Model-based analysis of sEMG signals using Stockwell transform features under varied muscle fiber composition and conduction velocity.

In this study, sEMG signals of adductor pollicis (AP) and triceps brachii (TB) muscles that vary in fiber type proportion are generated at different levels of maximum voluntary contraction (MVC) by integrating various model components reported in existing studies. The current distribution function of the existing sEMG model is modified with time-varying action potential conduction velocity values for type I and II motor units of the muscles. To validate the model, sEMG signals were recorded from both muscles at 30%, 50%, and 70% of maximum voluntary contraction (MVC) until fatigue; AP using a pulley-rope setup and TB during isometric contractions with dumbbells. Stockwell transform (S transform) is used to compute the time-frequency (TF) spectrum of the initial and final 2 s segments of the signals. From the obtained singular values (SVs), features such as maximum SV, SV energy, and SV entropy are computed. The statistical analysis performed using the Mann-Whitney U test showed significant differences (p < 0.05) in the extracted features of AP and TB for most of the aspects. The Bland-Altman analysis demonstrated a high degree of agreement between simulated and experimental features, with the mean difference falling within the 95% confidence interval in most cases. The TF spectrum generated using the S transform shows a shift in frequency components towards lower frequencies during the final segment of simulated and recorded signals at the selected levels of MVCs. The proposed model helps to study the fiber-type characteristics of other skeletal muscles under different neuromuscular conditions.