Utilizing Deep Learning Methods for Heart Defect Identification via Electrocardiogram (ECG): A Literature Review



  • Darwan IAIN Syekh Nurjati Cirebon
  • Faiz Muqorrir Kaffah Department of Informatics, IAIN Syekh Nurjati Cirebon, West Java, Indonesia.
  • Heru Purnomo Kurniawan Department of Informatics, IAIN Syekh Nurjati Cirebon, West Java, Indonesia.
  • Lia Farhatuaini Department of Informatics, IAIN Syekh Nurjati Cirebon, West Java, Indonesia.
  • Ibnu Rusydi Department of Software Engineering, Universitas Dharmawangsa, Medan, Indonesia.


Electrocardiogram, deep learning, dataset, pre-processing, identification


In recent years the application of Deep Learning is widely used in various fields of science, such as in the military, agriculture, health and even in other fields. In the field of health many studies use deep learning, especially related to heart disorders. Identification and detection of heart defects that are widely used are by using an electrocardiogram (ECG). Research related to ECG and the application of deep learning methods is very interesting for researchers, because researchers trace that there are still few studies that focus on researching related to it. This article aims to explain the trend of ECG research using deep learning approaches in recent years. We reviewed journals with the keyword title "ECG Deep Learning" and published from 2016 to October 2023. The articles that have been obtained are then classified based on the most frequently discussed topics including: data sets, pre-processing, feature extraction, and classification/identification methods. The approach used by some researchers is mostly to get the best results from the use of deep learning methods. This article will provide further explanation of the most widely used algorithms for ECG research with a deep learning approach. Of the deep learning methods used, almost 84% use the Convolutional Neural Network method. In this article, critical aspects of ECG research can be carried out in the future, namely the use of data in the form of other data from ECG, and the use of deep learning is a very big opportunity for researchers in the future.


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How to Cite

Darwan, Kaffah, F. M. ., Kurniawan, H. P. ., Farhatuaini, L. ., & Rusydi, I. . (2024). Utilizing Deep Learning Methods for Heart Defect Identification via Electrocardiogram (ECG): A Literature Review . Journal of Applied Artificial Intelligence, 5(1), 28–40. https://doi.org/10.48185/jaai.v5i1.1007