Performance analysis of classifiers in detection of conscious state from alcoholic EEG signals

In different social orders, mixed drinks are generally utilized. A decent and sound way of life can be leaded if the utilization of liquor is negligible and periodic. Notwithstanding, with the over admission of liquor, it upsets the dynamic cycle of the individual and it prompts serious mental, phys...

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Bibliographic Details
Main Authors:	Rajaguru, Harikumar, Shankar, M. Gowri, Irfan, S. Mohammed, Balaji, C. Mukesh
Format:	Conference Proceeding
Language:	eng
Subjects:	Classifiers Electroencephalography Kernels Liquor Parameter sensitivity Particle swarm optimization Principal components analysis Regression Signal classification
Online Access:	Get full text
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Summary:	In different social orders, mixed drinks are generally utilized. A decent and sound way of life can be leaded if the utilization of liquor is negligible and periodic. Notwithstanding, with the over admission of liquor, it upsets the dynamic cycle of the individual and it prompts serious mental, physical and enthusiastic medical issues. The liquor identification level is imperative to decide the individual's ability to make a specific showing. In this study the detection of alcoholic effects in the brain function is analyzed through alcoholic EEG Signals. Particle swarm optimization (PSO), Features are extracted from Alcoholic EEG signals which are classified through seven classifiers namely, Nonlinear Regression, Logistic Regression, Bayesian Linear discriminant Classifier (BDLC), Principal Component Analysis (PCA), Kernel PCA, Log Kernel PCA and Firefly classifier. The University of California, Irvine-Knowledge Discovery databases also known as the UCI KDD online database, provided the alcoholic EEG data analyzed in this study. Only ten well labeled alcoholic patients were considered in this study among 122 alcoholic and normal patients were available. The performance of the classifiers were analyzed and compared based on parameters like, Sensitivity, Specificity, Accuracy, GDR and Error rate. Results show that the Nonlinear Regression and BLDC classifier exceeds the other five classifiers in terms of accuracy of 94.011% and low error rate of 11.98%.
ISSN:	0094-243X 1551-7616