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.
Highlights
Cardiovascular disease (CVD) is one of the major causes of mortality in the world, which has seriously influenced sustainable human development [1]
We identified a group of baseline CVD-free subjects in an open database, and further divided them into two groups, CVD group and non-CVD group, according to the presence of CVD during follow-up years
We aimed to investigate whether hypnopompic heart rate variability (HRV) had a better ability for the prediction of short-term CVD events than long-term outcomes
Summary
Cardiovascular disease (CVD) is one of the major causes of mortality in the world, which has seriously influenced sustainable human development [1]. Evidence in scientific literature clearly demonstrated an uneven distribution of the occurrence of cardiovascular accidents during the 24 h [3]. CVD events have the tendency to occur at the end of the sleep period and during wakefulness after sleep, instead of later in the day [4]. It’s still unclear what is the mechanism responsible for the increased occurrence of cardiovascular events in the morning, surges in sympathetic activity are generally considered as a primary cause [4,5,6,7,8,9]
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have