Yang Su, Bornot Jose Miguel Sanchez, Fernandez Ricardo Bruña, Deravi Farzin, Wong-Lin KongFatt, Prasad Girijesh
Department of Computer Science, Swansea University, Swansea, UK.
Intelligent Systems Research Centre, School of Computing, Eng & Intel. Sys, Ulster University, Derry-Londonderry, Northern Ireland, UK.
Brain Inform. 2021 Nov 2;8(1):24. doi: 10.1186/s40708-021-00145-1.
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.
脑磁图（MEG）已与机器学习技术相结合，用于识别最常见的痴呆形式之一——阿尔茨海默病（AD）。然而，以前的大多数研究都局限于二元分类，且未充分利用两种可用的MEG模态（使用磁力计和梯度计传感器提取）。AD由几个进展阶段组成，本研究通过使用磁力计和梯度计数据，以三类分类问题的形式区分AD患者、与AD相关的轻度认知障碍（MCI）患者和健康对照（HC）参与者，解决了这一局限性。开发并评估了一系列基于小波的生物标志物，这些生物标志物同时利用了信号的空间、频率和时域特征。提出了一种基于改进的分数级融合方法的双峰识别系统，以加强对磁力计和梯度计捕获的大脑活动的解释。在这项初步研究中，发现源自梯度计的标志物往往优于基于磁力计的标志物。有趣的是，在总共10个感兴趣区域中，左额叶在AD/MCI/HC分类中的平均识别率比表现第二好的区域（左颞叶）高出约8%。在这项工作中提出的四种类型的标志物中，使用小波系数开发的空间标志物在三分类中提供了最佳识别性能。总体而言，所提出的方法为利用双峰MEG数据进行AD/MCI/HC三分类的潜力提供了有前景的结果。
