Schizophrenia recognition based on the phase space dynamic of EEG signals and graphical features. (August 2021)

• Record Type: Journal Article
• Authors: Akbari, Hesam; Ghofrani, Sedigheh; Zakalvand, Pejman; Tariq Sadiq, Muhammad
• Is Part Of: Biomedical signal processing and control. Volume 69(2021)
• Publication Date: 2021-08
• Subjects: Electroencephalography (EEG) -- Phase space dynamic -- Schizophrenic -- Graphical feature -- Classification; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102917
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Schizophrenia is a mental disorder that causes adverse effects on the mental capacity of a person, emotional inclinations, and quality of personal and social life. The official statistics reported that about 20 million people suffer from this severe mental illness worldwide. The manual screening of schizophrenic patients is tedious, time-consuming, costly, and prone to human error. Thus, it is necessary to provide a fully automatic, fairly accurate, and reasonably inexpensive system to diagnose schizophrenia patients. Electroencephalography (EEG) is commonly employed to evaluate and detect the brain's functions and disorders. The purpose of this study is to introduce, apply, and examine a novel framework to automatically diagnose schizophrenia disorders. This procedure is performed by using the phase space dynamic (PSD) of EEG signals. The two-dimensional PSD of EEG signals is first plotted on Cartesian space. Then, fifteen graphical features are extracted to evaluate the chaotic behavior of PSD based on healthy and schizophrenic subjects. Also, a sizeable number of remarkable features and optimum channels are obtained by the forward selection algorithm (FSA). Finally, eight different classifiers are tested for schizophrenia detection. In this case, the K-nearest neighbor (KNN) and generalized regression neural network (GRNN) showed better performance than the others. As a result, using a 10-fold cross-validation strategy, the KNN classifier with City-block distance reached the level of the average classification accuracy (ACC) of 94.80%, the sensitivity (SEN) of 94.30%, and the specificity (SPE) of 95.20%. The findings of the study confirmed that the PSD shape of the Cz channel for schizophrenia groups is more regular than the healthy ones. It can be applied as a biomarker for the medical team to diagnose schizophrenia disorder. It was found that the frontal and parietal lobes reflect the effects of schizophrenia disorder better than the other lobes. Consequently, the proposed framework contributes to realizing a real-time, easily accessible, and fairly inexpensive method in clinics and hospitals to quickly detect schizophrenia disorder.