Compression of high-density EMG signals for trapezius and gastrocnemius muscles

Abstract

BackgroundNew technologies for data transmission and multi-electrode arrays increased the demand for compressing high-density electromyography (HD EMG) signals. This article aims the compression of HD EMG signals recorded by two-dimensional electrode matrices at different muscle-contraction forces. It also shows methodological aspects of compressing HD EMG signals for non-pinnate (upper trapezius) and pinnate (medial gastrocnemius) muscles, using image compression techniques.MethodsHD EMG signals were placed in image rows, according to two distinct electrode orders: parallel and perpendicular to the muscle longitudinal axis. For the lossless case, the images obtained from single-differential signals as well as their differences in time were compressed. For the lossy algorithm, the images associated to the recorded monopolar or single-differential signals were compressed for different compression levels.ResultsLossless compression provided up to 59.3% file-size reduction (FSR), with lower contraction forces associated to higher FSR. For lossy compression, a 90.8% reduction on the file size was attained, while keeping the signal-to-noise ratio (SNR) at 21.19 dB. For a similar FSR, higher contraction forces corresponded to higher SNRConclusionsThe computation of signal differences in time improves the performance of lossless compression while the selection of signals in the transversal order improves the lossy compression of HD EMG, for both pinnate and non-pinnate muscles.

Efficient Real-Time Lossless EMG Data Transmission to Monitor Pre-Term Delivery in a Medical Information System

“…Due to the scarcity of research into the compression of EMG signals of maternal patients, studies of EMG signals, regardless of disease type, were used for comparison (refer to Section 4.2). The studies by Balouchestani [14], Itiki [15], Norris [16], Berger [17], Filho [18], and Trabuco [7] were selected. Specific summary details of the selected researches are shown in Table 1.…”

Segmentação Automática de S-EMG em Protocolos Dinâmicos para Compressão Bidimensional

“…Nos últimos anos, houve crescimento no número de cientistas que atuam na área de processamento digital de sinais biológicos, como sinais cardíacos (ECG) [18][19][20], sinais neurológicos (EEG) [21][22][23] e, recentemente, sinais de eletromiografia (EMG) [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Tal interesse é motivado pela importância que a pesquisa nessa área tem apresentado no diagnóstico e no tratamento de diversas doenças.…”

Electromyographic Signal Compression Through Image Encoders and Preprocessing Techniques

“…In addition, such a problem is even worse when high-density electromyography [13] is employed, due to the large amount of data. In summary, S-EMG signal compression is a bandwidth-demading technique that consequently raises the need for efficient compression methods, which besides producing a more compact data representation, must also be able to preserve the associated clinical/useful information [2], [3], [6], [14], [15].…”

“…Recently, a new trend has been noticed, regarding S-EMG compressors: algorithms that compress S-EMG signals as if they were two-dimensional, that is, S-EMG records compressed as images, with one [16], [17] or multiple electrodes [13], as show in Fig. 1.…”