Open Access

ARTICLE

Arrhythmia Prediction on Optimal Features Obtained from the ECG as Images

Fuad A. M. Al-Yarimi^*
Department of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia
* Corresponding Author: Fuad A. M. Al-Yarimi. Email:

Computer Systems Science and Engineering 2023, 44(1), 129-142. https://doi.org/10.32604/csse.2023.024297

Received 12 October 2021; Accepted 31 December 2021; Issue published 01 June 2022

Abstract

A critical component of dealing with heart disease is real-time identification, which triggers rapid action. The main challenge of real-time identification is illustrated here by the rare occurrence of cardiac arrhythmias. Recent contributions to cardiac arrhythmia prediction using supervised learning approaches generally involve the use of demographic features (electronic health records), signal features (electrocardiogram features as signals), and temporal features. Since the signal of the electrical activity of the heartbeat is very sensitive to differences between high and low heartbeats, it is possible to detect some of the irregularities in the early stages of arrhythmia. This paper describes the training of supervised learning using features obtained from electrocardiogram (ECG) image to correct the limitations of arrhythmia prediction by using demographic and electrocardiographic signal features. An experimental study demonstrates the usefulness of the proposed Arrhythmia Prediction by Supervised Learning (APSL) method, whose features are obtained from the image formats of the electrocardiograms used as input.

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Keywords

ECG records; electrocardiogram; morphological features (MF); empirical mode decomposition algorithm; HOS

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