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Journal of Integrative Neuroscience  2018, Vol. 17 Issue (1): 11-18    DOI: 10.31083/JIN-170033
Research article Previous articles | Next articles
Functional analysis of ADHD in children using nonlinear features of EEG signals
Shiva Khoshnoud1, Mohammad Ali Nazari1, Mousa Shamsi1, *()
1 Biomedical Engineering Faculty, Sahand University of Technology, Tabriz, Iran;
2 Cognitive Neuroscience Laboratory, Department of Psychology, University of Tabriz, Tabriz, Iran
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Abstract  

Attention deficit hyperactivity disorder is a neurodevelopmental condition associated with varying levels of hyperactivity, inattention, and impulsivity. This study investigated brain function in children with attention deficit hyperactivity disorder using measures of nonlinear dynamics in electroencephalogram signals during rest. During eyes-closed resting, 19 channel electroencephalogram signals were recorded from 12 ADHD and 12 normal age-matched children. The multifractal singularity spectrum, the largest Lyapunov exponent, and approximate entropy were employed to quantify the chaotic nonlinear dynamics of these electroencephalogram signals. As confirmed by Wilcoxon rank sum test, the largest Lyapunov exponent over left frontal-central cortex exhibited a significant difference between attention deficit hyperactivity disorder subjects and the age-matched control groups. Further, mean approximate entropy was significantly lower in attention deficit hyperactivity disorder subjects in prefrontal cortex. The singularity spectrum was also considerably altered in attention deficit hyperactivity disorder subjects when compared to control children. Evaluation of these features was performed with two classifiers: a support vector machine and a radial basis function neural network. For better comparison, subject classification based on frequency band power was assessed using the same types of classifiers. Nonlinear features provided better discrimination between attention deficit hyperactivity disorder and control than band power features. Under four-fold cross-validation testing, the support vector machine gave 83.33% accurate classification results.

Key words:  Attention deficit hyperactivity disorder      EEG signals      largest Lyapunov exponent      approximate entropy      multifractal DFA      classification     
Submitted:  23 January 2017      Accepted:  08 May 2017      Published:  15 February 2018     
*Corresponding Author(s):  Mousa Shamsi     E-mail:  shamsi@sut.ac.ir

Cite this article: 

Shiva Khoshnoud, Mohammad Ali Nazari, Mousa Shamsi. Functional analysis of ADHD in children using nonlinear features of EEG signals. Journal of Integrative Neuroscience, 2018, 17(1): 11-18.

URL: 

https://jin.imrpress.com/EN/10.31083/JIN-170033     OR     https://jin.imrpress.com/EN/Y2018/V17/I1/11

Fig.1.  Overall procedure for functional brain dynamic analysis of ADHD and control children using nonlinear dynamical features of EEG signals. First step: Signal was preprocessed to remove any noise and artefacts. Followed by calculation of LLE, ApEn, and multifractal spectra for every channel of the recorded EEG signals. In parallel with nonlinear features, frequency band power estimates were also calculated. Subsequently, the principal component coefficients of these features were computed and the 15 largest components of each feature category employed as final features. Finally, subject group classification was implemented with two classifiers: a support vector machine classifier and a radial basis function neural network.

Fig.2.  Results of the Wilcoxon rank sum test for the mean ApEn feature between ADHD and control group at channels: (a) Fp1, (b) Fp2. Mean ApEn in subjects with ADHD was significantly lower than control in these two scalp electrodes.

Fig.3.  Black disks indicate channels with multifractal spectrum height differences (p-value < 0.05) between ADHD and normal subjects. The multifractal spectrum for ADHD subjects was significantly higher than the control group over these scalp electrodes.

Fig.4.  Group classification using frequency band powers.

Table 1  Classification using nonlinear dynamical properties
Classifier Nonlinear features
Largest Approximate Multi fractal
Lyapunov entropy spectrum
exponent
SVM 70.83 59 79.17
RBF 79.17 75 66.67
Table 2  Classification using combined features
Band power features Nonlinear features
Classifier Without With PCA Without With PCA
PCA (%) (%) PCA (%) (%)
SVM 79.17 79.17 83.33 83.33
RBF 83.33 70.83 70.83 66.67
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