Subtle Micro-Tremor Fusion: A Cross-Modal AI Framework for Early Detection of Parkinson’s Disease from Voice and Handwriting Dynamics

H. Ahmed¹, Naglaa E. Ghannam^2,*, H. Mancy³, Esraa A. Mahareek⁴
1 Mathematics Department, College of Science and Humanities in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
2 Department of Computer Engineering and Information, College of Engineering in Wadi Alddawasir, Prince Sattam Bin Abdulaziz University, Al-kharj, Saudi Arabia
3 Department of Computer Science, College of Engineering and Computer Sciences, Prince Sattam Bin Abdulaziz University, Al-kharj, Saudi Arabia
4 Department of Mathematics, Faculty of Science, Al-Azhar University (Girls’ Branch), Cairo, Egypt
* Corresponding Author: Naglaa E. Ghannam. Email: email
(This article belongs to the Special Issue: Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.075732

Received 07 November 2025; Accepted 20 January 2026; Published online 04 February 2026

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Abstract

Parkinson’s disease remains a major clinical issue in terms of early detection, especially during its prodromal stage when symptoms are not evident or not distinct. To address this problem, we proposed a new deep learning 2-based approach for detecting Parkinson’s disease before any of the overt symptoms develop during their prodromal stage. We used 5 publicly accessible datasets, including UCI Parkinson’s Voice, Spiral Drawings, PaHaW, NewHandPD, and PPMI, and implemented a dual stream CNN–BiLSTM architecture with Fisher-weighted feature merging and SHAP-based explanation. The findings reveal that the model’s performance was superior and achieved 98.2%, a F1-score of 0.981, and AUC of 0.991 on the UCI Voice dataset. The model’s performance on the remaining datasets was also comparable, with up to a 2–7 percent betterment in accuracy compared to existing strong models such as CNN–RNN–MLP, ILN–GNet, and CASENet. Across the evidence, the findings back the diagnostic promise of micro-tremor assessment and demonstrate that combining temporal and spatial features with a scatter-based segment for a multi-modal approach can be an effective and scalable platform for an “early,” interpretable PD screening system.

Keywords

Early Parkinson diagnosis; explainable AI (XAI); feature-level fusion; handwriting analysis; microtremor detection; multimodal fusion; Parkinson’s disease; prodromal detection; voice signal processing

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Subtle Micro-Tremor Fusion: A Cross-Modal AI Framework for Early Detection of Parkinson’s Disease from Voice and Handwriting Dynamics

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