Autism Diagnosis from EEG Signals Using Machine Learning Algorithms and Convolutional Neural Networks

Articles in Press

Document Type : Original Article

Authors

1 Department of Electrical Engineering, Technical and Vocational University, Tehran, Iran

2 Department of Electrical Engineering, Engineering Faculty, Ardakan University, Ardakan, Iran

3 Department of Electrical Engineering, Yazd University, Yazd, Iran

10.22052/scj.2024.253566.1185

Abstract

A neurodevelopmental disorder recognized by insufficiency in social communication and repetitive behaviours is expressed as an autism spectrum disorder (ASD). One of the most useful tools for diagnosing autism is the use of electroencephalography (EEG) signals because these signals accurately represent the brain's function. The recorded EEG of each person contains a lot of information that is very difficult to study and check visually. The main goal of machine learning algorithms is to train the machine in such a way that it finally has a diagnosis close to that of the human brain. This paper evaluates the appropriate strategies for further exploitation of deep learning capabilities in the feature extraction block of autism diagnosis without using classical feature extraction methods. In this research, a convolutional neural network (CNN) structure is used to check the available data in order to extract features. Classification has been done with five machine learning classifiers, covering support vector machine (SVM), linear discriminant analysis (LDA), decision tree (DT), simple Bayes classification (GNB), and random forest (RF). The accuracy obtained from the use of classifiers with SVM methods is 100%, LDA 82%, DT 80.5%, GNB 100%, and RF 100%. The proposed idea of convolutional neural networks for feature extraction and classification with different machine learning methods has provided high-accuracy results that are equal to the other amazing methods for autism diagnosis.

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Soft Computing Journal

Articles in Press, Accepted Manuscript
Available Online from 30 January 2024

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History

  • Receive Date: 10 September 2023
  • Revise Date: 21 November 2023
  • Accept Date: 28 January 2024

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