Depression, threatening the well-being of millions, has become one of the major diseases in the past decade. However, the current method of diagnosing depression is questionnaire-based interviews, which is labor-intensive and highly dependent on doctors’ experience. Thus, objective and cost-efficient methods are needed. In this paper, we present a case-based reasoning model for identifying depression. Electroencephalography data were collected using a portable three-electrode EEG device, and then processed to remove artifacts and extract features. We applied multiple classifiers. The best performing k-Nearest Neighbor (KNN) was selected as the evaluation function to select the effective features which were then used to create the case base. Based on the weight set of standard deviations, the similarity was calculated using normalized Euclidean distance to get the optimal recognition rate of depression. The accuracy of optimal similarity identification of patients with depression was 91.25 percent, which was improved compared to the accuracy using KNN classifier (81.44 percent) or previously reported classifiers. Thus, we provide a novel pervasive and effective method for automatic detection of depression.

中文翻译：

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基于三电极脑电数据的抑郁症个案推理模型

抑郁症威胁着数百万人的福祉，已成为过去十年中的主要疾病之一。然而，目前诊断抑郁症的方法是基于问卷的访谈，这种方法劳动密集，高度依赖医生的经验。因此，需要客观且具有成本效益的方法。在本文中，我们提出了一个基于案例的推理模型来识别抑郁症。使用便携式三电极 EEG 设备收集脑电图数据，然后进行处理以去除伪影并提取特征。我们应用了多个分类器。选择性能最好的 k-最近邻 (KNN) 作为评估函数，以选择有效特征，然后将其用于创建案例库。基于标准差的权重集，使用归一化欧氏距离计算相似度，得到抑郁症的最佳识别率。抑郁症患者最佳相似度识别的准确率为91.25%，与使用KNN分类器（81.44%）或先前报道的分类器的准确率相比有所提高。因此，我们提供了一种新的普遍有效的方法来自动检测抑郁症。