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Multi-Class SVM Based on Sleep Stage Identification Using EEG Signal

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dc.contributor.author Aboalayon, Khald A.I.
dc.contributor.author Faezipour, Miad
dc.date.accessioned 2015-04-23T14:19:36Z
dc.date.available 2015-04-23T14:19:36Z
dc.date.issued 2015-03-27
dc.identifier.uri https://scholarworks.bridgeport.edu/xmlui/handle/123456789/1160
dc.description.abstract Currently, sleep disorders are considered as one of the major human life issues. Human sleep is a regular state of rest for the body in which the eyes are not only usually closed, but also have several nervous centers being inactive; hence, rendering the person either partially or completely unconscious and making the brain a less complicated network. This paper introduces an efficient technique towards differentiating sleep stages to assist physicians in the diagnosis and treatment of related sleep disorders. The idea is based on easily implementable filters in any hardware device and feasible discriminating features of the Electroencephalogram (EEG) signal by employing the one-against-all method of the multiclass Support Vector machine (SVM) to recognize the sleep stages and identify if the acquired signal is corresponding to wake, stage1, stage2, stage3 or stage4.The experimental results on several subjects achieve 92% of classification accuracy of the proposed work. A comparison of our proposed technique with some recent available work in the literature also presents the high classification accuracy performance. en_US
dc.language.iso en_US en_US
dc.subject Sleep disorder en_US
dc.subject Electroencephalography (EEG) en_US
dc.subject Support vector machine en_US
dc.title Multi-Class SVM Based on Sleep Stage Identification Using EEG Signal en_US
dc.type Presentation en_US
dc.institute.department School of Engineering en_US
dc.institute.name University of Bridgeport en_US
dc.event.location Bridgeport, CT en_US
dc.event.name Faculty Research Day en_US


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