EEG signal classification using universum support vector machine.
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| Title: | EEG signal classification using universum support vector machine. |
|---|---|
| Authors: | Richhariya, B.1 phd1701241001@iiti.ac.in, Tanveer, M.1 mtanveer@iiti.ac.in |
| Source: | Expert Systems with Applications. Sep2018, Vol. 106, p169-182. 14p. |
| Subjects: | Support vector machines, Electroencephalography, Digital signal processing, Classification algorithms, Diagnosis of neurological disorders |
| 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 129608228 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: EEG signal classification using universum support vector machine. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Richhariya%2C+B%2E%22">Richhariya, B.</searchLink><relatesTo>1</relatesTo><i> phd1701241001@iiti.ac.in</i><br /><searchLink fieldCode="AR" term="%22Tanveer%2C+M%2E%22">Tanveer, M.</searchLink><relatesTo>1</relatesTo><i> mtanveer@iiti.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Expert+Systems+with+Applications%22">Expert Systems with Applications</searchLink>. Sep2018, Vol. 106, p169-182. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Electroencephalography%22">Electroencephalography</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+signal+processing%22">Digital signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Classification+algorithms%22">Classification algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnosis+of+neurological+disorders%22">Diagnosis of neurological disorders</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.eswa.2018.03.053 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 169 Subjects: – SubjectFull: Support vector machines Type: general – SubjectFull: Electroencephalography Type: general – SubjectFull: Digital signal processing Type: general – SubjectFull: Classification algorithms Type: general – SubjectFull: Diagnosis of neurological disorders Type: general Titles: – TitleFull: EEG signal classification using universum support vector machine. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Richhariya, B. – PersonEntity: Name: NameFull: Tanveer, M. IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 09 Text: Sep2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 09574174 Numbering: – Type: volume Value: 106 Titles: – TitleFull: Expert Systems with Applications Type: main |
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