An Autocorrelative Approach for EMG Time-Frequency Analysis

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 strat...

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Auteurs principaux:	Mohannad K. Sabir, Noor K. Muhsin
Format:	article
Langue:	EN
Publié:	Al-Khwarizmi College of Engineering – University of Baghdad 2017
Sujets:	Digital Signal processing Nonstationary Processing of Biomedical Signals Time-Frequency Analysis of Biosignals Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
Accès en ligne:	https://doaj.org/article/17df7edaffd649c593d2b2a4bdc19053
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id	oai:doaj.org-article:17df7edaffd649c593d2b2a4bdc19053
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spelling	oai:doaj.org-article:17df7edaffd649c593d2b2a4bdc190532021-12-02T10:52:26ZAn Autocorrelative Approach for EMG Time-Frequency Analysis1818-11712312-0789https://doaj.org/article/17df7edaffd649c593d2b2a4bdc190532017-12-01T00:00:00Zhttp://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/157https://doaj.org/toc/1818-1171https://doaj.org/toc/2312-0789 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. Mohannad K. SabirNoor K. MuhsinAl-Khwarizmi College of Engineering – University of BaghdadarticleDigital Signal processingNonstationary Processing of Biomedical SignalsTime-Frequency Analysis of BiosignalsChemical engineeringTP155-156Engineering (General). Civil engineering (General)TA1-2040ENAl-Khawarizmi Engineering Journal, Vol 9, Iss 1 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Digital Signal processing Nonstationary Processing of Biomedical Signals Time-Frequency Analysis of Biosignals Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Digital Signal processing Nonstationary Processing of Biomedical Signals Time-Frequency Analysis of Biosignals Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040 Mohannad K. Sabir Noor K. Muhsin An Autocorrelative Approach for EMG Time-Frequency Analysis
description	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.
format	article
author	Mohannad K. Sabir Noor K. Muhsin
author_facet	Mohannad K. Sabir Noor K. Muhsin
author_sort	Mohannad K. Sabir
title	An Autocorrelative Approach for EMG Time-Frequency Analysis
title_short	An Autocorrelative Approach for EMG Time-Frequency Analysis
title_full	An Autocorrelative Approach for EMG Time-Frequency Analysis
title_fullStr	An Autocorrelative Approach for EMG Time-Frequency Analysis
title_full_unstemmed	An Autocorrelative Approach for EMG Time-Frequency Analysis
title_sort	autocorrelative approach for emg time-frequency analysis
publisher	Al-Khwarizmi College of Engineering – University of Baghdad
publishDate	2017
url	https://doaj.org/article/17df7edaffd649c593d2b2a4bdc19053
work_keys_str_mv	AT mohannadksabir anautocorrelativeapproachforemgtimefrequencyanalysis AT noorkmuhsin anautocorrelativeapproachforemgtimefrequencyanalysis AT mohannadksabir autocorrelativeapproachforemgtimefrequencyanalysis AT noorkmuhsin autocorrelativeapproachforemgtimefrequencyanalysis
_version_	1718396515065528320

An Autocorrelative Approach for EMG Time-Frequency Analysis

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