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Integrated space–frequency–time domain feature extraction for MEG-based Alzheimer’s disease classification

Scott Yang Orcid Logo, Jose Miguel Sanchez Bornot, Ricardo Bruña Fernandez, Farzin Deravi, KongFatt Wong-Lin, Girijesh Prasad

Brain Informatics, Volume: 8, Issue: 1, Start page: 24

Swansea University Author: Scott Yang Orcid Logo

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DOI (Published version): 10.1186/s40708-021-00145-1

Abstract

Magnetoencephalography (MEG) has been combined with machine learning techniques, to recognize the Alzheimer’s disease (AD), one of the most common forms of dementia. However, most of the previous studies are limited to binary classification and do not fully utilize the two available MEG modalities (...

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Published in: Brain Informatics
ISSN: 2198-4018 2198-4026
Published: Springer Science and Business Media LLC 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa58561
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AD consists of several stages of progression, this study addresses this limitation by using both magnetometer and gradiometer data to discriminate between participants with AD, AD-related mild cognitive impairment (MCI), and healthy control (HC) participants in the form of a three-class classification problem. A series of wavelet-based biomarkers are developed and evaluated, which concurrently leverage the spatial, frequency and time domain characteristics of the signal. A bimodal recognition system based on an improved score-level fusion approach is proposed to reinforce interpretation of the brain activity captured by magnetometers and gradiometers. In this preliminary study, it was found that the markers derived from gradiometer tend to outperform the magnetometer-based markers. Interestingly, out of the total 10 regions of interest, left-frontal lobe demonstrates about 8% higher mean recognition rate than the second-best performing region (left temporal lobe) for AD/MCI/HC classification. Among the four types of markers proposed in this work, the spatial marker developed using wavelet coefficients provided the best recognition performance for the three-way classification. 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spelling 2022-10-31T13:50:32.4590703 v2 58561 2021-11-08 Integrated space–frequency–time domain feature extraction for MEG-based Alzheimer’s disease classification 81dc663ca0e68c60908d35b1d2ec3a9b 0000-0002-6618-7483 Scott Yang Scott Yang true false 2021-11-08 SCS Magnetoencephalography (MEG) has been combined with machine learning techniques, to recognize the Alzheimer’s disease (AD), one of the most common forms of dementia. However, most of the previous studies are limited to binary classification and do not fully utilize the two available MEG modalities (extracted using magnetometer and gradiometer sensors). AD consists of several stages of progression, this study addresses this limitation by using both magnetometer and gradiometer data to discriminate between participants with AD, AD-related mild cognitive impairment (MCI), and healthy control (HC) participants in the form of a three-class classification problem. A series of wavelet-based biomarkers are developed and evaluated, which concurrently leverage the spatial, frequency and time domain characteristics of the signal. A bimodal recognition system based on an improved score-level fusion approach is proposed to reinforce interpretation of the brain activity captured by magnetometers and gradiometers. In this preliminary study, it was found that the markers derived from gradiometer tend to outperform the magnetometer-based markers. Interestingly, out of the total 10 regions of interest, left-frontal lobe demonstrates about 8% higher mean recognition rate than the second-best performing region (left temporal lobe) for AD/MCI/HC classification. Among the four types of markers proposed in this work, the spatial marker developed using wavelet coefficients provided the best recognition performance for the three-way classification. Overall, the proposed approach provides promising results for the potential of AD/MCI/HC three-way classification utilizing the bimodal MEG data. Journal Article Brain Informatics 8 1 24 Springer Science and Business Media LLC 2198-4018 2198-4026 Research, Multi-domain, Magnetoencephalography, Biomarkers, Spatio-temporal features, Alzheimer’s disease, Mild cognitive impairment 2 11 2021 2021-11-02 10.1186/s40708-021-00145-1 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University Northern Ireland Functional Brain Mapping Project Facility Grant: 1303/101154803 2022-10-31T13:50:32.4590703 2021-11-08T08:54:04.7194836 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Scott Yang 0000-0002-6618-7483 1 Jose Miguel Sanchez Bornot 2 Ricardo Bruña Fernandez 3 Farzin Deravi 4 KongFatt Wong-Lin 5 Girijesh Prasad 6 58561__21433__fd926e2534e54a238f26e1c7ce6a46ea.pdf 40708_2021_Article_145.pdf 2021-11-08T08:54:04.7191123 Output 1569229 application/pdf Version of Record true The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made true eng http://creativecommons.org/licenses/by/4.0/
title Integrated space–frequency–time domain feature extraction for MEG-based Alzheimer’s disease classification
spellingShingle Integrated space–frequency–time domain feature extraction for MEG-based Alzheimer’s disease classification
Scott Yang
title_short Integrated space–frequency–time domain feature extraction for MEG-based Alzheimer’s disease classification
title_full Integrated space–frequency–time domain feature extraction for MEG-based Alzheimer’s disease classification
title_fullStr Integrated space–frequency–time domain feature extraction for MEG-based Alzheimer’s disease classification
title_full_unstemmed Integrated space–frequency–time domain feature extraction for MEG-based Alzheimer’s disease classification
title_sort Integrated space–frequency–time domain feature extraction for MEG-based Alzheimer’s disease classification
author_id_str_mv 81dc663ca0e68c60908d35b1d2ec3a9b
author_id_fullname_str_mv 81dc663ca0e68c60908d35b1d2ec3a9b_***_Scott Yang
author Scott Yang
author2 Scott Yang
Jose Miguel Sanchez Bornot
Ricardo Bruña Fernandez
Farzin Deravi
KongFatt Wong-Lin
Girijesh Prasad
format Journal article
container_title Brain Informatics
container_volume 8
container_issue 1
container_start_page 24
publishDate 2021
institution Swansea University
issn 2198-4018
2198-4026
doi_str_mv 10.1186/s40708-021-00145-1
publisher Springer Science and Business Media LLC
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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description Magnetoencephalography (MEG) has been combined with machine learning techniques, to recognize the Alzheimer’s disease (AD), one of the most common forms of dementia. However, most of the previous studies are limited to binary classification and do not fully utilize the two available MEG modalities (extracted using magnetometer and gradiometer sensors). AD consists of several stages of progression, this study addresses this limitation by using both magnetometer and gradiometer data to discriminate between participants with AD, AD-related mild cognitive impairment (MCI), and healthy control (HC) participants in the form of a three-class classification problem. A series of wavelet-based biomarkers are developed and evaluated, which concurrently leverage the spatial, frequency and time domain characteristics of the signal. A bimodal recognition system based on an improved score-level fusion approach is proposed to reinforce interpretation of the brain activity captured by magnetometers and gradiometers. In this preliminary study, it was found that the markers derived from gradiometer tend to outperform the magnetometer-based markers. Interestingly, out of the total 10 regions of interest, left-frontal lobe demonstrates about 8% higher mean recognition rate than the second-best performing region (left temporal lobe) for AD/MCI/HC classification. Among the four types of markers proposed in this work, the spatial marker developed using wavelet coefficients provided the best recognition performance for the three-way classification. Overall, the proposed approach provides promising results for the potential of AD/MCI/HC three-way classification utilizing the bimodal MEG data.
published_date 2021-11-02T04:15:11Z
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