Cardiopulmonary Analysis of Sleep Apnea Based on Weighted Limited Penetrable Visibility Graph

Cardiopulmonary coupling (CPC) has been recognized as an important and low-cost technique for in-home sleep monitoring. Clinically, sleep staging is a prior for evaluation of sleep state, especially for the sleep disorders, such as sleep apnea. However, there are few studies on CPC-based characteristic parameters that could be applied to sleep staging. In this study, inspired by the Visibility Graph and complex networks, we realized CPC analysis using ECG-derived respiration (EDR) technique and mapped the EDR time series of different sleep stages for 16 sleep apnea patients into networks and calculated characteristic parameters using Weighted Limited Penetrable Visibility Graph (WLPVG), including the characteristic path length L, the weighted clustering coefficient entropy ECW and the weight distribution entropy E
ω
. Each characteristic parameter was evaluated among different sleep stages. Results showed the significant variation of characteristic parameters during different sleep stages. In particular, the characteristic path length L out of three parameters presented high sensitivity for capturing the difference in different stages. This study explored the mechanism of the cardiorespiratory dynamic system during sleep and supported an interpretable basis for subsequent characterization and differentiation of sleep stages.