Sleep stage prediction with raw acceleration and photoplethysmography heart rate data derived from a consumer wearable device
Open accessAbstract
Wearable, multisensor, consumer devices that estimate sleep are now commonplace, but the algorithms used by these devices to score sleep are not open source, and the raw sensor data is rarely accessible for external use. As a result, these devices are limited in their usefulness for clinical and research applications, despite holding much promise. We used a mobile application of our own creation to collect raw acceleration data and heart rate from the Apple Watch worn by participants undergoing polysomnography, as well as during the ambulatory period preceding in lab testing. Using this data, we compared the contributions of multiple features (motion, local standard deviation in heart rate, and “clock proxy”) to performance across several classifiers. Best performance was achieved using neural nets, though the differences across classifiers were generally small. For sleep-wake classification, our method scored 90% of epochs correctly, with 59.6% of true wake epochs (specificity) and 93% of true sleep epochs (sensitivity) scored correctly. Accuracy for differentiating wake, NREM sleep, and REM sleep was approximately 72% when all features were used. We generalized our results by testing the models trained on Apple Watch data using data from the Multi-ethnic Study of Atherosclerosis (MESA), and found that we were able to predict sleep with performance comparable to testing on our own dataset. This study demonstrates, for the first time, the ability to analyze raw acceleration and heart rate data from a ubiquitous wearable device with accepted, disclosed mathematical methods to improve accuracy of sleep and sleep stage prediction.
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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 [221]
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- Authors
- Walch, O., Huang, Y., Forger, D., & Goldstein, C.
- Affiliations
- University of Michigan (US)
- Dartmouth College (US)
- Corresponding author
- Walch, Olivia
- Venue
- Sleep
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-08-13
- Volume
- 42
- Issue
- 12
- ISSN
- 0161-8105 (ISSN-L)
- 1550-9109
- PMC
- PMC6930135
- OpenAlex
- W2937191485
- Cited by
- 240
- References
- 56
- Citations by year
- 2026: 20 · 2025: 42 · 2024: 33 · 2023: 38 · 2022: 39 · 2021: 40 · 2020: 28
- FWCI
- 9.43
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- SLEEP (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Smith ScholarWorks (Smith College) (submittedVersion, cc-by-nc)
- Funders
- Exercise & Sport Science Initiative University of Michigan (U056400, U049702, NSF DMS 1714094)
- 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
- wearable devices, Apple Watch, accelerometry, photoplethysmography, heart rate, sleep-wake classification, NREM sleep, REM sleep, polysomnography, artificial neural networks, classifier comparison, MESA, cross-dataset generalization
- MeSH terms
- Acceleration, Wearable Electronic Devices, Adult, Algorithms, Data Interpretation, Statistical, Electroencephalography, Female, Forecasting, Heart Rate, Humans, Male, Middle Aged, Sleep Stages, Photoplethysmography, Polysomnography, Young Adult
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