A Novel End-To-End Learning Framework Based on Optimised Residual Gated Units and Stacked Pre-Trained Layers for Detection of Autism Spectrum Disorders (ASD)

Autism Spectrum Disorder (ASD) is distinguished in detailed with diverse assemblage neural behaviours, diagnosed over the recent time of years. These ASD are developed using the increased internal connectivity among neural units of functional brain. Since Autism characteristics tend to occur over the months or even years, cognitive system for an early diagnosis of ASD is badly required to overcome the neural complexities of ASD with an accurate prediction. Electroencephalogram (EEG) is common technique for analysing brain’s electrical activity and widely used as the major tool for the gathering the neural information. Recent ensemble of EEG and Artificial Intelligence (AI) has thrown the brighter light in the ASD early prediction, but still computational overhead hurdles in achieving the highest performance. In this research article, novel deep learning framework that consist of hybrid combination of multiple tiers of pre-trained convolutional models and residual gated recurrent units with the hyper-parameters are tuned by the chaotic Cat swarm optimisation. As the first step, EEG signals are collected and translated into two-dimensional (2D) spectrograms followed by the pre-processing and feature engineering using the multi-scale stacked pre-trained models. Finally, the residual gated units are entangled for extracting the temporal features which are used to train the optimised classification networks. The proposed model is validated using EEG signals extracted from the 43 subjects and performance including accuracy, precision, recall and F1-score are measured then juxtaposed through the other cutting-edge learning models. The accuracy of detection is found to be 98.5%, precision was 98.2% recall was 98% and F1-score was 98% for the real time EEG subjects and also statistically proved its stability in handling the non-linear EEG datasets.