Wavelet Transform-Based Classification of Electromyogram Signals Using an Anova Technique

Abstract

Wavelet analysis of surface electromyogram (sEMG) signals has been investigated. Methods to remove noise before processing and further analysis are rather significant for these signals. The sEMG signals were estimated with the following steps, first, the obtained signal was decomposed using wavelet transform; then, decomposed coefficients were analyzed by threshold methods, and, finally, reconstruction was performed. Comparison of the Daubechies wavelet family for effective removing noise from the recorded sEMGs was executed preciously. As was found, wavelet transform db4 performs denoising best among the aforesaid wavelet family. Results inferred that Daubechies wavelet families (db4) were more suitable for the analysis of sEMG signals related to different upper limb motions, and a classification accuracy of 88.90% was achieved. Then, a statistical technique (one-way repeated factorial analysis) for the experimental coefficient was done to investigate the class separ ability among different motions.