title: Motor unit number estimation based on high-density surface electromyography decomposition.
abstract: To advance the motor unit number estimation (MUNE) technique using high density surface electromyography (EMG) decomposition.
  
  
  
  The K-means clustering convolution kernel compensation algorithm was employed to detect the single motor unit potentials (SMUPs) from high-density surface EMG recordings of the biceps brachii muscles in eight healthy subjects. Contraction forces were controlled at 10%, 20% and 30% of the maximal voluntary contraction (MVC). Achieved MUNE results and the representativeness of the SMUP pools were evaluated using a high-density weighted-average method.
  
  
  
  Mean numbers of motor units were estimated as 288±132, 155±87, 107±99 and 132±61 by using the developed new MUNE at 10%, 20%, 30% and 10-30% MVCs, respectively. Over 20 SMUPs were obtained at each contraction level, and the mean residual variances were lower than 10%.
  
  
  
  The new MUNE method allows a convenient and non-invasive collection of a large size of SMUP pool with great representativeness. It provides a useful tool for estimating the motor unit number of proximal muscles.
  
  
  
  The present new MUNE method successfully avoids the use of intramuscular electrodes or multiple electrical stimuli which is required in currently available MUNE techniques; as such the new MUNE method can minimize patient discomfort for MUNE tests.
authors.affiliation: Department of Biomedical Engineering, Cullen College of Engineering, University of Houston, Houston, TX 77204, USA.
authors.forename: Yun
authors.initials: Y
authors.lastname: Peng
authors.affiliation: School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo, China.
authors.forename: Jinbao
authors.initials: J
authors.lastname: He
authors.affiliation: Department of Physical Medicine and Rehabilitation, University of Texas Health Science Center, and TIRR Memorial Hermann Research Center, Houston, TX, USA.
authors.forename: Bo
authors.initials: B
authors.lastname: Yao
authors.affiliation: Department of Physical Medicine and Rehabilitation, University of Texas Health Science Center, and TIRR Memorial Hermann Research Center, Houston, TX, USA.
authors.forename: Sheng
authors.initials: S
authors.lastname: Li
authors.affiliation: Department of Physical Medicine and Rehabilitation, University of Texas Health Science Center, and TIRR Memorial Hermann Research Center, Houston, TX, USA.
authors.forename: Ping
authors.initials: P
authors.lastname: Zhou
authors.affiliation: Department of Biomedical Engineering, Cullen College of Engineering, University of Houston, Houston, TX 77204, USA; Guangdong Provincial Work Injury Rehabilitation Center, Guangzhou 510000, China. Electronic address: yzhang94@uh.edu.
authors.forename: Yingchun
authors.initials: Y
authors.lastname: Zhang
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000328:Adult; D004576:Electromyography / Q000379:methods*; D006801:Humans; D008297:Male; D018482:Muscle, Skeletal / Q000502:physiology*; D011999:Recruitment, Neurophysiological / Q000502:physiology*
keywords: Bicep brachii; Decomposition; Electromyography; High-density; Motor unit number estimation