ScholarWorks@성균관대학교

초록

Early detection of Parkinson's disease (PD) remains a critical challenge in healthcare, as current diagnostic methods often rely on subjective clinical assessments when motor symptoms have already progressed. This paper presents a novel deep learning approach for automated PD detection using digital handwriting analysis. We propose a dual-stream attention-enhanced bidirectional long short-term memory (BiLSTM) network that separately processes spatial trajectory features and dynamic behavioral features through dedicated pathways. Our architecture employs stream-specific attention mechanisms to identify discriminative temporal segments within each feature modality, followed by an attention-based fusion layer that integrates complementary representations from both streams. Extensive experiments on a comprehensive dataset of handwriting samples from both PD patients and healthy controls demonstrate that our approach achieves state-of-the-art performance with 94.7% accuracy and 0.9872 AUC. The attention mechanism provides interpretable insights into which temporal phases of handwriting are most indicative of PD symptoms, offering potential clinical value for understanding disease progression. Our findings suggest that automated analysis of digital handwriting can serve as an effective, non-invasive screening tool for early PD detection.

키워드