Department of Computer Science and Engineering, Birla Institute of Technology, Ranchi, Mesra, 835215, India.
J Med Syst. 2019 Dec 13;44(1):28. doi: 10.1007/s10916-019-1486-z.
Depression or Major Depressive Disorder (MDD) is a mental illness which negatively affects how a person thinks, acts or feels. MDD has become a major disease affecting millions of people presently. The diagnosis of depression is questionnaire based and is not based on any objective criteria. In this paper, feature extracted from EEG signal are used for the diagnosis of depression. Alpha, alpha1, alpha2, beta, delta and theta power and theta asymmetry was used as feature. Alpha1, alpha2 along with theta asymmetry was also used as a feature. Multi-Cluster Feature Selection (MCFS) was used for feature selection when feature combination was used. The classifiers used were Support Vector Machine (SVM), Logistic Regression (LR), Naïve-Bayesian (NB) and Decision Tree (DT). Alpha2 showed higher classification accuracy than alpha1 and alpha power in all applied classifier. From t-test it was found that there was a significant difference in the theta power of left and right hemisphere of normal subjects, but there was no significant difference in depression patients. Average theta asymmetry in normal subjects is higher than MDD patients but the difference in theta asymmetry in normal subjects and MDD patients is not significant. The combination of alpha2 and theta asymmetry showed the highest classification accuracy of 88.33% in SVM.
抑郁症或重度抑郁症（MDD）是一种精神疾病，会对一个人的思维、行为和感觉产生负面影响。目前，MDD 已成为影响数百万人的主要疾病。抑郁症的诊断是基于问卷的，而不是基于任何客观标准。本文使用 EEG 信号提取的特征来诊断抑郁症。使用 alpha、alpha1、alpha2、beta、delta 和 theta 功率和 theta 不对称作为特征。alpha1、alpha2 以及 theta 不对称也被用作特征。当使用特征组合时，使用多聚类特征选择（MCFS）进行特征选择。使用的分类器是支持向量机（SVM）、逻辑回归（LR）、朴素贝叶斯（NB）和决策树（DT）。在所有应用的分类器中，alpha2 的分类准确率均高于 alpha1 和 alpha 功率。通过 t 检验发现，正常受试者左右半球的 theta 功率存在显著差异，但抑郁症患者无显著差异。正常受试者的平均 theta 不对称性高于 MDD 患者，但正常受试者和 MDD 患者的 theta 不对称性差异不显著。alpha2 和 theta 不对称的组合在 SVM 中表现出最高的分类准确率为 88.33%。
