PPG-Based Sleep Staging Using SleepPPGNet: Extension to Wearables, Improvements, Limitations
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Author
Constantin, Loris
Aguet, Clémentine
Brill, Anne-Kathrin
Horvath, Christian M
Thiran, Jean-Philippe
Lemay, Mathieu
Braun, Fabian
DOI
10.1109/EMBC53108.2024.10781611
Abstract
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-theart 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.
Publication Reference
EMBC 2024, Orlando, FL (USA), pp. 1-4
Year
2024-07-15