PPG-Based Sleep Staging Using SleepPPGNet: Extension to Wearables, Improvements, Limitations

Applies SleepPPG-Net to authors’ own wrist-worn reflective PPG: ~78% accuracy / Cohen’s κ ~0.68 on adults. Adding activity counts as input improved accuracy from 78.5% to 80.0% and κ from 0.67 to 0.69 on the main dataset. Notes substantial performance drop (~10% accuracy) in cardiac arrhythmia vs control — important limitation for wearable deployment.

• Form factor: wrist

The diagnosis of sleep disorders is still often based on polysomnography, an in-lab exam allowing experts to perform accurate sleep staging, although this is labor-intensive, expensive, and exposing patients to unusual sleep conditions. A state-of-the-art deep learning model - called SleepPPGNet - was recently proposed. It achieves an accuracy of 82% and a Cohen's kappa of 0.74 on a completely new dataset through transfer learning, using only raw fingertip photoplethysmography (PPG) as input, paving the way toward more efficient sleep staging methods.We applied this model to PPG data collected with our own wrist-worn devices in adults and reached 78% accuracy and a Cohen's kappa of 0.68. This is encouraging in the prospect of patients collecting their own data at home. In addition, we built upon the model's architecture to include activity counts as additional input. This increased global accuracy from 78.5% to 80.0% and Cohen's kappa from 0.67 to 0.69 on our main dataset. Finally, although this model has demonstrated remarkable potential on subjects with normal cardiac rhythms, it has shown limitations when applied to patients with cardiac arrhythmia, with an accuracy drop of 10% compared to a control group.

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