The aim of this study was to build a machine learning model to discriminate Attention Deficit Hyperactivity Disorder (ADHD) patients and healthy controls using information from both time and frequency analysis of Event Related Potentials (ERP) obtained from Electroencephalography (EEG) signals while participants performed an auditory oddball task. The study included 23 unmedicated ADHD patients and 23 healthy controls. The EEG signal was analyzed in time domain by nonlinear brain dynamics and morphological features, and in time-frequency domain with wavelet coefficients. Selected features were applied to various machine learning techniques including; Multilayer Perceptron, Naïve Bayes, Support Vector Machines, k-nearest neighbor, Adaptive Boosting, Logistic Regression and Random Forest to classify ADHD patients and healthy controls. Longer P300 latencies and smaller P300 amplitudes were observed in ADHD patients relative to controls. In fractal dimension calculation relative to the control group, the ADHD group demonstrated reduced complexity. In addition, certain wavelet coefficients provided significantly different values in both groups. Combining these extracted features, our results indicated that Multilayer Perceptron method provided the best classification with an accuracy rate of 91.3% and a high level of reliability of concurrence (Kappa = 0.82).

The results showed that combining time and frequency domain features can be a useful and discriminative for diagnostic purposes in ADHD. The study presents a supporting diagnostic tool that uses EEG signal processing and machine learning algorithms. The findings would be helpful in the objective diagnosis of ADHD.

这项研究的目的是建立一个机器学习模型，以使用参与者在进行脑电图（EEG）信号时获得的事件相关电位（ERP）的时间和频率分析中的信息来区分注意力缺陷多动障碍（ADHD）患者和健康对照听觉怪胎任务。该研究包括23名未经药物治疗的ADHD患者和23名健康对照。脑电信号在时域中通过非线性大脑动力学和形态特征进行分析，在时频域中采用小波系数进行分析。选定的功能已应用于各种机器学习技术，包括：多层感知器，朴素贝叶斯，支持向量机，k近邻，自适应增强，逻辑回归和随机森林对ADHD患者和健康对照进行分类。相对于对照组，ADHD患者观察到更长的P300潜伏期和更小的P300振幅。在相对于对照组的分形维数计算中，ADHD组表现出降低的复杂性。另外，某些小波系数在两组中提供了明显不同的值。结合这些提取的特征，我们的结果表明，多层感知器方法提供了最佳分类，准确率为91.3％，并发可靠性很高（Kappa = 0.82）。

结果表明，时域和频域特征的组合对于ADHD的诊断目的可能是有用的和有区别的。这项研究提出了一种支持性诊断工具，该工具使用EEG信号处理和机器学习算法。这些发现将有助于多动症的客观诊断。