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.