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

Abstract 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.

[1]  D. Zytnicki,et al.  Alpha, beta and gamma motoneurons: functional diversity in the motor system's final pathway. , 2011, Journal of integrative neuroscience.

[2]  C. D. De Luca,et al.  High-yield decomposition of surface EMG signals , 2010, Clinical Neurophysiology.

[3]  Maria Piotrkiewicz,et al.  Recurrent inhibition of human firing motoneurons (experimental and modeling study) , 2004, Biological Cybernetics.

[4]  C. J. Luca Control properties of motor units , 1985 .

[5]  F. Awiszus Spike train analysis , 1997, Journal of Neuroscience Methods.

[6]  Mustafa Yilmaz,et al.  Classification of EMG signals using wavelet neural network , 2006, Journal of Neuroscience Methods.

[7]  Hossein Parsaei,et al.  EMG Signal Decomposition Using Motor Unit Potential Train Validity , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[8]  Andrzej P. Dobrowolski,et al.  Multiresolution MUAPs decomposition and SVM-based analysis in the classification of neuromuscular disorders , 2012, Comput. Methods Programs Biomed..

[9]  Kevin C. McGill,et al.  EMGLAB: An interactive EMG decomposition program , 2005, Journal of Neuroscience Methods.

[10]  Computer system for identification and analysis of motor unit potential trains , 2004 .

[11]  K C McGill,et al.  Automatic decomposition of multichannel intramuscular EMG signals. , 2009, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.