Key result
Adaptive wavelet technique outperforms standard wavelets in percent residual difference for a given compression factor.
Why the study?
An adaptive wavelet technique with optimized filter banks is needed to minimize distortion in compressed surface electromyographic signals.
An adaptive wavelet technique improves the compression of surface EMG signals compared to standard non-optimized techniques.
Adaptive wavelet compression may aid EMG signal handling in research; leaves open any clinical cardiovascular applications.
This paper presents an adaptive wavelet technique for compression of surface electromyographic signals. The technique employs an optimization algorithm to adjust the wavelet filter bank in order to minimize the distortion of the compressed signal. Orthogonality of the transform is ensured by using a restriction-free parametrization described elsewhere. A case study involving real-life isotonic and isometric electromyographic signals is presented for illustration. The results show that the proposed approach outperforms the standard non-optimized wavelet technique in terms of the percent residual difference for a given compression factor.
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Paiva et al. (2008) studied this question. Adaptive wavelet technique vs. Standard non-optimized wavelet technique was evaluated on Percent residual difference for a given compression factor. An adaptive wavelet technique outperformed the standard non-optimized wavelet technique in terms of the percent residual difference for a given compression factor.
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