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http://dx.doi.org/10.9717/kmms.2021.24.10.1336

Machine Learning-Based EEG Classification for Assisting the Diagnosis of ADHD in Children  

Kim, Min-Ki (Dept. of Computer Science, Gyeongsang National University, Engineering Research Institute)
Publication Information
Journal of Korea Multimedia Society / v.24, no.10, 2021 , pp. 1336-1345 More about this Journal
Abstract
Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurological disorders in children. The diagnosis of ADHD in children is based on the interviews and observation reports of parents or teachers who have stayed with them. Since this approach cannot avoid long observation time and the bias of observers, another approach based on Electroencephalography(EEG) is emerging. The goal of this study is to develop an assistive tool for diagnosing ADHD by EEG classification. This study explores the frequency bands of EEG and extracts the implied features in them by using the proposed CNN. The CNN architecture has three Convolution-MaxPooling blocks and two fully connected layers. As a result of the experiment, the 30-60 Hz gamma band showed dominant characteristics in identifying EEG, and when other frequency bands were added to the gamma band, the EEG classification performance was improved. They also show that the proposed CNN is effective in detecting ADHD in children.
Keywords
Attention Deficit Hyperactivity Disorder (ADHD); EEG; Gamma band; CNN;
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1 A.E. Alchalabi, M. Elsharnouby, S. Shirmohammaadi, and A.N. Eddin, "Feability of Detecting ADHD Patients' Attention Levels by Classifying Their EEG Signals," Proceedings of the IEEE International Symposium on Medical Measurements and Applications, pp. 314-319, 2017.
2 M. L. Danielson R. H. Bitsko, R. M. Ghandour, J. R. Holbrook, M. D. Kogan, and S. J. Blumberg. "Prevalence of Parent-Reported ADHD Diagnosis and Associated Treatment Among U.S. Children and Adolescents," Journal of Clinical Child and Adolescent Psychology, Vol. 47, No. 2, pp. 199-212, 2018.   DOI
3 H. Oh and K. Kim, "Effect of Multi-Sensory Play Therapy Program on the Attention and Learning of Children with ADHD," Journal of the Korean Society of Integrative Medicine, Vol. 7, No. 4, pp. 23-32, 2019.   DOI
4 S.D. Silva, S. Dayarathna, G. Ariyarathne, and D. Meedenjya, "A Survey of Attention Deficit Hyperactivity Disorder Identification Using Psychophyiologial Data," International Journal of Online and Biomedical Engineering, Vol. 15, No. 13, pp. 61-76, 2019.   DOI
5 M.R. Mohammadi, A. Khaleghi, A.M. Nasrabadi, S. Rafieivand, M. Begol, and H. Zarafshan, "EEG Classification of ADHD and Normal Children Using Non-linear Features and Netural Network," Biomedical Engineering Letter, Vol. 6, pp. 66-73, 2016.   DOI
6 B. TaghiBeyglou, N. Hasanzadeh, and F. Bagheri, "ADHD Diagnosis in Children Using Common Spatial Pattern and Nonlinear Analysis of Filter Banked EEG," Proceedings of the Iranian Conference on Electrical Engineering, pp. 1-5, 2020.
7 W. Mao, H.I.K. Fathurrahman, Y. Lee, and T.W. Chang, "EEG Dataset Classification Using CNN Method," Journal of Physics: Conference Series, Vol. 1456, pp. 1-7, 2020.
8 P. Sandheep, S. Vineeth, P. Meljo, and D.P. Subha, "Performance Analysis of Deep Learning CNN in Classification of Depression EEG Signals," Proceedings of IEEE Region 10 Conference, pp. 1339-1344, 2019.
9 M. Moghaddari, M.Z. Lighvan, and S. Danishvar, "Diagnose ADHD Disorder in Children Using Convolutional Neural Network Based on Continuous Mental Task EEG," Computer Methods and Programs in Biomedicine, Vol. 197, pp. 1-12, 2020.
10 H. Chen, Y. Song, and X. Li, "A Deep Learning Framework for Identifying Children with ADHD Using an EEG-Based Brain Network," Neurocomputing, Vol. 356, pp. 83-86, 2019.   DOI
11 Y. Roy, H. Banville, I. Albuquerque, A. Gramfort, T.H. Falk, and J. Faubert, "Deep Learning-Based Electroencephalography Analysis: A Systematic Review," Journal of Neural Engineering, Vol. 16, No. 5, pp. 1-37, 2019.
12 T. Aleksandar, M.S. Silvana, K. Ljupco, P.J. Jordan, M. Andreas, and C. Gian, "Machine Learning Approach for Classification of ADHD Adults," International Journal of Psychophysiology, Vol. 93, pp. 162-166, 2014.   DOI
13 D.O. Bos, "EEG-based Emotion Recognition," The Influence of Visual and Auditory Stimuli, Vol. 56, pp. 1-17, 2006.
14 L. Breiman, "Random Forests," Machine Learning, Vol. 45, pp. 5-32, 2001.   DOI
15 G. Joo, C. Park, and H. Im, "Performance Evaluation of Machine Learning Optimizers," Journal of Institute of Korean Electrical and Electronics Engineers, Vol. 24, No. 3, pp. 766-776, 2020.
16 B. T. Felt, B. Biermann, J. G. Christner, P. Kochhar, and R. V. Harrison, "Diagnosis and Management of ADHD in Children," American Family Physician, Vol. 90, No. 7, pp. 456-464, 2014.
17 A. Allahverdy, A.K. Moghaddam, M.R. Mohammadi, and A.M. Nasrabadi, "Detecting ADHD Children using the Attention Continuity as Nonlinear Feature of EEG," Frontiers in Biomedical Technologies, Vol. 3, No. 1-2, pp. 28-33, 2016.
18 A. Vahid, A. Bluschke, V. Roessner, S. Stober, and C. Beste, "Deep Learning Based on EventRelated EEG Differentiates Children with ADHD from Healthy Controls," Journal of Clinical Medicine, Vol. 8, No. 1055, pp. 1-15, 2019.
19 EEG Data for ADHD/Control Children, Shahed University of Iran, https://ieee-dataport.org/open-access/eeg-data-adhd-control-children (accessed October 1, 2012).
20 Q. Wang, O. Sourina, and M.K. Nguyen, "EEG-based 'Serious' Games Design for Medical Applications," Proceedings of the International Conference on Cyberworlds, pp. 270-276, 2010.