Lacsogram: a new EEG tool to diagnose Alzheimer's disease

Rodrigues, Pedro M.; Bispo, Bruno; Garrett, Carolina; Alves, Dílio; Teixeira, João Paulo; Freitas, Diamantino Silva

Publication

Lacsogram: a new EEG tool to diagnose Alzheimer's disease

2021Journal article

dc.contributor.author	Rodrigues, Pedro M.
dc.contributor.author	Bispo, Bruno
dc.contributor.author	Garrett, Carolina
dc.contributor.author	Alves, Dílio
dc.contributor.author	Teixeira, João Paulo
dc.contributor.author	Freitas, Diamantino Silva
dc.date.accessioned	2023-02-09T16:41:04Z
dc.date.available	2023-02-09T16:41:04Z
dc.date.issued	2021
dc.description.abstract	This work proposes the application of a new electroencephalogram (EEG) signal processing tool - the lacsogram - to characterize the Alzheimer’s disease (AD) activity and to assist on its diagnosis at different stages: Mild Cognitive Impairment (MCI), Mild and Moderate AD (ADM) and Advanced AD (ADA). Statistical analyzes are performed to lacstral distances between conventional EEG subbands to find measures capable of discriminating AD in all stages and characterizing the AD activity in each electrode. Cepstral distances are used for comparison. Comparing all AD stages and Controls (C), the most important significances are the lacstral distances between subbands and (p=0.0014<0.05). The topographic maps show significant differences in parietal, temporal and frontal regions as AD progresses. Machine learning models with a leave-one-out cross-validation process are applied to lacstral/cepstral distances to develop an automatic method for diagnosing AD. The following classification accuracies are obtained with an artificial neural network: 95.55% for All vs All, 98.06% for C vs MCI, 95.99% for C vs ADM, 93.85% for MCI vs ADM-ADA. In C vs MCI, C vs ADM and MCI vs ADM-ADA, the proposed method outperforms the stateof- art methods by 5%, 1%, and 2%, respectively. In All vs All, it outperforms the state-of-art EEG and non-EEG methods by 6% and 2%, respectively. These results indicate that the proposed method represents an improvement in diagnosing AD.	pt_PT
dc.description.version	info:eu-repo/semantics/publishedVersion	pt_PT
dc.identifier.citation	Rodrigues, Pedro M.; Bispo, Bruno C.; Garrett, Carolina ; Alves, Dilio; Teixeira, Joao Paulo; Freitas, Diamantino (2021). Lacsogram: a new EEG tool to diagnose Alzheimer's disease. IEEE Journal of Biomedical and Health Informatics. 25:9, p. 3384 3395	pt_PT
dc.identifier.doi	10.1109/JBHI.2021.3069789	pt_PT
dc.identifier.uri	http://hdl.handle.net/10198/26865
dc.language.iso	eng	pt_PT
dc.peerreviewed	yes	pt_PT
dc.publisher	IEEE Journal of Biomedical and Health Informatics	pt_PT
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	pt_PT
dc.subject	Alzheimer’s disease	pt_PT
dc.subject	Mild-cognitive impairment	pt_PT
dc.subject	Diagnose	pt_PT
dc.subject	Artificial neural networks	pt_PT
dc.subject	Cepstrum	pt_PT
dc.subject	Lacsogram	pt_PT
dc.title	Lacsogram: a new EEG tool to diagnose Alzheimer's disease	pt_PT
dc.type	journal article
dspace.entity.type	Publication
oaire.citation.endPage	3395	pt_PT
oaire.citation.issue	9	pt_PT
oaire.citation.startPage	3384	pt_PT
oaire.citation.title	IEEE Journal of Biomedical and Health Informatics	pt_PT
oaire.citation.volume	25	pt_PT
person.familyName	Rodrigues
person.familyName	Teixeira
person.givenName	Pedro M.
person.givenName	João Paulo
person.identifier	663194
person.identifier.ciencia-id	C919-D413-5E83
person.identifier.ciencia-id	4F15-B322-59B4
person.identifier.orcid	0009-0002-4920-7398
person.identifier.orcid	0000-0002-6679-5702
person.identifier.rid	N-6576-2013
person.identifier.scopus-author-id	56108282100
person.identifier.scopus-author-id	57069567500
rcaap.rights	restrictedAccess	pt_PT
rcaap.type	article	pt_PT
relation.isAuthorOfPublication	55f77b39-347e-4ad5-97c6-def43d656014
relation.isAuthorOfPublication	33f4af65-7ddf-46f0-8b44-a7470a8ba2bf
relation.isAuthorOfPublication.latestForDiscovery	55f77b39-347e-4ad5-97c6-def43d656014