Non-invasive Analysis of Fiber Type Composition in Lower Limb Skeletal Muscles Using Reduced Interference Rihaczek Distribution.

Fiber composition is an important factor influencing force generation and endurance of different skeletal muscles. The analysis of the heterogeneous composition of muscles has gained importance in the field of sports biomechanics and biomedicine. In this work, an attempt is made to analyze the fiber composition of Rectus femoris (type II dominant) and Soleus (type I dominant) muscles using surface electromyography. Isometric signals are acquired from the muscles of 15 participants using a well-defined protocol and are further processed using reduced interference Rihaczek distribution. Instantaneous median frequency (IMDF) is extracted from the non-fatigue (NF) and fatigue (F) segments of the signals and is analyzed. From the distributions, it is found that the spectral power increases in the lower frequencies of the signal recorded from the rectus femoris and in the higher frequencies of signals recorded from the soleus during fatigue. The soleus is showing higher IMDF values than the rectus femoris in both segments. A reduction of 14% and an increase of 10% is observed in the IMDF during fatigue for rectus femoris and soleus, respectively. Thus, the extracted feature is found to be sensitive and statistically significant (p<0.05) to differentiate fiber types as well as the NF and F states of the two muscles.Clinical Relevance- This study may be extended to non-invasively analyze the fiber type shifts in muscles due to athletic training and pathological conditions.

Muscle Fatigue Analysis by Visualization of Dynamic Surface EMG Signals Using Markov Transition Field.

Muscle fatigue analysis is important in the diagnosis of neuromuscular diseases. Analysis of surface electromyography (sEMG) signals by non-linear probabilistic approach is useful in studying their transitions and thus the neuromuscular system. In this study, a method to visualize sEMG signals using Markov transition field (MTF) under fatigue conditions is proposed. sEMG signals are acquired from 45 healthy participants during biceps curl exercise. They are filtered and divided into ten equal segments. Markov transition matrix is constructed and corresponding MTF image is generated. The average self-transition probability is extracted and compared for both non-fatigue and fatigue segments. It is observed that the extracted feature shows high statistical significance with p value less than 0.001. The increase in average self-transition probability under fatigue condition correlates with the reduction in the degree of signal complexity. Thus, encoding of sEMG signals to images is helpful in analyzing the complexity of the neuromuscular system. Clinical Relevance- This approach may be helpful in analyzing muscle fatigue related with various myoneural conditions.