Abstract
Manual sleep-stage scoring based on full-night polysomnography data recorded in a sleep lab has been the gold standard of clinical sleep medicine. This costly and time-consuming approach is unfit for long-term studies as well as assessment of sleep on a population level. With the vast amount of physiological data becoming available from wrist-worn devices, deep learning techniques provide an opportunity for fast and reliable automatic sleep-stage classification tasks. However, training a deep neural network requires large annotated sleep databases, which are not available for long-term epidemiological studies. In this paper, we introduce an end-to-end temporal convolutional neural network able to automatically score sleep stages from raw heartbeat RR interval (RRI) and wrist actigraphy data. Moreover, a transfer learning approach enables the training of the network on a large public database (Sleep Heart Health Study, SHHS) and its subsequent application to a much smaller database recorded by a wristband device. The transfer learning significantly shortens training time and improves sleep-scoring accuracy from 68.9% to 73.8% and inter-rater reliability (Cohen's kappa) from 0.51 to 0.59. We also found that for the SHHS database, automatic sleep-scoring accuracy using deep learning shows a logarithmic relationship with the training size. Although deep learning approaches for automatic sleep scoring are not yet comparable to the inter-rater reliability among sleep technicians, performance is expected to significantly improve in the near future when more large public databases become available. We anticipate those deep learning techniques, when combined with our transfer learning approach, will leverage automatic sleep scoring of physiological data from wearable devices and enable the investigation of sleep in large cohort studies.
Original language | English |
---|---|
Article number | 107193 |
Number of pages | 11 |
Journal | Computers in Biology and Medicine |
Volume | 163 |
DOIs | |
State | Published - Sep 2023 |
Bibliographical note
Publisher Copyright:© 2023 Elsevier Ltd
Funding
This work was supported by the German Israeli Foundation (GIF) Grant no I-1372-303.7/2016 . The Sleep Heart Health Study (SHHS) was supported by National Heart, Lung, and Blood Institute cooperative agreements U01HL53916 ( University of California, Davis ), U01HL53931 ( New York University ), U01HL53934 ( University of Minnesota ), U01HL53937 and U01HL64360 ( Johns Hopkins University ), U01HL53938 ( University of Arizona ), U01HL53940 ( University of Washington ), U01HL53941 ( Boston University ), and U01HL63463 ( Case Western Reserve University ). The National Sleep Research Resource was supported by the National Heart, Lung, and Blood Institute ( R24 HL114473 , 75N92019R002 ).
Funders | Funder number |
---|---|
German Israeli Foundation | I-1372-303.7/2016 |
National Heart, Lung, and Blood Institute | U01HL53916 |
New York University | U01HL53934 |
Boston University | U01HL63463 |
University of Minnesota | U01HL53937, U01HL64360 |
University of California, Davis | U01HL53931 |
University of Washington | U01HL53941 |
Johns Hopkins University | U01HL53938 |
University of Arizona | U01HL53940 |
Case Western Reserve University | 75N92019R002, R24 HL114473 |
Keywords
- Convolutional neural network
- Deep learning
- Epidemiological studies
- Heart-rate variability
- Sleep-stage scoring
- Transfer learning
- Wrist actigraphy