A new methodological approach based on stationarity and permutation entropy of EMG Bursts for assessing muscle function alterations in a Parkinson’s disease animal model

Abstract

The EMG signal is the electrical manifestation of motor unit recruitment processes underlying the contractile dynamics of muscle fibers. The analysis methodology frequently carried out includes a preprocessing stage based on artifact removal, stationarity testing, a feature extraction and interpretation stage. Generally, stationarity criteria are difficult to meet when EMG signals are evoked by momentary activations (bursting activity). Thus, the study of contractile patterns evoked in free-moving protocols require particular treatments. Here, we propose a new approach for quantitatively measuring stationarity by using mean, variance and autocovariance test (MVA-test) and the Permutation Entropy for measuring uncertainty degree. This methodology was applied to EMG signals obtained from a Parkinson’s disease (PD) lesion model to longitudinally study the muscle function alterations. The MVA-test revealed that the temporal structure of EMG around the reference contractile zone presents incremental non-stationary characteristics over post-injury time. Likewise, it was observed that the initial phase of motor recruitment in the biceps femoris (BF) muscle (around the onset) presents a high non-stationary component, which increases over post-injury time. Permutation entropy measures throughout the contractile dynamics of the BF muscle revealed that the uncertainty degree decreases in the initial phase of contraction as the animal’s post-injury time increases. The analysis proposed allowed for a longitudinal characterization of muscle function alterations in an animal model of PD in terms of the stationarity properties of EMG signals. Furthermore, it was observed that permutation entropy could serve as a robust biomarker for quantifying neuromuscular remodeling caused by PD progression.