Detection of arrhythmias and myocardial infarction using SVM and ANN algorithms
DOI:
https://doi.org/10.21123/bsj.2024.8168Keywords:
ECG features extraction, myocardial infraction, neural network classifier, SVM, Wavelet transformationAbstract
The heart is one of the most vital organs in humans, and any defect in its function is reflected in the general health of the patient. Therefore, the heart and its diseases have been largely studied in order to help the doctors diagnose heart diseases and reduce errors as much as possible. This study aims to investigate and suggest a method for diagnosing heart diseases from electrocardiogram (ECG) signals. Initially, noise was removed from the signal and the morphological and dynamic features of the ECG signal were extracted by an appropriate feature extraction algorithm and wavelet transformation. A support vector machine (SVM) classifier was then proposed to identify the sound signals from the pathological signals, followed by the use of an appropriate neural classifier in order to classify the pathological signals and extract the results. The system has reached an accuracy of 97 % of the diseased varieties from the healthy ones, out of 150 samples. The proposed system was also able to identify 96% of the number of pathological samples, and attributing them to 3 categories (myocardial infarction, arrhythmias, and other categories).
Received 26/11/2023
Revised 14/07/2023
Accepted 16/07/2023
Published Online First 20/01/2024
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