Adaptive wavelet EMG compression based on local optimization of filter banks.
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.
Paiva
Juliana Pereira Lisboa M
JP
Department of Biomedical Engineering, Universidade do Vale do Paraiba-UNIVAP, São Jose dos Campos, Brazil. lisboa_ju@yahoo.com.br
Kelencz
Carlos Alberto
CA
Paiva
Henrique Mohallem
HM
Galvão
Roberto Kawakami H
RK
Magini
Marcio
M
D000328:Adult; D004576:Electromyography / Q000379:methods*; D005374:Filtration; D006801:Humans; D012815:Signal Processing, Computer-Assisted