Show simple item record



dc.contributor.authorCejnek M.
dc.contributor.authorVyšata O.
dc.contributor.authorVališ M.
dc.contributor.authorBukovsky I.
dc.date.accessioned2021-11-19T12:14:39Z
dc.date.available2021-11-19T12:14:39Z
dc.date.issued2021
dc.identifierV3S-351487
dc.identifier.citationCEJNEK, M., et al. Novelty detection-based approach for Alzheimer’s disease and mild cognitive impairment diagnosis from EEG. Medical and Biological Engineering and Computing. 2021, 59(11-12), 2287-2296. ISSN 0140-0118. DOI 10.1007/s11517-021-02427-6.
dc.identifier.issn0140-0118 (print)
dc.identifier.urihttp://hdl.handle.net/10467/98536
dc.description.abstractAlzheimer’s disease is diagnosed via means of daily activity assessment. The EEG recording evaluation is a supporting tool that can assist the practitioner to recognize the illness, especially in the early stages. This paper presents a new approach for detecting Alzheimer’s disease and potentially mild cognitive impairment according to the measured EEG records. The proposed method evaluates the amount of novelty in the EEG signal as a feature for EEG record classification. The novelty is measured from the parameters of EEG signal adaptive filtration. A linear neuron with gradient descent adaptation was used as the filter in predictive settings. The extracted feature (novelty measure) is later classified to obtain Alzheimer’s disease diagnosis. The proposed approach was cross-validated on a dataset containing EEG records of 59 patients suffering from Alzheimer’s disease; seven patients with mild cognitive impairment (MCI) and 102 controls. The results of cross-validation yield 90.73% specificity and 89.51% sensitivity. The proposed method of feature extraction from EEG is completely new and can be used with any classifier for the diagnosis of Alzheimer’s disease from EEG records.eng
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherPeter Peregrinus
dc.relation.ispartofMedical and Biological Engineering and Computing
dc.rightsCreative Commons Attribution (CC BY) 4.0
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectNovelty detectioneng
dc.subjectAlzheimer's diseaseeng
dc.subjectGradiend descenteng
dc.subjectEEGeng
dc.subjectLinear Neural Uniteng
dc.titleNovelty detection-based approach for Alzheimer’s disease and mild cognitive impairment diagnosis from EEGeng
dc.typečlánek v časopisecze
dc.typejournal articleeng
dc.identifier.doi10.1007/s11517-021-02427-6
dc.rights.accessopenAccess
dc.identifier.wos000696775600001
dc.type.statusPeer-reviewed
dc.type.versionpublishedVersion
dc.identifier.scopus2-s2.0-85115045182


Files in this item


This item appears in the following Collection(s)

Show simple item record

Creative Commons Attribution (CC BY) 4.0
Except where otherwise noted, this item's license is described as Creative Commons Attribution (CC BY) 4.0