title: Decomposition of surface EMG signals from cyclic dynamic contractions.
abstract: Over the past 3 decades, various algorithms used to decompose the electromyographic (EMG) signal into its constituent motor unit action potentials (MUAPs) have been reported. All are limited to decomposing EMG signals from isometric contraction. In this report, we describe a successful approach to decomposing the surface EMG (sEMG) signal collected from cyclic (repeated concentric and eccentric) dynamic contractions during flexion/extension of the elbow and during gait. The increased signal complexity introduced by the changing shapes of the MUAPs due to relative movement of the electrodes and the lengthening/shortening of muscle fibers was managed by an incremental approach to enhancing our established algorithm for decomposing sEMG signals obtained from isometric contractions. We used machine-learning algorithms and time-varying MUAP shape discrimination to decompose the sEMG signal from an increasingly challenging sequence of pseudostatic and dynamic contractions. The accuracy of the decomposition results was assessed by two verification methods that have been independently evaluated. The firing instances of the motor units had an accuracy of ∼90% with a MUAP train yield as high as 25. Preliminary observations from the performance of motor units during cyclic contractions indicate that during repetitive dynamic contractions, the control of motor units is governed by the same rules as those evidenced during isometric contractions. Modifications in the control properties of motoneuron firings reported by previous studies were not confirmed. Instead, our data demonstrate that the common drive and hierarchical recruitment of motor units are preserved during concentric and eccentric contractions.
authors.affiliation: NeuroMuscular Research Center, Boston University, Boston, Massachusetts; Department of Biomedical Engineering, Boston University, Boston, Massachusetts; Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts; Department of Neurology, Boston University, Boston, Massachusetts; Department of Physical Therapy, Boston University, Boston, Massachusetts; and Delsys, Natick, Massachusetts cjd@bu.edu.
authors.forename: Carlo J
authors.initials: CJ
authors.lastname: De Luca
authors.affiliation: Delsys, Natick, Massachusetts.
authors.forename: Shey-Sheen
authors.initials: SS
authors.lastname: Chang
authors.affiliation: NeuroMuscular Research Center, Boston University, Boston, Massachusetts; Department of Physical Therapy, Boston University, Boston, Massachusetts; and.
authors.forename: Serge H
authors.initials: SH
authors.lastname: Roy
authors.affiliation: NeuroMuscular Research Center, Boston University, Boston, Massachusetts;
authors.forename: Joshua C
authors.initials: JC
authors.lastname: Kline
authors.affiliation: Department of Biomedical Engineering, Boston University, Boston, Massachusetts; Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts;
authors.forename: S Hamid
authors.initials: SH
authors.lastname: Nawab
mesh_terms: D000328:Adult; D001132:Arm / Q000502:physiology; D004576:Electromyography / Q000379:methods*; D005260:Female; D006801:Humans; D007537:Isometric Contraction; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D010507:Periodicity
keywords: dynamic contractions; firing rate; gait; motor units