An Advanced Analysis System for Identifying Alcoholic Brain State Through EEG Signals

doi:10.1007/s11633-019-1178-7

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	An Advanced Analysis System for Identifying Alcoholic Brain State Through EEG Signals
	Siuly Siuly 1; Varun Bajaj 2; Abdulkadir Sengur 3; Yanchun Zhang 1,4
发表期刊	International Journal of Automation and Computing
ISSN	1476-8186
	2019
卷号	16 期号:6 页码:737-747
摘要	This paper addresses an advanced analysis system for the identification of alcoholic brain states from electroencephalogram (EEG) data in an automatic way. This study introduces an optimum allocation based sampling (OAS) scheme to discover the most favourable representative data points from every single time-window of each EEG signal considering the minimal variability of the observations. Combining all representative samples of each time-window in a set, some statistical features are extracted from every set of each class. The Mann-Whitney U test is used to assess whether each of the features is significant between the two classes (e.g., alcoholic and control). In order to evaluate the effectiveness of the OAS-based features, four well-known machine learning methods (decision table, support vector machine (SVM), k-nearest neighbor (k-NN) and logistic regression) are considered for identification of alcoholic brain state. The experimental results on the UCI KDD (i.e., UCI knowledge discovery in databases) database demonstrate that the OAS based decision table algorithm yields the highest accuracy of 99.58% with a low false alarm rate 0.40%, which is an improvement of up to 9.58% over the existing algorithms. A proposed analysis system can be used to detect alcoholism and also to determine the level of alcoholism-related changes in EEG signals.
关键词	Electroencephalogram (EEG) alcoholism optimum allocation technique feature extraction decision table.
DOI	10.1007/s11633-019-1178-7
引用统计	被引频次：19[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/42371
专题	学术期刊_Machine Intelligence Research
作者单位	1.Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne VIC 3011, Australia 2.Discipline of Electronics and Communication Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur 482005, India 3.Deptartement of Electrical and Electronics Engineering, Faculty of Technology, Firat University, Elazig 23119, Turkey 4.Cyberspace Institute of Advanced Technology (CIAT), Guangzhou University, Guangzhou 510006, China
推荐引用方式 GB/T 7714	Siuly Siuly,Varun Bajaj,Abdulkadir Sengur,et al. An Advanced Analysis System for Identifying Alcoholic Brain State Through EEG Signals[J]. International Journal of Automation and Computing,2019,16(6):737-747.
APA	Siuly Siuly,Varun Bajaj,Abdulkadir Sengur,&Yanchun Zhang.(2019).An Advanced Analysis System for Identifying Alcoholic Brain State Through EEG Signals.International Journal of Automation and Computing,16(6),737-747.
MLA	Siuly Siuly,et al."An Advanced Analysis System for Identifying Alcoholic Brain State Through EEG Signals".International Journal of Automation and Computing 16.6(2019):737-747.

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