ConVnet BiLSTM for ASD Classification on EEG Brain Signal

Abstract

As a neurodevelopmental disability, Autism Spectrum Disorder (ASD) is classified as a continuum disorder. The availability of an automated technology system to classify the ASD trait would have a significant impact on paediatricians, as it would assist them in diagnosing ASD in children using a quantifiable method. In this paper, we propose a novel autism diagnosis method that is based on a hybrid of the deep learning algorithms. This hybrid consists of a convolutional neural network (ConVnet) architecture that merges two LSTM blocks (BiLSTM) with the other direction of propagation to classify the output state on the brain signal data from electroencephalogram (EEG) on individuals; typically development (TD) and autism (ASD) obtained from the Simon Foundation Autism Research Initiative (SFARI) database to classify the output state. For a 70:30 data distribution, an accuracy of 97.7 percent was achieved. Proposed methods outperformed the current state-of-the art in terms of autism classification efficiency and have the potential to make a significant contribution to neuroscience research, as demonstrated by the results.