An Autocorrelative Approach for EMG Time-Frequency Analysis

  • Mohannad K. Sabir Department of Biomedical Engineering/ Al-Khwarizmi College of Engineering/ University of Baghdad
  • Noor K. Muhsin Department of Biomedical Engineering/ Al-Khwarizmi College of Engineering/ University of Baghdad
Keywords: Digital Signal processing, Nonstationary Processing of Biomedical Signals, Time-Frequency Analysis of Biosignals

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.

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Published
2017-12-28
How to Cite
K. Sabir, M., & K. Muhsin, N. (2017). An Autocorrelative Approach for EMG Time-Frequency Analysis. Al-Khwarizmi Engineering Journal, 9(1), 39-46. Retrieved from http://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/157
Section
Articles