An integrated alcoholic index using tunable-Q wavelet transform based features extracted from EEG signals for diagnosis of alcoholism

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• 作者：Shivnarayan Patidar^a ; ^{shivnarayan.patidar@nitgoa.ac.in} ; Ram Bilas Pachori^b ; ^{pachori@iiti.ac.in} ; Abhay Upadhyay^b ; ^{phd1301102002@iiti.ac.in} ; U. Rajendra Acharya^c ; ^d ; ^e ; ^{aru@np.edu.sg}
• 关键词：Electroencephalogram ; Normal and alcoholic EEG signals ; Tunable-Q wavelet transform ; Correntropy ; Classifier
• 刊名：Applied Soft Computing
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：50
• 期：Complete
• 页码：71-78
• 全文大小：1726 K
• 卷排序：50

文摘

We propose a new method for diagnosis of alcoholism using TQWT. New feature set based on correntropy derived from TQWT have been proposed. The effects of Q on classification performance have been evaluated. A novel Alcoholism Risk Index (ARI) is developed using 3 clinically significant features. Performance has been compared with existing methods.