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Depression recognition based on the reconstruction of phase space of EEG signals and geometrical features
Applied Acoustics ( IF 3.4 ) Pub Date : 2021-04-10 , DOI: 10.1016/j.apacoust.2021.108078
Hesam Akbari , Muhammad Tariq Sadiq , Ateeq Ur Rehman , Mahdieh Ghazvini , Rizwan Ali Naqvi , Malih Payan , Hourieh Bagheri , Hamed Bagheri

Depression is a mental disorder that continues to make life difficult or impossible for a depressed person and, if left untreated, can lead to dangerous activities such as self-harm and suicide. Nowadays, Electroencephalogram (EEG) has become an important diagnostic tool for many brain disorders. In this article, a new method for the detection of depression based on the reconstructed phase space (RPS) of EEG signals and geometrical features has been proposed. The RPS of the EEG signals of 22 normal and 22 depressed subjects are plotted in two-dimensional space and, based on their shape, 34 geometrical features are extracted. The p-values for the proposed features were significantly lower (p-value 0) indicating the capacity of the proposed geometric features for the normal and depression EEG signals classification tasks. For the purpose of reducing feature vector arrays, the performance of four optimization algorithms is checked, namely: ant colony optimization (ACO), grey wolf optimization (GWO), genetic algorithm (GA) and particle swarm optimization (PSO), in which GA with the ability of 58.8% was better than the other optimization algorithms for decreasing the feature vector arrays. Selected features are fed to the support vector machine (SVM) classifier with radial basis function (RBF) kernel and K-nearest neighbors (KNN) classifier with Euclidean and city block distances in 10-fold cross-validation (CV) strategy. The proposed framework achieved a fairly good average classification accuracy (ACC) of 99.30% and a Matthews correlation coefficient (MCC) of 0.98 using the selected features of the PSO algorithm and the SVM classifier. We found that the RPS of normal EEG signals has a more irregular, complex and unpredictable shape than the RPS of depression EEG signals which has more regular (simple) with less variation and more predictable shape; therefore, we can say that RPS of EEG signals can be used as a biomarker for psychiatrists which are simpler than the EEG signals in visual depression diagnostics. We also found that EEG signals from the right hemisphere are significant for depression detection than the left hemisphere. The proposed framework may be used in clinics and hospitals to detect depression disorder quickly and precisely.



中文翻译:

基于脑电信号相空间和几何特征重构的抑郁识别

抑郁症是一种精神障碍,对于抑郁症患者来说,这继续使他们生活困难或无法生活,如果不加以治疗,可能会导致危险的活动,例如自残和自杀。如今,脑电图(EEG)已成为许多脑部疾病的重要诊断工具。本文提出了一种基于脑电信号重构相空间和几何特征的凹陷检测新方法。在二维空间中绘制了22位正常人和22位抑郁对象的EEG信号的RPS,并基于它们的形状提取了34个几何特征。拟议功能的p值明显较低(p值0)表示建议的几何特征用于正常和抑郁EEG信号分类任务的能力。为了减少特征向量数组,检查了四种优化算法的性能,即蚁群优化(ACO),灰狼优化(GWO),遗传算法(GA)和粒子群优化(PSO),其中GA减少特征向量数组的能力比其他优化算法提高了58.8%。选定的特征以10倍交叉验证(CV)策略提供给具有径向基函数(RBF)核的支持向量机(SVM)分类器和具有欧几里得距离和城市街区距离的K近邻(KNN)分类器。拟议的框架实现了99的相当不错的平均分类准确度(ACC)。使用PSO算法和SVM分类器的选定功能,可得到30%的Matthews相关系数(MCC)为0.98。我们发现正常的脑电信号的RPS具有比抑郁型脑电信号的RPS更规则,复杂和不可预测的形状,而抑郁型EEG信号的RPS具有更规则的(简单的)变化和可预测的形状。因此,我们可以说,EEG信号的RPS可用作精神科医生的生物标志物,在视觉抑郁症诊断中比EEG信号更简单。我们还发现,来自右半球的EEG信号比左半球对抑郁症的检测具有重要意义。所提出的框架可以在诊所和医院中使用,以快速,准确地检测出抑郁症。我们发现正常的脑电信号的RPS具有比抑郁型脑电信号的RPS更规则,复杂和不可预测的形状,而抑郁型EEG信号的RPS具有更规则的(简单的)变化和可预测的形状。因此,我们可以说,EEG信号的RPS可用作精神科医生的生物标志物,在视觉抑郁症诊断中比EEG信号更简单。我们还发现,来自右半球的EEG信号比左半球对抑郁症的检测具有重要意义。所提出的框架可以在诊所和医院中使用,以快速,准确地检测出抑郁症。我们发现正常的脑电信号的RPS具有比抑郁型脑电信号的RPS更规则,复杂和不可预测的形状,而抑郁型EEG信号的RPS具有更规则的(简单的)变化和可预测的形状。因此,我们可以说,EEG信号的RPS可用作精神科医生的生物标志物,在视觉抑郁症诊断中比EEG信号更简单。我们还发现,来自右半球的EEG信号比左半球对抑郁症的检测具有重要意义。所提出的框架可以在诊所和医院中使用,以快速,准确地检测出抑郁症。我们可以说,EEG信号的RPS可以用作精神科医生的生物标志物,比视觉抑郁症诊断中的EEG信号更简单。我们还发现,来自右半球的EEG信号比左半球对抑郁症的检测具有重要意义。所提出的框架可以在诊所和医院中使用,以快速,准确地检测出抑郁症。我们可以说,EEG信号的RPS可以用作精神科医生的生物标志物,比视觉抑郁症诊断中的EEG信号更简单。我们还发现,来自右半球的EEG信号比左半球对抑郁症的检测具有重要意义。所提出的框架可以在诊所和医院中使用,以快速,准确地检测出抑郁症。

更新日期:2021-04-11
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