TY - GEN
T1 - Categorizing Sleep in Older Adults with Wireless Activity Monitors Using LSTM Neural Networks
AU - Yildiz, Selda
AU - Opel, Ryan A.
AU - Elliott, Jonathan E.
AU - Kaye, Jeffrey
AU - Cao, Hung
AU - Lim, Miranda M.
N1 - Publisher Copyright:
© 2019 IEEE.
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.
KW - Actigraphy
KW - Long-short-term memory
KW - Neural network
KW - Sleep monitoring
KW - Wearable device
UR - http://www.scopus.com/inward/record.url?scp=85077901301&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85077901301&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2019.8857453
DO - 10.1109/EMBC.2019.8857453
M3 - Conference contribution
C2 - 31946603
AN - SCOPUS:85077901301
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 3368
EP - 3372
BT - 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Y2 - 23 July 2019 through 27 July 2019
ER -