ECG based Heart Disease Detection: Current State and Technological Advancements

This paper explores the fusion of deep learning, IoT, and state-of-the-art algorithms to enhance heart rate detection and analysis, crucial for addressing the global surge in cardiovascular diseases through early and precise detection for better preventive healthcare outcomes. It offers a comprehensive review of recent advancements and methods, highlighting the integration of computational and technological progress in improving cardiac health monitoring. The paper identifies and discusses significant research gaps such as the interpretability of deep learning models, the computational intensity of algorithms, and the need for diverse datasets for effective model training, along with challenges in integrating IoT for efficient, real-time cardiac monitoring. Emphasizing the importance of adaptive feature selection, and the utilization of machine and deep learning models for ECG signal analysis, it also points to innovative strategies like transfer learning for boosting detection accuracy. Stressing the need for cross-disciplinary efforts to close these research gaps, this study advocates for the development of more interpretable, efficient, and widely applicable heart rate detection systems, contributing to advanced cardiac health monitoring and fostering proactive, personalized healthcare to combat cardiovascular diseases.