ArticleSleep2020
Deep learning enables sleep staging from photoplethysmogram for patients with suspected sleep apnea.
Article in Sleep, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 2 of them syntheses that pooled it.
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Who cites it
48 citing papers in PubMed, 2 syntheses or guidelines pooled it, 125 citations in OpenAlex.
- Automatic Sleep Staging Using Cardiorespiratory Signals: A Systematic Review of Methodologies and Performance.Journal of medical systems · 2026Pooled it
- A Systematic Review of Sensing Technologies for Wearable Sleep Staging.Sensors (Basel, Switzerland) · 2021Pooled it
- Differential associations of arousals and respiratory events on oxygen desaturation and resaturation parameters in sleep.Sleep & breathing = Schlaf & Atmung · 2026Article
- Sleep Stage Classification During CPAP Therapy from CPAP-Airflow and Wearable Fingertip Signals.Sensors (Basel, Switzerland) · 2026Article
- Evaluation of an automated sleep apnea scoring algorithm via the Wesper Lab home sleep apnea test.Sleep medicine · 2026Article
- Sleep awake detection from leg-worn wearables using deep sensor fusion.Scientific reports · 2026Article
- An explainable deep learning approach for sleep staging in sleep apnea patients across all age subgroups from pulse oximetry signals.Engineering applications of artificial intelligence · 2025Article
- PDSRS-LD: Personalized Deep Learning-Based Sleep Recommendation System Using Lifelog Data.Sensors (Basel, Switzerland) · 2025Article
- Hypnogram and Hypnodensity Analysis of REM Sleep Behaviour Disorder Using Both EEG and HRV-Based Sleep Staging Models.Journal of sleep research · 2025Article
- Vital Sign and Biochemical Data Collection Using Non-contact Photoplethysmography and the Comestai Mobile Health App: Protocol for an Observational Study.JMIR research protocols · 2025Article
- Article
- Classification of sleep apnea syndrome using the spectrograms of EEG signals and YOLOv8 deep learning model.PeerJ. Computer science · 2025Article
- Attention-based CNN-BiLSTM for sleep state classification of spatiotemporal wide-field calcium imaging data.Journal of neuroscience methods · 2024Article
- Multisensor Integrated Platform Based on MEMS Charge Variation Sensing Technology for Biopotential Acquisition.Sensors (Basel, Switzerland) · 2024Article
- InsightSleepNet: the interpretable and uncertainty-aware deep learning network for sleep staging using continuous Photoplethysmography.BMC medical informatics and decision making · 2024Article
- Multicentre sleep-stage scoring agreement in the Sleep Revolution project.Journal of sleep research · 2024Article
- Respiratory event index underestimates severity of sleep apnea compared to apnea-hypopnea index.Sleep advances : a journal of the Sleep Research Society · 2024Article
- Variation in the Photoplethysmogram Response to Arousal From Sleep Depending on the Cause of Arousal and the Presence of Desaturation.IEEE journal of translational engineering in health and medicine · 2024Article
- EarSet: A Multi-Modal Dataset for Studying the Impact of Head and Facial Movements on In-Ear PPG Signals.Scientific data · 2023Article
- The 2023 wearable photoplethysmography roadmap.Physiological measurement · 2023Review
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
objectivesAccurate identification of sleep stages is essential in the diagnosis of sleep disorders (e.g. obstructive sleep apnea [OSA]) but relies on labor-intensive electroencephalogram (EEG)-based manual scoring. Furthermore, long-term assessment of sleep relies on actigraphy differentiating only between wake and sleep periods without identifying specific sleep stages and having low reliability in identifying wake periods after sleep onset. To address these issues, we aimed to develop an automatic method for identifying the sleep stages from the photoplethysmogram (PPG) signal obtained with a simple finger pulse oximeter.
methodsPPG signals from the diagnostic polysomnographies of susptected OSA patients (n = 894) were utilized to develop a combined convolutional and recurrent neural network. The deep learning model was trained individually for three-stage (wake/NREM/REM), four-stage (wake/N1+N2/N3/REM), and five-stage (wake/N1/N2/N3/REM) classification of sleep.
resultsThe three-stage model achieved an epoch-by-epoch accuracy of 80.1% with Cohen's κ of 0.65. The four- and five-stage models achieved 68.5% (κ = 0.54), and 64.1% (κ = 0.51) accuracies, respectively. With the five-stage model, the total sleep time was underestimated with a mean bias error (SD) of of 7.5 (55.2) minutes.
conclusionThe PPG-based deep learning model enabled accurate estimation of sleep time and differentiation between sleep stages with a moderate agreement to manual EEG-based scoring. As PPG is already included in ambulatory polygraphic recordings, applying the PPG-based sleep staging could improve their diagnostic value by enabling simple, low-cost, and reliable monitoring of sleep and help assess otherwise overlooked conditions such as REM-related OSA.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.