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A Pervasive Approach to EEG-Based Depression Detection

机译：一种普及的基于EEG的抑郁检测方法

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摘要

Nowadays, depression is the world's major health concern and economic burden worldwide. However, due to the limitations of current methods for depression diagnosis, a pervasive and objective approach is essential. In the present study, a psychophysiological database, containing 213 (92 depressed patients and 121 normal controls) subjects, was constructed. The electroencephalogram (EEG) signals of all participants under resting state and sound stimulation were collected using a pervasive prefrontal-lobe three-electrode EEG system at Fp1, Fp2, and Fpz electrode sites. After denoising using the Finite Impulse Response filter combining the Kalman derivation formula, Discrete Wavelet Transformation, and an Adaptive Predictor Filter, a total of 270 linear and nonlinear features were extracted. Then, the minimal-redundancy-maximal-relevance feature selection technique reduced the dimensionality of the feature space. Four classification methods (Support Vector Machine, K-Nearest Neighbor, Classification Trees, and Artificial Neural Network) distinguished the depressed participants from normal controls. The classifiers' performances were evaluated using 10-fold cross-validation. The results showed that K-Nearest Neighbor (KNN) had the highest accuracy of 79.27%. The result also suggested that the absolute power of the theta wave might be a valid characteristic for discriminating depression. This study proves the feasibility of a pervasive three-electrode EEG acquisition system for depression diagnosis.
机译：如今，抑郁症是全球全球主要的健康问题和经济负担。然而，由于目前抑郁症诊断方法的局限性，普遍性和客观方法至关重要。在本研究中，构建了含有213名（92名抑郁症患者和121名正常对照）受试者的心理生理数据库。在FP1，FP2和FPZ电极位点处使用普罗瓦特前叶三电极EEG系统收集所有参与者的脑电图（EEG）信号和声刺痛。在使用与卡尔曼推导公式的有限脉冲响应滤波器结合的有限脉冲响应滤波器后，离散小波变换和自适应预测器滤波器，则提取总共270个线性和非线性特征。然后，最小冗余最大关联特征选择技术减少了特征空间的维度。四种分类方法（支持向量机，K最近邻居，分类树和人工神经网络）区分抑郁的参与者与正常控制。使用10倍交叉验证评估分类器的性能。结果表明，K最近邻（KNN）的最高精度为79.27％。结果还表明，Theta波的绝对功率可能是歧视抑郁症的有效特征。本研究证明了抑郁三电极EEG采集系统对抑郁症诊断的可行性。

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  • 来源
    《Complexity》 |2018年第2期|共13页
  • 作者

    Cai Hanshu; Han Jiashuo; Chen Yunfei; Sha Xiaocong; Wang Ziyang; Hu Bin; Yang Jing; Feng Lei; Ding Zhijie; Chen Yiqiang; Gutknecht Jurg;

  • 作者单位

    Lanzhou Univ Sch Informat Sci &

    Engn Gansu Prov Key Lab Wearable Comp Lanzhou Gansu Peoples R China;

    Lanzhou Univ Sch Informat Sci &

    Engn Gansu Prov Key Lab Wearable Comp Lanzhou Gansu Peoples R China;

    Lanzhou Univ Sch Informat Sci &

    Engn Gansu Prov Key Lab Wearable Comp Lanzhou Gansu Peoples R China;

    Lanzhou Univ Sch Informat Sci &

    Engn Gansu Prov Key Lab Wearable Comp Lanzhou Gansu Peoples R China;

    Lanzhou Univ Sch Informat Sci &

    Engn Gansu Prov Key Lab Wearable Comp Lanzhou Gansu Peoples R China;

    Lanzhou Univ Sch Informat Sci &

    Engn Gansu Prov Key Lab Wearable Comp Lanzhou Gansu Peoples R China;

    Lanzhou Univ Hosp 2 Dept Child Psychol Lanzhou Gansu Peoples R China;

    Capital Med Univ Beijing Anding Hosp Beijing Peoples R China;

    Third Peoples Hosp Tianshui City Tianshui Peoples R China;

    Chinese Acad Sci Inst Comp Technol Beijing Peoples R China;

    ETH Comp Syst Inst Zurich Switzerland;

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  • 中图分类 大系统理论;
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