Spatial filtering for enhanced high-density surface electromyographic examination of neuromuscular changes and its application to spinal cord injury
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, b...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2020-12-01
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| Series: | Journal of NeuroEngineering and Rehabilitation |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12984-020-00786-z |
| _version_ | 1819173825370128384 |
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| author | Xu Zhang Xinhui Li Xiao Tang Xun Chen Xiang Chen Ping Zhou |
| author_facet | Xu Zhang Xinhui Li Xiao Tang Xun Chen Xiang Chen Ping Zhou |
| author_sort | Xu Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T20:29:14Z |
| format | Article |
| id | doaj.art-ca41cd9d26774079ba305ad9d0081687 |
| institution | Directory Open Access Journal |
| issn | 1743-0003 |
| language | English |
| last_indexed | 2024-12-22T20:29:14Z |
| publishDate | 2020-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of NeuroEngineering and Rehabilitation |
| spelling | doaj.art-ca41cd9d26774079ba305ad9d00816872022-12-21T18:13:39ZengBMCJournal of NeuroEngineering and Rehabilitation1743-00032020-12-0117111410.1186/s12984-020-00786-zSpatial filtering for enhanced high-density surface electromyographic examination of neuromuscular changes and its application to spinal cord injuryXu Zhang0Xinhui Li1Xiao Tang2Xun Chen3Xiang Chen4Ping Zhou5School of Information Science and Technology, University of Science and Technology of ChinaSchool of Information Science and Technology, University of Science and Technology of ChinaSchool of Information Science and Technology, University of Science and Technology of ChinaSchool of Information Science and Technology, University of Science and Technology of ChinaSchool of Information Science and Technology, University of Science and Technology of ChinaInstitute of Rehabilitation Engineering, University of RehabilitationAbstract 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.https://doi.org/10.1186/s12984-020-00786-zElectromyographyNoninvasive diagnosisNeuromuscular changesSpatial filteringSpinal cord injury |
| spellingShingle | Xu Zhang Xinhui Li Xiao Tang Xun Chen Xiang Chen Ping Zhou Spatial filtering for enhanced high-density surface electromyographic examination of neuromuscular changes and its application to spinal cord injury Journal of NeuroEngineering and Rehabilitation Electromyography Noninvasive diagnosis Neuromuscular changes Spatial filtering Spinal cord injury |
| title | Spatial filtering for enhanced high-density surface electromyographic examination of neuromuscular changes and its application to spinal cord injury |
| title_full | Spatial filtering for enhanced high-density surface electromyographic examination of neuromuscular changes and its application to spinal cord injury |
| title_fullStr | Spatial filtering for enhanced high-density surface electromyographic examination of neuromuscular changes and its application to spinal cord injury |
| title_full_unstemmed | Spatial filtering for enhanced high-density surface electromyographic examination of neuromuscular changes and its application to spinal cord injury |
| title_short | Spatial filtering for enhanced high-density surface electromyographic examination of neuromuscular changes and its application to spinal cord injury |
| title_sort | spatial filtering for enhanced high density surface electromyographic examination of neuromuscular changes and its application to spinal cord injury |
| topic | Electromyography Noninvasive diagnosis Neuromuscular changes Spatial filtering Spinal cord injury |
| url | https://doi.org/10.1186/s12984-020-00786-z |
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