Multi-Scale Temporal Analysis with a Dual-Branch Attention Network for Interpretable Gait-Based Classification of Neurodegenerative Diseases

The accurate diagnosis of neurodegenerative diseases (NDDs), such as Amyotrophic Lateral Sclerosis (ALS), Huntington's Disease (HD), and Parkinson's Disease (PD), remains a clinical challenge due to the complexity and subtlety of gait abnormalities. This paper proposes the Dual-Branch Attention-Enhanced Residual Network (DAERN), a novel deep learning architecture that integrates Dilated Causal Convolutions (DCCBlock) for local gait pattern extraction and Multi-Head Self-Attention (MHSA) for long-range dependency modeling. A CrossAttention Fusion module enhances feature integration, while SHapley Additive exPlanations (SHAP) and Integrated Gradients (IG) improve interpretability, providing clinically relevant insights into gait-based NDD classification. Uniform Manifold Approximation and Projection (UMAP) visualizations reveal well-separated clusters corresponding to distinct NDDs categories, demonstrating the model's ability to capture discriminative features. Comprehensive ablation studies validate the contributions of model components and preprocessing strategies, highlighting the significance of each in achieving state-of-the-art classification performance. Experimental evaluations on the Gait in Neurodegenerative Disease (GaitNDD) dataset demonstrate that DAERN achieves an accuracy of 99.64%, an F1-score of 99.65%, and an AUC of 0.9997, significantly outperforming conventional deep learning and machine learning baselines. These findings suggest that DAERN could be a valuable and interpretable tool for clinical gait assessment, aiding in early-stage monitoring and automated screening of NDDs, with potential applications in real-time wearable sensor-based gait analysis.