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

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 mod...

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Autores principales:	Su Yang, Jose Miguel Sanchez Bornot, Ricardo Bruña Fernandez, Farzin Deravi, KongFatt Wong-Lin, Girijesh Prasad
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Multi-domain Magnetoencephalography Biomarkers Spatio-temporal features Alzheimer’s disease Mild cognitive impairment Computer applications to medicine. Medical informatics R858-859.7 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/029afbddb6464c41975f8232c3136b33
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spelling	oai:doaj.org-article:029afbddb6464c41975f8232c3136b332021-11-07T12:11:48ZIntegrated space–frequency–time domain feature extraction for MEG-based Alzheimer’s disease classification10.1186/s40708-021-00145-12198-40182198-4026https://doaj.org/article/029afbddb6464c41975f8232c3136b332021-11-01T00:00:00Zhttps://doi.org/10.1186/s40708-021-00145-1https://doaj.org/toc/2198-4018https://doaj.org/toc/2198-4026Abstract 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.Su YangJose Miguel Sanchez BornotRicardo Bruña FernandezFarzin DeraviKongFatt Wong-LinGirijesh PrasadSpringerOpenarticleMulti-domainMagnetoencephalographyBiomarkersSpatio-temporal featuresAlzheimer’s diseaseMild cognitive impairmentComputer applications to medicine. Medical informaticsR858-859.7Computer softwareQA76.75-76.765ENBrain Informatics, Vol 8, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Multi-domain Magnetoencephalography Biomarkers Spatio-temporal features Alzheimer’s disease Mild cognitive impairment Computer applications to medicine. Medical informatics R858-859.7 Computer software QA76.75-76.765
spellingShingle	Multi-domain Magnetoencephalography Biomarkers Spatio-temporal features Alzheimer’s disease Mild cognitive impairment Computer applications to medicine. Medical informatics R858-859.7 Computer software QA76.75-76.765 Su Yang Jose Miguel Sanchez Bornot Ricardo Bruña Fernandez Farzin Deravi KongFatt Wong-Lin Girijesh Prasad Integrated space–frequency–time domain feature extraction for MEG-based Alzheimer’s disease classification
description	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.
format	article
author	Su Yang Jose Miguel Sanchez Bornot Ricardo Bruña Fernandez Farzin Deravi KongFatt Wong-Lin Girijesh Prasad
author_facet	Su Yang Jose Miguel Sanchez Bornot Ricardo Bruña Fernandez Farzin Deravi KongFatt Wong-Lin Girijesh Prasad
author_sort	Su Yang
title	Integrated space–frequency–time domain feature extraction for MEG-based Alzheimer’s disease classification
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
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/029afbddb6464c41975f8232c3136b33
work_keys_str_mv	AT suyang integratedspacefrequencytimedomainfeatureextractionformegbasedalzheimersdiseaseclassification AT josemiguelsanchezbornot integratedspacefrequencytimedomainfeatureextractionformegbasedalzheimersdiseaseclassification AT ricardobrunafernandez integratedspacefrequencytimedomainfeatureextractionformegbasedalzheimersdiseaseclassification AT farzinderavi integratedspacefrequencytimedomainfeatureextractionformegbasedalzheimersdiseaseclassification AT kongfattwonglin integratedspacefrequencytimedomainfeatureextractionformegbasedalzheimersdiseaseclassification AT girijeshprasad integratedspacefrequencytimedomainfeatureextractionformegbasedalzheimersdiseaseclassification
_version_	1718443461348163584

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

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