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A novel approach to automatic seizure detection using computer vision and independent component analysis

2023Artigo científico Acesso restrito
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dc.contributor.authorGarção, Vicente
dc.contributor.authorAbreu, Mariana
dc.contributor.authorPeralta, Ana
dc.contributor.authorBentes, Carla
dc.contributor.authorFred, Ana
dc.contributor.authorSilva, Hugo
dc.date.accessioned2023-10-16T14:20:44Z
dc.date.available2023-10-16T14:20:44Z
dc.date.issued2023
dc.description© 2023 International League Against Epilepsy.pt_PT
dc.description.abstractObjective: Epilepsy is a neurological disease that affects ~50 million people worldwide, 30% of which have refractory epilepsy and recurring seizures, which may contribute to higher anxiety levels and poorer quality of life. Seizure detection may contribute to addressing some of the challenges associated with this condition, by providing information to health professionals regarding seizure frequency, type, and/or location in the brain, thereby improving diagnostic accuracy and medication adjustment, and alerting caregivers or emergency services of dangerous seizure episodes. The main focus of this work was the development of an accurate video-based seizure-detection method that ensured unobtrusiveness and privacy preservation, and provided novel approaches to reduce confounds and increase reliability. Methods: The proposed approach is a video-based seizure-detection method based on optical flow, principal component analysis, independent component analysis, and machine learning classification. This method was tested on a set of 21 tonic-clonic seizure videos (5-30 min each, total of 4 h and 36 min of recordings) from 12 patients using leave-one-subject-out cross-validation. Results: High accuracy levels were observed, namely a sensitivity and specificity of 99.06% ± 1.65% at the equal error rate and an average latency of 37.45 ± 1.31 s. When compared to annotations by health care professionals, the beginning and ending of seizures was detected with an average offset of 9.69 ± 0.97 s. Significance: The video-based seizure-detection method described herein is highly accurate. Moreover, it is intrinsically privacy preserving, due to the use of optical flow motion quantification. In addition, given our novel independence-based approach, this method is robust to different lighting conditions, partial occlusions of the patient, and other movements in the video frame, thereby setting the base for accurate and unobtrusive seizure detection.pt_PT
dc.description.sponsorshipThis work was partially funded by Fundação para a Ciência e Tecnologia (FCT) under the grants 2021.08297.BD and UI/BD/154378/2023, by the FCT/Ministério da Ciência, Tecnologia e Ensino Superior (MCTES) through national funds and when applicable co-funded by EU funds under the project UIDB/50008/2020, and with the support of Centro Hospitalar Universitário Lisboa Norte, EPE, under the project “Pre_EpiSeizures”.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationEpilepsia. 2023 Sep;64(9):2472-2483pt_PT
dc.identifier.doi10.1111/epi.17677pt_PT
dc.identifier.eissn1528-1167
dc.identifier.issn0013-9580
dc.identifier.urihttp://hdl.handle.net/10451/59790
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherWileypt_PT
dc.relation2021.08297.BDpt_PT
dc.relationInvisibles for Health Monitoring in Chronic Conditions
dc.relationInstituto de Telecomunicações
dc.relation.publisherversionhttps://onlinelibrary.wiley.com/journal/15281167pt_PT
dc.subjectEpilepsypt_PT
dc.subjectMachine learningpt_PT
dc.subjectSignal processingpt_PT
dc.subjectVideo monitoringpt_PT
dc.titleA novel approach to automatic seizure detection using computer vision and independent component analysispt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardNumberUI/BD/154378/2023
oaire.awardNumberUIDB/50008/2020
oaire.awardTitleInvisibles for Health Monitoring in Chronic Conditions
oaire.awardTitleInstituto de Telecomunicações
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/OE/UI%2FBD%2F154378%2F2023/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50008%2F2020/PT
oaire.citation.endPage2483pt_PT
oaire.citation.issue9pt_PT
oaire.citation.startPage2472pt_PT
oaire.citation.titleEpilepsiapt_PT
oaire.citation.volume64pt_PT
oaire.fundingStreamOE
oaire.fundingStream6817 - DCRRNI ID
person.familyNameMayer Mendonça De Lima Garção
person.familyNameLima Lourinho Teixeira de Abreu
person.familyNamePeralta
person.familyNameBentes
person.familyNameNobre Fred
person.familyNamePlácido da Silva
person.givenNameVicente
person.givenNameMariana
person.givenNameAna Rita
person.givenNameCarla
person.givenNameAna Luisa
person.givenNameHugo Humberto
person.identifier.ciencia-id8E1B-1D7D-2237
person.identifier.ciencia-idFF1F-BCE1-43AD
person.identifier.ciencia-id1D1D-D037-54D8
person.identifier.ciencia-id7D1C-D5DD-C579
person.identifier.ciencia-id5F19-AD83-CE11
person.identifier.ciencia-idB415-0557-402B
person.identifier.orcid0000-0002-2945-3949
person.identifier.orcid0000-0002-9340-6610
person.identifier.orcid0000-0001-9449-431X
person.identifier.orcid0000-0003-2399-7678
person.identifier.orcid0000-0003-1320-5024
person.identifier.orcid0000-0001-6764-8432
person.identifier.scopus-author-id6603395191
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.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
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