Surface EMG signal normalisation and filtering improves sensitivity of equine gait analysis

Abstract

Low-frequency noise attenuation and normalisation are fundamental signal processing (SP) methods for surface electromyography (sEMG), but are absent, or not consistently applied, in equine biomechanics. The purpose of this study was to examine the effect of different band-pass filtering and normalisation conventions on sensitivity for identifying differences in sEMG amplitude-related measures, calculated from leading (LdH) and trailing hindlimb (TrH) during canter, where between-limb differences in vertical loading are known. sEMG and 3D-kinematic data were collected from the right Biceps Femoris in 10 horses during both canter leads. Peak hip and stifle joint angle and angular velocity were calculated during stance to verify between-limb biomechanical differences. Four SP methods, with and without normalisation and high-pass filtering, were applied to raw sEMG data. Methods 1 (M1) to 4 (M4) included DC-offset removal and full-wave rectification. Method 2 (M2) included additional normalisation relative to maximum sEMG across all strides. Method 3 (M3) included additional high-pass filtering (Butterworth 4th order, 40 Hz cut-off), for artefact attenuation. M4 included the addition of high-pass filtering and normalisation. Integrated EMG (iEMG) and average rectified value (ARV) were calculated using processed sEMG data from M1 – M4, with stride duration as the temporal domain. sEMG parameters, within M1 – M4, and kinematic parameters were grouped by LdH and TrH and compared using repeated measures ANOVA. Significant between-limb differences for hip and stifle joint kinematics were found, indicating functional differences in hindlimb movement. M2 and M4, revealed significantly greater iEMG and ARV for LdH than TrH (P<0.01), with M4 producing the lowest P-values and largest effect sizes. Significant between-limb differences in sEMG parameters were not observed with M1 and M3. The results indicate that equine sEMG SP should include normalisation and high-pass filtering to improve sensitivity for identifying differences in muscle function associated with biomechanical changes during equine gait.

Muscle Function and Kinematics during Submaximal Equine Jumping: What Can Objective Outcomes Tell Us about Athletic Performance Indicators?

“…sEMG signal amplitude is sensitive to several internal and external factors [49], so appropriate normalization techniques must be applied to reduce between and within-subject variation and to allow comparisons between subjects, muscles and trials [77,78]. In equine subjects, obtaining a maximum voluntary contraction (MVC) is not possible, but normalization to a submaximal RVC has been shown to improve sensitivity and accuracy of equine gait analysis [32]. In this study normalization to the maximum signal observed during canter permitted examination of the meaningful proportional change in muscle activity between the related activities of canter and jump.…”

“…Surface electromyography (sEMG) provides equine biomechanists with a non-invasive tool for quantifying superficial muscle activity. sEMG has been used to study equine muscle function during normal locomotion at walk, trot and canter [28][29][30][31][32][33][34][35], but only two known studies have evaluated muscle activity during jumping [36,37]. Unfortunately, a comparison of findings between these studies is confounded by methodological variation for sEMG signal detection and processing and differences in the jumping tasks studied.…”

“…Unfortunately, a comparison of findings between these studies is confounded by methodological variation for sEMG signal detection and processing and differences in the jumping tasks studied. Further, guidelines on best practice for equine sEMG signal processing only appeared after these publications [32,38] and were therefore not employed by either of the studies. Kinematic data were also not collected, which is recommended for developing a comprehensive understanding of the role of equine musculature during jumping [39].…”

Neural noise and cortical inhibition in schizophrenia

“…We investigated task-related motor noise at the level of muscle activation by analyzing EMG variability of the four recorded muscles. First, EMGs were low-pass filtered at 40Hz [41]. Then, over the same interval of 1s (as for force), the CV of the EMG (CV-EMG) was computed trial-by-trial.…”

Muscular tension as an indicator of acute stress in horses

“…Further steps (repeated scrubbing or further hair removal) were taken prior to data collection if an unacceptable level of impedance was observed making it unlikely the electrode preparation and attachment imposed limitations in the current study. Another of these recommendations for best practice is to normalize the recorded amplitudes to the maximum value for that individual as raw amplitude can be highly variable and is influenced by the muscle under consideration, the individual, and the electrode placement (St. George et al, 2019; Valentin & Zsoldos, 2016). The gold standard for normalization is a maximal voluntary contraction, where maximum contraction is voluntarily sustained over a period of time.…”