An application of a muscle model to study electromyographic signals
Introduction
Single fibre action potentials have earlier been simulated as well as signals obtained from other types of EMG electrodes [1], [2]. These simulations have given important information about the generation of EMG signals. Often the signal processing has been very time consuming and it has not been possible to use these techniques as on line tools for research and teaching. With faster computers and with an improved line source model [3] it is now feasible to build an advanced user friendly simulator for these purposes.
The aim of the study is to construct a simulator of a muscle with its muscle fibres, motor end- plates and motor units (MU), and to simulate the generated signals obtained with different types of EMG electrodes.
Section snippets
Mathematical model of the single fibre action potential
Single muscle fibre action potentials can be simulated using different types of mathematical models. The first were either very complex, e.g. volume conductor models [4], or simpler but less accurate, e.g. the core conductor model and the tripole and dipole models [5]. As a trade-off between these two extremes, the line source model was developed [6], [7], modified [8], and later improved by introducing an antialiasing filter [3], see Fig. 1. With the improved model, it is possible to use a
Getting started
EMG Simulator is started from a shortcut or by double-clicking a file (*.cmg) associated with the program. When EMG Simulator starts from the start menu or the desktop an empty muscle is created. To add MUs click with the left mouse button in the magnified transversal muscle view (the left view). Specify parameters for the MU in the dialog box (Fig. 3) and click ‘OK’. The fibres in the MU will be distributed according to the given parameters with the clicked point as center point. If a MU with
Discussion
A model for research and teaching EMG has been developed. It has a great number of parameters that may be optionally changed in simulations of normal and diseased muscle.
Hardware and software specification
Pentium 166 MHz CPU and higher.
windows 95, 98, me, 2000, nt.
soundblaster 16 compatible soundcard.
RAM (64 MB) recommended (48 MB minimum).
Availability of the software
The software is a commercial product, EMG Simulator, and is distributed by Medtronic Functional Diagnostics A/D, Copenhagen, Denmark. E-mail: [email protected], http://www.mfd.medtronic.com.
Acknowledgements
The study was supported by the Swedish Medical Research Council, Grant 135 ES.
References (18)
- et al.
How the size of the needle electrode leading-off surface influences the shape of the single muscle fibre action potentials in electromyography
Computer Programs in Biomedicine
(1973) Volume conductor fields of action currents
Biophysics
(1964)- et al.
Human single muscle fiber potentials at different radial distances from the fibers determined by a method of location
Experimetal Neurology
(1982) - et al.
Scanning EMG in normal muscle and in neuromuscular disorders
Electroencephalography and clinical Neurophysiology
(1991) - et al.
Simulation of the normal concentric needle electromyogram by using a muscle model
Clinical Neurophysiology
(2001) - et al.
Simulation of EMG in pathological situations
Clinical Neurophysiology
(2001) - et al.
Simulation techniques in electromyography
IEEE Transactions on Biomedical Engineering
(1985) - et al.
Novel ideas for fast muscle action potential simulations using the lince source model
Submitted to IEEE Transactions on Biomedical Engineering
(2002) Intra and extracellular potential fields of active nerve and muscle fibres: a physio-mathematical analysis of different models
Acta Physiologica Scandinavica
(1969)
Cited by (13)
A new method of simulating surface electromyograms using probability density functions
2008, Computers in Biology and MedicineA novel electromyographic signal simulator for muscle contraction studies
2008, Computer Methods and Programs in BiomedicineClassification of surface EMG signal using relative wavelet packet energy
2005, Computer Methods and Programs in BiomedicineClassification of sEMG signals using integrated neural network with small sized training data
2012, Biomedical Engineering - Applications, Basis and CommunicationsDiscussion on Computer Information Network Security Measures in the Era of Data Autonomy
2022, 3rd International Conference on Electronics and Sustainable Communication Systems, ICESC 2022 - ProceedingsBodyWorks: interactive interdisciplinary online teaching tools for biomechanics and physiology teaching
2021, Advances in Physiology Education