教師資料查詢 | 類別: 期刊論文 | 教師: 周建興 Chien-hsing Chou (瀏覽個人網頁)

標題:A Machine Learning Approach to Classify Vigilance States in Rats
學年100
學期1
出版(發表)日期2011/08/01
作品名稱A Machine Learning Approach to Classify Vigilance States in Rats
作品名稱(其他語言)
著者Yu, Zong-En; Kuo, Chung-Chih; Chou, Chien-Hsing; Yen, Chen-Tung; Chang, Fu
單位淡江大學電機工程學系
出版者Kidlington: Pergamon
著錄名稱、卷期、頁數Expert Systems and Applications 38(8), pp.10153-10160
摘要Identifying mammalian vigilance states has recently become an important topic in biological science research. The vigilance states are usually categorized in at least three states, including slow wave sleep (SWS), rapid eye movement sleep (REM), and awakening. To identify different vigilance states, even a well-trained expert must spend a lot of time analyzing a mass of physiological recording data. This study proposes an automatic vigilance stages classification method for analyzing EEG signals in rats. The EEG signals were transferred by fast Fourier transform before extracting features. These extracted features were then used as training patterns to construct the proposed classification system. The proposed classification system contains two functional units. The first unit is principle component analysis (PCA) method, which is used to project the high dimensional features into the lower dimensional subspace. The second unit is the k-nearest neighbor (k-NN) method, which identifies the physiological state in each EEG signal epoch. Based on the results of analyzing 810 epochs of EEG signal, the proposed classification method achieves satisfactory classification accuracy for vigilance states. Based on machine-learning algorithms, the classifier learns to approach the configuration that best fits the categorization task. Therefore, additional training in searching best parameters and thresholds can be avoided. Moreover, the PCA algorithm projects data instances into a 3-D space, making it possible to visualize state-changing dynamics. Experimental results show that the proposed machine-learning based classifier performs better than conventional vigilance state classification algorithms. The results also suggest that it is possible to identify the vigilance states with only EEG signals using the proposed pattern recognition technique.
關鍵字Vigilance stages; Pattern classification; EEG; Machine learning; PCA; k-NN
語言英文
ISSN0957-4174
期刊性質國外
收錄於SCI
產學合作
通訊作者Chou, Chien-Hsing
審稿制度
國別英國
公開徵稿
出版型式紙本
相關連結
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