Analysis of epileptic iEEG data by applying convolutional neural networks to low-frequency scalograms
Yükleniyor...
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE-Institute of Electrical Electronics Engineers INC.
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
In this paper, Convolutional Neural Networks (CNN) method was applied to low frequency scalograms in order to contribute to the development of diagnostic and early diagnosis systems of epileptic intracranial EEG (iEEG) signals of brain dynamics at preictal, ictal, and postictal states, and to achieve results that will be the basis for determining the pathological conditions of iEEG signals. As part of this study, iEEG data obtained from epileptic subjects were first decomposed into their subbands by discrete wavelet transformation, and then Shannon entropy was applied to these five subbands (delta, theta, alpha, beta, and gamma). The results obtained made us observe that the delta subband entropy value is lower than other subband entropy values. A low entropy value means that the data is less chaotic. A low degree of chaos means better predictability. Within this context, scalogram images of low-frequency delta subband were obtained at preictal, ictal, and postictal stages and treated with the CNN method, and consequently, a 93.33% accuracy rate was obtained.
Açıklama
WOS:000730450200001
Anahtar Kelimeler
Electroencephalography, Convolutional neural networks, Brain modeling, Entropy, Epilepsy, Deep learning, Data modelsIntracranial electroencephalogram (iEEG), Epilepsy, Entropy, Convolutional neural network (CNN), Delta subband
Kaynak
IEEE Access
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
9
Sayı
Künye
Bayram, M. ve Arserim, M.A. (2021). Analysis of epileptic iEEG data by applying convolutional neural networks to low-frequency scalograms. IEEE Access, 9, 162520-162529
