Photoplethysmography-based Bracelet for Automatic Sleep Stages Classification: Preliminary Results

Philippe Renevey, Patrick Celka, Simon Arberet, Enric Muntané Calvo, Josep Solá i Carós, Claudio Sartori, Mattia Bertschi, and Mathieu Lemay


Photoplethysmography, Sleep stages, REM, NREM, Principal Component Analysis , Neural Networks


In this ongoing study we present the preliminary results of a fully automatic sleep stages classification based on acceleration and photoplethysmography signals recorded at wrist. The device consists in a bracelet integrating sensors, processing unit, communication capabilities, and power management. The bracelet has been worn by two healthy volunteers during a night period at hospital in combination to a complete polysomnograph. Spectral analysis of heartbeat intervals in standard HRV frequency bands, as well as movement activity level have been performed and used to differentiate 3 sleep states: WAKE, REM and NREM. The automatic classification has been compared to the hypnogram provided by a professional clinician using standard polysomnography procedure. Classification rates up to 90% have been achieved for NREM state and between 44% and 72% for REM state. High confusion coefficients for WAKE state is reported and results from hypnographic misalignment with the algorithm output.

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