An EEG based nonlinearity analysis method for schizophrenia diagnosis

Zhao, Q. and Hu, B. and Liu, L. and Ratcliffe, M. and Peng, H. and Zhai, J. and Li, L. and Shi, Q. and Liu, Q. and Qi, Y. (2012) An EEG based nonlinearity analysis method for schizophrenia diagnosis. In: 9th IASTED International Conference on Biomedical Engineering, BioMed 2012, 15 February 2012 through 17 February 2012, Innsbruck; Austria.

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Abstract

In this paper, the complexity and chaos of EEG (electroencephalogram) signals exhibited in schizophrenic patients are analyzed using four nonlinear features: C0-complexity, Kolmogorov entropy together with an estimation of the correlation dimension and Lempel-Ziv complexity. The first two of these being novel applications of these measures. EEGs from 31 schizophrenic patients (18 males, 13 females, mean age 25.9 ±3.6 years) and 31 age/sex matched control subjects were recorded using 12 electrodes. In a t-test, it was found that all four nonlinear features had a significant variance between the schizophrenics and the control set (p ≤ 0.05). A classification accuracy of 91.7% was obtained by Back Propagation Neural Networks. Our results show that the discrimination of schizophrenic behavior is possible with respect to a control set using nonlinear analysis of EEG signals. We also assert that these methods may be the basis for a valuable tool set of EEG methods that could be used by psychiatrists when diagnosing schizophrenic patients.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Classification, EEG, Nonlinear method, Schizophrenia, Back propagation neural networks, Classification accuracy, Control set, Control subject, Correlation dimensions, EEG signals, Kolmogorov entropy, Lempel Ziv complexity, Mean ages, Non-linear methods, Nonlinear features, Nonlinearity analysis, Novel applications, Schizophrenia, Schizophrenic patients, Biomedical engineering, Classification (of information), Diseases, Neural networks, Nonlinear analysis, Electroencephalography
Subjects: C800 Psychology
G400 Computer Science
H900 Others in Engineering
Divisions: Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
UoA Collections > UoA11: Computer Science and Informatics
Depositing User: Hussen Farooq
Date Deposited: 21 Jul 2016 09:20
Last Modified: 21 Jul 2016 09:20
URI: http://www.open-access.bcu.ac.uk/id/eprint/2254

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