Autistic Spectrum Disorder: EEG Analysis and Classification

Abstract

Autistic 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.