Wang Baiyang, Kang Yuyun, Huo Dongyue, Feng Guifang, Zhang Jiawei, Li Jiadong
School of Information Science and Engineering, Linyi University, Linyi, China.
School of Logistics, Linyi University, Linyi, China.
Front Physiol. 2022 Oct 20;13:1029298. doi: 10.3389/fphys.2022.1029298. eCollection 2022.
Depression is an undetectable mental disease. Most of the patients with depressive symptoms do not know that they are suffering from depression. Since the novel Coronavirus pandemic 2019, the number of patients with depression has increased rapidly. There are two kinds of traditional depression diagnosis. One is that professional psychiatrists make diagnosis results for patients, but it is not conducive to large-scale depression detection. Another is to use electroencephalography (EEG) to record neuronal activity. Then, the features of the EEG are extracted using manual or traditional machine learning methods to diagnose the state and type of depression. Although this method achieves good results, it does not fully utilize the multi-channel information of EEG. Aiming at this problem, an EEG diagnosis method for depression based on multi-channel data fusion cropping enhancement and convolutional neural network is proposed. First, the multi-channel EEG data are transformed into 2D images after multi-channel fusion (MCF) and multi-scale clipping (MSC) augmentation. Second, it is trained by a multi-channel convolutional neural network (MCNN). Finally, the trained model is loaded into the detection device to classify the input EEG signals. The experimental results show that the combination of MCF and MSC can make full use of the information contained in the single sensor records, and significantly improve the classification accuracy and clustering effect of depression diagnosis. The method has the advantages of low complexity and good robustness in signal processing and feature extraction, which is beneficial to the wide application of detection systems.
抑郁症是一种难以察觉的精神疾病。大多数有抑郁症状的患者并不知道自己患有抑郁症。自2019年新型冠状病毒大流行以来，抑郁症患者数量迅速增加。传统的抑郁症诊断方法有两种。一种是由专业精神科医生为患者做出诊断结果，但这不利于大规模的抑郁症检测。另一种是使用脑电图（EEG）记录神经元活动。然后，采用人工或传统机器学习方法提取脑电图的特征，以诊断抑郁症的状态和类型。虽然这种方法取得了较好的效果，但它没有充分利用脑电图的多通道信息。针对这一问题，提出了一种基于多通道数据融合裁剪增强和卷积神经网络的抑郁症脑电图诊断方法。首先，将多通道脑电图数据经过多通道融合（MCF）和多尺度裁剪（MSC）增强后转换为二维图像。其次，通过多通道卷积神经网络（MCNN）进行训练。最后，将训练好的模型加载到检测设备中对输入的脑电图信号进行分类。实验结果表明，MCF和MSC的结合能够充分利用单个传感器记录中包含的信息，并显著提高抑郁症诊断的分类准确率和聚类效果。该方法在信号处理和特征提取方面具有低复杂度和良好的鲁棒性的优点，有利于检测系统的广泛应用。
