Spatial filtering for enhanced high-density surface electromyographic examination of neuromuscular changes and its application to spinal cord injury

Xu Zhang,Ping Zhou,Xiao Tang,Xun Chen,Xinhui Li,Xiang Chen

doi:10.1186/s12984-020-00786-z

Xu Zhang, Ping Zhou + Show 4 more

Open Access

https://doi.org/10.1186/s12984-020-00786-z

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Abstract

BackgroundSpatial 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.MethodsAimed 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).ResultsThe 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.ConclusionsThis 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.

Highlights

Spinal cord injury (SCI) is a leading cause of adult disability worldwide [1]
For the normal cloud consisting of all data points from the control muscle group, the clustering index (CI) showed a decreasing trend as the contraction level increased
This study presents three spatial filtering methods for preprocessing HD-surface electromyogram (sEMG) data to enhance the power of assessing neuromuscular abnormalities following SCI

Summary

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 lossZhang et al J NeuroEngineering Rehabil (2020) 17:160In clinical routine, an invasive approach using concentric needle is applied to electrophysiological examination of MU properties [4, 5]. The HD-sEMG measurement is able to better characterize the muscle’s structural and functional heterogeneity, which is regarded as the reflection of activities from different sources such as subcomponent muscles [11,12,13], muscle–tendon units [14,15,16], and even microscopic MUs [17,18,19,20] Such spatial information is helpful in suppressing muscular cross-talks within channels so as to improve the signal–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

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