@inproceedings{6506735940b34c75bec3a5100af80646,
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.}",
year = "2019",
month = jul
doi = "10.1109/EMBC.2019.8857453",
language = "English (US)",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3368--3372",
booktitle = "2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019",
note = "41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 ; Conference date: 23-07-2019 Through 27-07-2019",
}