Spatial filtering for enhanced high-density surface electromyographic examination of neuromuscular changes and its appli
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RESEARCH
Spatial filtering for enhanced high‑density surface electromyographic examination of neuromuscular changes and its application to spinal cord injury Xu Zhang1, Xinhui Li1, Xiao Tang1, Xun Chen1*, Xiang Chen1 and Ping Zhou2
Abstract Background: Spatial filtering of multi-channel signals is considered to be an effective pre-processing approach for improving signal-to-noise ratio. The use of spatial filtering for preprocessing high-density (HD) surface electromyogram (sEMG) helps to extract critical spatial information, but its application to non-invasive examination of neuromuscular changes have not been well investigated. Methods: Aimed at evaluating how spatial filtering can facilitate examination of muscle paralysis, three different spatial filtering methods are presented using principle component analysis (PCA) algorithm, non-negative matrix factorization (NMF) algorithm, and both combination, respectively. Their performance was evaluated in terms of diagnostic power, through HD-sEMG clustering index (CI) analysis of neuromuscular changes in paralyzed muscles following spinal cord injury (SCI). Results: The experimental results showed that: (1) The CI analysis of conventional single-channel sEMG can reveal complex neuromuscular changes in paralyzed muscles following SCI, and its diagnostic power has been confirmed to be characterized by the variance of Z scores; (2) the diagnostic power was highly dependent on the location of sEMG recording channel. Directly averaging the CI diagnostic indicators over channels just reached a medium level of the diagnostic power; (3) the use of either PCA-based or NMF-based filtering method yielded a greater diagnostic power, and their combination could even enhance the diagnostic power significantly. Conclusions: This study not only presents an essential preprocessing approach for improving diagnostic power of HD-sEMG, but also helps to develop a standard sEMG preprocessing pipeline, thus promoting its widespread application. Keywords: Electromyography, Noninvasive diagnosis, Neuromuscular changes, Spatial filtering, Spinal cord injury Introduction Spinal cord injury (SCI) is a leading cause of adult disability worldwide [1]. The disruption of communication between the brain and the spinal cord results in both loss *Correspondence: [email protected] 1 School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, Anhui, China Full list of author information is available at the end of the article
of voluntary movement (i.e., paraplegia) and loss of sensation [1, 2]. However, the effect of a paraplegia on the survival and function of motor unit (MU) in pathological muscles remains unclear. Since the MU is regarded as the basic functional unit and the final pathway of the neuromuscular control system, it is of great importance to identify MU changes induced by specific mechanisms following the SCI [3], which can offer guidance for the design of effective SCI rehabilitation protocols.
© The Author(s) 2020. Op
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