Liu Wei, Jia Kebin, Wang Zhuozheng, Ma Zhuo
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.
Beijing Laboratory of Advanced Information Networks, Beijing 100124, China.
Brain Sci. 2022 May 11;12(5):630. doi: 10.3390/brainsci12050630.
Depression has gradually become the most common mental disorder in the world. The accuracy of its diagnosis may be affected by many factors, while the primary diagnosis seems to be difficult to define. Finding a way to identify depression by satisfying both objective and effective conditions is an urgent issue. In this paper, a strategy for predicting depression based on spatiotemporal features is proposed, and is expected to be used in the auxiliary diagnosis of depression. Firstly, electroencephalogram (EEG) signals were denoised through the filter to obtain the power spectra of the three corresponding frequency ranges, Theta, Alpha and Beta. Using orthogonal projection, the spatial positions of the electrodes were mapped to the brainpower spectrum, thereby obtaining three brain maps with spatial information. Then, the three brain maps were superimposed on a new brain map with frequency domain and spatial characteristics. A Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) were applied to extract the sequential feature. The proposed strategy was validated with a public EEG dataset, achieving an accuracy of 89.63% and an accuracy of 88.56% with the private dataset. The network had less complexity with only six layers. The results show that our strategy is credible, less complex and useful in predicting depression using EEG signals.
抑郁症已逐渐成为全球最常见的精神障碍。其诊断准确性可能受到多种因素影响，而初步诊断似乎难以界定。找到一种能满足客观且有效的条件来识别抑郁症的方法是一个紧迫问题。本文提出了一种基于时空特征预测抑郁症的策略，有望用于抑郁症的辅助诊断。首先，通过滤波器对脑电图（EEG）信号进行去噪，以获取三个相应频率范围（Theta、Alpha和Beta）的功率谱。利用正交投影将电极的空间位置映射到脑功率谱上，从而获得三张具有空间信息的脑图。然后，将这三张脑图叠加到一张具有频域和空间特征的新脑图上。应用卷积神经网络（CNN）和门控循环单元（GRU）来提取序列特征。所提出的策略通过一个公共EEG数据集进行了验证，在私有数据集上实现了89.63%的准确率和88.56%的准确率。该网络仅有六层，复杂度较低。结果表明，我们的策略可靠、复杂度低且在利用EEG信号预测抑郁症方面很有用。
