CARDIAC ECG SIGNAL AUTOMATED ANALYSIS USING AI

Abstract

Accurate analysis of electrocardiogram (ECG) signals is crucial for early detection and management of cardiac arrhythmias and other heart conditions. Manual interpretation of ECG data can be time-consuming, prone to errors, and dependent on clinician expertise. Artificial intelligence (AI) algorithms, particularly deep learning models, offer automated, reliable, and efficient solutions for ECG signal analysis. This paper reviews current AI-based methodologies for automated ECG interpretation, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid approaches. Challenges such as data variability, noise, and model interpretability are discussed. The study highlights the potential of AI-driven systems to support clinicians, enhance diagnostic accuracy, and improve cardiovascular patient care.