Attention deficit hyperactivity disorder (ADHD) is a prevalent behavioral, cognitive, neurodevelopmental pediatric disorder. Clinical evaluations, symptom surveys, and neuropsychological assessments are some of the ADHD assessment methods, which are time-consuming processes and have a certain degree of uncertainty. This research investigates an efficient computer-aided technological solution for detecting ADHD from the acquired electroencephalography (EEG) signals based on different nonlinear entropy estimators and an artificial neural network classifier. Features extracted through fuzzy entropy, log energy entropy, permutation entropy, SURE entropy, and Shannon entropy are analyzed for effective discrimination of ADHD subjects from the control group. The experimented results confirm that the proposed techniques can effectively detect and classify ADHD subjects. The permutation entropy gives the highest classification accuracy of 99.82%, sensitivity of 98.21%, and specificity of 98.82%. Also, the potency of different entropy estimators derived from the t-test reflects that the Shannon entropy has a higher P-value (>.001); therefore, it has a limited scope than other entropy estimators for ADHD diagnosis. Furthermore, the considerable variance found from potential features obtained in the frontal polar (FP) and frontal (F) lobes using different entropy estimators under the eyes-closed condition shows that the signals received in these lobes will have more significance in distinguishing ADHD from normal subjects.

注意缺陷多动障碍 (ADHD) 是一种普遍存在的行为、认知、神经发育儿科疾病。临床评估、症状调查和神经心理评估是 ADHD 评估方法中的一些，这些方法耗时且具有一定程度的不确定性。本研究基于不同的非线性熵估计器和人工神经网络分类器，研究了一种有效的计算机辅助技术解决方案，用于从获得的脑电图 (EEG) 信号中检测 ADHD。分析通过模糊熵、对数能量熵、置换熵、SURE 熵和香农熵提取的特征，以有效区分 ADHD 受试者与对照组。实验结果证实，所提出的技术可以有效地检测和分类 ADHD 对象。排列熵给出了最高的分类准确率 99.82%、灵敏度 98.21% 和特异性 98.82%。此外，不同熵估计量的效力来自t检验反映香农熵具有较高的P值（>.001）；因此，与其他用于 ADHD 诊断的熵估计器相比，它的范围有限。此外，在闭眼条件下使用不同的熵估计器从额极 (FP) 和额叶 (F) 叶中获得的潜在特征中发现的相当大的差异表明，在这些叶中接收到的信号在区分 ADHD 与正常情况方面具有更重要的意义。科目。