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.
Highlights
New technologies for data transmission and multi-electrode arrays increased the demand for compressing high-density electromyography (HD EMG) signals
EMG signals differ from muscle to muscle and subject to subject, the basic shapes and discharge rates of the constituent motor unit action potential (MUAP) trains are similar and 90-95% of the signal power is within the 10-450 Hz range
These signals were recorded from the upper trapezius (UT) muscle of two healthy males, at twenty and forty percent of maximum voluntary contraction force (MVC), using a two-dimensional surface-electrode matrix of sixty-four electrodes distributed in five rows and thirteen columns [24], with rows positioned in the direction of muscle fibers
Summary
New 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 twodimensional electrode matrices at different muscle-contraction forces. Compression techniques have been primarily applied to medical images, electrocardiography, and electroencephalography [1,2,3,4,5,6,7] These techniques have been applied to electromyography (EMG) signals [8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23]. Compression techniques applied to EMG signals belong to two main groups: transform-based and linear prediction methods. Alternative approaches have been applied to one-dimensional EMG signal [16,17,18], including the segmentation of a single
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