AI-BASED AUTOMATED ANALYSIS OF ELECTROCARDIOGRAMS FOR EARLY DETECTION OF CARDIAC

Abstract

Cardiac arrhythmias, including atrial fibrillation, ventricular tachycardia, and premature ventricular contractions, are a major cause of cardiovascular morbidity and mortality worldwide. Early and accurate detection is critical for effective treatment, prevention of complications, and improved patient outcomes. Electrocardiography (ECG) is the primary diagnostic tool for arrhythmia detection; however, manual interpretation is time-consuming and prone to human error. Artificial intelligence (AI) and deep learning techniques provide automated, precise, and rapid analysis of ECG signals, enabling early identification of arrhythmias. This paper reviews current AI-based methodologies for ECG analysis, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid approaches. Challenges such as signal noise, limited annotated datasets, and model interpretability are discussed. The study emphasizes the potential of AI systems to improve diagnostic accuracy, enhance clinical workflow, and support timely intervention in cardiovascular care.