Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a deficit of social relationships, interaction, sense of imagination, and constrained interests. Early diagnosis of ASD will aid in devising appropriate training procedures and placing those children in the normal stream. The objective of this research is to analyze the brain response for auditory/visual stimuli in Typically Developing (TD) and children with autism through electroencephalography (EEG). Brain dynamics in the EEG signal can be analyzed well with the help of nonlinear feature primitives. Recent research reveals that, application of fractal-based techniques proves to be effective to estimate of degree of nonlinearity in a signal. This research attempts to analyze the effect of brain dynamics with Higuchi Fractal Dimension (HFD). Also, the performance of the fractal based techniques depends on the selection of proper hyper-parameters involved in it. One of the key parameters involved in computation of HFD is the time interval parameter ‘k’. Most of the researches arbitrarily fixes the value of ‘k’ in the range of all channels. This research proposes an algorithm to estimate the optimal value of the time parameter for each channel. Sub-band analysis was also carried out for the responding channels. Statistical analysis on the experimental reveals that a difference of 30% was observed between autistic and Typically Developing children.

中文翻译：

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使用 Higuchi 分形维数研究自闭症谱系障碍 (ASD) 的脑电图信号


自闭症谱系障碍 (ASD) 是一种神经发育障碍，表现为社会关系、互动、想象力和兴趣受限的缺陷。自闭症谱系障碍 (ASD) 的早期诊断将有助于设计适当的训练程序并将这些儿童置于正常状态。本研究的目的是通过脑电图 (EEG) 分析正常发育 (TD) 儿童和自闭症儿童对听觉/视觉刺激的大脑反应。借助非线性特征基元，可以很好地分析脑电图信号中的大脑动力学。最近的研究表明，基于分形的技术的应用被证明可以有效地估计信号的非线性程度。本研究试图利用 Higuchi 分形维数 (HFD) 来分析大脑动力学的影响。此外，基于分形的技术的性能取决于所涉及的适当超参数的选择。 HFD 计算中涉及的关键参数之一是时间间隔参数“k”。大多数研究任意固定所有通道范围内的“k”值。本研究提出了一种算法来估计每个通道的时间参数的最优值。还对响应通道进行了子带分析。实验统计分析表明，自闭症儿童和正常发育儿童之间存在 30% 的差异。