This paper describes an in vitro method for comparing surface-detected electromyographic median frequency (MF) and conduction velocity (CV) parameters with histochemical measurements of muscle fiber type composition and cross-sectional area (CSA). Electromyographic signals were recorded during electrically elicited tetanic contractions from rat soleus, extensor digitorum longus, and diaphragm muscles placed in an oxygenated Krebs bath. Fibers were typed as slow oxidative, fast oxidative glycolytic, and fast glycolytic based on histochemical enzyme stains. Muscles with a greater percentage of fast glycolytic and fast oxidative glycolytic fibers exhibited greater initial values of MF and CV as well as a greater reduction in these variables over the course of the contraction. Regression indicated that fiber type composition could be predicted based on two MF parameters. A weighted measure of muscle fiber CSA was found to be linearly related to both initial MF and CV. The results of this study suggest that MF and CV parameters recorded during a muscular contraction are related to muscle fiber type composition and muscle fiber CSA.

Experimental electromyogram (EMG) data from the human biceps brachii were simulated using the model described in [10] of this work. A multichannel linear electrode array, spanning the length of the biceps, was used to detect monopolar and bipolar signals, from which double differential signals were computed, during either voluntary or electrically elicited isometric contractions. For relatively low-level voluntary contractions (10%-30% of maximum force) individual firings of three to four-different motor units were identified and their waveforms were closely approximated by the model. Motor unit parameters such as depth, size, fiber orientation and length, location of innervation and tendonous zones, propagation velocity, and source width were estimated using the model. Two applications of the model are described. The first analyzes the effects of electrode rotation with respect to the muscle fiber direction and shows the possibility of conduction velocity (CV) over- and under-estimation. The second focuses on the myoelectric manifestations of fatigue during a sustained electrically elicited contraction and the interrelationship between muscle fiber CV, spectral and amplitude variables, and the length of the depolarization zone. It is concluded that a) surface EMG detection using an electrode array, when combined with a model of signal propagation, provides a useful method for understanding the physiological and anatomical determinants of EMG waveform characteristics and b) the model provides a way for the interpretation of fatigue plots.