An Autocorrelative Approach for EMG Time-Frequency Analysis

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ID: 71220
2017
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Abstract
As they are the smallest functional parts of the muscle, motor units (MUs) are considered as the basic building blocks of the neuromuscular system. Monitoring MU recruitment, de-recruitment, and firing rate (by either invasive or surface techniques) leads to the understanding of motor control strategies and of their pathological alterations. EMG signal decomposition is the process of identification and classification of individual motor unit action potentials (MUAPs) in the interference pattern detected with either intramuscular or surface electrodes. Signal processing techniques were used in EMG signal decomposition to understand fundamental and physiological issues. Many techniques have been developed to decompose intramuscularly detected signals with various degrees of automation. This paper investigates the application of autocorrelation function (ACF) method to decompose EMG signals to their frequency components. It was found that using the proposed method gives a quite good frequency resolution as compared to that resulting from using short time fast Fourier transform (STFFT); thus more MU’s can be distinguished.
Reference Key
sabir2017analkhawarizmi Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Sabir, Mohannad K.;Muhsin, Noor K.;
Journal al-khawarizmi engineering journal
Year 2017
DOI
DOI not found
URL
http://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/157
Keywords
Biology (General) Medicine (General) Chemical technology Engineering (General). Civil engineering (General) chemical engineering business social sciences telecommunication electronics

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