Advanced machine learning techniques reveal multidimensional EEG abnormalities in children with ADHD: a framework for automatic diagnosis.

Introduction: Attention deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder that affects attention, impulse control, and multitasking abilities in children and adults. Understanding electroencephalography (EEG) characteristics of children with ADHD can provide new diagnostic tools and personalized treatment plans. This study aims to explore potentially promising EEG features using advanced machine learning techniques and feature selection technique (i.e., SHapley Additive exPlanations (SHAP) algorithm) to reveal brain function abnormalities between pediatric children with ADHD and healthy controls (HC) in a data-driven manner. Methods: Multidimensional EEG characteristics were extracted from multiple domain (including power spectral density (PSD), fuzzy entropy (FuzEn), and functional connectivity features of mutual information (MI)) using a publicly-available dataset. Then, four widely-employed machine learning algorithms (including random forest (RF), XGBoost, CatBoost, and LightGBM) were used for classification calculations, and the SHAP algorithm was then used to assess the importance of the contributing features to interpret the model's decision process. Results: The results showed that the highest classification accuracy of 99.58% for pediatric ADHD detection was obtained with the CatBoost model based on the optimal feature subset of 206 features (PSD/FuzEn/MI = 53/5/148). According to the optimal feature subset statistics, there is an increase in the power of theta, alpha, and beta rhythms, an elevated power ratio between theta and beta (theta/beta ratio, TBR), and reorganization of whole-brain functional connectivity across all frequency bands in children with ADHD, primarily characterized by enhanced functional connectivity. Discussion: We showed that EEG features was effectively extracted by machine learning methods, which could play a critical role in classification between pediatric ADHD and HC. These findings provide strong evidence for revealing the electrophysiological mechanisms through multidimensional EEG characteristics and move a step forward towards future automatic diagnosis of ADHD.