Utilizing Machine Learning Techniques to Enhance Attention-Deficit Hyperactivity Disorder Diagnosis Using Resting-State EEG Data  

作　　者：Lina Han Liyan Li Yanyan Chen Xiaohan Wu Yang Yu Xu Liu Zihan Yang Ling Li Xinxian Peng 

机构地区：[1]Changchun Sixth Hospital,Changchun 130000,Jilin Province,China [2]The School of Communication Engineering,Jilin University,Changchun 130000,Jilin Province,China

出　　处：《Journal of Clinical and Nursing Research》2025年第1期209-217,共9页临床护理研究(英文)

基　　金：This study received financial support from the Jilin Province Health and Technology Capacity Enhancement Project(Project Number:222Lc132).

摘　　要：Objective: This study investigates the auxiliary role of resting-state electroencephalography (EEG) in the clinical diagnosis of attention-deficit hyperactivity disorder (ADHD) using machine learning techniques. Methods: Resting-state EEG recordings were obtained from 57 children, comprising 28 typically developing children and 29 children diagnosed with ADHD. The EEG signal data from both groups were analyzed. To ensure analytical accuracy, artifacts and noise in the EEG signals were removed using the EEGLAB toolbox within the MATLAB environment. Following preprocessing, a comparative analysis was conducted using various ensemble learning algorithms, including AdaBoost, GBM, LightGBM, RF, XGB, and CatBoost. Model performance was systematically evaluated and optimized, validating the superior efficacy of ensemble learning approaches in identifying ADHD. Conclusion: Applying machine learning techniques to extract features from resting-state EEG signals enabled the development of effective ensemble learning models. Differential entropy and energy features across multiple frequency bands proved particularly valuable for these models. This approach significantly enhances the detection rate of ADHD in children, demonstrating high diagnostic efficacy and sensitivity, and providing a promising tool for clinical application.

关 键 词：Attention-deficit hyperactivity disorder Machine learning EEG signals Feature extraction Ensemble learning models DIAGNOSIS 

分 类 号：R74[医药卫生—神经病学与精神病学]