Study on the prediction of cardiovascular disease based on sleep heart rate variability analysis.
10.7507/1001-5515.202004039
- Author:
Ye YANG
1
;
Xueya YAN
1
;
Fengzhen HOU
1
;
Lei PAN
1
Author Information
1. School of Science, China Pharmaceutical University, Nanjing 210009, P.R.China.
- Publication Type:Journal Article
- Keywords:
cardiovascular disease;
extreme gradient boosting;
heart rate variability;
sleep
- MeSH:
Algorithms;
Cardiovascular Diseases;
Heart Rate;
Humans;
Machine Learning;
Sleep
- From:
Journal of Biomedical Engineering
2021;38(2):249-256
- CountryChina
- Language:Chinese
-
Abstract:
The peak period of cardiovascular disease (CVD) is around the time of awakening in the morning, which may be related to the surge of sympathetic activity at the end of nocturnal sleep. This paper chose 140 participants as study object, 70 of which had occurred CVD events while the rest hadn't during a two-year follow-up period. A two-layer model was proposed to investigate whether hypnopompic heart rate variability (HRV) was informative to distinguish these two types of participants. In the proposed model, the extreme gradient boosting algorithm (XGBoost) was used to construct a classifier in the first layer. By evaluating the feature importance of the classifier, those features with larger importance were fed into the second layer to construct the final classifier. Three machine learning algorithms, i.e., XGBoost, random forest and support vector machine were employed and compared in the second layer to find out which one can achieve the highest performance. The results showed that, with the analysis of hypnopompic HRV, the XGBoost+XGBoost model achieved the best performance with an accuracy of 84.3%. Compared with conventional time-domain and frequency-domain features, those features derived from nonlinear dynamic analysis were more important to the model. Especially, modified permutation entropy at scale 1 and sample entropy at scale 3 were relatively important. This study might have significance for the prevention and diagnosis of CVD, as well as for the design of CVD-risk assessment system.
