Depression recognition based on the reconstruction of phase space of EEG signals and geometrical features. (August 2021)

• Record Type: Journal Article
• Authors: Akbari, Hesam; Sadiq, Muhammad Tariq; Ur Rehman, Ateeq; Ghazvini, Mahdieh; Naqvi, Rizwan Ali; Payan, Malih; Bagheri, Hourieh; Bagheri, Hamed
• Is Part Of: Applied acoustics. Volume 179(2021)
• Publication Date: 2021-08
• Subjects: EEG signal -- Depression disorder -- Reconstruction of phase space -- Geometrical features -- Feature selection -- Classification; Acoustical engineering -- Periodicals; Periodicals; 620.2
• DOI: 10.1016/j.apacoust.2021.108078
• Languages: English
• ISSNs: 0003-682X

Abstract

Abstract: 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.