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Advancements in ECG Heartbeat Classification: A Comprehensive Review of Deep Learning Approaches and Imbalanced Data Solutions

Authors

  • Amal P Varghese

    Author

  • Simy Mary Kurian

    Author

Abstract

This systematic literature review critically examines developments in electrocardiogram (ECG) heartbeat classification, focusing on the utilization of deep learning techniques and addressing challenges associated with imbalanced datasets. Covering articles published between 2012 and 2021, The primary objective is to uncover challenges related to imbalanced data in predicting heart diseases, specifically through the lens of machine learning applications utilizing ECG and patient data. The paper discusses the types of heart diseases, algorithms, applications, and solutions, shedding light on limitations and gaps in current approaches.

Keywords:

ECG Signal Processing, Convolutional Neural Network (CNN), AAMI Standard, MIT-BIH Dataset, INCART Dataset, Deep Learning, R-peak Detection, Electrocardiogram (ECG) Abnormalities, Machine Learning, Medical Signal Processing, RR Time Interval, Explainable AI
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Published

29-12-2023

Issue

Vol. 3 No. 2 (2023): IJERA

Section

Articles

License

Copyright (c) 2023 International Journal on Emerging Research Areas

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

All published work in this journal is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

How to Cite

[1]
A. P. Varghese and S. M. Kurian, “Advancements in ECG Heartbeat Classification: A Comprehensive Review of Deep Learning Approaches and Imbalanced Data Solutions”, IJERA, vol. 3, no. 2, Dec. 2023, Accessed: Jul. 05, 2025. [Online]. Available: https://ijera.in/index.php/IJERA/article/view/7