A new framework for automatic detection of patients with mild cognitive impairment using resting-state EEG signals

Siuly, Siuly and Alcin, Omer Faruk and Kabir, Enamul and Sengur, Abdulkadir and Wang, Hua and Zhang, Yanchun and Whittaker, Frank (2020) A new framework for automatic detection of patients with mild cognitive impairment using resting-state EEG signals. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28 (9). pp. 1966-1976. ISSN 1534-4320


Abstract

Mild cognitive impairment (MCI) can be an indicator representing the early stage of Alzheimier’s disease (AD). AD, which is the most common form of dementia, is a major public health problem worldwide. Efficient detection of MCI is essential to identify the risks of AD and dementia. Currently Electroencephalography (EEG) is the most popular tool to investigate the presenence of MCI biomarkers. This study aims to develop a new framework that can use EEG data to automatically distinguish MCI patients from healthy control subjects. The proposed framework consists of noise removal (baseline drift and power line interference noises), segmentation, data compression, feature extraction, classification, and performance evaluation. This study introduces Piecewise Aggregate Approximation (PAA) for compressing massive volumes of EEG data for reliable analysis. Permutation entropy (PE) and auto-regressive (AR) model features are investigated to explore whether the changes in EEG signals can effectively distinguish MCI from healthy control subjects. Finally, three models are developed based on three modern machine learning techniques: Extreme Learning Machine (ELM); Support Vector Machine (SVM) and K-Nearest Neighbours (KNN) for the obtained feature sets. Our developed models are tested on a publicly available MCI EEG database and the robustness of our models is evaluated by using a 10-fold cross validation method. The results show that the proposed ELM based method achieves the highest classification accuracy (98.78%) with lower execution time (0.281 seconds) and also outperforms the existing methods. The experimental results suggest that our proposed framework could provide a robust biomarker for efficient detection of MCI patients.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version, in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Date Deposited: 11 Sep 2020 05:13
Last Modified: 14 Sep 2020 01:54
Uncontrolled Keywords: mild cognitive impairment (MCI), Alzheimer’s disease (AD), electroencephalogram (EEG), piecewise aggregate proximation (PAA), auto-regressive (AR) model, permutation entropy (PE), extreme learning machine (ELM)
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: https://doi.org/10.1109/TNSRE.2020.3013429
URI: http://eprints.usq.edu.au/id/eprint/39527

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