An efficient classifier to diagnose of schizophrenia based on the EEG signals |
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| Authors: | Reza Boostani Khadijeh Sadatnezhad Malihe Sabeti |
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| Affiliation: | 1. National Laboratory of Pattern Recognition and Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;3. Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Center, Atlanta 30303, GA, USA;4. Peking University Sixth Hospital, Institute of Mental Health, Beijing 100191, China;5. Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing 100191, China;6. Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, Henan, China;7. Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, Henan, China;8. Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei, China;9. Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi’an 710032, Shaanxi, China;10. Zhumadian Psychiatric Hospital, Zhumadian 463000, Henan, China;11. Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan, China;12. Department of Psychology, Xinxiang Medical University, Xinxiang 453002, Henan, China;13. Center for Life Sciences/PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China;14. Queensland Brain Institute, University of Queensland, Brisbane 4072, QLD, Australia;15. CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;1. Department of Biomedical Engineering, Hanyang University, Seoul, South Korea;2. Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, South Korea;3. Berlin Institute of Technology, Machine Learning Group, Marchstrasse 23, Berlin 10587, Germany;4. Psychiatry Department, Ilsan Paik Hospital, Inje University, Goyang, South Korea |
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| Abstract: | In this paper, electroencephalogram (EEG) signals of 13 schizophrenic patients and 18 age-matched control participants are analyzed with the objective of classifying the two groups. For each case, multi-channels (22 electrodes) scalp EEG is recorded. Several features including autoregressive (AR) model parameters, band power and fractal dimension are extracted from the recorded signals. Leave-one (participant)-out cross validation is used to have an accurate estimation for the separability of the two groups. Boosted version of Direct Linear Discriminant Analysis (BDLDA) is selected as an efficient classifier which applied on the extracted features. To have comparison, classifiers such as standard LDA, Adaboost, support vector machine (SVM), and fuzzy SVM (FSVM) are applied on the features. Results show that the BDLDA is more discriminative than others such that their classification rates are reported 87.51%, 85.36% and 85.41% for the BDLDA, LDA, Adaboost, respectively. Results of SVM and FSVM classifiers were lower than 50% accuracy because they are more sensitive to outlier instances. In order to determine robustness of the suggested classifier, noises with different amplitudes are added to the test feature vectors and robustness of the BDLDA was higher than the other compared classifiers. |
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