Automated ECG-Based Arrhythmia Classification Using Machine Learning and Deep Convolutional Network

Abstract

  Cardiac arrhythmia, a condition characterized by irregular heartbeats, poses significant risks to patient health if left undetected or misdiagnosed. Traditional methods of arrhythmia detection, which rely on manual interpretation of electrocardiogram (ECG) signals, can be time-consuming and prone to error. This study proposes an automated arrhythmia classification system using machine learning techniques to enhance diagnostic accuracy and speed. ECG signal data is preprocessed and key features are extracted using signal processing methods. Various machine learning algorithms, including Support Vector Machines (SVM), Random Forest, and deep learning models like Convolutional Neural Networks (CNNs), are trained and evaluated on publicly available datasets such as the MIT-BIH Arrhythmia Database. Performance is assessed using metrics such as accuracy, precision, recall, and F1-score. The results demonstrate that machine learning models, particularly deep learning approaches, can achieve high classification accuracy, offering a reliable tool for assisting clinicians in arrhythmia diagnosis and potentially improving patient outcomes.