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A novel depression detection method based on pervasive EEG and EEG splitting criterion

机译：基于普适脑电和脑电分裂准则的抑郁症检测新方法

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Depression is a mental disorder characterized by persistent occurrences of lower mood states in the affected person. According to the study of World Health Organization (WHO), depression will become the second largest cause of illness threatening the life of human beings in 2020, so early detection, early diagnosis and early treatment of depression is very important to save the health and life of human beings. In order to alleviate the damage caused by depression and make early detection, early diagnosis and early treatment of depression, a portable and accurate depression detection and diagnosis method is most necessary. Due to the highly complexity, nonlinearity and non-stationarity of electroencephalogram (EEG) data in nature, we present a novel method for pervasive EEG-based detection and diagnosis of depression with the resting state eye-closed EEG data of Fp1, Fpz and Fp2 locations of scalp electrodes, which are closely related to emotion, collected through three-electrode pervasive EEG collection device in this paper. Experiment has been conducted and totally 170 (81 depressive patients and 89 normal subjects) subjects' pervasive EEG data have been collected in resting state and eye-closed. Then, Support Vector Machine (SVM) is utilized to analyze the pervasive EEG data and the average accuracy reaches 83.07%. After Friedman Test and post-hoc two-tailed Nemenyi Test, we propose a splitting criterion for pervasive EEG. The data analysis experimental results show that the proposed method for detecting and diagnosing depression is effective and convenient, and it also demonstrate that the three-electrode pervasive EEG collection device has broad prospects in depression detection and diagnosis.
机译：抑郁症是一种精神障碍，其特征在于在受影响的人中持续出现情绪低落状态。根据世界卫生组织（WHO）的研究，抑郁症将在2020年成为威胁人类生命的第二大疾病原因，因此对抑郁症进行早期发现，早期诊断和早期治疗对于挽救健康和生命至关重要人类。为了减轻抑郁症造成的损害并进行早期发现，早期诊断和早期治疗，最需要一种便携式，准确的抑郁症检测和诊断方法。由于自然界中脑电图（EEG）数据的高度复杂性，非线性和非平稳性，我们提出了一种基于Ep，Fp1，Fpz和Fp2静止状态闭眼式EEG数据的基于EEG的普适性抑郁症检测和诊断的新方法本文通过三电极普及型脑电图采集装置采集与情绪密切相关的头皮电极位置。已经进行了实验，总共收集了170名（81名抑郁症患者和89名正常受试者）在静息状态和闭眼状态下普遍的脑电图数据。然后，利用支持向量机（SVM）对普适的脑电数据进行分析，平均准确率达到83.07 \％。经过Friedman检验和事后两尾Nemenyi检验，我们提出了普及性EEG的分裂准则。数据分析实验结果表明，所提出的抑郁症检测诊断方法是有效，方便的，也证明了三电极普及型脑电图采集装置在抑郁症的检测和诊断中具有广阔的前景。

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