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Imputing missing sleep data from wearables with neural networks in real-world settings

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Title
Imputing missing sleep data from wearables with neural networks in real-world settings
Author(s)
Lee, Minki P.; Hoang, Kien; Park, Sungkyu; Yun Min Song; Joo, Eun Yeon; Chang, Won; Kim, Jee Hyun; Jae Kyoung Kim
Publication Date
2023-10
Journal
SLEEP, v.47, no.1
Publisher
OXFORD UNIV PRESS INC
Abstract
Sleep is a critical component of health and well-being but collecting and analyzing accurate longitudinal sleep data can be challenging, especially outside of laboratory settings. We propose a simple neural network model titled SOMNI (Sleep data restOration using Machine learning and Non-negative matrix factorIzation [NMF]) for imputing missing rest-activity data from actigraphy, which can enable clinicians to better handle missing data and monitor sleep-wake cycles of individuals with highly irregular sleep-wake patterns. The model consists of two hidden layers and uses NMF to capture hidden longitudinal sleep-wake patterns of individuals with disturbed sleep-wake cycles. Based on this, we develop two approaches: the individual approach imputes missing data based on the data from only one participant, while the global approach imputes missing data based on the data across multiple participants. Our models are tested with shift and non-shift workers' data from three independent hospitals. Both approaches can accurately impute missing data up to 24 hours of long dataset (>50 days) even for shift workers with extremely irregular sleep-wake patterns (AUC > 0.86). On the other hand, for short dataset (similar to 15 days), only the global model is accurate (AUC > 0.77). Our approach can be used to help clinicians monitor sleep-wake cycles of patients with sleep disorders outside of laboratory settings without relying on sleep diaries, ultimately improving sleep health outcomes.
URI
https://pr.ibs.re.kr/handle/8788114/14647
DOI
10.1093/sleep/zsad266
ISSN
0161-8105
Appears in Collections:
Pioneer Research Center for Mathematical and Computational Sciences(수리 및 계산과학 연구단) > 1. Journal Papers (저널논문)
Pioneer Research Center for Mathematical and Computational Sciences(수리 및 계산과학 연구단) > Biomedical Mathematics Group(의생명 수학 그룹) > 1. Journal Papers (저널논문)
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