Surface EMG Profiling in Parkinson's Disease: Advancing Severity Assessment with GCN-SVM

• URL: http://arxiv.org/abs/2507.14153v1
• Date: Fri, 04 Jul 2025 00:29:31 GMT
• Title: Surface EMG Profiling in Parkinson's Disease: Advancing Severity Assessment with GCN-SVM
• Authors: Daniel Cieślak, Barbara Szyca, Weronika Bajko, Liwia Florkiewicz, Kinga Grzęda, Mariusz Kaczmarek, Helena Kamieniecka, Hubert Lis, Weronika Matwiejuk, Anna Prus, Michalina Razik, Inga Rozumowicz, Wiktoria Ziembakowska,
• Abstract summary: This study introduces a novel approach utilizing surface electromyography (sEMG) to objectively assess Parkinson's disease severity.<n>Initial analysis of sEMG data from five PD patients and five healthy controls revealed significant neuromuscular differences.
• Score: 0.15557122832359727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Parkinson's disease (PD) poses challenges in diagnosis and monitoring due to its progressive nature and complex symptoms. This study introduces a novel approach utilizing surface electromyography (sEMG) to objectively assess PD severity, focusing on the biceps brachii muscle. Initial analysis of sEMG data from five PD patients and five healthy controls revealed significant neuromuscular differences. A traditional Support Vector Machine (SVM) model achieved up to 83% accuracy, while enhancements with a Graph Convolutional Network-Support Vector Machine (GCN-SVM) model increased accuracy to 92%. Despite the preliminary nature of these results, the study outlines a detailed experimental methodology for future research with larger cohorts to validate these findings and integrate the approach into clinical practice. The proposed approach holds promise for advancing PD severity assessment and improving patient care in Parkinson's disease management.

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