Akbari Hesam, Sadiq Muhammad Tariq, Siuly Siuly, Li Yan, Wen Paul
Department of Biomedical Engineering, Islamic Azad University, Tehran, 1584715414 Iran.
School of Architecture, Technology and Engineering, University of Brighton, Brighton, BN2 4AT UK.
Health Inf Sci Syst. 2022 Sep 1;10(1):24. doi: 10.1007/s13755-022-00187-7. eCollection 2022 Dec.
Early detection of depression is critical in assisting patients in receiving the best therapy possible to avoid negative repercussions. Depression detection using electroencephalogram (EEG) signals is a simple, low-cost, convenient, and accurate approach. This paper proposes a six-stage novel method for detecting depression using EEG signals. First, EEG signals are recorded from 44 subjects, with 22 subjects being normal and 22 subjects being depressed. Second, a simple notch filter with EEG signals differencing approach is employed for effective preprocessing. Third, the variational mode decomposition (VMD) approach is implemented for nonlinear and non-stationary EEG signals analysis, resulting in many modes. Fourth, mutual information-based novel modes selection criterion is proposed to select the most informative modes. In the fifth step, a combination of linear and nonlinear features are extracted from selected modes and at last, classification is performed with neural networks. In this study, a novel single feature is also proposed, which is made using Log energy, norm entropies and fluctuation index, which delivers 100% classification accuracy, sensitivity and specificity. By using these features, a novel depression diagnostic index is also proposed. This integrated index would assist in quicker and more objective identification of normal and depression EEG signals. The proposed computerized framework and the DDI can help health workers, large enterprises, and product developers build a real-time system.
早期发现抑郁症对于帮助患者接受最佳治疗以避免负面影响至关重要。利用脑电图（EEG）信号检测抑郁症是一种简单、低成本、便捷且准确的方法。本文提出了一种利用EEG信号检测抑郁症的六阶段新颖方法。首先，从44名受试者记录EEG信号，其中22名受试者为正常，22名受试者为抑郁症患者。其次，采用带有EEG信号差分方法的简单陷波滤波器进行有效预处理。第三，实施变分模态分解（VMD）方法对非线性和非平稳的EEG信号进行分析，得到多个模态。第四，提出基于互信息的新颖模态选择准则来选择最具信息性的模态。在第五步中，从选定的模态中提取线性和非线性特征的组合，最后，用神经网络进行分类。在本研究中，还提出了一种新颖的单一特征，它由对数能量、范数熵和波动指数构成，其分类准确率、灵敏度和特异性均达到100%。通过使用这些特征，还提出了一种新颖的抑郁症诊断指数。这个综合指数将有助于更快、更客观地识别正常和抑郁症EEG信号。所提出的计算机化框架和DDI可以帮助卫生工作者、大型企业和产品开发者构建一个实时系统。
