Machine learning approaches for predicting sleep arousal response based on heart rate variability, oxygen saturation, and body profiles.
Author(s)
Type
Journal Article
Abstract
OBJECTIVE: Obstructive sleep apnea is a global health concern, and several tools have been developed to screen its severity. However, most tools focus on respiratory events instead of sleep arousal, which can also affect sleep efficiency. This study employed easy-to-measure parameters-namely heart rate variability, oxygen saturation, and body profiles-to predict arousal occurrence. METHODS: Body profiles and polysomnography recordings were collected from 659 patients. Continuous heart rate variability and oximetry measurements were performed and then labeled based on the presence of sleep arousal. The dataset, comprising five body profiles, mean heart rate, six heart rate variability, and five oximetry variables, was then split into 80% training/validation and 20% testing datasets. Eight machine learning approaches were employed. The model with the highest accuracy, area under the receiver operating characteristic curve, and area under the precision recall curve values in the training/validation dataset was applied to the testing dataset and to determine feature importance. RESULTS: InceptionTime, which exhibited superior performance in predicting sleep arousal in the training dataset, was used to classify the testing dataset and explore feature importance. In the testing dataset, InceptionTime achieved an accuracy of 76.21%, an area under the receiver operating characteristic curve of 84.33%, and an area under the precision recall curve of 86.28%. The standard deviations of time intervals between successive normal heartbeats and the square roots of the means of the squares of successive differences between normal heartbeats were predominant predictors of arousal occurrence. CONCLUSIONS: The established models can be considered for screening sleep arousal occurrence or integrated in wearable devices for home-based sleep examination.
Date Issued
2023-10-13
Date Acceptance
2023-09-18
Citation
Digit Health, 2023, 9
ISSN
2055-2076
Journal / Book Title
Digit Health
Volume
9
Copyright Statement
© The Author(s) 2023. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
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License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/37846406
PII: 10.1177_20552076231205744
Subjects
arousal
heart rate variability
InceptionTime model
Obstructive sleep apnea
the square roots of the means of the squares of successive differences between normal heartbeats (RMSSD)
the standard deviations of the time intervals between successive normal heartbeats (SDNN)
Publication Status
Published
Coverage Spatial
United States
Date Publish Online
2023-10-13