Techniques for Decomposition of EMG Signals
نویسندگان
چکیده
The electrical signals produced by the muscles and nerves are analyzed to assess the state of neuromuscular function in subjects with suspected neuromuscular disorders. The repetitive activation of several individual motor units (MUs) results in a superposed pulse train and constitutes the electromyogram (EMG) signal. The analysis of the EMG is based on its basic constituent i.e. motor-unit action potentials (MUAPs). The motor unit is the smallest functional unit of a muscle, which can be activated voluntarily. It consists of a group of muscle fibers, which are innervated from the same motor nerve. The shape of MUAP reflects the pathological and functional states of the motor unit. With increasing muscle force, the EMG signal shows an increase in the number of activated MUAPs recruited at increasing firing rate, making it difficult for the neurophysiologist to distinguish individual MUAP waveforms. In most of the clinical EMG examinations, it is the shape of the action potential that is analyzed for diagnostics The shape and amplitude of MUAP waveform generally differ from motor unit to motor unit due to unique geometric arrangement of the muscle fibers in each motor unit. However, the MUAP ABSTRACT
منابع مشابه
Comparative Analysis of Wavelet-based Feature Extraction for Intramuscular EMG Signal Decomposition
Background: Electromyographic (EMG) signal decomposition is the process by which an EMG signal is decomposed into its constituent motor unit potential trains (MUPTs). A major step in EMG decomposition is feature extraction in which each detected motor unit potential (MUP) is represented by a feature vector. As with any other pattern recognition system, feature extraction has a significant impac...
متن کاملPeriodogram and Ensemble Empirica Mode Decomposition Analysis of Electromyography Processing
This work investigates the application of the Ensemble Empirical Mode Decomposition (EEMD) and the timefrequency techniques for treatment of the electromyography (EMG) signal. The EMG signals are usually corrupted by artifacts that hide useful information then the extraction of high-resolution EMG signals from recordings contaminated with back ground noise becomes an important problem. The Ense...
متن کاملComparative Study of Different EMG Signal decomposition Techniques
EMG signals are electromyogram signals generated by firing of MUs (motor units) in muscle fibers. The decomposition of EMG signal of a muscle provides useful information for the diagnosis of neuro-muscular diseases by physician and neurologist. In decomposition of EMG signal different MUAPs (Motor Unit Action Potentials) are classified into different categories. This paper gives a review of dif...
متن کاملPrediction of Above-elbow Motions in Amputees, based on Electromyographic(EMG) Signals, Using Nonlinear Autoregressive Exogenous (NARX) Model
Introduction In order to improve the quality of life of amputees, biomechatronic researchers and biomedical engineers have been trying to use a combination of various techniques to provide suitable rehabilitation systems. Diverse biomedical signals, acquired from a specialized organ or cell system, e.g., the nervous system, are the driving force for the whole system. Electromyography(EMG), as a...
متن کاملFeature Extraction of Visual Evoked Potentials Using Wavelet Transform and Singular Value Decomposition
Introduction: Brain visual evoked potential (VEP) signals are commonly known to be accompanied by high levels of background noise typically from the spontaneous background brain activity of electroencephalography (EEG) signals. Material and Methods: A model based on dyadic filter bank, discrete wavelet transform (DWT), and singular value decomposition (SVD) was developed to analyze the raw data...
متن کاملProgressive FastICA Peel-Off and Convolution Kernel Compensation Demonstrate High Agreement for High Density Surface EMG Decomposition
Decomposition of electromyograms (EMG) is a key approach to investigating motor unit plasticity. Various signal processing techniques have been developed for high density surface EMG decomposition, among which the convolution kernel compensation (CKC) has achieved high decomposition yield with extensive validation. Very recently, a progressive FastICA peel-off (PFP) framework has also been deve...
متن کامل