A systematic review of Automated prediction of sudden cardiac deathusing ECG signals

Sudden Cardiac Death (SCD) stands as a life-threatening cardiac event capable of swiftly claiming lives. Researchers have devised numerous models aimed at automatically predicting SCD through a combination of diverse feature extraction techniques and classifiers. We did a rigorous review of research publications ranging from 2011 to 2023, with a specific focus on the automated prediction of SCD, a growing health concern on a global scale. Over the past two decades, Machine Learning (ML) techniques have emerged and evolved for this purpose. Notably, since 2021, Deep Learning (DL) technology has also been incorporated into automatically predicting SCD. This literature review comprehensively analyzes ML and DL models employed in predicting SCD. The analysis yielded valuable insights into the fundamental structure of cardiac fatalities, extracting relevant characteristics from ECG and HRV signals, using databases, and evaluating classifier performance. The review offers a succinct yet thorough examination of automated SCD prediction methodologies, emphasizing current constraints and underscoring the necessity for further advancements. It serves as a valuable resource, providing valuable insights and outlining potential research directions for aspiring scholars in the domain of SCD prediction. These automated methodologies have demonstrated the potential to achieve remarkable prediction accuracy, reaching levels as high as 97%, and can forecast SCD events with a lead time of 30-70 minutes. Despite these promising outcomes, the quest for even greater accuracy and reliability persists. While ML and DL methodologies have shown great promise, their performance is intrinsically linked to the volume of training data available. Most predictive models rely on small-scale databases, raising concerns about their applicability in real-world scenarios. Furthermore, these models predominantly utilize electrocardiogram and heart rate variability signals, often overlooking the potential contributions of other physiological signals. Developing real-time, clinically applicable models also represents a critical avenue for further exploration in this field.