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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: email

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

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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Cite This Article

APA Style
Al-Yarimi, F.A.M. (2023). Arrhythmia prediction on optimal features obtained from the ECG as images. Computer Systems Science and Engineering, 44(1), 129-142. https://doi.org/10.32604/csse.2023.024297
Vancouver Style
Al-Yarimi FAM. Arrhythmia prediction on optimal features obtained from the ECG as images. Comput Syst Sci Eng. 2023;44(1):129-142 https://doi.org/10.32604/csse.2023.024297
IEEE Style
F.A.M. Al-Yarimi, "Arrhythmia Prediction on Optimal Features Obtained from the ECG as Images," Comput. Syst. Sci. Eng., vol. 44, no. 1, pp. 129-142. 2023. https://doi.org/10.32604/csse.2023.024297



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