M/EEG-based bio-markers to predict the MCI and Alzheimer's disease: a review from the ML perspective

Yang, Su ORCID: https://orcid.org/0000-0002-6618-7483, Bornot, Jose Miguel Sanchez, Wong-Lin, Kongfatt and Prasad, Girijesh (2019) M/EEG-based bio-markers to predict the MCI and Alzheimer's disease: a review from the ML perspective. IEEE Transactions on Biomedical Engineering, 66 (10). pp. 2924-2935. ISSN 0018-9294

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Abstract

This paper reviews the state-of-the-art neuromarkers development for the prognosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). The first part of this paper is devoted to reviewing the recently emerged machine learning (ML) algorithms based on electroencephalography (EEG) and magnetoencephalography (MEG) modalities. In particular, the methods are categorized by different types of neuromarkers. The second part of the review is dedicated to a series of investigations that further highlight the differences between these two modalities. First, several source reconstruction methods are reviewed and their source-level performances explored, followed by an objective comparison between EEG and MEG from multiple perspectives. Finally, a number of the most recent reports on classification of MCI/AD during resting state using EEG/MEG are documented to show the up-to-date performance for this well-recognized data collecting scenario. It is noticed that the MEG modality may be particularly effective in distinguishing between subjects with MCI and healthy controls, a high classification accuracy of more than 98% was reported recently; whereas the EEG seems to be performing well in classifying AD and healthy subjects, which also reached around 98% of the accuracy. A number of influential factors have also been raised and suggested for careful considerations while evaluating the ML-based diagnosis systems in the real-world scenarios.

Item Type: Article
Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.
Uncontrolled Keywords: Alzheimer's disease, mild cognitive impairment, MEG, EEG, biomarkers, neuromarkers
Subjects: Computing
Medicine and health
Related URLs:
Depositing User: Su Yang
Date Deposited: 03 Jun 2021 13:46
Last Modified: 28 Aug 2021 07:15
URI: http://repository.uwl.ac.uk/id/eprint/7925

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