| Summary: | 碩士 === 輔仁大學 === 資訊工程學系碩士班 === 101 === The Kleine-Levin syndrome is a rare sleep disorder to predispose young boys. Recurrent hypersomnia and cognitive or abnormal behavior are the common symptoms of the patients. In clinical, the signals of polysomnography and multiple sleep latency tests can not diagnose the disease. Actually, the pathophysiology mechanism and diagnostic criterion of the Kleine-Levin syndrome are not clear and definded easily at the present time. Therefore, how to establish a systematic approach to help the physicians to identify the patients with Kleine-Levin syndrome is a necessary task. This paper introduces a Kleine-Levin syndrome detection system based on Electroencephalogram frequency variation. The system uses the electroencephalogram and physiological signals of the patients with Kleine-Levin syndrome to conduct feature extraction. The fuzzy clustering algorithm is applied to help the physicians to examination of the features. Experimental results show that the system can precisely identify the patients during Kleine-Levin syndrome attack period.
Keywords: Kleine-Levin syndrome, polysomnography, electroencephalogram, feature extraction, fuzzy clustering
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