Wavelet-Attention deep model for pediatric ADHD diagnosis via EEG

Abstract

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in children, impacting academic performance, social interactions, and overall well-being. Early and accurate diagnosis is crucial, yet current methods rely heavily on subjective assessments. This study presents a novel Wavelet-Attention deep model for objective ADHD diagnosis using electroencephalography signals. The model integrates a wavelet transform for feature extraction with a deep residual network (ResNet) augmented by an attention mechanism to enhance focus on salient features. Rigorous preprocessing, including Independent Component Analysis for artifact removal, was applied to a publicly available dataset of 121 children. To ensure a robust and clinically relevant evaluation that avoids data leakage, a strict Leave-One-Subject-Out cross-validation protocol was employed. The proposed model demonstrated strong diagnostic performance, achieving an accuracy of 96.69%, a sensitivity of 95.08%, and a specificity of 98.33% in distinguishing between children with ADHD and healthy controls. Furthermore, model-agnostic interpretability analysis revealed that features derived from frontal lobe channels and low-frequency wavelet coefficients were most critical for the model's decisions, aligning with established neurophysiological markers of ADHD. The results suggest that this approach holds significant potential for developing a reliable and objective diagnostic tool for ADHD, facilitating earlier and more personalized interventions.