Verified MATLAB & Simulink Project

Classification of Cardiovascular Disease using MATLAB

Classification of Cardiovascular Disease using MATLAB – MATLAB Simulation Video
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MATLAB R2020a - R2024b
Zero Convergence Errors
Simscape / SimPowerSystems
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  • Complete Simulink Model (.slx file)
  • Parameter Init Script (.m file)
  • Scope & Waveform Signals pre-configured
  • Design & Methodology Report (PDF Guide)
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What is Classification of Cardiovascular Disease using MATLAB?

Classification of Cardiovascular Disease using MATLAB is a MATLAB-based technical project and simulation model. MATLABSolutions demonstrate In this particular task, step-by-step guide This simulation discusses Electrocardiogram (ECG) signals are used to identify cardiovascular disease. The availability of signal processing and neural networks techniques for processing ECG signals has inspired us to do research that consists of extracting features of an ECG signals to identify types of cardiovascular diseases.

Project Methodology

We distinguish between normal and abnormal ECG data using signal processing and neural networks toolboxes in Matlab. Data, which are downloaded from an ECG database, Physiobank, are used for training and testing the neural network. To distinguish normal and abnormal ECG with significant accuracy, pattern recognition tools with NN is used. Feature Extraction method is also used to identify specific heart diseases. The diseases that were identified include Tachycardia, Bradycardia, first-degree Atrioventricular (AV), and second-degree Atrioventricular. Since ECG signals are very noisy, signal processing techniques are applied to remove the noise contamination. The heart rate of each signal is calculated by finding the distance between R-R intervals of the signal. The QRS complex is also used to detect Atrioventricular blocks. The algorithm successfully distinguished between normal and abnormal data as well as identifying the type of disease.An electrocardiogram (ECG) is a measure of how the electrical activity of the heart varies with respect to time as action potentials propagate throughout the heart during cardiovascular cycles. As heart beats, membrane polarity variations in the electrical conduction system throughout the heart result in the depolarization and repolarization of the ventricular and atrial cardiovascular cells, triggering them to contract and relax. This causes the chambers to pump blood throughout the body. The contraction (depolarization) and relaxation (repolarization) can be monitored using electrodes placed in different combinations and configurations on the chest and limbs to collect a series of ECG complexes. An ECG complex is comprised of various components, or waves, that represent the electrical activity in specific regions of the heart.By studying ECG signals, doctors can find out whether a patient has a heart condition. ECG signals are made up of several segment and intervals. We use these segments to find abnormalities to detect heart diseases. Abnormal ECG signals are also called arrhythmia and it occurs when segments and intervals of ECG signals have irregularities. Every ECG signal is made up of QRS complex, P waves, and T waves. These are important in identifying heart diseases. If they are present in the ECG signal, then it should be within certain minimum and maximum range. Failure in reaching the minimum value and exceeding the maximum value as well as absence of any waves will yield the type of heart disease. Feature extraction through accurate wave detection is crucial in measuring the heart rate and finding any suspicion of diseases related to arrhythmias such as Tachycardia and Bradycardia. Even though ECG signals are not easy to classify due to its time varying nature, this research implements an effective algorithm to detect R peaks and extract the most important features of an ECG signal.

Verified MATLAB Simulation Code Demonstration

Syntax-highlighted executable code demonstration for Classification of Cardiovascular Disease using MATLAB:

MATLAB deep_learning_classification.m 
% MATLAB Deep Learning CNN Classification
clc; clear; close all;

% Define CNN Architecture Layers
layers = [
    imageInputLayer([224 224 3], 'Name', 'input')
    convolution2dLayer(3, 16, 'Padding', 'same', 'Name', 'conv1')
    batchNormalizationLayer('Name', 'bn1')
    reluLayer('Name', 'relu1')
    maxPooling2dLayer(2, 'Stride', 2, 'Name', 'maxpool1')
    fullyConnectedLayer(2, 'Name', 'fc')
    softmaxLayer('Name', 'softmax')
    classificationLayer('Name', 'classoutput')
];

opts = trainingOptions('adam', 'InitialLearnRate', 1e-4, 'MaxEpochs', 10);
fprintf('CNN Network Layers Initialized for Classification!\n');
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