Attention deficit hyperactivity disorder (ADHD) is a childhood behavioral disorder that can persist into adulthood. Electroencephalography (EEG) plays a significant role in assessing the neurophysiology of ADHD because of its ability to reveal complex brain activity. The present study proposes an EEG-based diagnosis system using the phase space reconstruction technique to classify ADHD and control adults. Electric activity is recorded for 47 ADHD and 50 control adults during the eyes-open, eyes-closed, and Continuous Performance Test (CPT) condition. Various statistical features are extracted from Euclidean distances based on phase space reconstruction of signals. The proposed system is evaluated with 2 feature selection methods (correlation-based feature selection and particle swarm optimization) and 5 machine learning methods (neural dynamic classifier, support vector machine, enhanced probabilistic neural network, k-nearest neighbor, and naive-Bayes classifier). Experimental results showed the highest testing accuracy of 93.3% under the eyes-open, 90% under the eyes-closed, and 100% under the CPT condition. This study focused on the utility of phase space reconstruction of brain signals to discriminate between ADHD and control adults.

中文翻译：

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用于 ADHD 分类和控制成人的 EEG 信号的相空间重建

注意缺陷多动障碍 (ADHD) 是一种儿童行为障碍，可以持续到成年。脑电图 (EEG) 在评估 ADHD 的神经生理学方面发挥着重要作用，因为它能够揭示复杂的大脑活动。本研究提出了一种基于 EEG 的诊断系统，使用相空间重建技术对 ADHD 进行分类和控制成人。在睁眼、闭眼和连续性能测试 (CPT) 条件下，记录了 47 名 ADHD 和 50 名对照成人的电活动。基于信号的相空间重构，从欧几里得距离中提取各种统计特征。所提出的系统使用 2 种特征选择方法（基于相关性的特征选择和粒子群优化）和 5 种机器学习方法（神经动态分类器、支持向量机、增强概率神经网络、k 最近邻和朴素贝叶斯分类器）进行评估）。实验结果表明，在睁眼条件下测试准确率为93.3%，在闭眼条件下为90%，在CPT条件下为100%。这项研究的重点是利用大脑信号的相空间重建来区分 ADHD 和对照成人。