Abstract
Novel approaches are needed to accurately classify and monitor sleep patterns in older adults, particularly those with cognitive impairment and non-normative sleep. Traditional methods ignore underlying sleep architecture in these patient populations, and other modern approaches tend to focus on healthy, normative patient populations. In this paper, we developed a model using a long-short-term memory neural network (LSTM) and trained it on a sample of older, non-normative patients. The 22 nights of data collected were trained on gold-standard polysomnography (PSG) as ground truth and were compared against the clinical standard threshold-based method for sleep detection. The LSTM more than doubled the traditional method's ability to detect clinically-relevant wakefulness during sleep (37.7% vs. 15%) without significantly sacrificing accuracy (67.7% vs. 75%) or precision (90.7% vs. 94%) of sleep classification.
Original language | English |
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Title of host publication | 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 3368-3372 |
Number of pages | 5 |
ISBN (Electronic) | 9781538613115 |
DOIs | |
Publication status | Published - Jul 2019 |
Externally published | Yes |
Event | 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Germany Duration: 23 Jul 2019 → 27 Jul 2019 |
Publication series
Name | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS |
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ISSN (Print) | 1557-170X |
Conference
Conference | 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 |
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Country/Territory | Germany |
City | Berlin |
Period | 23/07/19 → 27/07/19 |
Bibliographical note
Publisher Copyright:© 2019 IEEE.
Funding
This material is the result of work supported with resources and the use of facilities at the VA Portland Health Care System, VA Career Development Award IK2 BX002712, NIH EXITO Institutional Core, UL1GM118964, the Portland VA Research Foundation to M.M.L., Oregon Roybal Center for Translational Research on Aging NIH P30 AG024978-15 to R.A.O., M.M.L., S.Y., and J.K., NIH P30- AG008017 to J.K, and NIH NIA U19 PO#S9001796 (PEACE-AD) to J.E.E, J.K., and M.M.L., and NIH NCCIH K99AT010158 to S.Y. The contents do not represent the views of the U.S. Department of Veterans Affairs or the United States Government. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health
Funders | Funder number |
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NIH NCCIH | K99AT010158 |
NIH NIA | U19 PO#S9001796 |
Oregon Roybal Center for Translational Research on Aging NIH | P30- AG008017, P30 AG024978-15 |
National Institutes of Health | UL1GM118964 |
National Institute on Aging | P30AG024978 |
Portland VA Research Foundation |
Keywords
- Actigraphy
- Long-short-term memory
- Neural network
- Sleep monitoring
- Wearable device