Detection of Abnormalities based on Gamma Wave EEG Signal for Autism Spectrum Disorder
Keywords:ASD, EEG, Gamma Wave, GRNN, PNN
AbstractDiagnosing Autism Spectrum Disorder (ASD) by using the traits of abnormalities in their gamma waveform has been proposed in this study to suggest an objective method to detect the disorder using Electroencephalography (EEG) signal. Gamma waveform plays an important role in learning, memory and information processing where it shows slower activities in ASD person compared to a normal person, thus, causing the patients to have trouble in processing knowledge, communicate and pay attention. This study applies Probabilistic Neural Network (PNN) and General Regression Neural Network (GRNN) to classify the data into normal and abnormal classes. Classification algorithm by PNN was used as a benchmark for the outcomes. The results show that even though PNN and GRNN have similar architecture, but with fundamental difference, the outcomes are different. In this case, PNN performs better than GRNN. To obtain the desired results, we used three and four statistical features (mean, minimum, maximum and standard deviation) for both methods. The outcomes of using PNN with four features are more accurate (99.5% for normal class and 80.5% for abnormal class) compared to only three features. Furthermore, the outcomes of using GRNN with four features also have improvement (95% for normal class and 63.5% for abnormal class) compared to only three features.
W. Jamal, S. Das, I.A. Oprescu, K. Maharatna, F. Apicella and F. Sicca, “Classification of Autism Spectrum Disorder using Supervised Learning of Brain Connectivity Measures Extracted from Synchrostates,” Journal of Neural Engineering, pp. 1-27, 2014.
J. L. Matson, J. Wilkins, and M. Gonzalez, “Early Identification and Diagnosis in Autism Spectrum Disorders in Young Children and Infants: How Early is Too Early?,” Research in Autism Spectrum Disorders 2, pp. 75-84, 2008.
"Causes - Autism Society". Autism Society. N.p., 2016. Web. 5 Nov. 2016.
Sudirman, S. Saidin, N. M. Safri. “Study of Electroencephalography Signal of Autism and Down Syndrome Children using FFT,” IEEE Symposium on Industrial Electronics and Applications (ISIEA 2010), pp. 401-406, 2010.
J. Wang, J. Barstein, L. Ethridge, M. Mosconi, Y. Takarae and J. Sweeney, "Resting state EEG abnormalities in autism spectrum disorders," Journal of Neurodevelopmental Disorders, vol. 5, no. 1, pp. 24, 2013.
N. N. Boutros, R. L. Neill, A. Zillgitt, A. E. Richard, and S. M. Bowyer, “EEG Changes Associated with Autistic Spectrum Disorders,” Neuropsychiatric Electrophysiology, vol. 3, no. 1, pp. 1-20, 2015.
M. R. Berthold, “Constructive Training of Probabilistic Neural Networks,” Neurocomputing 19, pp. 167-183, 1998.
V. Cheung, K. Cannons, “An Introduction to Probabilistic Neural Networks”, 2002.
S. A. Hannan, “Generalized Regression Neural Network and Radial Basis Function for Heart Disease Diagnosis,” International Journal of Computer Applications, no. 13, pp. 7-13, 2010.
S. Nesil, F. Günes, U. Özkaya1 and B. Türetken, “Generalized Regression Neural Network based Phase Characterization of a Reflectarray Employing Minkowski Element of Variable Size,” Electronic and Communication Engineering, Yıldız Technical University, Istanbul, Turkey, pp. 1-4.
I. Popescu, A. Kanatas, P. Constantinou, I. Naforniţǎ, “Applications of General Regression Neural Networks for Path Loss Prediction,” Neural Networks 1, pp. 3, 2002.
How to Cite
TRANSFER OF COPYRIGHT AGREEMENT
The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):
- All proprietary right other than copyright, such as patent rights
- The right to make further copies of all or part of the published article for my use in classroom teaching
- The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
- The right to make copies of the published work for internal distribution within the institution that employs me
I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.