Fusion of Convolutional and Recurrent Networks for Autism Detection from EEG Signals
Keywords:
Autism Spectrum Disorder (ASD), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Data Science, Signal Processing, Temporal-Spatial Feature Extraction, Health Monitoring, Neurodevelopmental DisordersAbstract
Autism Spectrum Disorder (ASD) is amultifaceted neurodevelopmental condition that affectscommunication, behavior, and social interaction. Early andaccurate diagnosis is essential for effective intervention, yetexisting clinical assessments are often time-consuming andsubject to human interpretation. This study presents a novel
References
A. M. Roy, "CNN-Based Model with Feature Integration for Classification of MI EEG Subjects in Brain-Machine Interfaces,"
bioRxiv, Jan. 5, 2022. doi: 10.1101/2022.01.05.475058.
K. Palani Thanaraj, B. Parvathavarthini, U. John Tanik, V. Rajinikanth, S. Kadry, and K. Kamalanand, "Utilizing Deep Neural Networks for
EEG Signal Classification via Gramian Angular Summation Field for Epilepsy Diagnosis," arXiv:2003.04534.
Downloads
Published
2025-07-22
How to Cite
Mahesh KhadtareDr. Gajanan K. Kharate ,Dr. Dnyaneshwar Ahire,Pragati Dharmale. (2025). Fusion of Convolutional and Recurrent Networks for Autism Detection from EEG Signals. Journal of Computational Analysis and Applications (JoCAAA), 34(7), 108–113. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/3277
Issue
Section
Articles
