Schizophrenia EEG Signal Classification Based on Swarm Intelligence Computing

Abstract

One of the serious mental disorders where people interpret reality in an abnormal state is schizophrenia. A combination of extremely disordered thinking, delusion, and hallucination is caused due to schizophrenia, and the daily functions of a person are severely disturbed because of this disorder. A wide range of problems are caused due to schizophrenia such as disturbed thinking and behaviour. In the field of human neuroscience, the analysis of brain activity is quite an important research area. For general cognitive activity analysis, electroencephalography (EEG) signals are widely used as a low-resolution diagnosis tool. The EEG signals are a great boon to understand the abnormality of the brain disorders, especially schizophrenia. In this work, schizophrenia EEG signal classification is performed wherein, initially, features such as Detrend Fluctuation Analysis (DFA), Hurst Exponent, Recurrence Quantification Analysis (RQA), Sample Entropy, Fractal Dimension (FD), Kolmogorov Complexity, Hjorth exponent, Lempel Ziv Complexity (LZC), and Largest Lyapunov Exponent (LLE) are extracted initially. The extracted features are, then, optimized for selecting the best features through four types of optimization algorithms here such as Artificial Flora (AF) optimization, Glowworm Search (GS) optimization, Black Hole (BH) optimization, and Monkey Search (MS) optimization, and finally, it is classified through certain classifiers. The best results show that, for normal cases, a classification accuracy of 87.54% is obtained when BH optimization is utilized with Support Vector Machine-Radial Basis Function (SVM-RBF) kernel, and for schizophrenia cases, a classification accuracy of 92.17% is obtained when BH optimization is utilized with SVM-RBF kernel.

Detection of liver abnormalities—A new paradigm in medical image processing and classification techniques

“…If the CCA value is greater than 0.5, the two classes are highly correlated, and vice versa. Based on the CCA, we utilize higher classifiers 29 . The CCA is used to find the linear combinations of two vectors C and D, as follows: The CCA for the GLCM and statistical features are 0.6410 and 0.4063, respectively.…”

“…Based on the CCA, we utilize higher classifiers. 29 The CCA is used to find the linear combinations of two vectors C and D, as follows:…”

SASDL and RBATQ: Sparse Autoencoder With Swarm Based Deep Learning and Reinforcement Based Q-Learning for EEG Classification

“…As the paper discusses schizophrenia classification also from EEG signals, recent literature about it is also discussed in the paper as follows. Schizophrenia is a serious mental disorder where people interpret reality in an abnormal manner Schizophrenia results in a combination of delusion, hallucination, and disordered thinking thereby the daily functions are severely impaired [31] . Therefore, schizophrenia involves a range of problems with cognition, emotion, and behaviour.…”

“…EEG signals are a great boon to analyze this disorder and some of the famous works are utilized in this field are as follows. For schizophrenia EEG analysis, the EEG series splitting reported an accuracy of 92.91% [33] , deep convolutional neural networks reported 98.07% for non-subject based testing and 81.26% for subject based testing [34] , spectral based analysis reporting 96.77% [35] , swarm computing techniques with classifiers reporting 92.l7% [31] , Short Time Fourier Transform (STFT) with CNN reporting 97.00% [36] , Partial Least Squares technique reporting 98.77% [37] , multivariate Empirical Mode Decomposition (EMD) reporting 93.00% [38] , continuous wavelet transform (CWT) with CNN reporting an accuracy of 98.60% [39] , a simple CNN reporting 98.96% [40] , sparse depiction with nature inclined classification and deep cum transfer learning reporting 98.72% [30] and Collatz pattern reporting an accuracy of 99.47% [41] are some of the most famous works proposed recently. In this work, the key contributions are as follows and no previous works have been reported in literature using the two developed novel deep learning models.…”

Time-Frequency Distribution Analysis for Electroencephalogram Signals of Patients With Schizophrenia and Normal Participants

“…Kim et al 20 claimed that EEG microstate features have reasonable accuracy in the classification of patients with schizophrenia from normal individuals. Prabhakar et al 21 used several EEG features such as detrend fluctuation method, Hurst exponent, and recurrence quantification analysis and they achieved an acceptable classification accuracy for participants with schizophrenia. Sabeti et al 22 reported a classification accuracy of 86% and 90% by linear discriminant analysis and Adaboost classifiers in differentiation of patients with schizophrenia from normal ones based on both linear and nonlinear EEG features.…”