Siamese based deep neural network for ADHD detection using EEG signal.

BACKGROUND

Detecting Attention-Deficit/Hyperactivity Disorder (ADHD) in children is crucial for timely intervention and personalized treatment.

OBJECTIVE

This study aims to utilize deep learning techniques to analyze brain maps derived from Power Spectral Density (PSD) of Electroencephalography (EEG) signals in pediatric subjects for ADHD detection.

METHODS

We employed a Siamese-based Convolutional Neural Network (CNN) to analyze EEG-based brain maps. Gradient-weighted class activation mapping (Grad-CAM) was used as an explainable AI (XAI) visualization method to identify significant features.

RESULTS

The CNN model achieved a high classification accuracy of 99.17 %. Grad-CAM analysis revealed that PSD features from the theta band of the frontal and occipital lobes are effective discriminators for distinguishing children with ADHD from healthy controls.

CONCLUSION

This study demonstrates the effectiveness of deep learning in ADHD detection and highlights the importance of regional PSD metrics in accurate classification. By utilizing Grad-CAM, we elucidate the discriminative power of specific brain regions and frequency bands, thereby enhancing the understanding of ADHD neurobiology for improved diagnostic precision in pediatric populations.