Epileptic seizure detection using artificial neural network and a new feature extraction approach based on equal width discretization

In this study, we proposed a new feature extraction approach based on equal width discretization (EWD) method and used the statistical features obtained by means of this approach as the inputs of multilayer perceptron neural network (MLPNN) model in the detection of epileptic seizure from Electroencephalogram (EEG) signals. For this aim, EEG signals were discretized by EWD method, histograms of the signals were obtained according to the density of each discrete interval, and finally these histograms were used as the inputs of MLPNN models both without any hidden layer and with a hidden layer which has 5 neurons. Both of them detected epileptic seizures from EEG signals with high classification success ratios. This result showed that a linear classifier can also solve the problem of epileptic seizure detection by means of the offered feature extraction approach. Consequently, EWD approach may be used as a new feature extraction method in the biomedical signal processing.

Eser Adı
[dc.title]
Epileptic seizure detection using artificial neural network and a new feature extraction approach based on equal width discretization
Yazar
[dc.contributor.author]
Orhan, Umut
Yazar
[dc.contributor.author]
Hekim, Mahmut
Yazar
[dc.contributor.author]
Özer, Mahmut
Yayın Yılı
[dc.date.issued]
2011
Yayıncı
[dc.publisher]
GAZI UNIV, FAC ENGINEERING ARCHITECTURE
Yayın Türü
[dc.type]
article
Özet
[dc.description.abstract]
In this study, we proposed a new feature extraction approach based on equal width discretization (EWD) method and used the statistical features obtained by means of this approach as the inputs of multilayer perceptron neural network (MLPNN) model in the detection of epileptic seizure from Electroencephalogram (EEG) signals. For this aim, EEG signals were discretized by EWD method, histograms of the signals were obtained according to the density of each discrete interval, and finally these histograms were used as the inputs of MLPNN models both without any hidden layer and with a hidden layer which has 5 neurons. Both of them detected epileptic seizures from EEG signals with high classification success ratios. This result showed that a linear classifier can also solve the problem of epileptic seizure detection by means of the offered feature extraction approach. Consequently, EWD approach may be used as a new feature extraction method in the biomedical signal processing.
Açıklama
[dc.description]
WOS: 000295217600008
Kayıt Giriş Tarihi
[dc.date.accessioned]
2019-12-23
Açık Erişim Tarihi
[dc.date.available]
2019-12-23
Yayın Dili
[dc.language.iso]
tur
Konu Başlıkları
[dc.subject]
EEG signals
Konu Başlıkları
[dc.subject]
equal width discretization
Konu Başlıkları
[dc.subject]
biomedical signal processing
Konu Başlıkları
[dc.subject]
epileptic seizure detection
Konu Başlıkları
[dc.subject]
multilayer perceptron neural network
Konu Başlıkları
[dc.subject]
histogram
Haklar
[dc.rights]
info:eu-repo/semantics/closedAccess
Alternatif Başlık
[dc.title.alternative]
Eşit ağırlıklı ayrıklaştırma yöntemine dayalı yeni bir özellik çıkarma yaklaşımı ve yapay sinir ağı kullanarak epileptik atak testi
ISSN
[dc.identifier.issn]
1300-1884
İlk Sayfa Sayısı
[dc.identifier.startpage]
575
Son Sayfa Sayısı
[dc.identifier.endpage]
580
Dergi Adı
[dc.relation.journal]
JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY
Dergi Sayısı
[dc.identifier.issue]
3
Dergi Cilt Bilgisi
[dc.identifier.volume]
26
Tek Biçim Adres
[dc.identifier.uri]
https://hdl.handle.net/20.500.12628/2559
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signals approach method epileptic extraction feature seizure histograms detection inputs hidden obtained processing signal neurons detected biomedical seizures classification Consequently success ratios offered problem result showed classifier linear without discretization neural perceptron multilayer features statistical
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