Exploring deep residual network based features for automatic schizophrenia detection from EEG

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dc.contributor.authorSiuly, Siuly
dc.contributor.authorGuo, Yanhui
dc.contributor.authorAlcin, Omer Faruk
dc.contributor.authorLi, Yan
dc.contributor.authorWen, Peng
dc.contributor.authorWang, Hua
dc.date.accessioned2026-06-19T06:40:58Z
dc.date.available2026-06-19T06:40:58Z
dc.date.issued2023
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractSchizophrenia is a severe mental illness which can cause lifelong disability. Most recent studies on the Electroencephalogram (EEG)-based diagnosis of schizophrenia rely on bespoke/hand-crafted feature extraction techniques. Traditional manual feature extraction methods are time-consuming, imprecise, and have a limited ability to balance accuracy and efficiency. Addressing this issue, this study introduces a deep residual network (deep ResNet) based feature extraction design that can automatically extract representative features from EEG signal data for identifying schizophrenia. This proposed method consists of three stages: signal pre-processing by average filtering method, extraction of hidden patterns of EEG signals by deep ResNet, and classification of schizophrenia by softmax layer. To assess the performance of the obtained deep features, ResNet softmax classifier and also several machine learning (ML) techniques are applied on the same feature set. The experimental results for a Kaggle schizophrenia EEG dataset show that the deep features with support vector machine classifier could achieve the highest performances (99.23% accuracy) compared to the ResNet classifier. Furthermore, the proposed model performs better than the existing approaches. The findings suggest that our proposed strategy has capability to discover important biomarkers for automatic diagnosis of schizophrenia from EEG, which will aid in the development of a computer assisted diagnostic system by specialists.
dc.description.sponsorshipCAUL
dc.description.sponsorshipOpen Access funding enabled and organized by CAUL and its Member Institutions. The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
dc.identifier.doi10.1007/s13246-023-01225-8
dc.identifier.endpage574
dc.identifier.issn2662-4729
dc.identifier.issn2662-4737
dc.identifier.issue2
dc.identifier.orcid0000-0002-8465-0996
dc.identifier.orcid0000-0003-2491-0546
dc.identifier.orcid0000-0002-2917-3736
dc.identifier.orcid0000-0003-1814-9682
dc.identifier.pmid36947384
dc.identifier.scopus2-s2.0-85150722070
dc.identifier.scopusqualityQ2
dc.identifier.startpage561
dc.identifier.urihttps://doi.org/10.1007/s13246-023-01225-8
dc.identifier.urihttps://hdl.handle.net/20.500.12899/6010
dc.identifier.volume46
dc.identifier.wosWOS:000954674200001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofPhysical and Engineering Sciences in Medicine
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260612
dc.subjectElectroencephalogram (Eeg) Signal
dc.subjectSchizophrenia Detection
dc.subjectDeep Residual Network
dc.subjectFeature Extraction
dc.subjectClassification
dc.titleExploring deep residual network based features for automatic schizophrenia detection from EEG
dc.typeArticle

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