Source-Based Multifractal Detrended Fluctuation Analysis for Discrimination of Adhd Children in a Time Reproduction Paradigm



Khoshnoud S^{1, 3} ; Nazari MA² ; Shamsi M³ Show Affiliations
Source: BIOSIGNALS 2020 - 13th International Conference on Bio-Inspired Systems and Signal Processing# Proceedings; Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies# BIOSTEC 2020 Published:2020

Abstract

Electroencephalography recordings have a scale-invariant structure and multifractal detrended fluctuation analysis (MF-DFA) could quantify the fluctuation dynamics of these recordings in different brain states. However, the channel-based electrical activity of the brain has low spatial resolution and considering the source-level activity patterns is a good answer for this restriction. In this work, the multifractal spectrum parameters of the channel-based EEG, as well as the corresponding source-based independent components in children with Attention Deficit Hyperactivity Disorder (ADHD) and the age-matched control group, has been investigated. Considering the perceptual timing deficit in children with ADHD, for the analysis of the multifractality, two brain states including the eyes-open rest and the time reproduction condition have been considered. The results obtained showed that switching from rest to the time reproduction condition increases the degree of multifractality and so the complexity and non-uniformity of the signal. While the channel-based multifractal properties could not significantly distinguish two groups, the results for the source-based multifractal analysis showed a significantly decreased degree of multifractality for children with ADHD in prefrontal, mid-frontal and right frontal source clusters suggesting reduced activation of these clusters in this group. Utilizing support vector machine (SVM) classifier it is found that, the source-based multifractal features provide a significantly higher accuracy rate of 86.67% in comparison to the channel-based measures. Copyright © 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.