Features based on variational mode decomposition for identification of neuromuscular disorder using EMG signals

Neuromuscular disorder is a muscular and nervous disorder resulting in muscular weakness and progressively damages nervous control, such as amyotrophic lateral sclerosis (ALS) and myopathy (MYO). Its diagnosis can be possible by classification of ALS, MYO, and normal electromyogram (EMG) signals. In this paper, an effective method based on variational mode decomposition (VMD) is proposed for identification of neuromuscular disorder of EMG signals. VMD is an adaptive signal decomposition which decomposes EMG signals nonrecursively into band-limited functions or modes. These modes are used for extraction of spectral features, particularly spectral flatness, spectral spread, spectral decrease and statistical features like kurtosis, mean absolute deviation, and interquartile range. The extracted features are fed to the extreme learning machine classifier in order to classify neuromuscular disorder of EMG signals. The performance of obtained results shows that the method used provides a better classification for neuromuscular disorder of EMG signals as compared to existing methods.