An Automated Detection of Amyotrophic Lateral Sclerosis from Resting-State MEG Data Using 3D Deep Convolutional Neural Network
An Automated Detection of Amyotrophic Lateral Sclerosis from Resting-State MEG Data Using 3D Deep Convolutional Neural Network
复制标题
DOI：
10.1109/smc53992.2023.10393987
复制
发表时间：
2023-10
期刊：
影响因子：
--
通讯作者：
Kaniska Samanta;Sujit Roy;V. Marchand-Pauvert;Shirin Dora;Stephanie Duguez;Muskaan Singh;Girijesh Prasad
中科院分区：
文献类型：
--
作者：
Kaniska Samanta;Sujit Roy;V. Marchand-Pauvert;Shirin Dora;Stephanie Duguez;Muskaan Singh;Girijesh Prasad
提出了一种新的三维深卷积神经网络(3D-CNN)模型MEGNet3D。MEGNet3D旨在根据肌萎缩侧索硬化症(ALS)患者的静息状态(睁眼和闭眼状态)传感器水平脑磁图(MEG)数据区分肌萎缩侧索硬化(ALS)和健康人。原始的脑磁图数据最初被转换成它们的时频表示，然后被用作MEGNet3D的输入。磁力仪和梯度仪的记录都分别进行了研究。该模型在大多数分类条件下的准确率都在75%以上。因此，MEGNet3D能够处理高度的受试者变异性，并表明静息状态脑磁图数据的光谱-时间表示产生与ALS的存在相关的神经标记物。此外，还观察到闭眼静息状态比睁眼静息状态具有更好的分类精度。
A novel 3D deep convolutional neural network (3D-CNN) model called MEGNet3D has been proposed in the paper. MEGNet3D is designed to differentiate between amyotrophic lateral sclerosis (ALS) and healthy individuals from their resting state (eyes open and eyes closed condition) sensor-level magnetoencephalography (MEG) data. The raw MEG data is initially transformed into their time-frequency representation, which are then used as inputs to MEGNet3D. Both magnetometer and gradiometer recordings have been investigated separately. The proposed model exhibits an accuracy of over 75% for most classification conditions. Thus, MEGNet3D is capable of handling high subject variability and shows that spectral-temporal representation of resting-state MEG data yields relevant neural markers related to the existence of ALS. Furthermore, it has also been observed resting state with eyes closed yields better classification accuracy as compared to the resting state with eyes open condition.