A High Performance Algorithm for EMG Signal Denoising With Classification Using Multilevel Dwt

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. An electromyography detects the electrical potential generated by muscle cells when these cells are electrically or neurologically activated. The signals can be analyzed to detect medical abnormalities, activation level, or recruitment order or to analyze the biomechanics of human or animal movement.Wavelet Transform (WT) has been applied for removing noise from the surface EMG. Gaussianity tests are conducted to understand changes in muscle contraction and to quantify the effectiveness of the noise removal process.Wavelets are functions that satisfy certain mathematical requirements and are used in representing data or other functions. In this paper we analyze the performance of different level DWT for EMG signal denoising and compare the results considering mean square error (MSE).The Denoising analysis concludes using bior multi level wavelet and the mean was optimal in nature for different global threshold.