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EEG classification of ADHD and normal children using non-linear features and neural network

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Abstract

Purpose

Attention-Deficit Hyperactivity Disorder (ADHD) is a neuro-developmental disorder that is characterized by hyperactivity, inattention and abrupt behaviors. This study proposes an approach for distinguishing ADHD children from normal children using their EEG signals when performing a cognitive task.

Methods

In this study, 30 children with ADHD and 30 age-matched healthy children without neurological disorders underwent electroencephalography (EEG) when performing a task to stimulate their attention. Fractal dimension (FD), approximate entropy and lyapunov exponent were extracted from EEG signals as non-linear features. In order to improve the classification results, double input symmetrical relevance (DISR) and minimum Redundancy Maximum Relevance (mRMR) methods were used to select the best features as inputs to multi-layer perceptron (MLP) neural network.

Results

As expected, children with ADHD had more delays and were less accurate in doing the cognitive task. Also, the extracted non-linear features revealed that non-linear indices were greater in different regions of the brain of ADHD children compared to healthy children. This could indicate a more chaotic behavior of ADHD children while performing a cognitive task. Finally, the accuracy of 92.28% and 93.65% were achieved using mRMR method and DISR method using MLP, respectively.

Conclusions

The results of this study demonstrate the ability of the non-linear features to distinguish ADHD children from healthy children.

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Author information

Authors and Affiliations

  1. Psychiatry & Psychology Research Center, Tehran University of Medical Sciences, Tehran, Iran

    Mohammad Reza Mohammadi, Ali Khaleghi & Hadi Zarafshan

  2. Biomedical Engineering Department, Shahed University, Tehran, Iran

    Ali Moti Nasrabadi

  3. Faculty of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran

    Safa Rafieivand

  4. Department of Biomedical Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran

    Moslem Begol

Authors
  1. Mohammad Reza Mohammadi
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  2. Ali Khaleghi
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  3. Ali Moti Nasrabadi
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  4. Safa Rafieivand
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  5. Moslem Begol
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  6. Hadi Zarafshan
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Corresponding author

Correspondence to Ali Khaleghi.

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Mohammadi, M.R., Khaleghi, A., Nasrabadi, A.M. et al. EEG classification of ADHD and normal children using non-linear features and neural network. Biomed. Eng. Lett. 6, 66–73 (2016). https://doi.org/10.1007/s13534-016-0218-2

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  • DOI: https://doi.org/10.1007/s13534-016-0218-2

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