| Title |
Detection of Arousal in Patients with Respiratory Sleep Disorder Using Single Channel EEG |
| Authors |
조성필(Cho, Sung-Pil) ; 최호선(Choi, Ho-Seon) ; 이경중(Lee, Kyoung-Joung) |
| Keywords |
Arousal ; Sleep Fragment ; Time-Frequency Analysis ; Electroencephalogram ; Support Vector Machine |
| Abstract |
Frequent arousals during sleep degrade the quality of sleep and result in sleep fragmentation. Visual inspection of physiological signals to detect the arousal events is cumbersome and time-consuming work. The purpose of this study is to develop an automatic algorithm to detect the arousal events. The proposed method is based on time-frequency analysis and the support vector machine classifier using single channel electroencephalogram (EEG). To extract features, first we computed 6 indices to find out the informations of a subject's sleep states. Next powers of each of 4 frequency bands were computed using spectrogram of arousal region. And finally we computed variations of power of EEG frequency to detect arousals. The performance has been assessed using polysomnographic (PSG) recordings of twenty patients with sleep apnea, snoring and excessive daytime sleepiness (EDS). We could obtain sensitivity of 79.65%, specificity of 89.52% for the data sets. We have shown that proposed method was effective for detecting the arousal events. |