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EEG Mu Rhythm in Typical and Atypical Development
Published on: April 9, 2014
Attention Deficit Hyperactivity Disorder Diagnosis using non-linear univariate and multivariate EEG measurements: a
Maryam Rezaeezadeh1, Sina Shamekhi2, Mousa Shamsi1
1Biomedical Engineering Faculty, Sahand University of Technology, Sahand New Town, 5331817634, Tabriz, Iran.
Insights
This study developed advanced algorithms to differentiate children with Attention Deficit Hyperactivity Disorder (ADHD) from neurotypical children using resting-state electroencephalography (EEG) signals, achieving high classification accuracy.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Signal Processing
Background:
- Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder in childhood.
- Resting-state Electroencephalography (EEG) offers a non-invasive method to study brain activity.
- Distinguishing ADHD from typical development using EEG requires sophisticated analytical techniques.
Purpose of the Study:
- To propose and evaluate two novel classification algorithms for discriminating ADHD from normal children using EEG signals.
- To investigate the utility of non-linear entropy measures as features for ADHD classification.
- To compare the performance of univariate and multivariate feature extraction methods.
Main Methods:
- Extracted linear and non-linear univariate features (e.g., Theta/Beta Ratio, Sample Entropy) from individual EEG channels.
- Extracted non-linear multivariate features (e.g., multivariate Sample Entropy) from brain lobes.
- Employed Support Vector Machines (SVM), k-Nearest Neighbor (kNN), and Probabilistic Neural Network (PNN) classifiers.
- Utilized Multiscale Sample Entropy (MSE) for complexity analysis and entropy mapping for visualization.
Main Results:
- ADHD children exhibited higher brain activity and Theta/Beta Ratio (TBR) compared to controls.
- A more regular neural system and reduced dynamical complexity were observed in ADHD.
- Classification accuracy reached 99.58% using univariate non-linear features with Radial Basis Function (RBF) SVM.
- Classification using multivariate features achieved 90.63% accuracy with PNN.
Conclusions:
- Non-linear entropy measures are effective biomarkers for ADHD detection in EEG.
- Univariate feature analysis, particularly with non-linear measures, demonstrates superior performance for ADHD classification.
- The findings support the use of advanced signal processing techniques for objective ADHD diagnosis.
Abstract:
Attention Deficit Hyperactivity Disorder (ADHD) is a common neuro-developmental disorder of childhood. In this study we propose two classification algorithms for discriminating ADHD children from normal children using their resting state Electroencephalography (EEG) signals. One algorithm is based on the univariate features extracted from individual EEG recording channels and the other is based on the multivariate features extracted from brain lobes. We focused on entropy measures as non-linear univariate and multivariate features. Average power, Theta/Beta Ratio (TBR), Shannon Entropy (ShanEn), Sample Entropy (SampEn), Dispersion Entropy (DispEn) and Multiscale SampEn (MSE) were extracted as linear and non-linear univariate features. Besides, multivariate SampEn (mvSE) and multivariate MSE (mvMSE) were extracted as non-linear multivariate features. Classification was followed by three classifiers: Support Vector Machines (SVM) with different kernels, k-Nearest Neighbor (kNN) and Probabilistic Neural Network (PNN). Complexity analysis of multi-channel EEG data was performed using mvMSE approach. Entropy mapping as a useful tool was used to visually track changes of entropies in various brain regions. Based on achieved results, ADHD children have higher brain activity and TBR compared to normal children, while their neural system is more regular. Besides, ADHD children have reduced dynamical complexity of neural system. Finally, the accuracy of 99.58% was achieved in classification based on a combination of non-linear univariate features by Radial Basis Function (RBF) SVM. For classification based on brain regions using multivariate features, 90.63% accuracy was achieved by PNN.
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