Analysis of Muscle Fiber Type Proportions in Surface Electromyography Signals of Athletes Using Reassigned Morlet Scalogram

The selection of proper measurement systems and signal processing methods is crucial while analyzing complex physiological signals like surface electromyography (sEMG). The reassignment technique is a powerful signal analysis method that enables efficient cross-term suppression in time-frequency distributions (TFDs) and provides excellent time-frequency resolution. In this work, the fiber type proportions in the gastrocnemius lateralis (GL) of athletes (AT) are analyzed using sEMG signals and a reassigned Morlet scalogram. To compare the results, signals recorded from non-athletes (NAT) are considered. Signals are acquired during an isometric calf raise exercise with loads until fatigue and are processed using a reassigned Morlet scalogram. Features namely, instantaneous mean frequency (IMNF), instantaneous median frequency (IMDF), energy spectral density (ESD), and instantaneous power (IP) are extracted. A least square linear regression technique is used to study the variation of features over time and slope values are calculated. The mean percentage difference (MPD) of features from non-fatigue (NF) to fatigue (F) state is computed. AT demonstrates higher slope values and lower MPD for features reflecting the dominance of fast-twitch fibers in their GL. Features derived from the reassigned Morlet scalogram exhibited higher statistical significance in differentiating the signals of NAT and AT when compared to previously reported fiber-type sensitive sEMG features. ESD exhibited a maximum statistical significance of
p
= 8.42E-07 in differentiating the signals during a NF state. Thus, the proposed method is found reliable for the noninvasive assessment of fast-twitch fiber dominance in AT. The study can be extended for the automatic identification of muscle typology in aspiring AT which may find applications in sports science.