Integrated space-frequency-time domain feature extraction for MEG-based Alzheimer's disease classification.

Autor: Yang S; Department of Computer Science, Swansea University, Swansea, UK. su.yang@swansea.ac.uk., Bornot JMS; Intelligent Systems Research Centre, School of Computing, Eng & Intel. Sys, Ulster University, Derry-Londonderry, Northern Ireland, UK., Fernandez RB; Centre for Biomedical Technology, Technical University of Madrid, Madrid, Spain., Deravi F; School of Engineering and Digital Arts at the University of Kent, Canterbury, UK., Wong-Lin K; Intelligent Systems Research Centre, School of Computing, Eng & Intel. Sys, Ulster University, Derry-Londonderry, Northern Ireland, UK., Prasad G; Intelligent Systems Research Centre, School of Computing, Eng & Intel. Sys, Ulster University, Derry-Londonderry, Northern Ireland, UK.
Jazyk: angličtina
Zdroj: Brain informatics [Brain Inform] 2021 Nov 02; Vol. 8 (1), pp. 24. Date of Electronic Publication: 2021 Nov 02.
DOI: 10.1186/s40708-021-00145-1
Abstrakt: 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.
(© 2021. The Author(s).)
Databáze: MEDLINE
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