Sample Entropy on Multidistance Signal Level Difference for Epileptic EEG Classification.
Epilepsy is a disorder of the brain's nerves as a result of excessive brain cell activity. It is generally characterized by the recurrent unprovoked seizures. This neurological abnormality can be detected and evaluated using Electroencephalogram (EEG) signal. Many algorithms have been applied to ach...
| Publicado en: | Scientific World Journal pp. 1 - 7 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Wiley-Blackwell
9/12/2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=131726834&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 131726834 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 9/12/2018 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 131726834 131726834 NLM30279635 10.1155/2018/8463256 NLM30279635 131726834 ppf: 1 ppct: 6 formats: tig: atl: Sample Entropy on Multidistance Signal Level Difference for Epileptic EEG Classification. aug: au: Rizal, Achmad Hadiyoso, Sugondo affil: School of Electrical Engineering, Telkom University, Bandung 40257, Indonesia sug: subj: Epilepsy Classification Physics Electroencephalography Classification Electroencephalography Methods Brain Physiopathology Epilepsy Physiopathology ab: Epilepsy is a disorder of the brain's nerves as a result of excessive brain cell activity. It is generally characterized by the recurrent unprovoked seizures. This neurological abnormality can be detected and evaluated using Electroencephalogram (EEG) signal. Many algorithms have been applied to achieve high performance for the EEG classification of epileptic. However, the complexity and randomness of EEG signals become a challenge to researchers in applying the appropriate algorithms. In this research, sample entropy on Multidistance Signal Level Difference (MSLD) was applied to obtain the characteristic of EEG signals, especially towards the epilepsy patients. The test was performed on three classes of EEG data: EEG signals of epilepsy patient in ictal (seizure), interictal conditions (occurring between seizures) and normal EEG signals from healthy subjects with a closed eye condition. In this study, classification and verification were done using the Support Vector Machine (SVM) method. Through the 5-fold cross-validation, experimental results showed the highest accuracy of 97.7%. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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