Abstract
In this work a new method of automatic detection of apnea–hypopnea episodes is presented. It combines snore/nonsnore classification with information about body and limbs movements. The snore/nonsnore detection is performed using Discrete Fourier Transform and energy calculation. The feature space is reduced using Linear Discriminant Analysis and a linear classifier was obtained. The feasibility of this method was tested on the set of 8 full-night polysomnography recordings of which 2 indicate sleep apnea syndrome. The result shows that the method is effective in detection of apneic events.
This work was partially supported by the Warsaw University of Technology, Faculty of Mechatronics Dean’s Grant 504/02801.
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Rostek, K. (2018). Detection of Apnea–Hypopnea Events Using Actigraphy and Sleep Sounds. In: Kościelny, J., Syfert, M., Sztyber, A. (eds) Advanced Solutions in Diagnostics and Fault Tolerant Control. DPS 2017. Advances in Intelligent Systems and Computing, vol 635. Springer, Cham. https://doi.org/10.1007/978-3-319-64474-5_27
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