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Sleep stage detection: a clinical validation study of a custom-built single-channel in-ear EEG sensor

2024-10-25Conference object Restricted access
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dc.contributor.authorBorges, Daniel Filipe
dc.contributor.authorSoares, Joana I.
dc.contributor.authorSilva, Heloísa
dc.contributor.authorFelgueiras, João
dc.contributor.authorBatista, Carla
dc.contributor.authorFerreira, Simão
dc.contributor.authorRocha, Nuno
dc.contributor.authorLeal, Alberto
dc.date.accessioned2024-10-28T16:04:10Z
dc.date.available2024-10-28T16:04:10Z
dc.date.issued2024-10-25
dc.description.abstractIntroduction:Sleep is vital for health. It has regenerative and protective functions, and its disruption reduces the quality of life and increases susceptibility to disease. During sleep, there is a cyclicity of distinct phases that are studied using polysomnography (PSG), a costly and technically demanding method that compromises the quality of natural sleep. The search for simpler devices for recording biological signals at home addresses some of these issues. Objective: To clinically validate a custom-built single-channel in-ear EEG sensor for sleep classification by assessing various sleep metrics and staging decisions with simultaneously recorded PSG. Methods: Prospective cross-sectional study with 28 participants, divided into two groups: healthy volunteers and clinical patients. In both groups, PSG, individual in-ear EEG- with two different electrode configurations- and actigraphic recordings (only in the healthy group) were performed simultaneously for a whole night. Statistical analysis focussed on the four main sleep metrics: TRT (total recording time), TST (total sleep time), SE (sleep efficiency), SL (sleep latency) and the 5-class classifications (wakefulness, N1, N2, N3 and REM sleep). This included correlation analyses between methods and Bland-Altman plots, Cohen’s K coefficient, and confusion matrices aiming 30-second epoch-wise agreement with an automatic sleep classification algorithm using visual sleep classification by an ERSR-certified human expert as the gold standard according to current AASM guidelines. Results: The analysed sleep data comprised 30960 epochs. The correlation analysis revealed strong positive correlations (0.90) for all variables for the in-ear sensor. The Bland-Altman plots show a high level of agreement and consistency (+- 1.87 SD), with minimal bias between methods. The average kappa values (0.75) and the confusion matrices with each method's sensitivity and specificity also show a very high level of concordance.Conclusions: In both groups, the in-ear EEG sensor showed strong correlation, agreement and reliability with the gold standard, supporting accurate sleep classification.pt_PT
dc.description.versioninfo:eu-repo/semantics/acceptedVersionpt_PT
dc.identifier.citationBorges, D. F., Soares, J. I., Silva, H., Felgueiras, J., Batista, C., Ferreira, S., Barbosa Rocha, N., & Leal, A. (2024, outubro). Sleep stage detection: A clinical validation study of a custom-built single-channel in-ear EEG sensor. Congresso Nacional de Neurologia, Centro de Congressos do Hotel Cascais Miragem.
dc.identifier.urihttp://hdl.handle.net/10400.22/26270
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSociedade Portuguesa de Neurologiapt_PT
dc.subjectSleeppt_PT
dc.subjectPolysomnographypt_PT
dc.titleSleep stage detection: a clinical validation study of a custom-built single-channel in-ear EEG sensorpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceCascais, Portugalpt_PT
person.familyNameBorges
person.familyNameSoares
person.familyNameBatista
person.familyNameFerreira
person.familyNameRocha
person.familyNameRodrigues Leal
person.givenNameDaniel Filipe
person.givenNameJoana I.
person.givenNameCarla
person.givenNameSimão
person.givenNameNuno
person.givenNameAlberto João
person.identifier3235598
person.identifier2680796
person.identifier192266
person.identifier.ciencia-id0217-87F9-58DF
person.identifier.ciencia-idB81F-1A58-0242
person.identifier.ciencia-idB11E-1BD3-5F34
person.identifier.ciencia-idAE16-A494-5F8B
person.identifier.ciencia-id5F15-94D8-F634
person.identifier.orcid0000-0003-0189-7908
person.identifier.orcid0000-0002-3549-6873
person.identifier.orcid0000-0002-6453-8665
person.identifier.orcid0000-0001-8233-2217
person.identifier.orcid0000-0002-3139-2786
person.identifier.orcid0000-0002-3178-2530
person.identifier.ridJVO-1831-2024
person.identifier.ridM-9821-2013
person.identifier.scopus-author-id57912703700
person.identifier.scopus-author-id57359736400
person.identifier.scopus-author-id32867975300
rcaap.rightsclosedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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