A dementia classification framework using frequency and time-frequency features based on EEG signals

Simple item page
dc.contributor.authorDurongbhan, Pholpat
dc.contributor.authorZhao, Yifan
dc.contributor.authorChen, Liangyu
dc.contributor.authorZis, Panagiotis
dc.contributor.authorDe Marco, Matteo
dc.contributor.authorUnwin, Zoe C.
dc.contributor.authorVenneri, Annalena
dc.contributor.authorHe, Xiongxiong
dc.contributor.authorLi, Sheng
dc.contributor.authorZhao, Yitian
dc.contributor.authorBlackburn, Daniel J.
dc.contributor.authorSarrigiannis, Ptolemaios G.
dc.date.accessioned2019-05-29T10:18:52Z
dc.date.available2019-05-29T10:18:52Z
dc.date.issued2019-04-04
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 approaches; 2) the features extracted using the wavelet transform produced better classification performance in comparison to the features based on FFT; and 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_UK
dc.identifier.citationDurongbhan P, Zhao Y, Chen L, Zis P, De Marco M, Unwin JC, Venneri A, He X, Li Sheng, Zhao Y, Blackburn DJ and Sarrigiannis PG., A dementia classification framework using frequency and time-frequency features based on EEG signals, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Volume 27, Issue number 5, pp. 826-835.en_UK
dc.identifier.issn1534-4320
dc.identifier.urihttps://doi.org/10.1109/TNSRE.2019.2909100
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/14210
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectElectroencephalogramen_UK
dc.subjectAlzheimer’s Diseaseen_UK
dc.subjectMachine Learningen_UK
dc.subjectK-Nearest Neighbouren_UK
dc.subjectSignal Processingen_UK
dc.titleA dementia classification framework using frequency and time-frequency features based on EEG signalsen_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
A_dementia_classification_framework_using_frequency-2019.pdf
Size:
1.55 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SATM)