Motor unit number estimation based on high-density surface electromyography decomposition

Abstract

Objective: To advance the motor unit number estimation (MUNE) technique using high density surface electromyography (EMG) decomposition.

Methods: 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.

Results: 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%.

Conclusions: 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.

Significance: 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.

Keywords: Bicep brachii; Decomposition; Electromyography; High-density; Motor unit number estimation.