Performance Evaluation of Neural Network Models for Autism Detection Using EEG Data

Authors

  • Nazmul Hasan School of Computer Engineering and Mathematical Sciences, Defence Institute of Advanced Technology, Pune, India
  • Priyasha Paul Department of Biosciences, Manipal University Jaipur, Jaipur, India
  • Manisha Jitendra Nene School of Computer Engineering and Mathematical Sciences, Defence Institute of Advanced Technology, Pune, India

DOI:

https://doi.org/10.46604/aiti.2024.13951

Keywords:

Autism, detection, EEG, machine learning, neural network

Abstract

This study aims to leverage a promising avenue for the precise and early detection of Autism. Autism is a multifaceted neurodevelopmental condition marked by challenges in social interaction, communication, and repetitive behaviors. Traditional diagnosis relies on time-consuming behavioral assessments, necessitating reliable and non-intrusive biomarkers for early and accurate detection. This paper analyzes eleven linear and non-linear features across time and frequency domains from an EEG dataset. Four neural network models, such as convolutional neural network (CNN), deep neural network (DNN), long short-term memory (LSTM), and a custom neural network are employed for classification. The CNN achieves the lowest accuracy at 89.02%, while the custom neural network reaches the highest accuracy at 94.02%, and the DNN and LSTM achieve 91.98% and 93.83% accuracy, respectively. Other metrics such as precision, recall, specificity, and F1-score, are also evaluated. This research underscores the efficacy of neural network in detecting Autism, advancing diagnostic tools.

References

N. Hasan and M. J. Nene, “Determinants of Technological Interventions for Children With Autism - A Systematic Review,” Journal of Educational Computing Research, vol. 62, no. 1, pp. 250-289, March 2024.

N. Hasan, M. N. Islam, and N. Choudhury, “Evaluation of an Interactive Computer-Enabled Tabletop Learning Tool for Children With Special Needs,” Journal of Educational Computing Research, vol. 60, no. 8, pp. 2105-2137, January 2023.

N. Hasan and M. J. Nene, “LEFA: Framework to Develop Learnability of Children with Autism,” International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON), pp. 15-20, December 2022.

N. Hasan and M. J. Nene, “MAPE: An Interactive Learning Model for the Children with ASD,” Proceedings of International Conference on Communication and Computational Technologies, pp. 355-367, February 2022.

N. Hasan and M. J. Nene, “ICT Based Learning Solutions for Children with ASD: A Requirement Engineering Study,” International Journal of Special Education, vol. 37, no. 1, pp. 112-126, 2022.

N. Hasan and M. J. Nene, “An Agent-Based Basic Educational Model for the Children with ASD Using Persuasive Technology,” International Conference for Advancement in Technology (ICONAT), pp. 1-6, January 2022.

N. Hasan and M. N. Islam, “Exploring the Design Considerations for Developing an Interactive Tabletop Learning Tool for Children with Autism Spectrum Disorder,” Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019), pp. 834-844, December 2019.

A. M. Gonçalves and P. Monteiro, “Autism Spectrum Disorder and Auditory Sensory Alterations: A Systematic Review on the Integrity of Cognitive and Neuronal Functions Related to Auditory Processing,” Journal of Neural Transmission, vol. 130, no. 3, pp. 325-408, March 2023.

Y. Hus and O. Segal, “Challenges Surrounding the Diagnosis of Autism in Children,” Neuropsychiatric Disease and Treatment, vol. 17, pp. 3509-3529, 2021.

R. C. Sheldrick, A. S. Carter, A. Eisenhower, T. I. Mackie, M. B. Cole, N. Hoch, et al., “Effectiveness of Screening in Early Intervention Settings to Improve Diagnosis of Autism and Reduce Health Disparities,” JAMA Pediatrics, vol. 176, no. 3, pp. 262-269, March 2022.

E. Helmy, A. Elnakib, Y. ElNakieb, M. Khudri, M. Abdelrahim, J. Yousaf, et al., “Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey,” Biomedicines, vol. 11, no. 7, article no. 1858, July 2023.

M. Parellada, A. Andreu-Bernabeu, M. Burdeus, A. San José Cáceres, E. Urbiola, L. L. Carpenter, et al., “In Search of Biomarkers to Guide Interventions in Autism Spectrum Disorder: A Systematic Review,” American Journal of Psychiatry, vol. 180, no. 1, pp. 23-40, January 2023.

