Classification of muscle fatigue using surface electromyography signals and multifractals

Abstract

Muscle fatigue is commonly experienced in both normal and subjects with neuromuscular disorders. Surface electromyography (sEMG) signals are useful technique for analyzing muscle fatigue. sEMG signals are highly nonstationary and exhibit complex nonlinear characteristic in dynamic contractions. In this work, an attempt is made to classify sEMG signals recorded from biceps brachii muscles in nonfatigue and fatigue using multifractal features. The signals are recorded from 26 healthy normal adult subjects while performing standard experimental protocol involving dynamic contraction. The preprocessed signals are divided into six segments. The first and last segments are considered as nonfatigue and fatigue conditions respectively. The signals are then subjected to multifractal detrended moving average algorithm and eight multifractal features are extracted from both conditions. Further, information gain (IG) based ranking is used for reducing the number of features. Three different classification algorithms are employed namely, k-Nearest Neighbor algorithm (kNN), Naive Bayes (NB) and logistic regression (LR) for classification. The results show that signals exhibit multifractal characteristics and the multifractal features such as, generalized Hurst exponent, degree of multifractality and scaling exponent slope are significantly different in fatigue condition. The Hurst exponent for small fluctuation and degree of multifractality are found to be very highly significant feature. The LR and kNN classifier performance gave an accuracy of 84% and 82% respectively. This method of using multifractal features appears to be useful in classifying sEMG signals in dynamic contraction. This study can also be extended to classify fatigue condition in various neuromuscular disorders.