Extending Multiscale Characterization of Heart Rate Variability via Deep Learning for Mortality Risk Prediction.

Abstract

To improve mortality risk prediction from heart rate variability (HRV) signals by capturing nonlinear scaling patterns often overlooked by traditional linear analyses. This study combines detrended moving average (DMA) analysis with convolutional neural networks (CNNs). DMA curves were computed from 2-hour overlapping windows of 24-hour Holter ECG recordings in 916 survivors and 70 nonsurvivors. A CNN was trained to extract features from these curves and benchmarked against models using traditional HRV and clinical features. The CNN achieved an ROC-AUC of 0.72 and an adjusted hazard ratio of 2.129 for daytime recordings, outperforming standard models. Two patient groups emerged based on DMA scaling patterns. Group 1, with dominant short-term scaling, exhibited reduced slopes in nonsurvivors, suggesting impaired autonomic adaptability. Group 2 showed earlier transitions between short- and long-term behavior, where reduced long-term slopes more strongly predicted mortality. Integrated gradients analysis identified key timescales in the DMA curve driving model predictions. DMA combined with CNNs enhances HRV-based mortality risk stratification and reveals distinct physiological scaling patterns associated with survival outcomes. This study highlights the potential of DMA and CNNs in improving mortality risk stratification and providing mechanistic insights into HRV dynamics, with implications for personalized health monitoring.