Abstract
This paper presents and clinically validates a new method to accurately classify sleep phases within a wrist-worn device (e.g., smartwatch, fitnessband). The method combines inertial and optical sensors to compute the wearer’s motion, breathing rate, and pulse rate variability, and to estimate the different sleep stages (WAKE, REM and NREM). The presented method achieves a sensitivity and specificity for the REM of \(89.2\,\%\) and \(77.9\,\%\) respectively; for the NREM class \(83.4\,\%\) and \(84.9\,\%\) respectively; and a median accuracy of \(81.4\,\%\). The assessment of the performance was obtained by comparing to the gold standard measure in sleep monitoring, polysomnography.
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Renevey, P. et al. (2018). Optical wrist-worn device for sleep monitoring. In: Eskola, H., Väisänen, O., Viik, J., Hyttinen, J. (eds) EMBEC & NBC 2017. EMBEC NBC 2017 2017. IFMBE Proceedings, vol 65. Springer, Singapore. https://doi.org/10.1007/978-981-10-5122-7_154
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DOI: https://doi.org/10.1007/978-981-10-5122-7_154
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