Functional brain dynamic analysis of ADHD and control children using nonlinear dynamical features of EEG signals
Shiva Khoshnoud1, Mohammad Ali Nazari2, Mousa Shamsi1
1Electrical Engineering Faculty, Sahand University of Technology, Tabriz, Iran.
Insights
This study reveals distinct brain activity patterns in children with attention deficit hyperactivity disorder (ADHD) using nonlinear dynamics in EEG signals. Nonlinear EEG analysis accurately differentiated ADHD from control groups, offering new diagnostic insights.
Area of Science:
- Neuroscience
- Pediatric Neurology
- Biomedical Engineering
Background:
- Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder impacting children.
- Characterized by hyperactivity, inattention, and impulsivity, ADHD necessitates improved diagnostic methods.
- Understanding brain function alterations in ADHD is crucial for effective interventions.
Purpose of the Study:
- To investigate differences in brain function between children with ADHD and neurotypical controls using nonlinear dynamics of EEG signals.
- To evaluate the efficacy of nonlinear EEG features in classifying ADHD subjects.
- To compare the diagnostic power of nonlinear features against traditional frequency band power analysis.
Main Methods:
- Recorded 19-channel resting-state EEG data from 12 children with ADHD and 12 age-matched controls.
- Quantified nonlinear dynamics using multifractal singularity spectrum, largest Lyapunov exponent, and approximate entropy.
- Employed Support Vector Machine (SVM) and Radial Basis Function (RBF) Neural Networks for classification, with four-fold cross-validation.
Main Results:
- Significant differences in largest Lyapunov exponent were observed over the left frontal-central cortex in ADHD subjects.
- Mean approximate entropy was significantly lower in the prefrontal cortex of children with ADHD.
- The singularity spectrum showed considerable alterations in ADHD compared to controls.
- Nonlinear EEG features outperformed frequency band power features in discriminating between ADHD and control groups.
- SVM achieved 83.33% classification accuracy using nonlinear features.
Conclusions:
- Nonlinear dynamics analysis of EEG signals reveals significant alterations in brain function in children with ADHD.
- These nonlinear EEG features demonstrate superior potential for ADHD classification compared to conventional methods.
- The findings support the use of advanced EEG signal analysis for improved ADHD diagnosis and understanding.
Abstract:
Attention deficit hyperactivity disorder is a neurodevelopmental condition associated with varying levels of hyperactivity, inattention, and impulsivity. This study investigates brain function in children with attention deficit hyperactivity disorder using measures of nonlinear dynamics in EEG signals during rest. During eyes-closed resting, 19 channel EEG signals were recorded from 12 ADHD and 12 normal age-matched children. We used the multifractal singularity spectrum, the largest Lyapunov exponent, and approximate entropy to quantify the chaotic nonlinear dynamics of these EEG signals. As confirmed by Wilcoxon rank sum test, largest Lyapunov exponent over left frontal-central cortex exhibited a significant difference between ADHD and the age-matched control groups. Further, mean approximate entropy was significantly lower in ADHD subjects in prefrontal cortex. The singularity spectrum was also considerably altered in ADHD compared to control children. Evaluation of these features was performed by 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 ADHD and control than band power features. Under four-fold cross validation testing, support vector machine gave 83.33% accurate classification results.
