Assessment of elbow spasticity with surface electromyography and mechanomyography based on support vector machine

• Main Authors: Hui Wang, Lei Wang, Yun Xiang, Ning Zhao, Xiangxin Li, Shixiong Chen, Chuang Lin, Guanglin Li
• Format: Conference Proceeding
• Language: English
• Subjects: Correlation; Elbow; Electromyography; Feature extraction; Kernel; Muscles; Support vector machines
• ISSN: 1557-170X; 2694-0604
• DOI: 10.1109/EMBC.2017.8037699

The Modified Ashworth Scale (MAS) is the gold standard in clinical for grading spasticity. However, its results greatly depend on the physician evaluations and are subjective. In this study, we investigated the feasibility of using support vector machine (SVM) to objectively assess elbow spasticity based on both surface electromyography (sEMG) and mechanomyography (MMG). sEMG signals and tri-axial accelerometer mechanomyography (ACC-MMG) signals were recorded simultaneously on patients' biceps and triceps when they extended or bended elbow passively. 39 post-stroke patients participated in the study, and were divided into four groups regarding MAS level (MAS=0, 1, 1+ or 2). The three types of features, root mean square (RMS), mean power frequency (MPF), and median frequency (MF), were calculated from sEMG and MMG signal recordings. Spearman correlation analysis was used to investigate the relationship between the features and spasticity grades. The results showed that the correlation between MAS and each of the five features (MMG-RMS of the biceps, MMG-RMS of the triceps, the EMG-RMS of the biceps, EMG-RMS of the triceps, EMG-MPF of the triceps) was significant (p