Detection of epileptic seizure using EEG signals analysis based on deep learning techniques

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dc.contributor.authorAbdulwahhab, Ali H.
dc.contributor.authorAbdulaal, Alaa Hussein
dc.contributor.authorThary Al-Ghrairi, Assad H.
dc.contributor.authorMohammed, Ali Abdulwahhab
dc.contributor.authorValizadeh, Morteza
dc.date.accessioned2024-03-30T10:12:09Z
dc.date.available2024-03-30T10:12:09Z
dc.date.issued2024en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractThe brain neurons' electrical activities represented by Electroencephalogram (EEG) signals are the most common data for diagnosing Epilepsy seizure, which is considered a chronic nervous disorder that cannot be controlled medically using surgical operation or medications with more than 40 % of Epilepsy seizure case. With the progress and development of artificial intelligence and deep learning techniques, it becomes possible to detect these seizures over the observation of the non-stationary-dynamic EEG signals, which contain important information about the mental state of patients. This paper provides a concerted deep machine learning model consisting of two simultaneous techniques detecting the activity of epileptic seizures using EEG signals. The time-frequency image of EEG waves and EEG raw waves are used as input components for the convolution neural network (CNN) and recurrent neural network (RNN) with long- and short-term memory (LSTM). Two processing signal methods have been used, Short-Time Fourier Transform (STFT) and Continuous Wavelet Transformation (CWT), have been used for generating spectrogram and scalogram images with sizes of 77 × 75 and 32 × 32, respectively. The experimental results showed a detection accuracy of 99.57 %, 99.57 % using CWT Scalograms, and 99.26 %, 97.12 % using STFT spectrograms as CNN input for the Bonn University dataset and the CHB-MIT dataset, respectively. Thus, the proposed models provide the ability to detect epileptic seizures with high success compared to previous studies.en_US
dc.identifier.citationAbdulwahhab, A. H., Abdulaal, A. H., Thary Al-Ghrairi, A. H., Mohammed, A. A., Valizadeh, M. (2024). Detection of epileptic seizure using EEG signals analysis based on deep learning techniques. Chaos, Solitons and Fractals, 181. 10.1016/j.chaos.2024.114700en_US
dc.identifier.issn0960-0779
dc.identifier.scopus2-s2.0-85187239300
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4649
dc.identifier.volume181en_US
dc.identifier.wosWOS:001209349700001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAbdulwahhab, Ali H.
dc.language.isoen
dc.publisherElsevieren_US
dc.relation.ispartofChaos, Solitons and Fractals
dc.relation.isversionof10.1016/j.chaos.2024.114700en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConvolutional neural networken_US
dc.subjectDeep learningen_US
dc.subjectEEGen_US
dc.subjectElectroencephalogramen_US
dc.subjectEpileptic seizureen_US
dc.subjectRecurrent neural networken_US
dc.titleDetection of epileptic seizure using EEG signals analysis based on deep learning techniques
dc.typeArticle

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