Epilepsy diagnosis using probability density functions of EEG signals

In this paper, the equal frequency discretization (EFD) based probability density approach was proposed to be used in the diagnosis of epilepsy from electroencephalogram (EEG) signals. For this aim, EEG signals were decomposed by using the discrete wavelet discretization (DWT) method into subbands, the coefficients in each subband were discretized to several intervals by EFD method, and the probability density of each subband of each EEG segment was computed according to the number of coefficients in discrete intervals. Then, two probability density functions were defined by means of the curve fitting over the probability densities of the sets of both healthy subjects and epilepsy patients. EEG signals were classified by applying the mean square error (MSE) criterion to these functions. The result of the classification was evaluated by using the ROC analysis, which indicated 82.50% success in the diagnosis of epilepsy. As a result, the EFD based probability density approach may be considered as an alternative way to diagnose epilepsy disease on EEG signals. © 2011 IEEE.

Dergi Adı INISTA 2011 - 2011 International Symposium on INnovations in Intelligent SysTems and Applications
Sayfalar 626 - 630
Yayın Yılı 2011
Eser Adı
[dc.title]
Epilepsy diagnosis using probability density functions of EEG signals
Yazar
[dc.contributor.author]
Orhan, Umut
Yazar
[dc.contributor.author]
Hekim, Mahmut
Yazar
[dc.contributor.author]
Özer, Mahmut
Yazar
[dc.contributor.author]
Provaznik, Ivo
Yayın Yılı
[dc.date.issued]
2011
Yayın Türü
[dc.type]
proceedings
Açıklama
[dc.description]
TUBITAK;IEEE
Açıklama
[dc.description]
2011 International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2011 -- 15 June 2011 through 18 June 2011 -- Istanbul-Kadikoy -- 85879
Özet
[dc.description.abstract]
In this paper, the equal frequency discretization (EFD) based probability density approach was proposed to be used in the diagnosis of epilepsy from electroencephalogram (EEG) signals. For this aim, EEG signals were decomposed by using the discrete wavelet discretization (DWT) method into subbands, the coefficients in each subband were discretized to several intervals by EFD method, and the probability density of each subband of each EEG segment was computed according to the number of coefficients in discrete intervals. Then, two probability density functions were defined by means of the curve fitting over the probability densities of the sets of both healthy subjects and epilepsy patients. EEG signals were classified by applying the mean square error (MSE) criterion to these functions. The result of the classification was evaluated by using the ROC analysis, which indicated 82.50% success in the diagnosis of epilepsy. As a result, the EFD based probability density approach may be considered as an alternative way to diagnose epilepsy disease on EEG signals. © 2011 IEEE.
Kayıt Giriş Tarihi
[dc.date.accessioned]
2019-12-23
Açık Erişim Tarihi
[dc.date.available]
2019-12-23
Yayın Dili
[dc.language.iso]
eng
Konu Başlıkları
[dc.subject]
curve fitting
Konu Başlıkları
[dc.subject]
EEG signals
Konu Başlıkları
[dc.subject]
epilepsy
Konu Başlıkları
[dc.subject]
equal frequency discretization
Konu Başlıkları
[dc.subject]
mean square error
Konu Başlıkları
[dc.subject]
probability density
Konu Başlıkları
[dc.subject]
wavelet transform
Künye
[dc.identifier.citation]
Orhan, U., Hekim, M., Ozer, M. ve Provaznik, I. (2011). Epilepsy diagnosis using probability density functions of EEG signals. 2011 International Symposium on Innovations in Intelligent Systems and Applications içinde (ss. 626–630). doi:10.1109/INISTA.2011.5946171
Haklar
[dc.rights]
info:eu-repo/semantics/closedAccess
İlk Sayfa Sayısı
[dc.identifier.startpage]
626
Son Sayfa Sayısı
[dc.identifier.endpage]
630
Dergi Adı
[dc.relation.journal]
INISTA 2011 - 2011 International Symposium on INnovations in Intelligent SysTems and Applications
Tek Biçim Adres
[dc.identifier.uri]
https://dx.doi.org/10.1109/INISTA.2011.5946171
Tek Biçim Adres
[dc.identifier.uri]
https://hdl.handle.net/20.500.12628/5649
Görüntülenme Sayısı ( Şehir )
Görüntülenme Sayısı ( Ülke )
Görüntülenme Sayısı ( Zaman Dağılımı )
Görüntülenme
36
09.12.2022 tarihinden bu yana
İndirme
1
09.12.2022 tarihinden bu yana
Son Erişim Tarihi
09 Şubat 2024 12:12
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probability signals epilepsy density functions coefficients method discrete approach subband result intervals discretization diagnosis square analysis evaluated criterion classification indicated alternative disease diagnose considered classified success applying according patients electroencephalogram wavelet decomposed subbands proposed frequency
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