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From Sound Perception to Automatic Detection of Schizophrenia: An EEG-Based Deep Learning Approach

2022-02-17Journal article Open access
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dc.contributor.authorBarros, Carla
dc.contributor.authorRoach, Brian
dc.contributor.authorFord, Judith M.
dc.contributor.authorPinheiro, Ana P.
dc.contributor.authorSilva, Carlos
dc.date.accessioned2024-02-23T10:18:04Z
dc.date.available2024-02-23T10:18:04Z
dc.date.issued2022-02-17
dc.date.updated2024-01-25T23:27:24Z
dc.description.abstractDeep learning techniques have been applied to electroencephalogram (EEG) signals, with promising applications in the field of psychiatry. Schizophrenia is one of the most disabling neuropsychiatric disorders, often characterized by the presence of auditory hallucinations. Auditory processing impairments have been studied using EEG-derived event-related potentials and have been associated with clinical symptoms and cognitive dysfunction in schizophrenia. Due to consistent changes in the amplitude of ERP components, such as the auditory N100, some have been proposed as biomarkers of schizophrenia. In this paper, we examine altered patterns in electrical brain activity during auditory processing and their potential to discriminate schizophrenia and healthy subjects. Using deep convolutional neural networks, we propose an architecture to perform the classification based on multi-channels auditory-related EEG single-trials, recorded during a passive listening task. We analyzed the effect of the number of electrodes used, as well as the laterality and distribution of the electrical activity over the scalp. Results show that the proposed model is able to classify schizophrenia and healthy subjects with an average accuracy of 78% using only 5 midline channels (Fz, FCz, Cz, CPz, and Pz). The present study shows the potential of deep learning methods in the study of impaired auditory processing in schizophrenia with implications for diagnosis. The proposed design can provide a base model for future developments in schizophrenia research.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationBarros, C., Roach, B., Ford, J. M., Pinheiro, A. P., & Silva, C. A. (2022). From sound perception to automatic detection of schizophrenia: an EEG-based deep learning approach. Frontiers in Psychiatry, 12, 813460. https://doi.org/10.3389/fpsyt.2021.813460pt_PT
dc.identifier.doi10.3389/fpsyt.2021.813460pt_PT
dc.identifier.issn1664-0640
dc.identifier.slugcv-prod-2890267
dc.identifier.urihttp://hdl.handle.net/10451/62855
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherFrontiers Mediapt_PT
dc.relationI predict, therefore I do not hallucinate: a longitudinal study testing the neurophysiological underpinnings of auditory verbal hallucinations
dc.relationMicroelectromechanical Systems Research Unit
dc.relation.publisherversionhttps://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2021.813460/fullpt_PT
dc.subjectAuditory processingpt_PT
dc.subjectConvolutional neural networkpt_PT
dc.subjectDeep learningpt_PT
dc.subjectEEGpt_PT
dc.subjectSchizophreniapt_PT
dc.titleFrom Sound Perception to Automatic Detection of Schizophrenia: An EEG-Based Deep Learning Approachpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleI predict, therefore I do not hallucinate: a longitudinal study testing the neurophysiological underpinnings of auditory verbal hallucinations
oaire.awardTitleMicroelectromechanical Systems Research Unit
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/Projetos de Investigação Científica e Desenvolvimento Tecnológico - 2014 (P2020)/PTDC%2FMHC-PCN%2F0101%2F2014/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FEEA%2F04436%2F2019/PT
oaire.citation.titleFrontiers in Psychiatrypt_PT
oaire.citation.volume12pt_PT
oaire.fundingStreamProjetos de Investigação Científica e Desenvolvimento Tecnológico - 2014 (P2020)
oaire.fundingStream6817 - DCRRNI ID
person.familyNameBarros
person.familyNameRoach
person.familyNamePinheiro
person.familyNameSilva
person.givenNameCarla
person.givenNameBrian
person.givenNameAna
person.givenNameCarlos
person.identifier287434
person.identifier646262
person.identifier.ciencia-idC910-FC13-D0E3
person.identifier.ciencia-id6E1C-0855-073D
person.identifier.ciencia-id5F15-7B55-99FD
person.identifier.orcid0000-0003-2330-398X
person.identifier.orcid0000-0002-3264-1465
person.identifier.orcid0000-0002-7981-3682
person.identifier.orcid0000-0002-1015-5095
person.identifier.ridJ-1190-2014
person.identifier.scopus-author-id55775021600
person.identifier.scopus-author-id35079329100
person.identifier.scopus-author-id36172795600
person.identifier.scopus-author-id56325790600
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.cv.cienciaid6E1C-0855-073D | Ana Pinheiro
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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