Abstract：
:A technique is proposed that allows automatic decomposition of electromyographic (EMG) signals into their constituent motor unit action potential trains (MUAPTs). A specific iterative algorithm with a classification method using fuzzy-logic techniques was developed. The proposed classification method takes into account imprecise information, such as waveform instability and irregular firing patterns, that is often encountered in EMG signals. Classification features were determined by the combining of time position and waveform information. Statistical analysis of inter-pulse intervals and spike amplitude provided an accurate estimation of features used in the classification step. Algorithm performance was evaluated using simulated EMG signals composed of up to six different discharging motor units corrupted with white noise. The algorithm was then applied to real signals recorded by a high spatial resolution surface EMG device based on a Laplacian spatial filter. On six groups of 20 simulated signals, the decomposition algorithm performed with a maximum and an average mean error rate of 2.13% and 1.37%, respectively. On real surface EMG signals recorded at different force levels (from 10% to 40% of the maximum voluntary contraction), the algorithm correctly identified 21 MUAPTs, compared with the 29 MUAPTs identified by an experienced neurophysiologist. The efficiency of the decomposition on surface EMG signals makes this method very attractive for non-invasive investigation of physiological muscle properties. However, it can also be used to decompose intramuscularly recorded EMG signals.
journal_name
Med Biol Eng Computjournal_title
Medical & biological engineering & computingauthors
Chauvet E,Fokapu O,Hogrel JY,Gamet D,Duchêne Jdoi
10.1007/BF02349972keywords:
subject
Has Abstractpub_date
2003-11-01 00:00:00pages
646-53issue
6eissn
0140-0118issn
1741-0444journal_volume
41pub_type
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