Entropy-Based Measures of Hypnopompic Heart Rate Variability Contribute to the Automatic Prediction of Cardiovascular Events.

Surges in sympathetic activity near the end of nocturnal sleep may contribute to cardiovascular events. Analyzing heart rate variability (HRV) during the hypnopompic period (the transition from sleep to waking) helps predict cardiovascular disease (CVD). In 2,217 initially CVD-free subjects, those who later developed CVD showed significant alterations in hypnopompic HRV. Machine learning models using hypnopompic HRV metrics achieved 81.4% accuracy for short-term CVD prediction (within two years), a 10.7% improvement over long-term prediction. Removing HRV metrics reduced short-term predictive performance by over 6%. Entropy-based complexity measures of hypnopompic HRV contributed more to prediction than conventional HRV measures.

Study at a glance

Abstract

Surges in sympathetic activity should be a major contributor to the frequent occurrence of cardiovascular events towards the end of nocturnal sleep. We aimed to investigate whether the analysis of hypnopompic heart rate variability (HRV) could assist in the prediction of cardiovascular disease (CVD). 2217 baseline CVD-free subjects were identified and divided into CVD group and non-CVD group, according to the presence of CVD during a follow-up visit. HRV measures derived from time domain analysis, frequency domain analysis and nonlinear analysis were employed to characterize cardiac functioning. Machine learning models for both long-term and short-term CVD prediction were then constructed, based on hypnopompic HRV metrics and other typical CVD risk factors. CVD was associated with significant alterations in hypnopompic HRV. An accuracy of 81.4% was achieved in short-term prediction of CVD, demonstrating a 10.7% increase compared with long-term prediction. There was a decline of more than 6% in the predictive performance of short-term CVD outcomes without HRV metrics. The complexity of hypnopompic HRV, measured by entropy-based indices, contributed considerably to the prediction and achieved greater importance in the proposed models than conventional HRV measures. Our findings suggest that Hypnopompic HRV assists the prediction of CVD outcomes, especially the occurrence of CVD event within two years.