Abstract
AbstractMagnetoencephalography (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.
Funder
Northern Ireland Functional Brain Mapping Project Facility
Publisher
Springer Science and Business Media LLC
Subject
Cognitive Neuroscience,Computer Science Applications,Neurology
Reference33 articles.
1. Yang S, Miguel J, Bornot S, Wong-lin K, Prasad G, Member S (2019) M/EEG-based bio-markers to predict the mild cognitive impairment and Alzheimer ’ s disease : a review from the machine learning perspective. IEEE Trans Biomed Eng 66(10):2924–2935
2. Elekta Neuromag Vector View 306 Channel Meg | Mindset. https://www.mindsetconsultinggroup.com/index.php/what-we-do/medical-imaging/elekta-neuromag-vector-view-306-channel-meg. [Accessed: Apr-2021].
3. Elekta Neuromag® TRIUXTM Functional Mapping. https://www.elekta.com/diagnostic-solutions/elekta-neuromag-triux/. [Accessed: Oct-2021].
4. Suk HI, Shen D (2013) deep learning-based feature representation for AD/MCI Classification, Int. Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 583–590.
5. Fiscon G, Weitschek E, Cialini A, Felici G, Bertolazzi P, De Salvo S, Bramanti A, Bramanti P, De Cola MC (2018) Cola MC, Combining EEG signal processing with supervised methods for Alzheimer’ s patients classification”. BMC Med Inform Decis Mak 18:1–10
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献