caa a22 4500 606162992 CHVBK 20210128100641.0 cr unu---uuuuu 210128e20150801xx s 000 0 eng 10.1007/s11062-015-9537-7 doi (NATIONALLICENCE)springer-10.1007/s11062-015-9537-7 Karan V. Department of Electrical and Instrumentation Engineering, Thapar University, Patiala, India aut Wavelet Transform-Based Classification of Electromyogram Signals Using an Anova Technique [Elektronische Daten] [V. Karan] 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. Springer Science+Business Media New York, 2015 electromyogram nationallicence wavelet nationallicence analysis of variance nationallicence denoising nationallicence classifier nationallicence Neurophysiology Springer US; http://www.springer-ny.com 47/4(2015-08-01), 302-309 0090-2977 47:4<302 2015 47 11062 https://doi.org/10.1007/s11062-015-9537-7 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11062-015-9537-7 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Karan V. Department of Electrical and Instrumentation Engineering, Thapar University, Patiala, India aut NATIONALLICENCE 773 0- Neurophysiology Springer US; http://www.springer-ny.com 47/4(2015-08-01), 302-309 0090-2977 47:4<302 2015 47 11062