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 strate...

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Autores principales:	Mohannad K. Sabir, Noor K. Muhsin
Formato:	article
Lenguaje:	EN
Publicado:	Al-Khwarizmi College of Engineering – University of Baghdad 2013
Materias:	Digital Signal processing; Nonstationary Processing of Biomedical Signals; Time-Frequency Analysis of Biosignals. Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/69f47638a6fc4692ba70588dde1573a6
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id	oai:doaj.org-article:69f47638a6fc4692ba70588dde1573a6
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spelling	oai:doaj.org-article:69f47638a6fc4692ba70588dde1573a62021-12-02T07:09:50ZAn Autocorrelative Approach for EMG Time-Frequency Analysis1818-1171https://doaj.org/article/69f47638a6fc4692ba70588dde1573a62013-01-01T00:00:00Zhttp://www.iasj.net/iasj?func=fulltext&aId=69964https://doaj.org/toc/1818-1171As 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 MUs can be distinguished. Mohannad K. SabirNoor K. MuhsinAl-Khwarizmi College of Engineering – University of BaghdadarticleDigital Signal processing; Nonstationary Processing of Biomedical Signals; Time-Frequency Analysis of Biosignals.Chemical engineeringTP155-156Engineering (General). Civil engineering (General)TA1-2040ENAl-Khawarizmi Engineering Journal, Vol 9, Iss 1, Pp 39-46 (2013)
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 MUs 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	2013
url	https://doaj.org/article/69f47638a6fc4692ba70588dde1573a6
work_keys_str_mv	AT mohannadksabir anautocorrelativeapproachforemgtimefrequencyanalysis AT noorkmuhsin anautocorrelativeapproachforemgtimefrequencyanalysis AT mohannadksabir autocorrelativeapproachforemgtimefrequencyanalysis AT noorkmuhsin autocorrelativeapproachforemgtimefrequencyanalysis
_version_	1718399576233213952

An Autocorrelative Approach for EMG Time-Frequency Analysis

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