Detection of EEG Arousals in Patients with Respiratory Sleep Disorder

Abstract

This paper describes the detection of arousals from sleep in patients with respiratory sleep disorder using a single channel EEG signal and support vector machine classifier. Determining the occurrence and the frequency of occurrence of arousals from sleep is very important because it is directly related to the quality of sleep. In this paper we used twenty polysomnographic recordings of patients with respiratory sleep disorder. Six recordings were used as training sets and fourteen recordings were used as test sets. We extracted three types of features, which are six indices relating to sleep states, the powers of each of four frequency bands and variations of power of EEG frequency, using time-frequency analysis. We detected arousals from sleep using the above features and SVM classifier. From the results, the sensitivity of 79.65% and the specificity of 89.52% were obtained. The error between the total arousal time detected by the proposed method and the annotated data was 15.09±10.76 min and it showed the possibility of application for the detection of arousal from sleep using a single channel EEG signal.