Method of automatic recognition and other solutions used in new computer program for full decomposition of EMG signals

• Autorzy: Młoźniak D. , Piotrkiewicz M.

Identyfikatory

DOI

10.1016/j.bbe.2014.05.002

• Języki publikacji: EN

Abstrakty

EN

The analysis of electromyographic signals can be very time consuming. In designing a program for EMG signal analysis, there are two competing factors: the accuracy of the final result and its speed. In scientific work, accuracy is the most important factor. All of the existing decomposition programs used in neurophysiology require a final phase of manual corrections, if reliable results are to be obtained. This phase is considerably longer than the phase of automatic recognition. The solutions presented below, used in our new MUR program, allow for the accurate decomposition of complex EMG signals in a reasonable amount of time. The decomposition is performed interactively with optimal time division between automatic and manual tasks. All of this is achieved through a simple method of automatic recognition with the use of the modified coefficient of determination and the method of multiple subtractions of potentials.

Słowa kluczowe

EN

motor unit; potential; EMG signal; decomposition; automatic recognition; modified coefficient of determination

PL

jednostka ruchowa; potencjał; sygnał EMG; dekompozycja; rozpoznawanie automatyczne; współczynnik determinacji

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2015
• Tom: Vol. 35, no. 1
• Strony: 22--29
• Opis fizyczny: Bibliogr. 12 poz., rys.

Twórcy

autor

Młoźniak D.

• Nałęcz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, ul. Ks. Trojdena 4, 02-109 Warszawa, Poland

autor

Piotrkiewicz M.

• Nałęcz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, ul. Ks. Trojdena 4, 02-109 Warszawa, Poland

Bibliografia

• [1] Manuel M, Zytnicki D. Alpha, beta and gamma motoneurons: functional diversity in the motor system's final pathway. J Integr Neurosci 2011;10(3):243–76.
• [2] De Luca CJ. Control properties of motor units. J Exp Biol 1985;115:125–36.
• [3] Parsaei H, Stashuk DW. EMG signal decomposition using motor unit potential train validity. IEEE Trans Neural Syst Rehabil Eng 2013;21(2):265–74.
• [4] Awiszus F. Spike train analysis. J Neurosci Meth 1997;74 (2):155–66.
• [5] Florestal JR, Mathieu PA, McGill KC. Automatic decomposition of multichannel intramuscular EMG signals. J Electromyogr Kinesiol 2009;19(1):1–9.
• [6] McGill KC, Lateva ZC, Marateb HR. EMGLAB: an interactive EMG decomposition program. J Neurosci Meth 2005;149 (2):121–33.
• [7] Mazurkiewicz Ł, Piotrkiewicz M. Computer system for identification and analysis of motor unit potential trains. Biocyber Biomed Eng 2004;24(6):15–23.
• [8] Piotrkiewicz M, Kudina L, Mierzejewska J. Recurrent inhibition of human firing motoneurons (experimental and modeling study). Biol Cybern 2004;91(4):243–57.
• [9] Nawab SH, Chang SS, De Luca CJ. High-yield decomposition of surface EMG signals. Clin Neurophysiol 2010;121(10):1602–15.
• [10] Raez MB, Hussain MS, Mohd-Yasin F. Techniques of EMG signal analysis: detection, processing, classification and applications. Biol Proced Online 2006;8:11–35.
• [11] Subasi A, Yilmaz M, Ozcalik HR. Classification of EMG signals using wavelet neural network. J Neurosci Meth 2006;156(1–2):360–7.
• [12] Dobrowolski AP, Wierzbowski M, Tomczykiewicz K. Multiresolution MUAPs decomposition and SVM-based analysis in the classification of neuromuscular disorders. Comput Meth Prog Bio 2012;107(3):393–403.