ECG Machine Learning Classification Techniques for Arrhythmia Heart Disease

Abstract

This paper reviews machine learning techniques for electrocardiogram (ECG)-based classification of cardiac arrhythmias as effective alternatives to conventional diagnostic approaches. Cardiac arrhythmia remains one of the most prevalent cardiovascular conditions worldwide, and ECG signals play a vital role in the early detection and assessment of heart-related abnormalities. However, traditional diagnostic methods are often limited by the shortage of medical experts, the complexity of ECG interpretation, and the similarity of symptoms across different cardiac conditions. This review focuses on key stages of ECG-based machine learning classification, including signal preprocessing, feature extraction, algorithmic analysis, and performance evaluation. Twenty-five research studies are examined, with particular attention to commonly applied classification methods, including Support Vector Machine, Random Forest, K-Nearest Neighbor, Artificial Neural Network, and Convolutional Neural Network. The review highlights the strengths and limitations of these approaches and compares their reported performance in arrhythmia classification.