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 (US) |
|---|---|
| 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 | |
| State | Published - Jul 2019 |
| Event | 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Germany Duration: Jul 23 2019 → Jul 27 2019 |
Publication series
| Name | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS |
|---|---|
| ISSN (Print) | 1557-170X |
Conference
| Conference | 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 |
|---|---|
| Country | Germany |
| City | Berlin |
| Period | 7/23/19 → 7/27/19 |
Fingerprint
Keywords
- Actigraphy
- Long-short-term memory
- Neural network
- Sleep monitoring
- Wearable device
ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics
Cite this
Categorizing Sleep in Older Adults with Wireless Activity Monitors Using LSTM Neural Networks. / Yildiz, Selda; Opel, Ryan A.; Elliott, Jonathan E.; Kaye, Jeffrey; Cao, Hung; Lim, Miranda M.
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).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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.
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.
ER -