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Classification of autistic children using polar-based lagged state-space indices of EEG signals

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Abstract

One of the most widespread disorders in childhood is called autism spectrum disorder (ASD), which affects the brain's function. Previously, many efforts have been made to develop an intelligent system to detect disease using brain activity. However, accurate diagnosis of ASD remains a challenging issue among scientists. The purpose of this study was to diagnose ASD at an early age using a low computationally algorithm based on electroencephalography (EEG) signal. In this study, we classified two groups of normal and autistic children using brain signals at resting-state. Two brain channels (C3 and C4) of 61 children including 27 normal children and 34 autistic children in the age range of 4 to 8 years were studied. For the first time, we characterized the EEGs using innovative polar-based lagged state-space indices. The classification was performed using the support vector machine (SVM). The results demonstrated the highest average accuracy of 81.96% using the indices of two EEG channels. Using single-channel EEG measures, the maximum average classification rate of 78.68% was achieved using C4. To sum up, the results revealed that despite the limited number of brain channels and computational simplicity, the proposed algorithm was able to distinguish the two groups of normal and autistic children with satisfactory accuracy.

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Acknowledgements

The authors would like to extend their sincere thanks to Dr. Ghasem Sadeghi Bajestani for sharing data.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors”.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Imam Reza International University, Rezvan Campus (Female Students), Phalestine Sq., Mashhad, Razavi Khorasan, P.O. BOX 91735-553, Iran

    Nahid Ghoreishi, Ateke Goshvarpour, Samane Zare-Molekabad, Narjes Khorshidi & Somayeh Baratzade

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  1. Nahid Ghoreishi
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  2. Ateke Goshvarpour
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  3. Samane Zare-Molekabad
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  4. Narjes Khorshidi
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Correspondence to Ateke Goshvarpour.

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The files have been registered under the Ethics License #500685/95 from the Medical Sciences University of Mashhad [18].”

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Ghoreishi, N., Goshvarpour, A., Zare-Molekabad, S. et al. Classification of autistic children using polar-based lagged state-space indices of EEG signals. SIViP 15, 1805–1812 (2021). https://doi.org/10.1007/s11760-021-01928-z

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  • DOI: https://doi.org/10.1007/s11760-021-01928-z

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