Estimation of sleep stages in a healthy adult population from optical plethysmography and accelerometer signals
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OBJECTIVE: This paper aims to report on the accuracy of estimating sleep stages using a wrist-worn device that measures movement using a 3D accelerometer and an optical pulse photoplethysmograph (PPG). APPROACH: Overnight recordings were obtained from 60 adult participants wearing these devices on their left and right wrist, simultaneously with a Type III home sleep testing device (Embletta MPR) which included EEG channels for sleep staging. The 60 participants were self-reported normal sleepers (36 M: 24 F, age = 34 ± 10, BMI = 28 ± 6). The Embletta recordings were scored for sleep stages using AASM guidelines and were used to develop and validate an automated sleep stage estimation algorithm, which labeled sleep stages as one of Wake, Light (N1 or N2), Deep (N3) and REM (REM). Features were extracted from the accelerometer and PPG sensors, which reflected movement, breathing and heart rate variability. MAIN RESULTS: Based on leave-one-out validation, the overall per-epoch accuracy of the automated algorithm was 69%, with a Cohen's kappa of 0.52 ± 0.14. There was no observable bias to under- or over-estimate wake, light, or deep sleep durations. REM sleep duration was slightly over-estimated by the system. The most common misclassifications were light/REM and light/wake mislabeling. SIGNIFICANCE: The results indicate that a reasonable degree of sleep staging accuracy can be achieved using a wrist-worn device, which may be of utility in longitudinal studies of sleep habits.
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Algorithms for sleep stage classification have used PPG-derived HR, PRV, RR, pulse wave morphology, and accelerometry in combination with PPG.
“Algorithms have used PPG-derived HR [221], PRV [222], [223], RR [222], pulse wave morphology [223], and accelerometry in combination with PPG [222].”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [222]
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Self-citation
- Authors
- Beattie, Z., Oyang, Y., Statan, A., Ghoreyshi, A., Pantelopoulos, A., Russell, A., & Heneghan, C.
- Affiliations
- Fitbit (United States) (US)
- Corresponding author
- Heneghan, C
- Venue
- Physiological Measurement
- Publisher
- IOP Publishing
- Published
- 2017-10-31
- Volume
- 38
- Issue
- 11
- Pages
- 1968-1979
- ISSN
- 0967-3334 (ISSN-L)
- 1361-6579
- OpenAlex
- W2765638029
- Cited by
- 198
- References
- 18
- Citations by year
- 2026: 15 · 2025: 18 · 2024: 23 · 2023: 27 · 2022: 27 · 2021: 32 · 2020: 24 · 2019: 27 · 2018: 5
- FWCI
- 6.91
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Physiological Measurement (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Fitbit Inc
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Keywords
- sleep stage estimation, automated sleep staging, accelerometer, wrist-worn devices, home sleep testing, REM sleep, light sleep, slow-wave sleep, heart rate variability, breathing, healthy adults
- MeSH terms
- Adult, Female, Humans, Male, Signal Processing, Computer-Assisted, Sleep Stages, Photoplethysmography, Accelerometry, Healthy Volunteers
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