Detection of Apnea-Hypopnea Events Using Actigraphy and Sleep Sounds

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.