New approaches to epileptic seizure prediction based on EEG signals using hybrid CNNs

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dc.authorid0009-0002-4501-1662
dc.authorid0000-0002-8061-8059
dc.contributor.authorNour, Majid
dc.contributor.authorArabacı, Bahadır
dc.contributor.authorÖcal, Hakan
dc.contributor.authorPolat, Kemal
dc.date.accessioned2024-09-25T19:59:58Z
dc.date.available2024-09-25T19:59:58Z
dc.date.issued2024
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü en_US
dc.description.abstractThis study employs the University of Bonn Dataset to address the importance of frequency information in EEG data and introduces a methodology utilising the short-time Fourier transform. The proposed method transforms conventional 1D EEG signals into informative 2D spectrograms, offering an approach for advancing the detection of neurological diseases. Integrating advanced CNN architectures with the conversion of EEG signals into 2D spectrograms forms the foundation of our proposed methodology. The 1D CNN model utilised in this study demonstrates exceptional performance metrics, achieving a specificity of 0.996, an overall test accuracy of 0.991, a sensitivity of 0.987, and an F1 score of 0.989. Shifting to the 2D approach discloses a slight reduction in accuracy to 0.987, sensitivity of 0.976, specificity of 0.988, and an F1 score of 0.97. This analysis provides nuanced insights into the performance of 1D and 2D CNNs, clarifying respective strengths in the context of neurological disease detection.en_US
dc.description.sponsorshipInstitutional Fund Projects [IFPIP: 1038-135-1443]; Ministry of Education; King Abdulaziz University, DSR, Jeddah, Saudi Arabiaen_US
dc.description.sponsorshipThis research work was funded by Institutional Fund Projects under Grant No. (IFPIP: 1038-135-1443). The authors gratefully acknowledge technical and financial support provided by the Ministry of Education and King Abdulaziz University, DSR, Jeddah, Saudi Arabia.en_US
dc.identifier.doi10.1504/IJIEI.2024.137706
dc.identifier.issn1758-8715
dc.identifier.issn1758-8723
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85189666246en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1504/IJIEI.2024.137706
dc.identifier.urihttps://hdl.handle.net/20.500.12491/14006
dc.identifier.volume12en_US
dc.identifier.wosWOS:001196022700001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.institutionauthorid0000-0003-1840-9958
dc.language.isoenen_US
dc.publisherInderscience Enterprises Ltden_US
dc.relation.ispartofInternational Journal of Intelligent Engineering Informaticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzYK_20240925en_US
dc.subjectSeizure Predictionen_US
dc.subjectEpilepsyen_US
dc.subjectEEG Signalsen_US
dc.subject1D Convolutional Neural Networken_US
dc.subjectDeep Learningen_US
dc.subjectClassificationen_US
dc.titleNew approaches to epileptic seizure prediction based on EEG signals using hybrid CNNsen_US
dc.typeArticleen_US

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