Enhanced Adaptive Threshold Algorithms for Real-Time Cardiovascular Risk Prediction from Wearable HRV Data

Abstract

Heart rate variability monitoring through consumer wearable devices offers unprecedented opportunities for continuous cardiovascular health assessment. This research presents enhanced adaptive threshold algorithms that address critical challenges in wearable-based cardiac risk prediction, including motion artifacts, individual baseline variability, and computational constraints. The proposed methodology establishes personalized baselines through multi-day data collection and implements a three-stage threshold adaptation mechanism combining Bayesian updating with signal quality assessment. Experimental validation across public datasets (MIMIC-III, MIT-BIH) and real-world deployments demonstrates superior performance with sensitivity of 82.7%, specificity of 89.1%, and AUROC of 0.893, representing meaningful improvements over static threshold and machine learning approaches. Clinical validation confirms average warning times of 47 minutes before cardiac events with false alarm rates reduced by 67%. The implementation achieves real-time processing on resource-constrained devices with 512KB memory footprint, enabling practical deployment in cardiovascular rehabilitation and remote monitoring applications.