Iterative Filtering-Based Automated Method for Detection of Normal and ALS EMG Signals

Abstract

Electromyogram (EMG) signals have been proved very useful in identification of neuromuscular diseases (NMDs). In the proposed work, we have proposed a new method for the classification of normal and abnormal EMG signals to identify amyotrophic lateral sclerosis (ALS) disease. First, we have obtained all motor unit action potentials (MUAPs) from EMG signals. Extracted MUAPs are then decomposed using iterative filtering (IF) decomposition method and intrinsic mode functions (IMFs) are obtained. Features like Euclidean distance quadratic mutual information (QMIED), Cauchy–Schwartz quadratic mutual information (QMICS), cross information potential (CIP), and correntropy (COR) are computed for each level of IMFs separately. Statistical analysis of features has been performed by the Kruskal–Wallis statistical test. For classification, the calculated features are given as an input to the three different classifiers: JRip rules classifier, reduces error pruning (REP) tree classifier, and random forest classifier for the classification of normal and ALS EMG signals. The results obtained from classification process show that proposed classification method provides very accurate classification of normal and ALS EMG signals and better than the previously existing methods.