Cardiovascular diseases (CVDs) continue to be a leading cause of morbidity and mortality worldwide. Early diagnosis and continuous monitoring are critical in managing these conditions effectively. Recent advancements in artificial intelligence (AI), particularly in deep learning (DL) techniques, have shown promising results in improving the diagnostic and prognostic accuracy in CVDs, especially when combined with electrocardiography (ECG). This systematic review aims to provide an overview of the integration of deep learning methods with ECG in the diagnosis and management of cardiovascular diseases. The review explores various deep learning models used for ECG signal processing, classification, arrhythmia detection, and risk prediction. The findings indicate that deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models, have significantly improved the performance of ECG-based diagnostic tools, offering substantial advantages in terms of accuracy, speed, and scalability. However, challenges such as data privacy, generalizability, and clinical integration remain. Future research should focus on addressing these challenges and further enhancing the clinical applicability of AI in cardiovascular healthcare.

Deep Learning, Electrocardiography (ECG), Cardiovascular Care

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