A Deep Learning Neural Network Method Using Linear Eigenvalue Statistics for Schizophrenic EEG Data Classification

Haichun Liu, Lanzhen Li, Yumeng Ye, Changchun Pan (), Genke Yang (), Tao Chen, Tianhong Zhang, Jijun Wang and Caiming (Robert) Qiu
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Haichun Liu: Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
Lanzhen Li: Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
Yumeng Ye: Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
Changchun Pan: Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
Genke Yang: Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
Tao Chen: Department of Statistic and Actuarial Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Tianhong Zhang: Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200001, China
Jijun Wang: Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200001, China
Caiming (Robert) Qiu: School of Electronic Information and Communication, Huazhong University of Science and Technology, Wuhan 430030, China

Mathematics, 2023, vol. 11, issue 23, 1-21

Abstract: Electroencephalography (EEG) signals can be used as a neuroimaging indicator to analyze brain-related diseases and mental states, such as schizophrenia, which is a common and serious mental disorder. However, the main limiting factor of using EEG data to support clinical schizophrenia diagnosis lies in the inadequacy of both objective characteristics and effective data analysis techniques. Random matrix theory (RMT) and its linear eigenvalue statistics (LES) can provide an effective mathematical modeling method for exploring the statistical properties of non-stationary nonlinear systems, such as EEG signals. To obtain an accurate classification and diagnosis of schizophrenia, this paper proposes a LES-based deep learning network scheme in which a series of random matrixes, consisting of EEG data sliding window sampling and their eigenvalues, are employed as features for deep learning. Due to the fact that the performance of the LES-based scheme is sensitive to the LES’s test function, the proposed LES-based deep learning network is embedded with two ways of combining LES’s test functions with learning techniques: the first is to have the LES’s test function assigned, while, using the second way, the optimal LES’s test function should be solved in a functional optimization problem. In this paper, various test functions and different optimal learning methods were coupled in experiments. Our results revealed a binary classification accuracy of nearly 90% in distinguishing between healthy controls (HC) and patients experiencing the first episode of schizophrenia (FES). Additionally, we achieved a ternary classification accuracy of approximately 70% by including clinical high risk for psychosis (CHR). The LES-embedded approach yielded notably higher classification accuracy compared to conventional machine learning methods and standard convolutional neural networks. As the performance of schizophrenia classification is strongly influenced by test functions, a functional optimization problem was proposed to identify an optimized test function, and an approximated parameter optimization problem was introduced to limit the search area of suitable basis functions. Furthermore, the parameterization test function optimization problem and the deep learning network were coupled to be synchronously optimized during the training process. The proposal approach achieved higher classification accuracy rates of 96.87% between HC and FES, with an additional 89.06% accuracy when CHR was included. The experimental studies demonstrated that the proposed LES-based method was significantly effective for schizophrenic EEG data classification.

Keywords: resting state EEG data; schizophrenia; deep learning; random matrix theory; linear eigenvalue statistics; functional optimization problem (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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