Classification of electromyography signal using wavelet decomposition method

Understanding cognitive responses of human brain is one of the significant research fields where electroencephalography plays vital role in analyzing brain functionality with respect to brain signals. Electromyography is another modality to understand cognitive responses with respect to muscle activation. In this research work, a data set consists of healthy and myopathy has been considered from physionet data repository. Signal has been decomposed using wavelet transformation. Features such as Shannon, spectral and approximate entropy have been extracted from decomposed signal. Support vector machine has been used for classification. Result shows that first level and third level coefficient shows better classification accuracy than other components. Spectral entropy has good classification results than Shannon and approximate entropy.