Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32586
EEG-Based Screening Tool for School Student’s Brain Disorders Using Machine Learning Algorithms

Authors: Abdelrahman A. Ramzy, Bassel S. Abdallah, Mohamed E. Bahgat, Sarah M. Abdelkader, Sherif H. ElGohary


Attention-Deficit/Hyperactivity Disorder (ADHD), epilepsy, and autism affect millions of children worldwide, many of which are undiagnosed despite the fact that all of these disorders are detectable in early childhood. Late diagnosis can cause severe problems due to the late treatment and to the misconceptions and lack of awareness as a whole towards these disorders. Moreover, electroencephalography (EEG) has played a vital role in the assessment of neural function in children. Therefore, quantitative EEG measurement will be utilized as a tool for use in the evaluation of patients who may have ADHD, epilepsy, and autism. We propose a screening tool that uses EEG signals and machine learning algorithms to detect these disorders at an early age in an automated manner. The proposed classifiers used with epilepsy as a step taken for the work done so far, provided an accuracy of approximately 97% using SVM, Naïve Bayes and Decision tree, while 98% using KNN, which gives hope for the work yet to be conducted.

Keywords: ADHD, autism, epilepsy, EEG, SVM.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 870


[1] National Health Service. (Online). Available:
[2] Nations International Children’s Emergency Fund (UNICEF). (Online). Available:
[3] Children and Adults with Attention-Deficit/Hyperactivity Disorder (CHADD). (Online). Available:
[4] World Health Organization (WHO). (Online). Available:
[5] Laura M. Guilhoto, “Absence epilepsy: Continuum of clinical presentation and epigenetics”, Seizure European Journal of Epilepsy.
[6] Hye Ran Park, Jae Meen Lee, Hyo Eun Moon, Dong Soo Lee, Bung-Nyun Kim, Jinhyun Kim, Dong Gyu Kim & Sun Ha Paek, “A Short Review on the Current Understanding of Autism Spectrum Disorders”, Experimental Neurobiology journal.
[7] William J. Bosl, Helen Tager-Flusberg & Charles A. Nelson, “EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach”, Nature journal of science.
[8] Enzo Grossi, Chiara Olivieri & Massimo Buscema, “Diagnosis of autism through EEG processed by advanced computational algorithms: A pilot study”.
[9] Abo-Zahhad, M & Ahmed, Sabah & Seha, Sherif Nagib. (2015). “A New EEG Acquisition Protocol for Biometric Identification Using Eye Blinking Signals”. International Journal of Intelligent Systems and Applications (IJISA). 07. 48-54. 10.5815/ijisa.2015.06.05.
[10] Ralph G. Andrzejak, Klaus Lehnertz, Florian Mormann, Christoph Rieke, Peter David & Christian E. Elger “Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state”, Phys. Rev. E, 64, 061907.
[11] Jennifer L. DeWolfe & Beth Ann Malow, “Approach to Sleep-Related Seizure Identification and Management”, Therapy in Sleep Medicine, 2012.
[12] Veloso, L., McHugh, J. R., von Weltin, E., Obeid, I., & Picone, J. (2017). Big Data Resources for EEGs: Enabling Deep Learning Research. In I. Obeid & J. Picone (Eds.), Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (p. 1). Philadelphia, Pennsylvania, USA: IEEE.
[13] Arnaud Delorme & Scott Makeig, “EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis”, Journal of Neuroscience Methods 134:9-21.
[14] Duann, Jeng-Ren, Jung, Tzyy-Ping, and Makeig, Scott “FMRLAB: A MATLAB toolbox for functional imaging analysis”, WWW Site, Swartz Center for Computational Neurobiology, Institute for Neural Computation, University of California San Diego,, (World Wide Web Publication), 2002.