Detection of seizures in EEG signal using weighted locally linear embedding and SVM classifier
To diagnose the structural disorders of brain, electroencephalography (EEG) is routinely used for observing the epileptic seizures in neurology clinics, which is one of the major brain disorders till today. In this work, we present a new, EEG-based, brain-state identification method which could form...
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| creator | Yaozhang Pan Shuzhi Sam Ge Al Mamun, A. Feng Ru Tang |
| description | To diagnose the structural disorders of brain, electroencephalography (EEG) is routinely used for observing the epileptic seizures in neurology clinics, which is one of the major brain disorders till today. In this work, we present a new, EEG-based, brain-state identification method which could form the basis for detecting epileptic seizure. We aim to classify the EEG signals and diagnose the epileptic seizures directly by using weighted locally linear embedding (WLLE) and support vector machine (SVM). Firstly, we use WLLE to do feature extraction of the EEG signal to obtain more compact representations of the internal characteristic and structure in the original data, which captures the information necessary for further manipulations. Then, SVM classifier is used to identify the seizures onset state from normal state of the patients. |
| doi_str_mv | 10.1109/ICCIS.2008.4670889 |
| format | Conference Proceeding |
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Then, SVM classifier is used to identify the seizures onset state from normal state of the patients.</description><subject>Animals</subject><subject>Clustering algorithms</subject><subject>Electroencephalography</subject><subject>Epilepsy</subject><subject>Feature extraction</subject><subject>Frequency synchronization</subject><subject>locally linear embedding</subject><subject>Mice</subject><subject>seizures detection</subject><subject>Support vector machine classification</subject><subject>Support vector machines</subject><subject>Transmitters</subject><subject>weighted distance measurement</subject><subject>weighted locally linear embedding</subject><issn>2326-8123</issn><isbn>1424416736</isbn><isbn>9781424416738</isbn><isbn>1424416744</isbn><isbn>9781424416745</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFUE1PAjEQrVESAfkDeukfWGw7bbc9mhWQBOMB9Sjp7k6xpixmu8Tgr3eNJM7l5U3mfWQIueZsyjmzt8uiWK6ngjEzlTpnxtgzMuJSSMl1LuX5PwF9QYYChM4MFzAgo16UW1DG8EsySemD9SNVv1BD8naPHVZd2Dd072nC8H1oMdHQ0NlsQVPYNi7SQwrNln5h2L53WNO4r1yMRxpDg66luCuxrn8vXFPT9esjraJLKfiA7RUZeBcTTk44Ji_z2XPxkK2eFsvibpVVHGyXCQngJLc5lOBy64Qyuq9XK8VKaT2IPqH0jiM6XmqjpOYOWI2sBI_KcxiTmz_fgIibzzbsXHvcnB4FP7HFWHM</recordid><startdate>200809</startdate><enddate>200809</enddate><creator>Yaozhang Pan</creator><creator>Shuzhi Sam Ge</creator><creator>Al Mamun, A.</creator><creator>Feng Ru Tang</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200809</creationdate><title>Detection of seizures in EEG signal using weighted locally linear embedding and SVM classifier</title><author>Yaozhang Pan ; Shuzhi Sam Ge ; Al Mamun, A. ; Feng Ru Tang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c139t-2433a41973b3a79a2586585d550b49f32bedbfa1eea1b685461a30de0b3fe5f13</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Animals</topic><topic>Clustering algorithms</topic><topic>Electroencephalography</topic><topic>Epilepsy</topic><topic>Feature extraction</topic><topic>Frequency synchronization</topic><topic>locally linear embedding</topic><topic>Mice</topic><topic>seizures detection</topic><topic>Support vector machine classification</topic><topic>Support vector machines</topic><topic>Transmitters</topic><topic>weighted distance measurement</topic><topic>weighted locally linear embedding</topic><toplevel>online_resources</toplevel><creatorcontrib>Yaozhang Pan</creatorcontrib><creatorcontrib>Shuzhi Sam Ge</creatorcontrib><creatorcontrib>Al Mamun, A.</creatorcontrib><creatorcontrib>Feng Ru Tang</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yaozhang Pan</au><au>Shuzhi Sam Ge</au><au>Al Mamun, A.</au><au>Feng Ru Tang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Detection of seizures in EEG signal using weighted locally linear embedding and SVM classifier</atitle><btitle>2008 IEEE Conference on Cybernetics and Intelligent Systems</btitle><stitle>ICCIS</stitle><date>2008-09</date><risdate>2008</risdate><spage>358</spage><epage>363</epage><pages>358-363</pages><issn>2326-8123</issn><isbn>1424416736</isbn><isbn>9781424416738</isbn><eisbn>1424416744</eisbn><eisbn>9781424416745</eisbn><abstract>To diagnose the structural disorders of brain, electroencephalography (EEG) is routinely used for observing the epileptic seizures in neurology clinics, which is one of the major brain disorders till today. In this work, we present a new, EEG-based, brain-state identification method which could form the basis for detecting epileptic seizure. We aim to classify the EEG signals and diagnose the epileptic seizures directly by using weighted locally linear embedding (WLLE) and support vector machine (SVM). Firstly, we use WLLE to do feature extraction of the EEG signal to obtain more compact representations of the internal characteristic and structure in the original data, which captures the information necessary for further manipulations. Then, SVM classifier is used to identify the seizures onset state from normal state of the patients.</abstract><pub>IEEE</pub><doi>10.1109/ICCIS.2008.4670889</doi><tpages>6</tpages></addata></record> |
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| identifier | ISSN: 2326-8123 |
| ispartof | 2008 IEEE Conference on Cybernetics and Intelligent Systems, 2008, p.358-363 |
| issn | 2326-8123 |
| language | eng |
| recordid | cdi_ieee_primary_4670889 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Animals Clustering algorithms Electroencephalography Epilepsy Feature extraction Frequency synchronization locally linear embedding Mice seizures detection Support vector machine classification Support vector machines Transmitters weighted distance measurement weighted locally linear embedding |
| title | Detection of seizures in EEG signal using weighted locally linear embedding and SVM classifier |
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