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