Classification of BMD and ADHD patients using their EEG signals |
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| Authors: | Khadijeh Sadatnezhad Reza Boostani Ahmad Ghanizadeh |
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| Affiliation: | 1. CSE&IT Department, Faculty of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran;2. Research Center for Psychiatry and Behavioral Sciences, Department of Psychiatry, Shiraz University of Medical Sciences, Shiraz, Iran;1. Physiology Unit, Health Sciences Faculty, La Laguna University, Spain;2. Department of Industrial Engineering, School of Engineering and Technology, University of La Laguna, Tenerife, Spain;3. Clinical Neurophysiology Unit, University Hospital La Candelaria, Tenerife, Spain;4. Pediatric Unit, University Hospital N.S. La Candelaria, Tenerife, Spain;1. Human Psychobiology Lab, Experimental Psychology Department, University of Sevilla, 41018, Sevilla, Spain;2. Instituto Hispalense de Pediatría, Seville, Spain;3. UGC of Mental Health, Virgen Macarena Hospital, 41009, Sevilla, Spain;1. Faculty of New Sciences & Technologies, Semnan University, Semnan, Iran;2. Department of Electrical Engineering, Semnan University, Semnan, Iran;1. DSPRL-Wireless@VT-Electrical & Computer Engineering, Virginia Tech, Blacksburg, VA, 24060, United States;2. Psychology, Virginia Tech, Blacksburg VA 24060, United States;1. Biomarkers of Vulnerability Unit, Division of General Psychiatry, Department of Mental Health and Psychiatry, University Hospitals of Geneva, Belle Idée, Chemin du Petit-Bel-Air 2, 1225 Chêne-Bourg, Switzerland;2. Division of Psychiatric Specialties, Department of Mental Health and Psychiatry, University Hospitals of Geneva, 20bis rue de Lausanne, 1201 Geneva, Switzerland;3. Division of Geriatrics, Department of Internal Medicine, Rehabilitation and Geriatrics, University Hospitals of Geneva, Chemin du Pont Bochet 3, 1226 Thônex, Switzerland;4. Division of General Psychiatry, Department of Mental Health and Psychiatry, University Hospitals of Geneva, Belle Idée, Chemin du Petit-Bel-Air 2, 1225 Chêne-Bourg, Switzerland;5. INSERM U1039, Faculty of Medicine, Bâtiment Jean Roger, 38700 La Tronche, France |
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| Abstract: | Bipolar Mood Disorder (BMD) and Attention Deficit Hyperactivity Disorder (ADHD) patients mostly share clinical signs and symptoms in children; therefore, accurate distinction of these two mental disorders is a challenging issue among the psychiatric society. In this study, 43 subjects are participated including 21 patients with ADHD and 22 subjects with BMD. Their electroencephalogram (EEG) signals are recorded by 22 electrodes in two eyes-open and eyes-closed resting conditions. After a preprocessing step, several features such as band power, fractal dimension, AR model coefficients and wavelet coefficients are extracted from recorded signals. This paper is aimed to achieve a high classification rate between ADHD and BMD patients using a suitable classifier to their EEG features. In this way, we consider a piece wise linear classifier which is designed based on XCSF. Experimental results of XCSF-LDA showed a significant improvement (86.44% accuracy) compare to that of standard XCSF (78.55%). To have a fair comparison, the other state-of-art classifiers such as LDA, Direct LDA, boosted JD-LDA (BJDLDA), and XCSF are assessed with the same feature set that finally the proposed method provided a better results in comparison with the other rival classifiers. To show the robustness of our method, additive white noise with different amplitude is added to the raw signals but the results achieved by the proposed classifier empirically confirmed a higher robustness against noise compare to the other classifiers. Consequently, the proposed classifier can be considered as an effective method to classify EEG features of BMD and ADHD patients. |
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