Siamese based deep neural network for ADHD detection using EEG signal
Behnam Latifi1, Ali Amini1, Ali Motie Nasrabadi2
1Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran.
Insights
Deep learning accurately detects Attention-Deficit/Hyperactivity Disorder (ADHD) in children using brain maps from EEG signals. Specific brain regions and theta band activity are key indicators for diagnosis.
Area of Science:
- Neuroscience
- Artificial Intelligence
- Pediatric Medicine
Background:
- Early detection of Attention-Deficit/Hyperactivity Disorder (ADHD) in children is vital for effective intervention and tailored treatment.
- Understanding the neurobiological underpinnings of ADHD is essential for improving diagnostic accuracy.
Purpose of the Study:
- To apply deep learning models to analyze Electroencephalography (EEG) derived brain maps for ADHD detection in pediatric subjects.
- To leverage explainable AI (XAI) to identify key brain regions and signal features indicative of ADHD.
Main Methods:
- A Siamese-based Convolutional Neural Network (CNN) was utilized to process EEG-based brain maps.
- Power Spectral Density (PSD) analysis was performed on EEG signals.
- Gradient-weighted Class Activation Mapping (Grad-CAM) was employed for feature visualization and interpretation.
Main Results:
- The CNN model achieved a high classification accuracy of 99.17% for ADHD detection.
- Grad-CAM analysis identified theta band PSD features from the frontal and occipital lobes as significant discriminators.
- These findings highlight specific neurophysiological markers associated with ADHD in children.
Conclusions:
- Deep learning, particularly CNNs, demonstrates high efficacy in detecting ADHD in pediatric populations.
- Regional PSD metrics, especially in the theta band of frontal and occipital lobes, are crucial for accurate ADHD classification.
- Explainable AI (Grad-CAM) enhances the understanding of ADHD neurobiology, paving the way for improved diagnostic precision.
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.
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