Sleep stage prediction with raw acceleration and photoplethysmography heart rate data derived from a consumer wearable device
| Autor: | Cathy Goldstein, Daniel B. Forger, Yitong Huang, Olivia J. Walch |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Adult
Male Sleep Health and Disease ambulatory sleep monitoring Computer science Polysomnography Acceleration Wearable computer Non-rapid eye movement sleep Standard deviation 03 medical and health sciences Wearable Electronic Devices Young Adult 0302 clinical medicine Heart Rate Physiology (medical) Photoplethysmogram medicine Humans Photoplethysmography sleep tracking 030304 developmental biology validation 0303 health sciences Sleep Stages medicine.diagnostic_test Artificial neural network business.industry mathematical modeling of sleep Pattern recognition Electroencephalography Middle Aged Editor's Choice machine learning Data Interpretation Statistical Female Neurology (clinical) Artificial intelligence Sleep (system call) business 030217 neurology & neurosurgery Algorithms Forecasting |
| Zdroj: | Sleep |
| ISSN: | 1550-9109 0161-8105 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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