Combining mathematical model for HRV mapping and machine learning to predict sudden cardiac death

Sudden cardiac death, a prominent cause of mortality, often occurs within a narrow time window of less than an hour. This study introduces a novel methodology with the aim of early prediction of sudden cardiac death. The proposed approach involves the extraction of diverse features from the ECG signal, including the calculation of angles between two vectors, the computation of triangle areas formed by consecutive points, the determination of the shortest distance to a 450 line, and their combinations. Additionally, a thresholding technique is proposed to identify the risk period and predict the occurrence of sudden death. To assess the performance of the algorithm, data from the MIT-BH Holter database were utilized. The results obtained demonstrate that the angle feature achieves an average sensitivity of 93.75% with five false alarms, the area feature achieves an average sensitivity of 88.75% with nine false alarms, the shortest distance feature achieves an average sensitivity of 86.25% with 12 false alarms, and the combined feature achieves an average sensitivity of 96.25% with three false alarms. Remarkably, unlike existing methodologies in the literature, this method exhibits high accuracy in predicting the development of the risk of sudden cardiac death (SCD) even up to 30 min prior to onset. As a consequence, it plays a critical role in diagnosing patients' conditions and facilitating timely interventions. Moreover, the results confirm the feasibility of predicting cardiac arrest solely based on geometric features derived from variations in heart rate variability (HRV) dynamics.