Akademik Veri Yönetim Sistemi
JOURNAL OF MEDICAL SYSTEMS, cilt.49, sa.2025, ss.1-15, 2025 (SCI-Expanded)
Parkinson’s disease (PD) is a prevalent and complex neurodegenerative disorder, with early diagnosis playing a critical role in timely treatment and management. Handwriting dynamics has emerged as a promising biomarker for early detection of PD, yet current diagnostic methods often lack precision and robustness. This study introduces a novel multimodal deep learning-based decision support system to enhance PD diagnosis. Our approach leverages static and dynamic features of handwriting data by combining images of handwritten drawings with fused time-frequency representations of grip pressure, axial pressure, tilt, and accelerometer signals from the y- and z-axes recorded during handwriting. The timefrequency transformations employ Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) to generate spectrograms and scalograms. Results demonstrate that fusing STFT spectrograms achieves an accuracy of 85.41%, which improves to 97.92% when integrated into the multimodal CNN model. Similarly, fusing CWT scalograms achieves 92.08% accuracy, further enhanced to 96.66% with the multimodal approach. These findings highlight that fused time-frequency representations yield successful results for PD diagnosis. Furthermore, the CWT-based approach demonstrates superior performance compared to STFT. Finally, integrating fused time-frequency images with visualizations further improves the accuracy rates. We incorporate the Gradient-weighted Class Activation Mapping++(Grad-CAM++) eXplainable Artificial Intelligence (XAI) method to ensure interpretability, highlighting attention regions within the fused STFT and CWT images. These attention regions effectively differentiate between healthy controls (HC) and PD patients. Although the model achieved promising results on the NewHandPD dataset, further external validation on diverse and multi-center datasets is required to confirm its generalizability and clinical applicability. The findings underscore the potential of integrating handwriting-based static and dynamic features for high-precision PD diagnosis, offering a robust and explainable framework for clinical decision-making.