Autistic Spectrum Disorder: EEG Analysis and Classification
Keywords:Autism, Electroencephalogram, Multilayer Perceptron, Spectrum disorder,
AbstractAutistic spectrum disorder (ASD) which also known as autism is a syndrome shows neurological disorder found in brain development. Autistic patients suffer from communication disorder and lack of social interaction. This study is aimed to integrate the Electroencephalogram (EEG) signal processing and classification into a graphical user interface (GUI). In this study, severity of the autistic children is classified into three stages, namely, mild, moderate and severe which determined from their sensory response. An electrical signal is obtained by attaching the electrode onto the scalp by following the rules of the system. Then, sensory response test is carried out. The targeted channels on the scalp of the subject are C3, Cz and C4. The signal obtained from these three channels processed for artefact and noise removal suing band pass filter. Features extracted from the preprocessed signal is analysed using Short Time Fourier Transform (STFT.) These extracted features will undergo multilayer perceptron neural network and genetic algorithm for the classification process. The task is performed by implementing the algorithms of signal analysis and classification in the simplest form into GUI. The pattern of the signal and the result of the autism severity are shown in the window from GUI. The GUI also allows the user to insert the profile of the patient as a record to prevent mixing of data and for reference purpose. The GUI designed has to successfully classify the sensory data to identify the level of severity of the autistic child.
C. L. Chuin, “Electroencephalograph Signal Classification for Autistic Spectrum Disorder using Multilayer Perception,” Final Year Project Report, Universiti Teknologi Malaysia, Malaysia, 2015.
S. S. Hussin and R. Sudirman. “Sensory response through EEG interpretation on alpha wave and power spectrum,” in Malaysia Technical Universities Conference on Engineering & Technology 2012, Perlis, 2012, pp. 288-293.
R. Vigario, J. Sarela, V. Jousmaki, M. Hamalainen, and E. Oja, “Independent component approach to the analysis of EEG and MEG recordings,” IEEE Transactions on Biomedical Engineering, vol. 47, no. 5, pp. 589-593, 2000.
N. N. Boutros, R. Lajiness-O’Neill, A. Zillgitt, A. E Richard and S. M. Bowyer. “EEG changes associated with autistic spectrum disorders,” Neuropsychiatric Electrophysiology, vol. 1, no. 3, pp. 1- 20, 2015.
S. S. Hussin and R. Sudirman, “EEG interpretation through short time fourier transform for sensory response among children,” Australian Journal of Basic and Applied Sciences, vol. 8, no. 5, pp. 417- 422, 2014.
M. Okamoto and I. Dan, “Functional near-infrared spectroscopy for human brain mapping of taste-related cognitive functions, ”Journal of Bioscience and Bioengineering, vol. 103, no. 3, pp. 207-215, 2007.
A. Hyvärinen. “Fast and robust fixed-point algorithms for independent component analysis”. IEEE Transactions on Neural Networks, vol. 10, no. 3, pp. 626-634, 1999.
S. Reynolds and S. J. Lane, “Diagnostic validity of sensory overresponsivity: a review of the literature and case reports,” Journal of Autism Development Disorder, vol. 38, pp. 516- 529, 2008.
A. Sheikani, H. Behnam, M.R. Mohammadi and M. Noroozian, “Analysis of EEG background activity in Autism disease patients with bispectrum and STFT measure”, in 11th WSEAS International Conference on Communications, Agios Nikolaos, Crete Island, Greece, 2007, pp. 318-322.
M. K. Kiymik, I. Guler, A. Dizibuyuk and M. Akin, “Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application,” Computers in Biology and Medicine, vol. 35, no. 7, pp. 603- 616, 2005.
A. Delorme and S. Makeig, “EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis,” Journal of Neuroscience Methods, vol. 134, pp. 9-21, 2004.
S. K. Pal and S. Mitra, “Multilayer perceptron fuzzy sets, and classification”, IEEE Transactions on Neural Networks, vol. 3, no. 5, pp. 683- 697, 1992.
S. Chauhan and S. Dhingra, “Pattern recognition system using MLP neural networks,” International Journal of Engineering Research and Development, vol. 4, no. 9, pp. 43-46, 2012.
M. G. Bello, “Enhanced training algorithms, and integrated training/architecture selection for multilayer perceptron networks”, IEEE Transaction on Neural Networks, vol. 3, no. 6, pp.864- 875, 1992.
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