A method for analyzing sleep stage characteristics based on complex networks and very short-term heart rate variability

To study the non-EEG sleep assessment method suitable for wearable devices, a complex network-based sleep stage characteristic analysis method for short-term heart rate variability data is proposed. Short-term heartbeat interval time series in different sleep stages are mapped to directed unweighted networks, respectively. Then, several characteristic parameters, including the average out-degree, the entropy of the degree distribution, the average clustering coefficient, and the entropy of clustering coefficient are calculated and analyzed. The results indicate that the various network measures show significant differences in the amplitude levels during wakefulness, light sleep, deep sleep, and rapid eye movement sleep. These differences can reflect the dynamic characteristics of short-term heart rate interval series in different sleep stages, helping to reveal microvariations and patterns during sleep processes. Furthermore, these findings align with existing physiological cognition, offering new supplementary parameters for sleep staging research based on short-term heart rate variability, which has the potential to enhance the effectiveness of sleep staging in wearable devices.