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dc.contributor.authorDurongbhan, Pholpaten
dc.contributor.authorZhao, Yifanen
dc.contributor.authorChen, Liangyuen
dc.contributor.authorZis, Panagiotisen
dc.contributor.authorDe Marco, Matteoen
dc.contributor.authorUnwin, Zoe C.en
dc.contributor.authorVenneri, Annalenaen
dc.contributor.authorHe, Xiongxiongen
dc.contributor.authorLi, Shengen
dc.contributor.authorZhao, Yitianen
dc.contributor.authorBlackburn, Daniel J.en
dc.contributor.authorSarrigiannis, Ptolemaios G.en
dc.creatorDurongbhan, Pholpaten
dc.creatorZhao, Yifanen
dc.creatorChen, Liangyuen
dc.creatorZis, Panagiotisen
dc.creatorDe Marco, Matteoen
dc.creatorUnwin, Zoe C.en
dc.creatorVenneri, Annalenaen
dc.creatorHe, Xiongxiongen
dc.creatorLi, Shengen
dc.creatorZhao, Yitianen
dc.creatorBlackburn, Daniel J.en
dc.creatorSarrigiannis, Ptolemaios G.en
dc.description.abstractAlzheimer's disease (AD) accounts for 60%-70% of all dementia cases, and clinical diagnosis at its early stage is extremely difficult. As several new drugs aiming to modify disease progression or alleviate symptoms are being developed, to assess their efficacy, novel robust biomarkers of brain function are urgently required. This paper aims to explore a routine to gain such biomarkers using the quantitative analysis of electroencephalography (QEEG). This paper proposes a supervised classification framework that uses EEG signals to classify healthy controls (HC) and AD participants. The framework consists of data augmentation, feature extraction, K-nearest neighbor (KNN) classification, quantitative evaluation, and topographic visualization. Considering the human brain either as a stationary or a dynamical system, both the frequency-based and time-frequency-based features were tested in 40 participants. The results show that: 1) the proposed method can achieve up to a 99% classification accuracy on short (4s) eyes open EEG epochs, with the KNN algorithm that has best performance when compared with alternative machine learning approachesen
dc.description.abstract2) the features extracted using the wavelet transform produced better classification performance in comparison to the features based on FFTen
dc.description.abstractand 3) in the spatial domain, the temporal and parietal areas offer the best distinction between healthy controls and AD. The proposed framework can effectively classify HC and AD participants with high accuracy, meanwhile offering identification and the localization of significant QEEG features. These important findings and the proposed classification framework could be used for the development of a biomarker for the diagnosis and monitoring of disease progression in AD.en
dc.sourceIEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Societyen
dc.titleA Dementia Classification Framework Using Frequency and Time-Frequency Features Based on EEG Signalsen
dc.description.endingpage835Ιατρική Σχολή / Medical SchoolΙατρική Σχολή / Medical School
dc.source.abbreviationIEEE Trans Neural Syst Rehabil Engen
dc.contributor.orcidZis, Panagiotis [0000-0001-8567-3092]
dc.contributor.orcidSarrigiannis, Ptolemaios G. [0000-0002-8380-8755]
dc.contributor.orcidVenneri, Annalena [0000-0002-9488-2301]

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