Objective: To develop a diagnostic tool for Parkinson’s Disease (PD) resting tremor using surface Electromyography (sEMG) data and musculoskeletal simulations to identify key biomarkers.

Background: Resting tremor is a primary indicator of PD. However, reliance on subjective diagnostic methods highlights the urgent need for objective, digitally quantifiable tools to enhance PD diagnosis and treatment.

Method: This study analyzed and compared sEMG data from 10 PD patients who exhibited resting tremors and a control group within the same age range.  Specifically, data corresponding to the Flexor Carpi Radialis (FCR) muscle underwent processing, including filtering, rectification, and normalization, and were examined in both time and frequency domains. Furthermore, a Hill-type musculoskeletal model representing the FCR was developed, utilizing the processed data as muscle activation information in simulations to model the FCR muscle’s dynamics and properties. Both biological (from sEMG) and synthetic (from simulations) data features were analyzed. Feature ranking was conducted employing Principal Component Analysis (PCA), to discern their relevance in characterizing resting tremor and potentially defining them as biomarkers for this motor symptom. Linear Discriminant Analysis (LDA) was employed to assess the efficacy of these biomarkers in tremor classification.

Results: The analysis revealed distinct features selected as key biological biomarkers for assessing resting tremor, including the Mean Absolute Value (MAV), Root Mean Square (RMS) and mean frequency derived from sEMG signals. Moreover, synthetic biomarkers such as pennation angular velocity and fiber velocity obtained from simulations of the developed FCR muscle were also extracted to assess resting tremor. The integration of these features into the LDA model yielded a classification accuracy of 93.2%, surpassing analyses of individual biological (87.8%) or synthetic (89.5%) markers alone. This enhancement was observed in 70% of the cases.

Conclusion: This study presents a novel method for PD tremor symptom evaluation by merging sEMG data with musculoskeletal simulations to pinpoint relevant biomarkers for resting tremors. Although further studies with larger datasets are required for comprehensive validation, this approach promises a more objective and rapid diagnostic method, potentially surpassing existing practices in PD diagnosis.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/combining-emg-and-simulation-based-biomarkers-to-enhance-parkinsons-resting-tremor-diagnosis/