Journal article 722 views 112 downloads
Integrated space–frequency–time domain feature extraction for MEG-based Alzheimer’s disease classification
,
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
-
PDF | Version of Record
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
Download (1.5MB)
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 (...
| Published in: | Brain Informatics |
|---|---|
| ISSN: | 2198-4018 2198-4026 |
| Published: |
Springer Science and Business Media LLC
2021
|
| Online Access: |
Check full text
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa58561 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| 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 (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. |
|---|---|
| Keywords: |
Research, Multi-domain, Magnetoencephalography, Biomarkers, Spatio-temporal features, Alzheimer’s disease, Mild cognitive impairment |
| College: |
Faculty of Science and Engineering |
| Funders: |
Northern Ireland Functional Brain Mapping Project Facility Grant: 1303/101154803 |
| Issue: |
1 |
| Start Page: |
24 |