Diagnosis of Autism Disorder Based on Deep Network Trained by Augmented EEG Signals

Autism spectrum disorder is a neurodevelopmental disorder typically characterized by abnormalities in social interaction and stereotyped and repetitive behaviors. Diagnosis of autism is mainly based on behavioral tests and interviews. In recent years, studies involving the diagnosis of autism based on analysis of EEG signals have increased. In this paper, recorded signals from people suffering from autism and healthy individuals are divided to without overlap windows considered as images and these images are classified using a two-dimensional Deep Convolution Neural Network (2D-DCNN). Deep learning models require a lot of data to extract the appropriate features and automate data classification. But, in most neurological studies, preparing a large number of measurements is difficult (a few 1000s as compared to million natural images), due to the cost, time, and difficulty of recording these signals. Therefore, to make the appropriate number of data, in our proposed method, some of the data augmentation methods are used. These data augmentation methods are mainly introduced for image databases and should be generalized for EEG-as-an-image database. In this paper, one of the nonlinear image mixing methods is used that mixes the rows of two images. According to the fact that any row in our image is one channel of EEG signal, this method is named channel combination. The result is that in the best case, i.e., augmentation according to channel combination, the average accuracy of 88.29% is achieved in the classification of short signals of healthy people and ASD ones and 100% for ASD and epilepsy ones, using 2D-DCNN. After the decision on joined windows related to each subject, we could achieve 100% accuracy in detecting ASD subjects, according to long EEG signals.