EEG signal classification using universum support vector machine. (15th September 2018)

• Record Type: Journal Article
• Authors: Richhariya, B.; Tanveer, M.
• Is Part Of: Expert systems with applications. Volume 106(2018)
• Page Start: 169
• Page End: 182
• Publication Date: 2018-09-15
• Subjects: Universum -- Interictal -- Support vector machine -- Twin support vector machine; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2018.03.053
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Electroencephalogram signal classification is performed using universum learning. Support vector machine classifier uses prior information from interictal signals. Many feature extraction techniques are used for comparing the algorithms. Universum support vector machine is used first time for seizure classification. Abstract: Support vector machine (SVM) has been used widely for classification of electroencephalogram (EEG) signals for the diagnosis of neurological disorders such as epilepsy and sleep disorders. SVM shows good generalization performance for high dimensional data due to its convex optimization problem. The incorporation of prior knowledge about the data leads to a better optimized classifier. Different types of EEG signals provide information about the distribution of EEG data. To include prior information in the classification of EEG signals, we propose a novel machine learning approach based on universum support vector machine (USVM) for classification. In our approach, the universum data points are generated by selecting universum from the EEG dataset itself which are the interictal EEG signals. This removes the effect of outliers on the generation of universum data. Further, to reduce the computation time, we use our approach of universum selection with universum twin support vector machine (UTSVM) which has less computational cost in comparison to traditional SVM. For checking the validity of our proposed methods, we use various feature extraction techniques for different datasets consisting of healthy and seizure signals. Several numerical experiments are performed on the generated datasets and the results of our proposed approach are compared with other baseline methods. Our proposed USVM and proposed UTSVM show better generalization performance compared to SVM, USVM, Twin SVM (TWSVM) and UTSVM. The proposed UTSVM has achieved highest classification accuracy of 99% for the healthy and seizure EEG signals.