Validation of photoplethysmography-based sleep staging compared with polysomnography in healthy middle-aged adults
Abstract
Study Objectives: To compare the accuracy of automatic sleep staging based on heart rate variability measured from photoplethysmography (PPG) combined with body movements measured with an accelerometer, with polysomnography (PSG) and actigraphy. Methods: Using wrist-worn PPG to analyze heart rate variability and an accelerometer to measure body movements, sleep stages and sleep statistics were automatically computed from overnight recordings. Sleep-wake, 4-class (wake/N1 + N2/N3/REM) and 3-class (wake/NREM/REM) classifiers were trained on 135 simultaneously recorded PSG and PPG recordings of 101 healthy participants and validated on 80 recordings of 51 healthy middle-aged adults. Epoch-by-epoch agreement and sleep statistics were compared with actigraphy for a subset of the validation set. Results: The sleep-wake classifier obtained an epoch-by-epoch Cohen's κ between PPG and PSG sleep stages of 0.55 ± 0.14, sensitivity to wake of 58.2 ± 17.3%, and accuracy of 91.5 ± 5.1%. κ and sensitivity were significantly higher than with actigraphy (0.40 ± 0.15 and 45.5 ± 19.3%, respectively). The 3-class classifier achieved a κ of 0.46 ± 0.15 and accuracy of 72.9 ± 8.3%, and the 4-class classifier, a κ of 0.42 ± 0.12 and accuracy of 59.3 ± 8.5%. Conclusions: The moderate epoch-by-epoch agreement and, in particular, the good agreement in terms of sleep statistics suggest that this technique is promising for long-term sleep monitoring, although more evidence is needed to understand whether it can complement PSG in clinical practice. It also offers an improvement in sleep/wake detection over actigraphy for healthy individuals, although this must be confirmed on a larger, clinical population.
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PPG accuracy issues potentially affect health-related outcomes that rely on PPG, including sleep duration, sleep architecture, diagnosis of sleep apnea, long-term sleep patterns, and other sleep disorders.
“This potentially affects health-related outcomes that rely on PPG including a host of sleep-related indices such as sleep duration, architecture [8], diagnosis of sleep apnea [9], long-term sleep patterns, and other sleep disorders [10].”
From Limiting racial disparities and bias for wearable devices in health science research (2020) · cites this paper as [8]
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- Authors
- Fonseca, P., Weysen, T., Goelema, M. S., Møst, E. I. S., Radha, M., Lunsingh Scheurleer, C., van den Heuvel, L., & Aarts, R. M.
- Affiliations
- Eindhoven University of Technology (NL)
- Venue
- SLEEP
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-06-16
- Volume
- 40
- Issue
- 7
- ISSN
- 0161-8105 (ISSN-L)
- 1550-9109
- OpenAlex
- W2625272325
- Cited by
- 148
- References
- 50
- Citations by year
- 2026: 9 · 2025: 17 · 2024: 14 · 2023: 19 · 2022: 14 · 2021: 30 · 2020: 20 · 2019: 17 · 2018: 7 · 2017: 1
- FWCI
- 5.55
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- SLEEP (publishedVersion)
- PubMed (publishedVersion)
- TU/e Research Portal (submittedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Keywords
- photoplethysmography, sleep stage classification, heart rate variability, accelerometer, actigraphy, polysomnography, sleep-wake detection, long-term sleep monitoring, middle-aged adults
- MeSH terms
- Adult, Female, Heart Rate, Humans, Male, Middle Aged, Movement, Sleep Stages, Wakefulness, Wrist, Photoplethysmography, Polysomnography, Actigraphy, Healthy Volunteers
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