Visibility graph-based complex networks can effectively model cardiorespiratory interactions to differentiate human sleep stages.
May facilitate sleep staging via VG networks of cardiorespiratory signals; leaves open prospective clinical validation.
Human sleep comprises several stages including wake, rapid-eye-movement sleep, light sleep, and deep sleep. Cardiorespiratory activity has been shown to correlate with sleep stages due to the regulation of autonomic nervous system. Here, the cardiorespiratory interaction (CRI) during sleep is analyzed using a visibility graph (VG) method that represents the CRI time series in complex networks. We demonstrate that the dynamics of the interaction between heartbeats and respiration can be revealed by VG-based networks, whereby sleep stages can be characterized and differentiated.
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Long et al. (2014) studied this question.
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