Comparative study of multi-headed and baseline deep learning models for ADHD classification from EEG signals
Lamiaa A Amar1, Ahmed M Otifi2, Shimaa A Mohamed3
1Department of Networks and Distributed Systems, Informatic Research Institute, City of Scientific Research and Technological Applications, SRTA-CITY, Alexandria, 21934, Egypt.
Physical and Engineering Sciences in Medicine
|August 26, 2025
Summary
A novel multi-headed deep learning framework shows promise for diagnosing Attention-Deficit/Hyperactivity Disorder (ADHD) using electroencephalography (EEG) signals. This approach achieved 89.87% accuracy, outperforming traditional methods for ADHD detection.
Area of Science:
- Neuroscience
- Computational Psychiatry
- Machine Learning
Background:
- Rising prevalence of Attention-Deficit/Hyperactivity Disorder (ADHD) in children necessitates improved diagnostic tools.
- Electroencephalography (EEG) offers a noninvasive method for ADHD detection, but full channel utilization poses computational challenges.
- Model overfitting is a risk with complex EEG analyses.
Purpose of the Study:
- To compare a novel multi-headed deep learning framework against a traditional single-model approach for ADHD classification using EEG.
- To evaluate the efficacy of using a reduced set of strategically selected EEG channels.
- To assess the ability of multi-headed models to capture inter-channel relationships and temporal features.
Main Methods:
- Collected EEG data from 79 participants (37 with ADHD, 42 healthy) across four cognitive states.
- Utilized five strategically selected EEG channels to mitigate computational complexity.
- Implemented a multi-headed deep learning framework with parallel branches (Bidirectional Long Short-Term Memory, Long Short-Term Memory, Gated Recurrent Unit).
Main Results:
- The multi-headed framework, specifically the Long Short-Term Memory and Bidirectional Long Short-Term Memory combination, achieved the highest classification accuracy of 89.87%.
- This performance significantly surpassed all tested baseline single-model configurations.
- The multi-headed approach effectively extracted richer temporal features and inter-channel relationships.
Conclusions:
- Multi-headed deep learning models demonstrate significant potential for enhancing EEG-based ADHD diagnosis.
- Integrating diverse deep learning architectures within a multi-headed framework improves classification accuracy.
- This approach offers a computationally efficient and effective method for ADHD detection.
