Classification of patients with alzheimer's disease and dementia with lewy bodies using resting EEG selected features at sensor and source levels: A proof-of-concept study

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dc.authorid0000-0002-0860-0524
dc.authorid0000-0003-4292-5717
dc.contributor.authorSan Martin, Rodrigo
dc.contributor.authorJ Fraga, Francisco
dc.contributor.authorDel Percio, Claudio
dc.contributor.authorLizio, Roberta
dc.contributor.authorNoce, Giuseppe
dc.contributor.authorNobili, Flavio
dc.contributor.authorArnaldi, Dario
dc.contributor.authorD'Antonio, Fabrizia
dc.contributor.authorLena, Carlo De
dc.contributor.authorGüntekin, Bahar
dc.contributor.authorHanoğlu, Lütfü
dc.contributor.authorTaylor, John Paul
dc.contributor.authorMcKeith, Ian
dc.contributor.authorStocchi, Fabrizio
dc.contributor.authorFerri, Raffaele
dc.contributor.authorOnofrj, Marco
dc.contributor.authorLopez, Susanna
dc.contributor.authorBonanni, Laura
dc.contributor.authorBabiloni, Claudio
dc.date.accessioned2022-01-04T08:05:49Z
dc.date.available2022-01-04T08:05:49Z
dc.date.issued2021
dc.departmentİstanbul Medipol Üniversitesi, Tıp Fakültesi, Temel Tıp Bilimleri Bölümü, Biyofizik Ana Bilim Dalı
dc.departmentİstanbul Medipol Üniversitesi, Rektörlük, Rejeneratif ve Restoratif Tıp Araştırmaları Merkezi (REMER)
dc.departmentİstanbul Medipol Üniversitesi, Tıp Fakültesi, Nöroloji Ana Bilim Dalı
dc.description.abstractBackground: Early differentiation between Alzheimer's disease (AD) and Dementia with Lewy Bodies (DLB) is important for accurate prognosis, as DLB patients typically show faster disease progression. Cortical neural networks, necessary for human cognitive function, may be disrupted differently in DLB and AD patients, allowing diagnostic differentiation between AD and DLB. Objective: This proof-of-concept study assessed whether the application of machine learning techniques to data derived from resting-state electroencephalographic (rsEEG) rhythms (discriminant sensor power, 19 electrodes) and source connectivity (between five cortical regions of interest) allowed differentiation between DLB and AD. Methods: Clinical, demographic, and rsEEG datasets from DLB patients (N=30), AD patients (N=30), and control seniors (NOld, N=30), matched for age, sex, and education, were taken from our international database. Individual (delta, theta, alpha) and fixed (beta) rsEEG frequency bands were included. The rsEEG features for the classification task were computed at both sensor and source levels. The source level was based on eLORETA freeware toolboxes for estimating cortical source activity and linear lagged connectivity. Fluctuations of rsEEG recordings (band-pass waveform envelopes of each EEG rhythm) were also computed at both sensor and source levels. After blind feature reduction, rsEEG features served as input to support vector machine (SVM) classifiers. Discrimination of individuals from the three groups was measured with standard performance metrics (accuracy, sensitivity, and specificity). Results: The trained SVM two-class classifiers showed classification accuracies of 97.6% for NOld vs. AD, 99.7% for NOld vs. DLB, and 97.8% for AD vs. DLB. Three-class classifiers (AD vs. DLB vs. NOld) showed classification accuracy of 94.79%. Conclusion: These promising preliminary results should encourage future prospective and longitudinal cross-validation studies using higher resolution EEG techniques and harmonized clinical procedures to enable the clinical application of these machine learning techniques.
dc.description.sponsorshipFundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)en_US
dc.identifier.citationSan Martin, R., J Fraga, F., Del Percio, C., Lizio, R., Noce, G., Nobili, F. ... Babiloni, C. (2021). Classification of patients with alzheimer's disease and dementia with lewy bodies using resting EEG selected features at sensor and source levels: A proof-of-concept study. Current Alzheimer Research, 18(12), 956-969. https://doi.org/10.2174/1567205018666211027143944
dc.identifier.doi10.2174/1567205018666211027143944
dc.identifier.endpage969
dc.identifier.issn1567-2050
dc.identifier.issn1875-5828
dc.identifier.issue12
dc.identifier.scopusqualityQ2
dc.identifier.startpage956
dc.identifier.urihttps://doi.org/10.2174/1567205018666211027143944
dc.identifier.urihttps://hdl.handle.net/20.500.12511/8754
dc.identifier.volume18
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherBentham Science
dc.relation.ispartofCurrent Alzheimer Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectAlzheimer's Disease
dc.subjectEEG Source Connectivity
dc.subjectFeature Selection
dc.subjectLORETA
dc.subjectLewy Body Dementia
dc.subjectMachine Learning
dc.titleClassification of patients with alzheimer's disease and dementia with lewy bodies using resting EEG selected features at sensor and source levels: A proof-of-concept study
dc.typeArticle

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