BackgroundSurface electromyographic (S-EMG) signal processing has been emerging in the past few years due to its non-invasive assessment of muscle function and structure and because of the fast growing rate of digital technology which brings about new solutions and applications. Factors such as sampling rate, quantization word length, number of channels and experiment duration can lead to a potentially large volume of data. Efficient transmission and/or storage of S-EMG signals are actually a research issue. That is the aim of this work.MethodsThis paper presents an algorithm for the data compression of surface electromyographic (S-EMG) signals recorded during isometric contractions protocol and during dynamic experimental protocols such as the cycling activity. The proposed algorithm is based on discrete wavelet transform to proceed spectral decomposition and de-correlation, on a dynamic bit allocation procedure to code the wavelets transformed coefficients, and on an entropy coding to minimize the remaining redundancy and to pack all data. The bit allocation scheme is based on mathematical decreasing spectral shape models, which indicates a shorter digital word length to code high frequency wavelets transformed coefficients. Four bit allocation spectral shape methods were implemented and compared: decreasing exponential spectral shape, decreasing linear spectral shape, decreasing square-root spectral shape and rotated hyperbolic tangent spectral shape.ResultsThe proposed method is demonstrated and evaluated for an isometric protocol and for a dynamic protocol using a real S-EMG signal data bank. Objective performance evaluations metrics are presented. In addition, comparisons with other encoders proposed in scientific literature are shown.ConclusionsThe decreasing bit allocation shape applied to the quantized wavelet coefficients combined with arithmetic coding results is an efficient procedure. The performance comparisons of the proposed S-EMG data compression algorithm with the established techniques found in scientific literature have shown promising results.

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This paper presents algorithms designed for one-dimensional (1-D) and 2-D surface electromyographic (S-EMG) signal compression. The 1-D approach is a wavelet transform based encoder applied to isometric and dynamic S-EMG signals. An adaptive estimation of the spectral shape is used to carry out dynamic bit allocation for vector quantization of transformed coefficients. Thus, an entropy coding is applied to minimize redundancy in quantized coefficient vector and to pack the data. In the 2-D approach algorithm, the isometric or dynamic S-EMG signal is properly segmented and arranged to build a 2-D representation. The high efficient video codec is used to encode the signal, using 16-bit-depth precision, all possible coding/prediction unit sizes, and all intra-coding modes. The encoders are evaluated with objective metrics, and a real signal data bank is used. Furthermore, performance comparisons are also shown in this paper, where the proposed methods have outperformed other efficient encoders reported in the literature.

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In this paper, an S-EMG signal encoder based on wavelet transform and bit allocation for sub-bands is presented. Were implemented and compared two methods of allocating bits: linear decreasing spectral profile and hyperbolic tangent decreasing spectral profile. Comparisons with other encoders in the literature are shown. In the development of the encoder, always sought to maximum compression, but without loss of fidelity of the reconstructed signal, a fundamental characteristic of the analysis of electromyographic signals. Developed algorithm showed very satisfactory and promising results. Keywords --Data compression, discrete wavelet transform, surface electromyographic signal Resumo --Nesse trabalho é apresentado um codificador de sinais de eletromiografia baseado em transformada wavelet e alocação de bits por sub-bandas. Foram implementadas e comparadas duas formas de alocação de bits: perfil espectral linear decrescente e perfil espectral tangente hiperbólica decrescente. Comparações com outros codificadores presentes na literatura são apresentadas. No desenvolvimento do codificador, buscou-se sempre a máxima compressão, mas sem a perda da fidelidade do sinal reconstruído, característica fundamental da análise de sinais eletromiográficos. Verificouse que o algoritmo desenvolvido apresentou resultados muito satisfatórios e promissores.Palavras-chave --Compressão de dados, transformada wavelet discreta, eletromiografia de superfície