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: fuadalyarimi@gmail.com Received: 12 October 2021; Accepted: 31 December 2021 Abstract: A critical component of dealing with heart disease is real-time identifi- cation, which triggers rapid action. The main challenge of real-time identification is illustrated here by the rare occurrence of cardiac arrhythmias. Recent contribu- tions to cardiac arrhythmia prediction using supervised learning approaches gen- erally 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 electrocardio- graphic 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. Keywords: ECG records; electrocardiogram; morphological features (MF); empirical mode decomposition algorithm; HOS 1 Introduction ECGs are representations of the electrical activity of the heart muscle that varies over time, which is generally archived on paper for easier analysis. The cardiac muscle responds to electrical depolarization of its cells, as do other muscles. In an ECG, electrical activity aggregation is recorded & amplified for a few seconds, which reflects the variation in the electrical potential graph produced by the heart & measured at the body’ s surface. Currently, clinical information of patients has changed among health care providers. From ECG, the parameters are measured very extensively from ECG records. It would be very effective if the paper type of ECG records could be converted into digital files. Furthermore, the novel pre-requisites justify the need for tools in converting current ECG records to digital format, mainly for retrospective contributions. In the work [1,2] a computer program is presented for the conversion of ECG paper charts into ECG files digitally. Usually, ECG paper charts can be divided into three kinds: background with grid collared, background with the grid as black, and background without any grid, uniform background. In hospitals, the ECG signals are generally recorded on benchmark grid papers in This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Computer Systems Science & Engineering DOI: 10.32604/csse.2023.024297 Article ech T Press Science