ADHDeepNet From Raw EEG to Diagnosis: Improving ADHD Diagnosis through Temporal-Spatial Processing, Adaptive Attention Mechanisms, and Explainability in Raw EEG Signals

2025-09-10

AI Summary

To address the time-consuming and subjectivity-prone nature of clinical ADHD diagnosis, this study proposes ADHDeepNet—a novel end-to-end deep learning framework for automatic ADHD identification directly from raw EEG signals. Methodologically, it integrates dual-dimensional temporal-spatial feature modeling with a dynamic adaptive attention mechanism, and incorporates Gradient-weighted Class Activation Mapping (Grad-CAM) to localize discriminative brain regions and frequency bands, thereby enhancing model interpretability and clinical credibility. Robustness and generalizability are ensured via additive Gaussian noise augmentation, nested cross-validation, and t-SNE visualization. Evaluated on a dataset of 121 participants—including both children and adults—the model achieves 100% sensitivity and 99.17% accuracy, significantly outperforming existing approaches. This work establishes a new paradigm for precise, efficient, and interpretable objective ADHD diagnosis.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Attention Deficit Hyperactivity Disorder (ADHD) is a common brain disorder in children that can persist into adulthood, affecting social, academic, and career life. Early diagnosis is crucial for managing these impacts on patients and the healthcare system but is often labor-intensive and time-consuming. This paper presents a novel method to improve ADHD diagnosis precision and timeliness by leveraging Deep Learning (DL) approaches and electroencephalogram (EEG) signals. We introduce ADHDeepNet, a DL model that utilizes comprehensive temporal-spatial characterization, attention modules, and explainability techniques optimized for EEG signals. ADHDeepNet integrates feature extraction and refinement processes to enhance ADHD diagnosis. The model was trained and validated on a dataset of 121 participants (61 ADHD, 60 Healthy Controls), employing nested cross-validation for robust performance. The proposed two-stage methodology uses a 10-fold cross-subject validation strategy. Initially, each iteration optimizes the model's hyper-parameters with inner 2-fold cross-validation. Then, Additive Gaussian Noise (AGN) with various standard deviations and magnification levels is applied for data augmentation. ADHDeepNet achieved 100% sensitivity and 99.17% accuracy in classifying ADHD/HC subjects. To clarify model explainability and identify key brain regions and frequency bands for ADHD diagnosis, we analyzed the learned weights and activation patterns of the model's primary layers. Additionally, t-distributed Stochastic Neighbor Embedding (t-SNE) visualized high-dimensional data, aiding in interpreting the model's decisions. This study highlights the potential of DL and EEG in enhancing ADHD diagnosis accuracy and efficiency.

Problem

• Improving ADHD diagnosis precision using deep learning and EEG signals
• Developing ADHDeepNet with temporal-spatial processing and attention mechanisms
• Enhancing model explainability to identify key brain regions and frequencies

Innovation

• Uses temporal-spatial processing of raw EEG signals
• Implements adaptive attention mechanisms for analysis
• Incorporates explainability techniques for model interpretation