Intramuscular EMG Signal Decomposition

Abstract

Information regarding motor unit potentials (MUPs) and motor unit fi ring patterns during muscle contractions is useful for physiological investigation and clinical examinations either for the understanding of motor control or for the diagnosis of neuromuscular disorders. In order to obtain such information, composite electromyographic (EMG) signals are decomposed (i.e., resolved into their constituent motor unit potential trains [MUPTs]). The goals of automatic decomposition techniques are to create a MUPT for each motor unit that contributed significant MUPs to the original composite signal. Diagnosis can then be facilitated by decomposing a needle-detected EMG signal, extracting features of MUPTs, and finally analyzing the extracted features (i.e., quantitative electromyography). Herein, the concepts of EMG signals and EMG signal decomposition techniques are explained. The steps involved with the decomposition of an EMG signal and the methods developed for each step, along with their strengths and limitations, are discussed and compared. Finally, methods developed to evaluate decomposition algorithms and assess the validity of the obtained MUPTs are reviewed and evaluated.

Motor Unit Potential Train Validation and Its Application in EMG Signal Decomposition

“…Consequently, each active MU generates a train of MUPs during a muscle contraction known as MUPT. A MUPT is mathematically described as (De Luca, 1979;Basmajian & De Luca, 1985;Stashuk, 2001;Parsaei et al, 2010):…”

“…Assuming K MUs were active during a muscle contraction, the detected EMG signal can be mathematically represented as (De Luca,1979;Basmajian & De Luca, 1985;Stashuk, 2001;Parsaei et al, 2010):…”

“…This process that is conceptually shown in Fig.2 is implemented by employing digital signal processing and pattern recognition techniques in four/five steps: signal preprocessing, signal segmentation and MUP detection, feature extraction, and then clustering and possibly supervised classification of detected MUPs (Stashuk, 2001;Parsaei et al, 2010).The first step is to remove background noise and low-frequency information from the detected EMG signal, to shorten the duration of MUPs and decrease MUP temporal overlap, and to sharpen the MUPs and increase discrimination between them. The second step is to section the signal into segments containing possible MUPs that were generated by active MUs that contributed significantly to the detected EMG signal.…”

“…However, for detailed studies of MU control and muscle architecture, SMUPs must be resolved. A recent comprehensive review of the algorithms developed for the decomposition of indwelling EMG signals is provided by Parsaei et al (2010). Once the decomposition process is completed, the prototypical MUP shape (MUP template) and MU firing pattern statistics for each extracted MUPT are estimated for future analysis (especially for quantitative electromyography).…”

“…The MUP shape validity of a MUPT is assessed by an expert using raster/shimmer plots of its assigned MUPs ( Doherty & Stashuk,2003;Stashuk, 2001;Stashuk et al, 2004;Boe et al, 2005, Calder et al, 2008Parsaei et al, 2010). MU firing pattern validity of a MUPT is determined by viewing and qualitative evaluation of its IDI histogram and the plots of the firing rate as a function of time.…”