Detection of abnormal electromyograms employing DWT-based amplitude envelope analysis using Teager energy operator

In this contribution, detection and classification of healthy, myopathy and neuropathy electromyograms employing a novel discrete wavelet transform-based amplitude envelope analysis is proposed. Electromyograms of healthy, myopathy and neuropathy categories are initially decomposed into several frequency bands with the help of discrete wavelet transform-based multi resolution analysis. Following this, instead of using Hilbert transform, a novel technique for amplitude envelope extraction from different decomposed frequency sub-bands was performed using discrete energy separation algorithm implementing Teager energy operator. Three distinct features were extracted from the amplitude envelopes of each sub-band and analysis of variance (ANOVA) test was performed to substantiate their statistical significance. The extracted features were finally fed as input to the employed support vector machines classifier to classify different categories of electromyography signals. It was observed that 100% classification accuracy is obtained in this work, which is found to outperform the existing methods studied on the same database.