1. Sleep Position Detection for Closed-Loop Treatment of Sleep-Related Breathing Disorders
- Author
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Breuss, A, Vonau, N, Ungricht, C, Schwarz, E, Irion, M, Bradicich, M, Grewe, F A, Liechti, S, Thiel, S, Kohler, M, Riener, R, Wilhelm, E, University of Zurich, Breuss, A, Robotics and image-guided minimally-invasive surgery (ROBOTICS), and Discrete Technology and Production Automation
- Subjects
Male ,Sleep Apnea, Obstructive ,2742 Rehabilitation ,Polysomnography ,Respiration ,2208 Electrical and Electronic Engineering ,Supine Position ,Humans ,2207 Control and Systems Engineering ,610 Medicine & health ,10178 Clinic for Pneumology ,Sleep - Abstract
Reliable detection of sleep positions is essential for the development of technical aids for patients with position-dependent sleep-related breathing disorders. We compare personalized and generalizable sleeping position classifiers using unobtrusive eight-channel pressure-sensing mats. Data of six male patients with confirmed position-dependent sleep apnea was recorded during three subsequent nights. Personalized position classifiers trained using leave-one-night-out cross-validation on average reached an F1-score of 61.3% for supine/non-supine and an F1-score of 46.2% for supine/lateral-left/lateral-right classification. The generalizable classifiers reached average F1-scores of 62.1% and 49.1% for supine/non-supine and supine/lateral-left/lateral-right classification, respectively. In-bed presence ('bed occupancy') could be detected with an average F1-score of 98.1%. This work shows that personalized sleep-position classifiers trained with data from two nights have comparable performance to classifiers trained with large interpatient datasets. Simple eight-channel sensor mattresses can be used to accurately detect in-bed presence required for closed-loop systems but their use to classify sleep-positions is limited.
- Published
- 2022
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