashed black
stem(peaks2'.*((ecg-min(ecg))/(max(ecg)-min(ecg)))', ':k');
% Hold off the figure
hold off
end