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

TL;DR

This study presents a novel GCN-SVM approach using surface EMG data to improve the accuracy of Parkinson's disease severity assessment, showing promising preliminary results.

Contribution

Introduces a GCN-SVM model for PD severity assessment using sEMG, demonstrating improved accuracy over traditional methods.

Findings

SEMG data reveals neuromuscular differences in PD patients.

GCN-SVM achieves 92% accuracy in classifying PD severity.

Methodology sets foundation for larger-scale validation.

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.