Sleep heart rate variability assists the automatic prediction of long-term cardiovascular outcomes

Abstract

Objective: We aimed to investigate the association between sleep HRV and long-term cardiovascular disease (CVD) outcomes, and further explore whether HRV features can assist the automatic CVD prediction.

Methods: We retrospectively analyzed polysomnography (PSG) data obtained from 2111 participants in the Sleep Heart Health Study, who were followed up for a median of 11.8 years after PSG acquisition. During follow-up, 1252 participants suffered CVD events (CVD group) and 859 participants remained CVD-free (non-CVD group). HRV measures, derived from time-domain and frequency-domain, were calculated. Regression models were created to determine the independent predictor for long-term CVD outcomes, and to explore the association between HRV and CVD latency. Furthermore, based on HRV and other clinical features, a model was trained to automatically predict CVD outcomes using the eXtreme Gradient Boosting algorithm.

Results: Compared with the non-CVD group, decreased HRV during sleep was found in the CVD group. HRV, particularly its component of high frequency (HF), was demonstrated to be independent predictor of CVD outcomes. Moreover, normalized HF was positively correlated with CVD latency. The proposed prediction model achieved a total accuracy of 75.3%, in which sleep HRV features served as a supplement to the well-recognized CVD risk factors, such as aging, adiposity and sleep disorders.

Conclusions: Association between sleep HRV and long-term CVD outcomes was demonstrated here, suggesting that altered HRV during sleep might occur many years prior to the onset of CVD. Machine learning models, combining sleep HRV and other clinical characteristics, should be promising in the early prediction of CVD outcomes.

Keywords: Cardiovascular diseases; Heart rate variability; Machine learning; Sleep.