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

doi:10.1109/EMBC.2019.8857453

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

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

4 Atıf (Scopus)

Özet

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.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	3368-3372
Sayfa sayısı	5
ISBN (Elektronik)	9781538613115
DOI'lar	https://doi.org/10.1109/EMBC.2019.8857453
Yayın durumu	Yayınlandı - Tem 2019
Harici olarak yayınlandı	Evet
Etkinlik	41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Germany Süre: 23 Tem 2019 → 27 Tem 2019

Yayın serisi

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

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Ülke/Bölge	Germany
Şehir	Berlin
Periyot	23/07/19 → 27/07/19

Bibliyografik not

Publisher Copyright:
© 2019 IEEE.

Finansman

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

Finansörler	Finansör numarası
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

Belgeye Erişim

10.1109/EMBC.2019.8857453

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Yildiz, S., Opel, R. A., Elliott, J. E., Kaye, J., Cao, H., & Lim, M. M. (2019). Categorizing Sleep in Older Adults with Wireless Activity Monitors Using LSTM Neural Networks. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 (pp. 3368-3372). Makale 8857453 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2019.8857453

Yildiz, Selda ; Opel, Ryan A. ; Elliott, Jonathan E. et al. / Categorizing Sleep in Older Adults with Wireless Activity Monitors Using LSTM Neural Networks. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 3368-3372 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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title = "Categorizing Sleep in Older Adults with Wireless Activity Monitors Using LSTM Neural Networks",

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.",

keywords = "Actigraphy, Long-short-term memory, Neural network, Sleep monitoring, Wearable device",

author = "Selda Yildiz and Opel, {Ryan A.} and Elliott, {Jonathan E.} and Jeffrey Kaye and Hung Cao and Lim, {Miranda M.}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 ; Conference date: 23-07-2019 Through 27-07-2019",

year = "2019",

month = jul,

doi = "10.1109/EMBC.2019.8857453",

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series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

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Yildiz, S, Opel, RA, Elliott, JE, Kaye, J, Cao, H & Lim, MM 2019, Categorizing Sleep in Older Adults with Wireless Activity Monitors Using LSTM Neural Networks. in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019., 8857453, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers Inc., pp. 3368-3372, 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019, Berlin, Germany, 23/07/19. https://doi.org/10.1109/EMBC.2019.8857453

Categorizing Sleep in Older Adults with Wireless Activity Monitors Using LSTM Neural Networks. / Yildiz, Selda; Opel, Ryan A.; Elliott, Jonathan E. et al.
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 3368-3372 8857453 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

TY - GEN

T1 - Categorizing Sleep in Older Adults with Wireless Activity Monitors Using LSTM Neural Networks

AU - Yildiz, Selda

AU - Opel, Ryan A.

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AU - Kaye, Jeffrey

AU - Cao, Hung

AU - Lim, Miranda M.

PY - 2019/7

Y1 - 2019/7

N2 - 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.

AB - 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.

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ER -

Yildiz S, Opel RA, Elliott JE, Kaye J, Cao H, Lim MM. Categorizing Sleep in Older Adults with Wireless Activity Monitors Using LSTM Neural Networks. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 3368-3372. 8857453. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2019.8857453

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

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