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A Case-Based Reasoning Model for Depression Based on Three-Electrode EEG Data

机译：基于三电极EEG数据的抑郁案件推理模型

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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.
机译：抑郁症，威胁数百万的福祉，已成为过去十年的主要疾病之一。然而，目前诊断抑郁症的方法是基于调查问卷的访谈，其劳动密集型和高度依赖于医生的经验。因此，需要客观和有效的方法。在本文中，我们提出了一种识别抑郁症的基于案例的推理模型。使用便携式三电极EEG器件收集脑电图数据，然后加工以去除伪影和提取特征。我们应用了多个分类器。选择最佳的k最近邻（knn）作为评估功能，以选择用于创建壳体基础的有效特征。基于重量标准偏差，使用标准化的欧几里德距离计算相似性以获得抑郁症的最佳识别率。抑郁患者最佳相似性鉴定的准确性为91.25％，与使用KNN分类器（81.44％）或先前报告的分类器的准确性相比，改善。因此，我们为自动检测抑郁症提供了一种新的普遍性和有效方法。

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