DS-HViT: A Dual-Stream Hierarchical Vision Transformer for Multi-Scale Analysis of Parkinson's Disease Handwriting - Louja, Ayoub; Zaiouane, Yassin; Jamali, Abdellah; Naja, Najib (Engineering, Technology & Applied Science Research, 2025)
Early detection of Parkinson's Disease (PD) remains challenging in the medical field due to motor symptoms manifesting across different temporal scales. To address this, we introduce a Dual-Stream Hierarchical Vision Transformer (DS-HViT), a deep learning framework designed to capture the multi-scale temporal dynamics of PD-related Handwriting (HW) impairments. The architecture employs parallel micro- and macro-scale streams: the micro-scale stream models high-frequency tremor signatures, while the macro-scale stream captures gradual motor decline. The model was evaluated on the NewHandPD dataset using 5-fold patient-stratified cross-validation, complemented by bootstrap analysis and McNemar’s test for statistical validation. DS-HViT achieved an accuracy of 98.2 ± 0.8%, with sensitivity of 97.3 ± 1.2% and specificity of 99.0 ± 0.9%, significantly surpassing state-of-the-art methods (p < 0.01). Ablation studies confirmed the synergistic effect of dual-stream processing, while the model demonstrated excellent calibration with an Expected Calibration Error (ECE) of 0.043 and exceptional discriminative ability with a Diagnostic Odds Ratio (DOR) of 486.3.