Categorizing Sleep in Older Adults with Wireless Activity Monitors Using LSTM Neural Networks

Selda Yildiz, Ryan A. Opel, Jonathan E. Elliott, Jeffrey Kaye, Hung Cao, Miranda M. Lim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

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 languageEnglish
Title of host publication2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3368-3372
Number of pages5
ISBN (Electronic)9781538613115
DOIs
Publication statusPublished - Jul 2019
Externally publishedYes
Event41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Germany
Duration: 23 Jul 201927 Jul 2019

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Country/TerritoryGermany
CityBerlin
Period23/07/1927/07/19

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

FundersFunder number
NIH NCCIHK99AT010158
NIH NIAU19 PO#S9001796
Oregon Roybal Center for Translational Research on Aging NIHP30- AG008017, P30 AG024978-15
National Institutes of HealthUL1GM118964
National Institute on AgingP30AG024978
Portland VA Research Foundation

    Keywords

    • Actigraphy
    • Long-short-term memory
    • Neural network
    • Sleep monitoring
    • Wearable device

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