Neuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Phys Eng Sci Med. 2022 Sep;45(3):705-719. doi: 10.1007/s13246-022-01135-1. Epub 2022 May 30.
Major depressive disorder (MDD) as a psychiatric illness negatively affects the behavior and daily life of the patients.Therefore, the early MDD diagnosis can help to cure the patients more efficiently and prevent adverse effects, although its unclear manifestations make the early diagnosis challenging. Nowadays, many studies have proposed automatic early MDD diagnosis methods based on electroencephalogram (EEG) signals. This study also presents an automated EEG-based MDD diagnosis framework based on Dictionary learning (DL) approaches and functional connectivity features. Firstly, a feature space of MDD and healthy control (HC) participants were constructed via functional connectivity features.Next, DL-based classification approaches such as Label Consistent K-SVD (LC-KSVD) and Correlation-based Label Consistent K-SVD (CLC-KSVD) methods, were utilized to perform the classification task. A public dataset was used, consisting of EEG signals from 34 MDD patients and 30 HC subjects, to evaluate the proposed method. To validate the proposed method, 10-fold cross-validation technique with 100 iterations was employed, providing accuracy (AC), sensitivity (SE), specificity (SP), F1-score (F1), and false discovery rate (FDR) performance metrics. The results show that LC-KSVD2 and CLC-KSVD2 performed efficiently in classifying MDD and HC cases. The best classification performance was obtained by the LCKSVD2 method, with average AC of 99.0%, SE of 98.9%, SP of 99.2%, F1 of 99.0%, and FDR of 0.8%. According to the results, the proposed method provides an accurate performance and, therefore, it can be developed into a computer-aided diagnosis (CAD) tool for automatic MDD diagnosis.
重度抑郁症（MDD）作为一种精神疾病，会对患者的行为和日常生活产生负面影响。因此，早期 MDD 诊断可以帮助患者更有效地治疗疾病并预防不良后果，尽管其表现不明确，使得早期诊断具有挑战性。如今，许多研究已经提出了基于脑电图（EEG）信号的自动早期 MDD 诊断方法。本研究还提出了一种基于字典学习（DL）方法和功能连接特征的自动 EEG 基 MDD 诊断框架。首先，通过功能连接特征构建 MDD 和健康对照组（HC）参与者的特征空间。接下来，利用基于 DL 的分类方法，如标签一致 K-SVD（LC-KSVD）和基于相关的标签一致 K-SVD（CLC-KSVD）方法，进行分类任务。使用公共数据集，包括 34 名 MDD 患者和 30 名 HC 受试者的 EEG 信号，评估所提出的方法。为了验证所提出的方法，采用 10 折交叉验证技术，进行 100 次迭代，提供准确性（AC）、敏感度（SE）、特异性（SP）、F1 分数（F1）和假发现率（FDR）性能指标。结果表明，LC-KSVD2 和 CLC-KSVD2 在分类 MDD 和 HC 病例方面表现出色。LC-KSVD2 方法的分类性能最佳，平均 AC 为 99.0%，SE 为 98.9%，SP 为 99.2%，F1 为 99.0%，FDR 为 0.8%。根据结果，所提出的方法提供了准确的性能，因此可以开发成用于自动 MDD 诊断的计算机辅助诊断（CAD）工具。
