Advances in Artificial Intelligence for Electrocardiogram Analysis: A Comprehensive Review of Architectures, Clinical Applications, and Future Directions

Electrocardiogram (ECG) analysis is a fundamental tool in cardiology for diagnosing a wide range of heart conditions. The advent of artificial intelligence (AI), specifically deep learning, has opened new avenues for automated ECG interpretation, offering the potential for increased accuracy, efficiency, and wider access to cardiac care. This review provides a comprehensive analysis of the current state-of-the-art in AI-driven ECG analysis, covering deep learning architectures, clinical applications, validation strategies, and future research directions. We examine the core challenges in ECG analysis, such as the complexity of cardiac signals, noise interference, and the need for model interpretability. We then present a detailed overview of key deep learning approaches, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), graph neural networks (GCNs), and hybrid models. We place particular emphasis on innovations like deformable convolutions, attention mechanisms, and capsule networks, providing mathematical formulations and comparative performance analyses. The review highlights major clinical applications, including arrhythmia detection, myocardial infarction identification, heart failure prediction, and the integration of multi-modal data. Rigorous model validation, performance benchmarking against established datasets, and efforts towards clinical translation are critically assessed. Finally, we outline promising future research directions, including personalized medicine, explainable AI (XAI), edge computing, and the ethical considerations surrounding AI deployment in healthcare.