Abstract
Surface electromyography (sEMG) signals acquired with linear electrode array are useful in analyzing muscle anatomy and physiology. Most algorithms for signal processing, detection, and estimation require adequate quality of the input signals, however, multi-channel sEMG signals are commonly contaminated due to several noise sources. The sEMG signal needs to be enhanced prior to the digital signal and image processing to achieve the best results. This study is using spatio-temporal images to represent surface EMG signals. The motor unit action potential (MUAP) in these images looks like a linear structure, making certain angles with the x-axis, depending on the conduction velocity of the MU. A multi-scale Hessian-based filter is used to enhance the linear structure, i.e., the MUAP region, and to suppress the background noise. The proposed framework is compared with some of the existing algorithms using synthetic, simulated, and experimental sEMG signals. Results show improved detection accuracy of the motor unit action potential after the proposed enhancement as a preprocessing step.
Highlights
Human muscle is made up of a certain number of motor units (MU) which are comprised of a set of muscle fibers innervated by the motor neuron
We assume that in spatio-temporal surface electromyography (sEMG) images, the brightness of the bright portion is high in the middle and can decreases be modelled as a line-like structure transversal to its axis, i.e., as given gradually toward the end Gaussian (Figure 2a)
The performance of the multi-scale Hessian-based filter is studied and analyzed in different scenarios, such as synthetic images with gaussian propagating profile, simulated EMG signals using the model proposed by the authors in Reference [26], and the experimental EMG signals recorded
Summary
Human muscle is made up of a certain number of motor units (MU) which are comprised of a set of muscle fibers innervated by the motor neuron. The electrical activity of these motor units is called motor unit action potential (MUAP) [1]. The sum of these potentials for a muscle is called electromyograph (EMG). Surface EMG (sEMG) signals are recorded by placing the electrodes on muscle surface [2]. Multi-channel EMG signals are often recorded using a linear array of electrodes [3]. These multi-channel sEMG signals can be represented using spatio-temporal two-dimensional (2D)
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