A convolutional neural network model using LASSO-regularized EEG biomarkers achieved a superior testing accuracy of 97.75% for classifying pediatric ADHD.
Do machine learning and deep learning models using EEG biomarkers accurately detect pediatric ADHD?
A convolutional neural network model using LASSO-regularized EEG biomarkers achieved 97.75% accuracy in detecting pediatric ADHD, offering a highly effective automated screening tool.
This study presents a novel methodology for automating the classification of pediatric ADHD using electroencephalogram (EEG) biomarkers through machine learning and deep learning techniques. The primary objective is to develop accurate EEG-based screening tools to aid clinical diagnosis and enable early intervention for ADHD. The proposed system utilizes a publicly available dataset consisting of raw EEG recordings from 61 individuals with ADHD and 60 control subjects during a visual attention task. The methodology involves meticulous preprocessing of raw EEG recordings to isolate brain signals and extract informative features, including time, frequency, and entropy signal characteristics. The feature selection techniques, including least absolute shrinkage and selection operator (LASSO) regularization and recursive elimination, were applied to identify relevant variables and enhance generalization. The obtained features are processed by employing various machine learning and deep learning algorithms, namely CatBoost, Random Forest Decision Trees, Convolutional Neural Networks (CNNs), and Long Short-Term Memory Networks (LSTMs). The empirical results of the proposed algorithms highlight the effectiveness of feature selection approaches in matching informative biomarkers with optimal model classes. The convolutional neural network model achieves superior testing accuracy of 97.75% using LASSO-regularized biomarkers, underscoring the strengths of deep learning and customized feature optimization. The proposed framework advances EEG analysis to uncover discriminative patterns, significantly contributing to the field of ADHD screening and diagnosis. The suggested methodology achieved high performance compared with different existing systems based on AI approaches for diagnosing ADHD.
Alkahtani et al. (Mon,) conducted a other in Pediatric ADHD (n=121). Convolutional Neural Network (CNN) model using LASSO-regularized EEG biomarkers vs. Other machine learning models and existing systems was evaluated on Testing accuracy. A convolutional neural network model using LASSO-regularized EEG biomarkers achieved a superior testing accuracy of 97.75% for classifying pediatric ADHD.