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Towards automatic EEG cyclic alternating pattern analysis: a systematic review

2023Journal article Open access
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dc.contributor.authorMendonça, Fábio
dc.contributor.authorMostafa, Sheikh Shanawaz
dc.contributor.authorDias, Fernando Morgado
dc.contributor.authorRavelo-García, Antonio G.
dc.contributor.authorRosenzweig, Ivana
dc.date.accessioned2024-04-24T16:02:43Z
dc.date.available2024-04-24T16:02:43Z
dc.date.issued2023
dc.description.abstractThis study conducted a systematic review to determine the feasibility of automatic Cyclic Alternating Pattern (CAP) analysis. Specifically, this review followed the 2020 Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines to address the formulated research question: is automatic CAP analysis viable for clinical applica tion? From the identified 1,280 articles, the review included 35 studies that proposed various methods for examining CAP, including the classification of A phase, their subtypes, or the CAP cycles. Three main trends were observed over time regarding A phase classification, starting with mathematical models or features classified with a tuned threshold, followed by using conventional machine learning models and, recently, deep learning models. Regarding the CAP cycle detection, it was observed that most studies employed a finite state machine to implement the CAP scoring rules, which depended on an initial A phase classifier, stressing the importance of developing suitable A phase detection models. The assessment of A-phase subtypes has proven challenging due to various approaches used in the state-of-the-art for their detection, ranging from multiclass models to creating a model for each subtype. The review provided a positive answer to the main research question, concluding that automatic CAP analysis can be reliably performed. The main recommended research agenda involves validating the proposed methodologies on larger datasets, including more subjects with sleep-related disorders, and providing the source code for independent confirmationpt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1007/s13534-023-00303-wpt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/5648
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringerpt_PT
dc.relationLaboratory of Robotics and Engineering Systems
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectA phasept_PT
dc.subjectAutomatic classificationpt_PT
dc.subjectCAPpt_PT
dc.subjectEEGpt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.titleTowards automatic EEG cyclic alternating pattern analysis: a systematic reviewpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleLaboratory of Robotics and Engineering Systems
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50009%2F2020/PT
oaire.citation.endPage291pt_PT
oaire.citation.issue3pt_PT
oaire.citation.startPage273pt_PT
oaire.citation.titleBiomedical Engineering Letterspt_PT
oaire.citation.volume13pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameSilva Mendonça
person.familyNameMostafa
person.familyNameMorgado-Dias
person.familyNameRavelo-García
person.familyNameRosenzweig
person.givenNameFábio Rúben
person.givenNameSheikh Shanawaz
person.givenNameFernando
person.givenNameAntonio G.
person.givenNameIvana
person.identifier34497
person.identifier.ciencia-id7F1E-8AE9-3098
person.identifier.ciencia-idEE14-BEB3-F82B
person.identifier.ciencia-id7B14-DF07-AA6D
person.identifier.orcid0000-0002-5107-3248
person.identifier.orcid0000-0002-7677-0971
person.identifier.orcid0000-0001-7334-3993
person.identifier.orcid0000-0002-8512-965X
person.identifier.orcid0000-0003-2152-9694
person.identifier.ridN-9228-2015
person.identifier.scopus-author-id55489640900
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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