Classification of EEG Data Sets with Hilbert Transform
| dc.contributor.author | Seker, Mesut | |
| dc.contributor.author | Özerdem, Mehmet Sirac | |
| dc.date.accessioned | 2024-04-24T17:33:20Z | |
| dc.date.available | 2024-04-24T17:33:20Z | |
| dc.date.issued | 2016 | |
| dc.department | Dicle Üniversitesi | en_US |
| dc.description | 24th Signal Processing and Communication Application Conference (SIU) -- MAY 16-19, 2016 -- Zonguldak, TURKEY | en_US |
| dc.description.abstract | Electroencephalographic (EEG) records which are related to the electrical activity of the brain are one of the most useful tools which are used in diagnosis of neurologic diseases. The aim of this study was to classify different sets of EEG signals by using Hilbert transform and artificial neural networks (ANN). The EEG data used in this study has been acquired from database of Epileptology Department of Bonn University. The database constitutes of five data sets, namely A, B, C, D and E. Besides, each data set has a difference due to healthy/ epileptic subject, eyes open/ closed, the position of electrode, seizure-free or seizure activity. A-B, A-E, C-D and A-B-E signal groups are classified with each other. For classification, magnitude and phase difference components obtained with Hilbert transform were used. To get the different frequency (theta (4-7Hz), alpha (8-13Hz), beta (12-38Hz), all (0.5-40Hz)) band, the signal filtered with band pass filter. The classification result of phase difference is higher than the amplitude based result. The highest performances of data sets are; 100% for A-B (phase difference, 0.5-40Hz, 7x30x2 network structure), 99.93% for A-E (phase difference, theta band, 7x75x2 network structure), 99.68% for C-D (phase difference, 0.540Hz, 7x30x2 network structure) and 97.72% for A-B-E (phase difference, theta, 7x20x3 network structure). | en_US |
| dc.description.sponsorship | IEEE,Bulent Ecevit Univ, Dept Elect & Elect Engn,Bulent Ecevit Univ, Dept Biomed Engn,Bulent Ecevit Univ, Dept Comp Engn | en_US |
| dc.identifier.endpage | 1952 | en_US |
| dc.identifier.isbn | 978-1-5090-1679-2 | |
| dc.identifier.scopus | 2-s2.0-84982803669 | |
| dc.identifier.scopusquality | N/A | |
| dc.identifier.startpage | 1949 | en_US |
| dc.identifier.uri | https://hdl.handle.net/11468/20629 | |
| dc.identifier.wos | WOS:000391250900464 | |
| dc.identifier.wosquality | N/A | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | tr | en_US |
| dc.publisher | Ieee | en_US |
| dc.relation.ispartof | 2016 24th Signal Processing and Communication Application Conference (Siu) | |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Eeg | en_US |
| dc.subject | Classification | en_US |
| dc.subject | Hilbert Transform | en_US |
| dc.subject | Ann | en_US |
| dc.title | Classification of EEG Data Sets with Hilbert Transform | en_US |
| dc.title | Classification of EEG Data Sets with Hilbert Transform | |
| dc.type | Conference Object | en_US |
