S-EMG signal compression based on domain transformation and spectral shape dynamic bit allocation

Abstract

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.

Electromyographic Signal Compression Through Image Encoders and Preprocessing Techniques

“…As already mentioned, the methods presented by Norris, Englehart, and Lovely [38], Berger et al [27], and Trabuco et al [29], [30] are based on wavelets, while the one introduced by Filho, Silva, and Carvalho [31] employs a spatial domain approach, which approximates signal segments with elements from an adaptive dictionary. The scheme presented by Trabuco et al overcame the proposed method only for a CF of 85%, which probably happened due to the high performance of its bit allocation scheme, for this specific CF.…”

“…Indeed, the majority of available schemes aim to maintain signal shape [16], [26], [27], [29], [31], [40] and the ones with high performance regarding spectral parameters do not present expressive results regarding waveform errors [24], [48].…”

“…There are many approaches and most use the discrete cosine transform (DCT) [15], [25] or wavelets [26], however, the latter generally present better results. For instance, Norris, Englehart, and Lovely [26] applied the embedded zero-tree wavelet algorithm to S-EMG segments of 1024 samples, Berger et al [27] employed adaptive bit-allocation through an artificial neural network, Jain and Vig [28] used tree vectors for rearranging wavelet coefficients, which are then quantized, and, finally, Trabuco et al [29], [30] proposed an algorithm that uses wavelet transform followed by dynamic bit allocation, based on mathematical decreasing spectral shape models for reducing the bit depth of high-frequency coefficients, and entropy coding.…”

Avaliação de fadiga muscular localizada em sinais eletromiográficos utilizando taxa de amostragem variável no tempo

“…Entretanto, para a exploração da correlação bidimensional no processo de compressão, conforme se descreve nesses trabalhos, há a necessidade de se ter o sinal eletromiográfico completo previamente adquirido. Em [12], os autores propõem um algoritmo de compressão utilizando decomposição em wavelets e alocação dinâmica de bits em sub-bandas. Em comum, todos esses trabalhos citados fazem uso de transformadas ortogonaise da representação esparsa obtida no domínio transformadono processo de compressão.…”

Research on a Solution for Efficient ECG Data Transmission in IoT Environment

“…Among many efforts of advanced emergency system [6][7][8], first, our research team chose to apply cloud network to our solution, because ECG data brings about the need for large amounts of mass data for storing information of interest [9]. Second, we applied our proposed ECIoT(ECG Compression for IoT) compression algorithm to maximize the transmission efficiency of the ECG IoT environment.…”