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

Yildiz, Abdulnasir; Zan, Hasan; Said, Sherif

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

dc.authorid	0000-0002-8156-016X	en_US
dc.authorwosid	AAF-2775-2019	en_US
dc.contributor.author	Yildiz, Abdulnasir
dc.contributor.author	Zan, Hasan
dc.contributor.author	Said, Sherif
dc.date.accessioned	2023-12-18T15:07:07Z
dc.date.available	2023-12-18T15:07:07Z
dc.date.issued	2021	en_US
dc.department	MAÜ, Meslek Yüksekokulları, Mardin Meslek Yüksekokulu, Elektrik ve Enerji Bölümü	en_US
dc.description.abstract	Electrical 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.doi	https://doi.org/10.1016/j.bspc.2021.102720	en_US
dc.identifier.uri	https://doi.org/10.1016/j.bspc.2021.102720
dc.identifier.uri	https://hdl.handle.net/20.500.12514/4978
dc.identifier.volume	68	en_US
dc.identifier.wosquality	Q2	en_US
dc.institutionauthor	Zan, Hasan
dc.language.iso	en	en_US
dc.relation.ispartof	Biomedical Signal Processing and Control	en_US
dc.relation.publicationcategory	Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı	en_US
dc.rights	info:eu-repo/semantics/closedAccess	en_US
dc.title	Classification and analysis of epileptic EEG recordings using convolutional neural network and class activation mapping	en_US
dc.type	Article	en_US