Li Mengqian, Liu Yuan, Liu Yan, Pu Changqin, Yin Ruocheng, Zeng Ziqiang, Deng Libin, Wang Xing
Department of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, China.
Second Clinical Medical College, Nanchang University, Nanchang, China.
Front Physiol. 2022 Oct 10;13:956254. doi: 10.3389/fphys.2022.956254. eCollection 2022.
The study aimed to assess the value of the resting-state electroencephalogram (EEG)-based convolutional neural network (CNN) method for the diagnosis of depression and its severity in order to better serve depressed patients and at-risk populations. In this study, we used the resting state EEG-based CNN to identify depression and evaluated its severity. The EEG data were collected from depressed patients and healthy people using the Nihon Kohden EEG-1200 system. Analytical processing of resting-state EEG data was performed using Python and MATLAB software applications. The questionnaire included the Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS), Symptom Check-List-90 (SCL-90), and the Eysenck Personality Questionnaire (EPQ). A total of 82 subjects were included in this study, with 41 in the depression group and 41 in the healthy control group. The area under the curve (AUC) of the resting-state EEG-based CNN in depression diagnosis was 0.74 (95%CI: 0.70-0.77) with an accuracy of 66.40%. In the depression group, the SDS, SAS, SCL-90 subscales, and N scores were significantly higher in the major depression group than those in the non-major depression group ( < 0.05). The AUC of the model in depression severity was 0.70 (95%CI: 0.65-0.75) with an accuracy of 66.93%. Correlation analysis revealed that major depression AI scores were significantly correlated with SAS scores (r = 0.508, = 0.003) and SDS scores (r = 0.765, < 0.001). Our model can accurately identify the depression-specific EEG signal in terms of depression diagnosis and severity identification. It would eventually provide new strategies for early diagnosis of depression and its severity.
本研究旨在评估基于静息态脑电图（EEG）的卷积神经网络（CNN）方法在抑郁症诊断及其严重程度评估中的价值，以便更好地服务于抑郁症患者和高危人群。在本研究中，我们使用基于静息态EEG的CNN来识别抑郁症并评估其严重程度。使用日本光电EEG - 1200系统从抑郁症患者和健康人群中收集EEG数据。使用Python和MATLAB软件应用程序对静息态EEG数据进行分析处理。问卷包括自评焦虑量表（SAS）、自评抑郁量表（SDS）、症状自评量表90（SCL - 90）和艾森克人格问卷（EPQ）。本研究共纳入82名受试者，其中抑郁症组41名，健康对照组41名。基于静息态EEG的CNN在抑郁症诊断中的曲线下面积（AUC）为0.74（95%CI：0.70 - 0.77），准确率为66.40%。在抑郁症组中，重度抑郁症组的SDS、SAS、SCL - 90分量表和N得分显著高于非重度抑郁症组（<0.05）。该模型在抑郁症严重程度评估中的AUC为0.70（95%CI：0.65 - 0.75），准确率为66.93%。相关性分析显示，重度抑郁症人工智能得分与SAS得分（r = 0.508，= 0.003）和SDS得分（r = 0.765，<0.001）显著相关。我们的模型在抑郁症诊断和严重程度识别方面能够准确识别出抑郁症特异性的EEG信号。最终将为抑郁症及其严重程度的早期诊断提供新的策略。
