A review of machine learning algorithms to diagnose autism using EEG signal

Articles in Press

Document Type : Review Article

Authors

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

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

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

Abstract

Autism is a neurological and developmental disorder in which individuals often show limited symptoms or behaviors and are diagnosed based on a behavioral test. This neurological disorder is similar to other neurological disorders; therefore, diagnosing autism is a complicated task. If the disease is diagnosed at an early age, the severity of the disease and its complications will be reduced. In recent years, researchers have used electroencephalography (EEG) to diagnose autism. In the diagnosis of autism using EEG signals, machine learning algorithms have been used, and the human user has been able to diagnose this disease with a good percentage of accuracy by analyzing the extracted features. Here, the algorithms used in each of the articles have been reviewed, and the results obtained from the corresponding data review are the basis for further research. While examining the previously presented methods, the advantages and disadvantages of the methods are examined. The results show that the role of the methods used for pre-processing, extracting and selecting features from EEG images and the type of classifiers are effective factors in classification accuracy. Convolutional neural networks have been the most popular compared to other learning-based methods due to their ability to provide the best accuracy results. 

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Main Subjects

Soft Computing Journal

Articles in Press, Accepted Manuscript
Available Online from 10 September 2023

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History

  • Receive Date: 10 November 2022
  • Revise Date: 13 August 2023
  • Accept Date: 31 August 2023

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