Non-contact REM/NREM sleep staging from piezoelectric signals using respiratory and body-movement features with auxiliary TWED-based respiratory stability measures.

Introduction: Non-contact sleep monitoring based on under-mattress piezoelectric sensing is attractive for low-burden home use, but REM/NREM discrimination remains challenging. This study aimed to investigate whether respiratory pattern stability, quantified by Time Warp Edit Distance (TWED)-based respiratory interval sequence (RIS) similarity features, could improve discrimination between the two major sleep states within sleep itself. Methods: Overnight piezoelectric and polysomnography (PSG) data were collected simultaneously from 85 clinical subjects. PSG-labeled wake epochs were excluded, and the task was formulated as binary REM/NREM classification. From piezoelectric signals, we extracted conventional body-movement features, respiratory variability features, and multi-scale TWED-based RIS similarity features. Feature normalization was performed within each subject using the full-night unlabeled feature distribution, consistent with the intended Results: Feature normalization improved performance across all feature sets. The best result was achieved by combining conventional body-movement and respiratory variability features with TWED-based RIS similarity features, yielding an accuracy of 84.39 ± 12.76%, Cohen's Kappa of 0.524 ± 0.241, REM precision of 0.600 ± 0.210, REM recall of 0.735 ± 0.226, and REM F1-score of 0.603 ± 0.211 under nested LOOCV. Compared with the normalized conventional feature set without RIS similarity, adding the TWED-based features improved both Kappa and REM F1-score. In respiratory tests, PVDF-derived respiration showed low detection error and good agreement with the airflow reference under both posture-change and overnight conditions. Discussion: These findings indicate that TWED-based RIS similarity features provide useful complementary information beyond conventional descriptors and support the feasibility of using respiratory pattern stability derived from non-contact piezoelectric signals for within-sleep REM/NREM classification. At the current level of performance, the proposed method is better viewed as a low-burden adjunctive tool for offline whole-night longitudinal monitoring and trend assessment in home-like settings rather than as a replacement for PSG-based clinical diagnosis, real-time sleep staging, or full sleep-stage scoring.