Abstract
Recognition of sleep and wake states is one of the relevant parts of sleep analysis. Performing this measurement in a contactless way increases comfort for the users. We present an approach evaluating only movement and respiratory signals to achieve recognition, which can be measured non-obtrusively. The algorithm is based on multinomial logistic regression and analyses features extracted out of mentioned above signals. These features were identified and developed after performing fundamental research on characteristics of vital signals during sleep. The achieved accuracy of 87% with the Cohen’s kappa of 0.40 demonstrates the appropriateness of a chosen method and encourages continuing research on this topic.
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Notes
- 1.
Initial study was carried out in the Interdisciplinary Center for Sleep Medicine of Charité-Universitätsmedizin Berlin, Charitéplatz 1, D-10117 Berlin (Germany).
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Acknowledgments
We thank the Interdisciplinary Center for Sleep Medicine of Charité Clinic in Berlin and, in particular, Dr. rer. medic. Martin Glos for supporting the study.
This research was partially funded by the EU Interreg V-Program “Alpenrhein-Bodensee-Hochrhein”: Project “IBH Living Lab Active and Assisted Living”, grants ABH40, ABH41, and ABH66 and by the German Federal Ministry For Economic Affairs And Energy, ZiM project “Sleep Lab at Home” (SLaH) grant: ZF4825301AW9.
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Gaiduk, M. et al. (2022). Evaluating Body Movement and Breathing Signals for Identification of Sleep/Wake States. In: Saponara, S., De Gloria, A. (eds) Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2021. Lecture Notes in Electrical Engineering, vol 866. Springer, Cham. https://doi.org/10.1007/978-3-030-95498-7_29
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