Khoshnoud Shiva, Nazari Mohammad Ali, Shamsi Mousa
Electrical Engineering Faculty, Sahand University of Technology, Tabriz, Iran.
Cognitive Neuroscience Laboratory, Department of Psychology, University of Tabriz, Tabriz, Iran.
J Integr Neurosci. 2018 Aug 15;17(1):11-17. doi: 10.31083/JIN-170033.
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.
注意力缺陷多动障碍是一种神经发育疾病，与不同程度的多动、注意力不集中和冲动有关。本研究使用静息状态下脑电图（EEG）信号的非线性动力学测量方法，对患有注意力缺陷多动障碍的儿童的脑功能进行研究。在闭眼静息期间，从12名注意力缺陷多动障碍儿童和12名年龄匹配的正常儿童身上记录了19通道的EEG信号。我们使用多重分形奇异谱、最大Lyapunov指数和近似熵来量化这些EEG信号的混沌非线性动力学。经Wilcoxon秩和检验证实，左额中央皮层的最大Lyapunov指数在注意力缺陷多动障碍组和年龄匹配的对照组之间存在显著差异。此外，前额叶皮层中注意力缺陷多动障碍受试者的平均近似熵显著更低。与对照儿童相比，注意力缺陷多动障碍患者的奇异谱也有相当大的改变。使用两种分类器对这些特征进行评估：支持向量机和径向基函数神经网络。为了进行更好的比较，使用相同类型的分类器评估基于频段功率的受试者分类情况。非线性特征在区分注意力缺陷多动障碍和对照组方面比频段功率特征表现更好。在四重交叉验证测试中，支持向量机给出了83.33%的准确分类结果。
