Abstract
A nearly diagnosis and inference of Autism Spectrum Disorder (ASD) demands an automated intelligent system that can efficiently and accurately detect disorder from diverse dataset. The existing automated (machine/deep learning) state-of-the-art approaches have major limitation of being specific to the EEG dataset and have not been tested over EEG-datasets with varying metadata. Hence, this challenges how well the methods can be generalized. The present paper aims to propose and investigate a Bidirectional Stacked GRU network for ASD prediction considering diversity in EEG-signals recorded from different devices. The stacked layers in the proposed model structure performs complex mapping, nonlinear variation capturing, and temporal feature extraction for classifying individuals with ASD from Neurotypical individuals. To enhance the generalizability of the model and identify model robustness for practical implementation, the proposed framework is trained on three different EEG-dataset fed randomly to the system. The model in cross-validation framework showed an accuracy of 97% compared to the state-of-the-art methods ranging from 82 to 96% when applied on the same dataset. Furthermore, a receiver operating characteristic curve metric has quantified proposed model performance showing 0.998 as Area Under the Curve (AUC) compared to models with values in range 0.961 < AUC < 0.984.






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The datasets generated during and/or analysed during the current study are available from the First author (Dr. Tanu Wadhera) on reasonable request.
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Wadhera, T., Bedi, J. & Sharma, S. Autism spectrum disorder prediction using bidirectional stacked gated recurrent unit with time-distributor wrapper: an EEG study. Neural Comput & Applic 35, 9803–9818 (2023). https://doi.org/10.1007/s00521-023-08218-4
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DOI: https://doi.org/10.1007/s00521-023-08218-4