Analysis of surface myoelectric signals by linear prediction method

Waldemar Suszyński, Wiesława Kuniszyk-Jóźkowiak, Ireneusz Codello, Rafał Stęgierski, Karol Kuczyński, Janusz Jaszczuk

Abstract


The article presents a proposal to use linear prediction method for a quick analysis of surface myoelectric (EMG) signals. The spectra obtained with the linear prediction (LP) and Fourier methods were compared. The LP method allows for a precise determination of the location and amplitude of the spectrum maximum and observation of changes in muscle tension and contraction phases. EMG spectra of brachial biceps during flexion and extension of the forearm by four adults were analyzed. The optimal width of the time window for the averaging of motor unit action potentials that allows for the observation of changes during contraction was established. It has been found that maximum spectrum during flexion has a significantly higher frequency and amplitude than during the extension of the forearm.

Keywords


EMG, linear prediction; Fourier analysis; frequency spectrum

Full Text:

PDF

References


Betts B J, Binsted K, Jorgensen Ch. Small-vocabulary speech recognition using surface electromyography. Interacting with Computers 2006; 18: 1242-59.

Codello I, Kuniszyk-Jóźkowiak W. Digital signals analysis with the LPC method. Annales UMCS Informatica 2006; A15: 315-21.

Codello I, Kuniszyk-Jóźkowiak W.’Wave Blaster’ – A comprehensive tool for speech analysis and its application for vowel recognition using wavelet continuous transform with bark scale. In: Proc. 56th open Seminar in Acoustics 2009; 141-46.

Codello I, Kuniszyk-Jóźkowiak W, Smołka E, Suszyński W. Speaker Recognition Using Continuous Wavelet Transform with Bark Scales. Polish Journal of Environmental Studies 2009; 18 (3B): 78-82.

Coorevits P, Danneels L, Cambier D, Ramon H, Druyts H, Karlsson J S, De Moor G, Vanderstraeten G. Correlations between short-time Fourier and continuous wavelet transforms in the analysis of localized back and hip muscle fatigue during isometric contractions. Journal of Electromyography and Kinesiology 2008; 18: 637-44.

Fele-Žorž G, Kavšek G, Novak-Antolič Ž, Jager F. A comparison of various linear and non-linear processing techniques to separate EMG records of term and pre-term delivery groups. Med. Biol. Eng. Comp. 2008; 46: 911-22.

Flanders M. Choosing a wavelet for single-trial EMG. Journal of Neuroscience Methods 2002; 116: 165-177.

Manal K, Buchanan T S. A one-parameter neural activation to muscle activation model: estimating isometric joint moments from electromyograms. Journal of Biomechanics

; 36: 1197-1203.

Moshou D, Hostens I, Papaioannou, Ramon H. Dynamic muscle fatigue detection using self-organizing maps. Applied Soft Computing 2005; 5: 391-398.

Olmo G, Laterza F, Lo Presti L, Matched wavelet approach in stretching analysis of electrically evoked surface EMG signal. Signal Processing 2000; 80: 671-684.

Farina D, Falla D, Estimation of muscle conduction velocity from two-dimensional surface EMG recordings in dynamic tasks. Biomechanical Signal Processing and Control 2008; 3: 138-144.

Piotrkiewicz M, Hausmanowa-Petrusewicz I, Mierzejewska J. Are motoneurons involved in muscular dystrophy? Clinical Neurophysiology 1999; 110: 1111-22.

Piotrkiewicz M, Filipiuk M, Hausmanowa-Petrusewicz I. MU firing characteristics in human dystrophic muscle. Acta neurobiologiae experimentalis. 1993; 53(1): 313-18.

Piotrkiewicz M, Kudina l, Chen JJJ, Zalewska E, Hausmanowa-Petrusewicz I. Assessment of Human Motoneuron Afterhyperpolarization Duration in Health and Disease. Biocybernetics and Biomedical Engineering 2012; 32 (3): 43-61.

Rabiner LR, Schafer, Digital Processing of Speech Signals. New Jersey; Prentice-Hall, Inc. ; 1978.

von Tscharner V, Goephert B, Nigg BM. Changes in EMG signals for muscle tibialis anterior while running barefoot or with shoes resolved by non-lenarly scaled wavelets. Journal of Biomechanics 2003; 36: 1169-76.

Zalewska E, Hausmanowa-Petrusewicz I. Approximation of motor unit structure from the analysis of motor unit potential. Clinical Neurophysiology 2008; 119: 2501-06.

Zalewska E, Hausmanowa-Petrusewicz I. Effectiveness of motor unit potentials classification using various parameters and indexes. Clinical Neurophysiology 2000; 111: 1380-1387.

Zalewska E, Rowinska-Marcinska K, Gawel M, Hausmanowa-Petrusewicz I. Simulation studies on the motor unit potentials with satellite components in amyotrophic lateral sclerosis and spinal muscle atrophy, Muscle & Nerve 2012; 45 (4): 514-521.

Zieliński T P. Digital Signal Processing. Warszawa; Publisher Transport and Communications; 2005 (in Polish).




DOI: http://dx.doi.org/10.17951/ai.2016.16.1.62
Date of publication: 2016-10-04 00:00:00
Date of submission: 2016-05-17 10:55:24


Statistics


Total abstract view - 754
Downloads (from 2020-06-17) - PDF - 0

Indicators



Refbacks

  • There are currently no refbacks.


Copyright (c) 2016 Waldemar Suszyński, Wiesława Kuniszyk-Jóźkowiak, Ireneusz Codello, Rafał Stęgierski, Karol Kuczyński, Janusz Jaszczuk

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.