arrhythmia detection in matlab

To detect arrhythmia in MATLAB, we can use signal processing techniques along with machine learning algorithms. The most common way to detect arrhythmia is by analyzing electrocardiogram (ECG) signals which are recorded from the heart.

One popular method for arrhythmia detection is by using the Pan-Tompkins algorithm which is a real-time algorithm for QRS detection. It uses a moving average filter, derivative filter, and a threshold function to locate QRS complexes in the ECG signal.

Here's an example code snippet for arrhythmia detection using the Pan-Tompkins algorithm in MATLAB:

main.m
% Load ECG signal
load('ecg_signal.mat');

% Apply low-pass filter
fc = 30; % cutoff frequency
fs = 250; % sampling frequency
[b,a] = butter(1,fc/(fs/2),'low');
ecg_filtered = filtfilt(b,a,ecg_signal);

% Pan-Tompkins QRS detection
[delay,L] = pan_tompkin(ecg_filtered,fs);

% Plot detected QRS complexes
plot(ecg_filtered); hold on;
plot(delay,ecg_filtered(delay),'ro','LineWidth',2);
xlabel('Sample number'); ylabel('ECG Signal');
legend('ECG signal','Detected QRS complexes');
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After performing QRS detection, we can use machine learning algorithms such as decision trees or support vector machines to classify the ECG signals as normal or abnormal (i.e. arrhythmic). These algorithms can be trained on a labeled dataset of ECG signals with known arrhythmia labels.

Overall, arrhythmia detection in MATLAB involves using signal processing techniques to preprocess ECG signals and then applying machine learning algorithms for classification.