Objective: To define reference values for surface electromyography (EMG) -based signal parameters in Parkinson’s disease (PD) based on previously measured data. These reference values may help in the interpretation of home-based measurement data from PD patients.

Background: A wearable system was recently presented for ambulatory monitoring of surface EMG and kinematic measurements in PD [1]. The system assess motor fluctuations based on computed parameters of surface EMG and 3D acceleration as a function of time.

Method: The previously gathered surface EMG data consist of 225 patient measurements and 90 healthy control measurements. During the EMG measurements, the subjects were performing isometric and dynamic contractions of the biceps brachii (BB). The group mean ± std values were computed for the following EMG parameters: kurtosis, burst frequency, correlation dimension and recurrence rate. In addition, the sample entropy and the acceleration power at frequency bands 0.2-4 Hz and 4-9 Hz were computed and combined with EMG signal parameters to form the first principal component for evaluating the overall abnormality of the neuromuscular function and movement.

Results: [figure1] depicts the mean ± std values of the surface EMG parameters for the patients and healthy controls. All parameters were significantly different between the patients and the healthy controls (Kruskal-Wallis test, p < 0.001). The surface EMG parameters are visualized with the defined reference values for one PD patient with motor fluctuations during the home-based monitoring in [figure2].

Conclusion: Ambulatory EMG appears to reliably measure neuromuscular dysfunctions in PD.

References: [1] Rissanen SM, Koivu M, Hartikainen P, Pekkonen E. Ambulatory surface electromyography with accelerometry for evaluating daily motor fluctuations in Parkinson’s disease. Clin Neurophysiol. 2021;132(2):469-479.

« Back to MDS Virtual Congress 2021

MDS Abstracts - https://www.mdsabstracts.org/abstract/surface-emg-signal-features-of-parkinsons-disease-may-support-interpretation-of-home-based-measurement-data/