物理学报2017，Vol.66Issue(16)：37-46,10.DOI:10.7498/aps.66.160502
基于有限穿越水平可视图的短时睡眠心率变异性研究
Research of short-term heart rate variability during sleep based on limited penetrable horizontal visibility graph
摘要
Abstract
Heart rate is one of the most easily accessed human physiological data. In recent years, the analysis of sleep function based on heart rate variability has become a new popular feature of wearable devices used for daily health management. Consequently, it is needed to explore various types of short-term characteristic parameters which can be applied to the heartbeat interval time series within the standard sleep staging time window (about 30 s). Utilizing the recently reported limited penetrable horizontal visibility graph (LPHVG) algorithm, together with a weighted limited penetrable horizontal visibility graph (WLPHVG) algorithm proposed in this paper, the short-term heartbeat interval time series in different sleep stages are mapped to networks respectively. Then, 6 characteristic parameters, including the average clustering coefficient C, the characteristic path length L, the clustering coefficient entropy Ec, the distance distribution entropy Ed, the weighted clustering coefficient entropy ECw and the weight distribution entropy Ew are calculated and analyzed. The results show that the values of these characteristic parameters are significantly different in the states of wakefulness, light sleep, deep sleep and rapid eye movement, especially in the case of the limited penetrable distance Lp=1, indicating the effectiveness of LPHVG and WLPHVG algorithm in sleep staging based on short-term heartbeat interval time series. In addition, a preliminary comparison between proposed algorithm and the basic visibility graph (VG) algorithm shows that in this case, the LPHVG and WLPHVG algorithm are superior to the basic VG algorithm both in performance and in calculation speed. Meanwhile, based on the LPHVG and WLPHVG algorithm, the values of network parameters (the clustering coefficient entropy Ec and the weighted clustering coefficient entropy ECw) are calculated from heartbeat interval time series of healthy young and elder subjects in different sleep stages, to further study the aging effect on and sleep regulation over cardiac dynamics. It is found that despite an overall level difference between the values of Ec and ECw in young and elder groups, the stratification patterns across different sleep stages almost do not break down with advanced age, suggesting that the effect of sleep regulation on cardiac dynamics is significantly stronger than the effect of healthy aging. In addition, compared with the clustering coefficient entropy Ec based on LPHVG algorithm, the weighted clustering coefficient entropy ECw based on WLPHVG algorithm shows higher sensitivity to discriminating subtle differences in cardiac dynamics among different sleep states. Overall, it is shown that with the simple mapping criteria and low computational complexity, the proposed method could be used as a new auxiliary tool for sleep studies based on heart rate variability, and the corresponding network parameters could be used in wearable device as new auxiliary parameters for sleep staging.关键词
有限穿越水平可视图/睡眠/心率变异性Key words
limited penetrable horizontal visibility graph/sleep/heart rate variability引用本文复制引用
霍铖宇,马小飞,宁新宝..基于有限穿越水平可视图的短时睡眠心率变异性研究[J].物理学报,2017,66(16):37-46,10.基金项目
江苏省高校自然科学研究项目(批准号:15KJD310002)、国家自然科学基金(批准号:61402057)和江苏省高校优秀中青年教师和校长境外研修计划(2016年度)资助的课题.Project supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No.15KJD310002),the National Natural Science Foundation of China (Grant No.61402057),and Jiangsu Overseas Research& Training Program for University Prominent Young & Middle-aged Teachers and Presidents (2016). （批准号:15KJD310002）
