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

J NeuroEngineering Rehabil, 2020 · DOI: https://doi.org/10.1186/s12984-020-00786-z · Published: November 1, 2020

Spinal Cord Injury Rehabilitation Biomedical

Simple Explanation

This study explores how spatial filtering of high-density surface electromyogram (HD-sEMG) signals can improve the non-invasive examination of neuromuscular changes, particularly in paralyzed muscles after spinal cord injury (SCI). The research introduces three spatial filtering methods using principle component analysis (PCA), non-negative matrix factorization (NMF), and a combination of both, to enhance the signal-to-noise ratio in HD-sEMG data. The study concludes that spatial filtering, especially the combined PCA-NMF approach, enhances the diagnostic power of HD-sEMG, which helps in developing a standard preprocessing pipeline for widespread application.

Study Duration

Not specified

Participants

9 subjects with incomplete cervical SCI and 13 neurologically intact subjects

Evidence Level

Not specified

Key Findings

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.

Research Summary

This study introduces spatial filtering methods using PCA, NMF, and their combination to enhance HD-sEMG for examining neuromuscular changes, particularly in SCI patients. The experimental results show that spatial filtering improves the diagnostic power of HD-sEMG, with the combined PCA-NMF method yielding the highest diagnostic accuracy. The findings support the development of a standardized HD-sEMG preprocessing pipeline, facilitating more effective and practical applications in diagnosing neuromuscular conditions.

Practical Implications

Enhanced Diagnostic Accuracy

Spatial filtering techniques, particularly PCA-NMF, can significantly improve the accuracy of diagnosing neuromuscular changes after SCI.

Standardized Preprocessing

The study supports the development of a standard HD-sEMG preprocessing pipeline, making the technology more accessible and reliable for clinical applications.

Improved Clinical Utility

By enhancing the diagnostic power of HD-sEMG, this research facilitates the non-invasive examination of neuromuscular diseases and injuries.

Study Limitations

1
The study focuses on only three types of spatial filtering methods.
2
The sample size used in this study is relatively small.
3
The sEMG diagnostic approach requires more or less voluntary contraction ability of the examined muscle to emit sufficient sEMG activities.

Your Feedback

Was this summary helpful?

Back to Spinal Cord Injury