Epileptic seizure detection using neural fuzzy networks
The electroencephalogram (EEG) is a representative signal containing information about the condition of the brain. The shape of the wave may contain useful information about its state. However, the human observer cannot directly monitor these subtle details. Besides, since bio-signals are highly sub...
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| Format: | Journal article |
| Language: | English |
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2006
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| _version_ | 1797063649494302720 |
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| author | Sadati, N Mohseni, H Maghsoudi, A |
| author_facet | Sadati, N Mohseni, H Maghsoudi, A |
| author_sort | Sadati, N |
| collection | OXFORD |
| description | The electroencephalogram (EEG) is a representative signal containing information about the condition of the brain. The shape of the wave may contain useful information about its state. However, the human observer cannot directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the EEG signal parameters, extracted and analyzed using computers, are highly useful in diagnosis. The aim of this work is to compare the different classifiers when applied to EEG data from normal and epileptic subjects. For this purpose an adaptive neural fuzzy network (ANFN) to classify normal and epileptic EEG signals is proposed. The results are compared with other classifiers such as SVM (Support Vector Machine), ANFIS and FBNN (Feed forward Back-propagation Neural Network). It is shown that a classification accuracy of about 85.9% can be achieved using ANFN. © 2006 IEEE. |
| first_indexed | 2024-03-06T21:02:57Z |
| format | Journal article |
| id | oxford-uuid:3b79e86d-95fb-46a0-a83a-b3e5f53b5ca7 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T21:02:57Z |
| publishDate | 2006 |
| record_format | dspace |
| spelling | oxford-uuid:3b79e86d-95fb-46a0-a83a-b3e5f53b5ca72022-03-26T14:07:56ZEpileptic seizure detection using neural fuzzy networksJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:3b79e86d-95fb-46a0-a83a-b3e5f53b5ca7EnglishSymplectic Elements at Oxford2006Sadati, NMohseni, HMaghsoudi, AThe electroencephalogram (EEG) is a representative signal containing information about the condition of the brain. The shape of the wave may contain useful information about its state. However, the human observer cannot directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the EEG signal parameters, extracted and analyzed using computers, are highly useful in diagnosis. The aim of this work is to compare the different classifiers when applied to EEG data from normal and epileptic subjects. For this purpose an adaptive neural fuzzy network (ANFN) to classify normal and epileptic EEG signals is proposed. The results are compared with other classifiers such as SVM (Support Vector Machine), ANFIS and FBNN (Feed forward Back-propagation Neural Network). It is shown that a classification accuracy of about 85.9% can be achieved using ANFN. © 2006 IEEE. |
| spellingShingle | Sadati, N Mohseni, H Maghsoudi, A Epileptic seizure detection using neural fuzzy networks |
| title | Epileptic seizure detection using neural fuzzy networks |
| title_full | Epileptic seizure detection using neural fuzzy networks |
| title_fullStr | Epileptic seizure detection using neural fuzzy networks |
| title_full_unstemmed | Epileptic seizure detection using neural fuzzy networks |
| title_short | Epileptic seizure detection using neural fuzzy networks |
| title_sort | epileptic seizure detection using neural fuzzy networks |
| work_keys_str_mv | AT sadatin epilepticseizuredetectionusingneuralfuzzynetworks AT mohsenih epilepticseizuredetectionusingneuralfuzzynetworks AT maghsoudia epilepticseizuredetectionusingneuralfuzzynetworks |