Author ORCID Identifier
Yitong Huang: 0000-0002-5200-8077
Document Type
Article
Publication Date
12-1-2019
Publication Title
Sleep
Abstract
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.
Keywords
Ambulatory sleep monitoring, Machine learning, Mathematical modeling of sleep, Sleep tracking, Validation
Volume
42
Issue
12
DOI
10.1093/sleep/zsz180
ISSN
01618105
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Rights
© Sleep Research Society 2019. Published by Oxford University Press
Recommended Citation
Walch, Olivia; Huang, Yitong; Forger, Daniel; and Goldstein, Cathy, "Sleep Stage Prediction With Raw Acceleration and Photoplethysmography Heart Rate Data Derived from a Consumer Wearable Device" (2019). Mathematics Sciences: Faculty Publications, Smith College, Northampton, MA.
https://scholarworks.smith.edu/mth_facpubs/195
Comments
Archived as published.