Classification of Coherence Indices Extracted from EEG Signals of Mild and Severe Autism

Abstract

Autism spectrum disorder is a debilitating neurodevelopmental illness characterized by serious impairments in communication and social skills. Due to the increasing prevalence of autism worldwide, the development of a new diagnostic approach for autism spectrum disorder is of great importance. Also, diagnosing the severity of autism is very important for clinicians in the treatment process. Therefore, in this study, we intend to classify the electroencephalogram (EEG) signals of mild and severe autism patients. Twelve patients with mild autism and twelve patients with severe autism with the age range of 10-30 years participated in the present research. Due to the difficulties of working with autism patients and recording EEG signals from these patients in the awake state, the Emotiv Epoch headset device was utilized in this work. After signal preprocessing, we calculated short-range and long-range coherence values in the frequency range of 1-45 Hz, including short-and long-range intra-and inter-hemispheric coherence features.Then, statistical analysis was conducted to select coherence features with statistical differences between the two groups. Multilayer perceptron (MLP) neural network and support vector machine (SVM) with radial basis function (RBF) kernel were used in the classification stage. Our results showed that the best MLP classification performance was obtained by selected inter-hemispheric coherence features with accuracy, sensitivity and specificity of 96.82%, 97.82% and 96.92%, respectively. Also, the best SVM classification performance was obtained by selected inter-hemispheric coherence features with accuracy, sensitivity and specificity of 94.70%, 93.85% and 95.55%, respectively. However, it should be noted that the MLP neural network imposes a much higher computational cost than the SVM classifier. Considering that our simple system gives promising results in diagnosing autistic patients with mild and severe severities from EEG, there is scope for further work with a larger sample size and different ages and genders.

Sleep Apnea Detection Method Based on Improved Random Forest

“…Yang, et al propose a method to detect Parkinson's disease and predict disease severity by breathing at night [6]. SA can also cause neurasthenia, which affects the classification of autistic patients using electroencephalography [7]. Therefore, timely, accurate and convenient detection of SA is of great significance.…”