EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer's disease and schizophrenia
EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer's disease and schizophrenia
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DOI：
10.1088/2632-072x/ac5f8d
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发表时间：
2022-06-01
影响因子：
2.7
通讯作者：
Rodrigues, Francisco A.
中科院分区：
文献类型：
--
作者：
Alves, Caroline L.;Pineda, Aruane M.;Rodrigues, Francisco A.
精神障碍是全世界导致残疾的主要原因之一。治疗这些疾病的第一步是获得准确的诊断。正如我们在这项工作中描述的那样，机器学习算法可以为这个问题提供一种可能的解决方案。我们提出了一种基于脑电时间序列和深度学习得到的连接矩阵的精神障碍自动诊断方法。我们表明，我们的方法可以对阿尔茨海默病和精神分裂症患者进行高水平的分类。与使用原始脑电时间序列的传统案例的比较表明，该方法具有最高的精度。因此，深度神经网络在脑连接数据上的应用是一种非常有前途的神经疾病诊断方法。
Mental disorders are among the leading causes of disability worldwide. The first step in treating these conditions is to obtain an accurate diagnosis. Machine learning algorithms can provide a possible solution to this problem, as we describe in this work. We present a method for the automatic diagnosis of mental disorders based on the matrix of connections obtained from EEG time series and deep learning. We show that our approach can classify patients with Alzheimer's disease and schizophrenia with a high level of accuracy. The comparison with the traditional cases, that use raw EEG time series, shows that our method provides the highest precision. Therefore, the application of deep neural networks on data from brain connections is a very promising method for the diagnosis of neurological disorders.