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Classification of sleep events from polygraphic data
Bódi, Michal ; Smital, Lukáš (oponent) ; Králík, Martin (vedoucí práce)
This bachelor’s thesis discusses the detection and classification of sleep apnea. First, it explains the differences between individual sleep disorders and data acquisition methods. Creating an overview such as gain signals and then suggests an algorithm procedure for detection and classification with the help of wavelet transform, thresholding and machine learning model. The thesis continues with the program solution itself in the Matlab environment and its evaluation through the visualization of the confusion matrix and the F1 score. The highest value of F1 in detection reached 91.33% and in classification 43,64%. The created algorithm was supposed to look for sleep-related breathing disorders in all-night recordings.

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