J. Shan, Y. Gu, J. Zhang, X. Hu, H. Wu, T. Yuan, et al., “A Scoping Review of Physiological Biomarkers in Autism,” Frontiers in Neuroscience, vol. 17, article no. 1269880, September 2023.

C. Z. C. Hasan, R. Jailani, and N. M. Tahir, “Autism Spectrum Disorder and Normal Gait Classification Using Machine Learning Approach,” Southeast Europe Journal of Soft Computing, vol. 12, no. 1, pp. 57-63, 2023.

X. Geng, X. Fan, Y. Zhong, M. F. Casanova, E. M. Sokhadze, X. Li, et al., “Abnormalities of EEG Functional Connectivity and Effective Connectivity in Children with Autism Spectrum Disorder,” Brain Sciences, vol. 13, no. 1, article no. 130, January 2023.

F. Salto, C. Requena, P. Alvarez-Merino, V. Rodríguez, J. Poza, and R. Hornero, “Electrical Analysis of Logical Complexity: An Exploratory EEG Study of Logically Valid/Invalid Deducive Inference,” Brain Informatics, vol. 10, no. 1, article no. 13, December 2023.

G. Chiarion, L. Sparacino, Y. Antonacci, L. Faes, and L. Mesin, “Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends,” Bioengineering, vol. 10, no. 3, article no. 372, March 2023.

C. Clairmont, J. Wang, S. Tariq, H. T. Sherman, M. Zhao, and X. J. Kong, “The Value of Brain Imaging and Electrophysiological Testing for Early Screening of Autism Spectrum Disorder: A Systematic Review,” Frontiers in Neuroscience, vol. 15, article no. 812946, 2021.

T. Heunis, C. Aldrich, J. M. Peters, S. S. Jeste, M. Sahin, C. Scheffer, et al., “Recurrence Quantification Analysis of Resting State EEG Signals in Autism Spectrum Disorder – A Systematic Methodological Exploration of Technical and Demographic Confounders in the Search for Biomarkers,” BMC Medicine, vol. 16, article no. 101, 2018.

D. Haputhanthri, G. Brihadiswaran, S. Gunathilaka, D. Meedeniya, Y. Jayawardena, S. Jayarathna, et al., “An EEG Based Channel Optimized Classification Approach for Autism Spectrum Disorder,” Moratuwa Engineering Research Conference, pp. 123-128, July 2019.

D. Abdolzadegan, M. H. Moattar, and M. Ghoshuni, “A Robust Method for Early Diagnosis of Autism Spectrum Disorder From EEG Signals Based on Feature Selection and DBSCAN Method,” Biocybernetics and Biomedical Engineering, vol. 40, no. 1, pp. 482-493, January-March 2020.

M. Radhakrishnan, K. Ramamurthy, K. K. Choudhury, D. Won, and T. A. Manoharan, “Performance Analysis of Deep Learning Models for Detection of Autism Spectrum Disorder From EEG Signals,” Traitement Du Signal, vol. 38, no. 3, pp. 853-863, June 2021.

P. Garcés, S. Baumeister, L. Mason, C. H. Chatham, S. Holiga, J. Dukart, et al., “Resting State EEG Power Spectrum and Functional Connectivity in Autism: A Cross-Sectional Analysis,” Molecular Autism, vol. 13, article no. 22, 2022.

S. Peketi and S. B. Dhok, “Machine Learning Enabled P300 Classifier for Autism Spectrum Disorder Using Adaptive Signal Decomposition,” Brain Sciences, vol. 13, no. 2, article no. 315, February 2023.

“Global Datasets for Autism Disorder,” https://malhaddad.kau.edu.sa/Pages-BCI-Datasets.aspx, June 03, 2023.

R. Silva and P. Melo-Pinto, “t-SNE: A Study on Reducing the Dimensionality of Hyperspectral Data for the Regression Problem of Estimating Oenological Parameters,” Artificial Intelligence in Agriculture, vol. 7, pp. 58-68, March 2023.

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Published

2024-10-17

How to Cite

[1]
Nazmul Hasan, Priyasha Paul, and Manisha Jitendra Nene, “Performance Evaluation of Neural Network Models for Autism Detection Using EEG Data”, Adv. technol. innov., Oct. 2024.

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Articles