Durongbhan, P., Zhao, Y., Chen, L. et al. (9 more authors) (2019) A dementia classification framework using frequency and time-frequency features based on EEG signals. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27 (5). pp. 826-835. ISSN 1534-4320
Abstract
Alzheimer'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 study aims to explore a routine to gain such biomarkers using the quantitative analysis of Electroencephalography (QEEG). This paper proposes a supervised classification framework which uses EEG signals to classify healthy controls (HC) and AD participants. The framework consists of data augmentation, feature extraction, K-Nearest Neighbour (KNN) classification, quantitative evaluation and topographic visualisation. Considering the human brain either as a stationary or a dynamical system, both frequency-based and time-frequency-based features were tested in 40 participants. Results: a) The proposed method can achieve up to 99% classification accuracy on short (4s) eyes open EEG epochs, with the KNN algorithm that has best performance when compared to alternative machine learning approaches; b) The features extracted using the wavelet transform produced better classification performance in comparison to the features based on FFT; c) 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 localisation 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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Electroencephalogram; Alzheimer’s Disease; Machine Learning; K-Nearest Neighbour; Signal Processing; Electroencephalography; Feature extraction; Time-frequency analysis; Dementia; Biomarkers; Classification algorithms |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Sheffield Teaching Hospitals |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 11 Apr 2019 12:06 |
Last Modified: | 23 Nov 2021 10:41 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/TNSRE.2019.2909100 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:144851 |