Continuous wavelet transform based method for detection of arousal

Sleeping is a vital biological requirement in the homeostatic mechanism. Sleep disorders can cause personal health problems and life quality deterioration. Transient waveforms (k-complexes, sleep spindles, arousal, etc.) happens instantaneously in sleep, have distinctive structural features, amplitudes and frequencies, and are difficult to distinguish from the background of electroencephalography (EEG) which are called the microstructure of the EEG. Microstructure analysis is important for brain research, sleep studies, sleep stage scorings and assessment of sleep disorders. Detection of arousal, one of these structures, is performed by visual scoring of all night sleep records by an expert sleep physician. In particular, visual analysis of the microstructure is difficult and time-consuming task so it increases error rates in scoring. The aim of this study is automatic detection of arousals from EEG signals. A computer-aided diagnosis system that can detect arousal can give more objective results to the expert sleep experts for diagnosing. In this study, continuous wavelet transform analysis of scored EEG signals was performed and features were obtained. As a result, a decision support system algorithm for arousal has been developed using the obtained features and support vector machines. The specificity of the algorithm is 100% and sensitivity is 95.45%.