Attention Deficit Hyperactivity Disorder Diagnosis using non-linear univariate and multivariate EEG measurements: a preliminary study.

Physical and Engineering Sciences in Medicine
Maryam RezaeezadehMousa Shamsi

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, ...Continue Reading

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Citations

Jul 21, 2021·Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology·Aarti SathyanarayanaWilliam J Bosl
Jul 26, 2021·Research in Developmental Disabilities·Caroline Nunes GonzagaLuiz Carlos Marques Vanderlei

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Methods Mentioned

BETA
feature extraction
PCA

Software Mentioned

NCDF
mvSE
LM
SampEn
LOGSIG
DispEn
TANSIG

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