Multi-Channel Surface EMG Spatio-Temporal Image Enhancement Using Multi-Scale Hessian-Based Filters

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.

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Behaviour of motor unit action potential rate, estimated from surface EMG, as a measure of muscle activation level

Journal of NeuroEngineering and Rehabilitation ◽

10.1186/1743-0003-3-15 ◽

2006 ◽

Vol 3 (1) ◽

Cited By ~ 10

Author(s):

Laura AC Kallenberg ◽

Hermie J Hermens

Keyword(s):

Action Potential ◽

Motor Unit ◽

Muscle Activation ◽

Surface Emg ◽

Motor Unit Action Potential ◽

Activation Level ◽

Motor Unit Action ◽

Potential Rate ◽

Unit Action

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Spatio-temporal sEMG image enhancement and motor unit action potential (MUAP) detection: algorithms and their analysis

Journal of Ambient Intelligence and Humanized Computing ◽

10.1007/s12652-019-01411-1 ◽

2019 ◽

Vol 10 (10) ◽

pp. 3809-3819 ◽

Cited By ~ 1

Author(s):

Ihtesham ul Islam ◽

Khalil Ullah ◽

Muhammad Afaq ◽

Muhammad Hasanain Chaudary ◽

Muhammad Kashif Hanif

Keyword(s):

Action Potential ◽

Image Enhancement ◽

Motor Unit ◽

Motor Unit Action Potential ◽

Detection Algorithms ◽

Motor Unit Action ◽

Spatio Temporal ◽

Unit Action

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Models of recruitment and rate coding organization in motor-unit pools

Journal of Neurophysiology ◽

10.1152/jn.1993.70.6.2470 ◽

1993 ◽

Vol 70 (6) ◽

pp. 2470-2488 ◽

Cited By ~ 553

Author(s):

A. J. Fuglevand ◽

D. A. Winter ◽

A. E. Patla

Keyword(s):

Action Potential ◽

Firing Rate ◽

Motor Unit ◽

Action Potentials ◽

Surface Emg ◽

Motor Unit Action Potential ◽

Rate Coding ◽

Motor Unit Action ◽

Unit Action ◽

Excitatory Drive

1. Isometric muscle force and the surface electromyogram (EMG) were simulated from a model that predicted recruitment and firing times in a pool of 120 motor units under different levels of excitatory drive. The EMG-force relationships that emerged from simulations using various schedules of recruitment and rate coding were compared with those observed experimentally to determine which of the modeled schemes were plausible representations of the actual organization in motor-unit pools. 2. The model was comprised of three elements: a motoneuron model, a motor-unit force model, and a model of the surface EMG. Input to the neuron model was an excitatory drive function representing the net synaptic input to motoneurons during voluntary muscle contractions. Recruitment thresholds were assigned such that many motoneurons had low thresholds and relatively few neurons had high thresholds. Motoneuron firing rate increased as a linear function of excitatory drive between recruitment threshold and peak firing rate levels. The sequence of discharge times for each motoneuron was simulated as a random renewal process. 3. Motor-unit twitch force was estimated as an impulse response of a critically damped, second-order system. Twitch amplitudes were assigned according to rank in the recruitment order, and twitch contraction times were inversely related to twitch amplitude. Nonlinear force-firing rate behavior was simulated by varying motor-unit force gain as a function of the instantaneous firing rate and the contraction time of the unit. The total force exerted by the muscle was computed as the sum of the motor-unit forces. 4. Motor-unit action potentials were simulated on the basis of estimates of the number and location of motor-unit muscle fibers and the propagation velocity of the fiber action potentials. The number of fibers innervated by each unit was assumed to be directly proportional to the twitch force. The area of muscle encompassing unit fibers was proportional to the number of fibers innervated, and the location of motor-unit territories were randomly assigned within the muscle cross section. Action-potential propagation velocities were estimated from an inverse function of contraction time. The train of discharge times predicted from the motoneuron model determined the occurrence of each motor-unit action potential. The surface EMG was synthesized as the sum of all motor-unit action-potential trains. 5. Two recruitment conditions were tested: narrow (limit of recruitment < 50% maximum excitation) and broad recruitment range conditions (limit of recruitment > 70% maximum excitation).(ABSTRACT TRUNCATED AT 400 WORDS)

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