PaperarXiv:2404.06869

SleepPPG-Net2: Deep learning generalization for sleep staging from photoplethysmography

Preprint2024Added Sep 26, 2026

Authors: Shirel Attia, Revital Shani Hershkovich, Alissa Tabakhov, Angeleene Ang, Sharon Haimov, Riva Tauman et al. (7 authors)

arXiv preprint (cs.LG, cs.AI, eess.SP)

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Attia, Shirel et al., "SleepPPG-Net2: Deep learning generalization for sleep staging from photoplethysmography", arXiv:2404.06869, CC BY 4.0

TL;DR

Background: Sleep staging is a fundamental component in the diagnosis of sleep disorders and the management of sleep health. Traditionally, this analysis is conducted in clinical settings and involves a time-consuming scoring procedure. Recent data-driven algorithms for sleep staging, using the photoplethysmogram (PPG) time series, have shown high performance on local test sets but lower performance on external datasets due to data drift.

Form factor: other

Abstract

Background: Sleep staging is a fundamental component in the diagnosis of sleep disorders and the management of sleep health. Traditionally, this analysis is conducted in clinical settings and involves a time-consuming scoring procedure. Recent data-driven algorithms for sleep staging, using the photoplethysmogram (PPG) time series, have shown high performance on local test sets but lower performance on external datasets due to data drift. Methods: This study aimed to develop a generalizable deep learning model for the task of four class (wake, light, deep, and rapid eye movement (REM)) sleep staging from raw PPG physiological time-series. Six sleep datasets, totaling 2,574 patients recordings, were used. In order to create a more generalizable representation, we developed and evaluated a deep learning model called SleepPPG-Net2, which employs a multi-source domain training approach.SleepPPG-Net2 was benchmarked against two state-of-the-art models. Results: SleepPPG-Net2 showed consistently higher performance over benchmark approaches, with generalization performance (Cohen's kappa) improving by up to 19%. Performance disparities were observed in relation to age, sex, and sleep apnea severity. Conclusion: SleepPPG-Net2 sets a new standard for staging sleep from raw PPG time-series.

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Tasks & signals

Tasks

Sleep staging

Signals

PPG

Keywords

sleepstaginggeneralizationdeep-learning

How it works

Cite this paper

APA

Attia, S., Hershkovich, R. S., Tabakhov, A., Ang, A., Haimov, S., Tauman, R., et al. (2024). SleepPPG-Net2: Deep learning generalization for sleep staging from photoplethysmography. arXiv preprint. https://doi.org/10.48550/arXiv.2404.06869

BibTeX

@misc{attia2024sleepppgnet2,
  title         = {{SleepPPG-Net2: Deep learning generalization for sleep staging from photoplethysmography}},
  author        = {Attia, Shirel and Hershkovich, Revital Shani and Tabakhov, Alissa and Ang, Angeleene and Haimov, Sharon and Tauman, Riva and others},
  year          = {2024},
  howpublished  = {arXiv preprint},
  doi           = {10.48550/arXiv.2404.06869},
  eprint        = {2404.06869},
  archivePrefix = {arXiv}
}

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Sep 26, 2026Added to the ChatPPG research hub

SleepPPG-Net2: Deep learning generalization for sleep staging from photoplethysmography

TL;DR

Abstract

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