Attention deficit hyperactivity disorder (ADHD) is a common behavioural disorder that may be found in 5%-8% of the children. Early diagnosis of ADHD is crucial for treating the disease and reducing its harmful effects on education, employment, relationships, and life quality. On the other hand, non-linear analysis methods are widely applied in processing the electroencephalogram (EEG) signals. It has been proved that the brain neuronal activity and its related EEG signals have chaotic behaviour. Hence, chaotic indices can be employed to classify the EEG signals. In this study, a new approach is proposed based on the combination of some non-linear features to distinguish ADHD from normal children. Lyapunov exponent, fractal dimension, correlation dimension and sample, fuzzy and approximate entropies are the non-linear extracted features. For computing, the chaotic time series of obtained EEG in the brain frontal lobe (FP1, FP2, F3, F4, and Fz) need to be analysed. Experiments on a set of EEG signal obtained from 50 ADHD and 26 normal cases yielded a sensitivity, specificity, and accuracy of 98, 92.31, and 96.05%, respectively. The obtained accuracy provides a significant improvement in comparison to the other similar studies in identifying and classifying children with ADHD.

中文翻译：

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使用脑电信号的非线性分析诊断注意力缺陷多动障碍。

注意缺陷多动障碍（ADHD）是一种常见的行为障碍，可能在 5%-8% 的儿童中发现。ADHD 的早期诊断对于治疗疾病和减少其对教育、就业、人际关系和生活质量的有害影响至关重要。另一方面，非线性分析方法广泛应用于脑电图（EEG）信号的处理。已经证明，大脑神经元活动及其相关的脑电信号具有混沌行为。因此，可以使用混沌指数对脑电信号进行分类。在这项研究中，提出了一种基于一些非线性特征组合的新方法来区分多动症和正常儿童。Lyapunov指数、分形维数、相关维数和样本、模糊和近似熵是非线性提取的特征。对于计算，需要分析大脑额叶（FP1、FP2、F3、F4 和 Fz）中获得的 EEG 的混沌时间序列。对从 50 例 ADHD 和 26 例正常病例获得的一组脑电图信号进行的实验分别产生了 98%、92.31% 和 96.05% 的灵敏度、特异性和准确度。与识别和分类多动症儿童的其他类似研究相比，获得的准确性显着提高。