Deng Xin, Fan Xufeng, Lv Xiangwei, Sun Kaiwei
Key Laboratory of Data Engineering and Visual Computing, College of Computer Science and Technology, Chongqing University of Posts and Telecommunication, Chongqing, China.
Front Neuroinform. 2022 Jun 2;16:914823. doi: 10.3389/fninf.2022.914823. eCollection 2022.
Depression affects many people around the world today and is considered a global problem. Electroencephalogram (EEG) measurement is an appropriate way to understand the underlying mechanisms of major depressive disorder (MDD) to distinguish depression from normal control. With the development of deep learning methods, many researchers have adopted deep learning models to improve the classification accuracy of depression recognition. However, there are few studies on designing convolution filters for spatial and frequency domain feature learning in different brain regions. In this study, SparNet, a convolutional neural network composed of five parallel convolutional filters and the SENet, is proposed to learn EEG space-frequency domain characteristics and distinguish between depressive and normal control. The model is trained and tested by the cross-validation method of subject division. The results show that SparNet achieves a sensitivity of 95.07%, a specificity of 93.66%, and an accuracy of 94.37% in classification. Therefore, our results can conclude that the proposed SparNet model is effective in detecting depression using EEG signals. It also indicates that the combination of spatial information and frequency domain information is an effective way to identify patients with depression.
抑郁症如今影响着世界各地的许多人，被视为一个全球性问题。脑电图（EEG）测量是了解重度抑郁症（MDD）潜在机制以区分抑郁症与正常对照的一种合适方法。随着深度学习方法的发展，许多研究人员采用深度学习模型来提高抑郁症识别的分类准确率。然而，针对不同脑区进行空间和频域特征学习的卷积滤波器设计的研究较少。在本研究中，提出了一种由五个并行卷积滤波器和SENet组成的卷积神经网络SparNet，用于学习EEG的空间频域特征并区分抑郁与正常对照。该模型通过受试者划分的交叉验证方法进行训练和测试。结果表明，SparNet在分类中实现了95.07%的灵敏度、93.66%的特异性和94.37%的准确率。因此，我们的结果可以得出结论，所提出的SparNet模型在使用EEG信号检测抑郁症方面是有效的。这也表明空间信息和频域信息的结合是识别抑郁症患者的有效方法。
