School of Information Science and Technology, University of Science and Technology of China, Hefei, 230027, Anhui, China.
Institute of Rehabilitation Engineering, University of Rehabilitation, Qingdao, 266024, Shandong, China.
J Neuroeng Rehabil. 2020 Dec 3;17(1):160. doi: 10.1186/s12984-020-00786-z.
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.
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).
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.
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.
多通道信号的空间滤波被认为是提高信噪比的有效预处理方法。使用空间滤波对高密度（HD）表面肌电图（sEMG）进行预处理有助于提取关键的空间信息，但它在非侵入性检查神经肌肉变化中的应用尚未得到很好的研究。
本研究旨在评估空间滤波如何有助于检查肌肉瘫痪，分别使用主成分分析（PCA）算法、非负矩阵分解（NMF）算法和两者的组合提出了三种不同的空间滤波方法。通过对脊髓损伤（SCI）后瘫痪肌肉的 HD-sEMG 聚类指数（CI）分析，评估它们在诊断能力方面的表现。
实验结果表明：（1）常规单通道 sEMG 的 CI 分析可以揭示 SCI 后瘫痪肌肉的复杂神经肌肉变化，其诊断能力已被证实具有 Z 分数方差的特征；（2）诊断能力高度依赖于 sEMG 记录通道的位置。直接对通道的 CI 诊断指标进行平均，仅达到中等水平的诊断能力；（3）使用基于 PCA 或 NMF 的滤波方法中的任何一种都可以提高诊断能力，并且它们的组合甚至可以显著增强诊断能力。
本研究不仅提出了一种提高 HD-sEMG 诊断能力的基本预处理方法，还有助于开发标准的 sEMG 预处理流程，从而促进其广泛应用。
