Classification and analysis of epileptic EEG recordings using convolutional neural network and class activation mapping

dc.authorid0000-0002-8156-016Xen_US
dc.authorwosidAAF-2775-2019en_US
dc.contributor.authorYildiz, Abdulnasir
dc.contributor.authorZan, Hasan
dc.contributor.authorSaid, Sherif
dc.date.accessioned2023-12-18T15:07:07Z
dc.date.available2023-12-18T15:07:07Z
dc.date.issued2021en_US
dc.departmentMAÜ, Meslek Yüksekokulları, Mardin Meslek Yüksekokulu, Elektrik ve Enerji Bölümüen_US
dc.description.abstractElectrical bio-signals have the potential to be used in different applications due to their hidden nature and their ability to facilitate liveness detection. This paper investigates the feasibility of using the Convolutional Neural Network (CNN) to classify and analyze electroencephalogram (EEG) data with their time-frequency representations and class activation mapping (CAM) to detect epilepsy disease. Several types of pre-trained CNNs are employed for a multi-class classification task (AlexNet, GoogLeNet, ResNet-18, and ResNet-50) and their results are compared. Also, a novel convolutional neural network architecture comprised of two horizontally concatenated GoogLeNets is proposed with two inputs scalograms and spectrogram of the eplictic EEG signal. Four segment lengths (4097, 2048, 1024, and 512 sampling points) with three time-frequency representations (short-time Fourier, Wavelet, and Hilbert-Huang transform) are statistically evaluated. The dataset used in this research is collected at the University of Bonn. The dataset is reorganized as normal, interictal, and ictal. The maximum achieved accuracies for 4097, 2048, 1024, and 512 sampling points are 100 %, 100 %, 100 %, and 99.5 % respectively. The CAM method is used to analyze discriminative regions of time-frequency representations of EEG segments and networks' decisions. This method showed CNN models used different time and frequency regions of input images for each class with correct and incorrect predictions.en_US
dc.identifier.doihttps://doi.org/10.1016/j.bspc.2021.102720en_US
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2021.102720
dc.identifier.urihttps://hdl.handle.net/20.500.12514/4978
dc.identifier.volume68en_US
dc.identifier.wosqualityQ2en_US
dc.institutionauthorZan, Hasan
dc.language.isoenen_US
dc.relation.ispartofBiomedical Signal Processing and Controlen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.titleClassification and analysis of epileptic EEG recordings using convolutional neural network and class activation mappingen_US
dc.typeArticleen_US

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