State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
Peking University Sixth Hospital / Institute of Mental Health, Key Laboratory of Ministry of Health (Peking University), Beijing 100191, China.
Neuroscience. 2019 May 15;406:444-456. doi: 10.1016/j.neuroscience.2019.03.048. Epub 2019 Mar 26.
The electroencephalogram (EEG) is an informative neuroimaging tool for studying attention-deficit/hyperactivity disorder (ADHD); one main goal is to characterize the EEG of children with ADHD. In this study, we employed the power spectrum, complexity and bicoherence, biomarker candidates for identifying ADHD children in a machine learning approach, to characterize resting-state EEG (rsEEG). We built support vector machine classifiers using a single type of feature, all features from a method (relative spectral power, spectral power ratio, complexity or bicoherence), or all features from all four methods. We evaluated effectiveness and performance of the classifiers using the permutation test and the area under the receiver operating characteristic curve (AUC). We analyzed the rsEEG from 50 ADHD children and 58 age-matched controls. The results show that though spectral features can be used to build a convincing model, the prediction accuracy of the model was unfortunately unstable. Bicoherence features had significant between-group differences, but classifier performance was sensitive to brain region used. rsEEG complexity of ADHD children was significantly lower than controls and may be a suitable biomarker candidate. Through a machine learning approach, 14 features from various brain regions using different methods were selected; the classifier based on these features had an AUC of 0.9158 and an accuracy of 84.59%. These findings strongly suggest that the combination of rsEEG characteristics obtained by various methods may be a tool for identifying ADHD.
脑电图（EEG）是研究注意力缺陷多动障碍（ADHD）的一种有价值的神经影像学工具；其主要目标之一是描述 ADHD 儿童的 EEG 特征。在这项研究中，我们采用了功率谱、复杂度和双谱相干性等方法来识别 ADHD 儿童的生物标志物，对静息态 EEG（rsEEG）进行特征描述。我们使用支持向量机（SVM）分类器，基于单一特征、一种方法的所有特征（相对谱功率、谱功率比、复杂度或双谱相干性）或所有四种方法的所有特征构建分类器。我们使用置换检验和接收器操作特征曲线（ROC）下面积（AUC）评估分类器的有效性和性能。我们分析了 50 名 ADHD 儿童和 58 名年龄匹配的对照组的 rsEEG。结果表明，虽然频谱特征可用于构建可信度较高的模型，但模型的预测精度却很不稳定。双谱相干性特征存在显著的组间差异，但分类器性能对所使用的脑区很敏感。ADHD 儿童的 rsEEG 复杂度显著低于对照组，可能是一个合适的生物标志物候选者。通过机器学习方法，从不同脑区使用不同方法选择了 14 个特征；基于这些特征的分类器具有 0.9158 的 AUC 和 84.59%的准确率。这些发现强烈表明，使用各种方法获得的 rsEEG 特征的组合可能是识别 ADHD 的一种工具。
