Surface EMG Profiling in Parkinson's Disease: Advancing Severity Assessment with GCN-SVM
This work addresses the challenge of diagnosis and monitoring for Parkinson's disease patients, but it is incremental as it builds on existing methods with a hybrid approach on a small dataset.
This study tackled the problem of objectively assessing Parkinson's disease severity by using surface electromyography (sEMG) data from the biceps brachii muscle, achieving up to 92% accuracy with a GCN-SVM model compared to 83% with a traditional SVM.
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.