Data-driven sleep structure deciphering based on cardiorespiratory signals

Background and Objective

:

Cardiorespiratory signals provide a novel perspective for understanding sleep structure through the physiological mechanism of cardiopulmonary coupling. This mechanism divides the coupling spectrum into high-frequency (HF) and low-frequency (LF) bands, indicating that signal segments of 4–8 min are optimal for analysis. However, the lack of labels tailored to these signals has led to reliance on the American Academy of Sleep Medicine (AASM) definitions, which are primarily designed for electroencephalogram (EEG) and electrooculogram (EOG) data. This study aims to address the challenge of transitioning from AASM-defined labels to cardiorespiratory-oriented ones and to evaluate the feasibility of using these signals for accurate sleep structure recognition.

Methods:

To align with the physiological characteristics of cardiorespiratory signals, AASM labels were modified by excluding the N2 stage due to its overlap of stable and unstable non-rapid eye movement (NREM) phases, which introduces ambiguity. The modified dataset focused on the wake, N1, deep sleep (N3), and rapid eye movement (REM) stages. A physiologically-inspired deep-learning model (PIDM) was developed to extract features from cardiorespiratory time series and classify sleep stages. Post-analysis assessed the physiological validity of the model’s N2 predictions by evaluating the HF-to-LF ratio and respiratory variability.

Results:

The pipeline, combining the modified labeling scheme with the PIDM model, achieved balanced accuracy scores of 0.83, 0.86, and 0.78 for wake, deep sleep, and REM stages, respectively in the normal group; and 0.92, 0.95, and 0.90 in the mild and moderate sleep apnea groups. Post-analysis revealed that most N2 samples were attributed to stable NREM sleep, characterized by higher HF-to-LF ratios and lower respiratory variability, aligning with physiological understanding.

Conclusions:

This study highlights the physiological relevance of cardiorespiratory signals for sleep structure recognition. By addressing the uncertainty in N2 classification through exclusion and redefinition, the proposed pipeline effectively distinguished wake, deep sleep, and REM stages. These findings demonstrate the potential of cardiorespiratory signals as a robust, practical, and EEG-independent tool for sleep analysis, particularly in home healthcare settings.