Department of Biomedical Engineering, Lund University, 221 00, Lund, Sweden.
Department of Radiation Sciences, Biomedical Engineering, Radiation Physics, Umeå University, Umeå, Sweden.
Biomed Eng Online. 2023 Feb 7;22(1):10. doi: 10.1186/s12938-023-01076-0.
Individual motor units have been imaged using ultrafast ultrasound based on separating ultrasound images into motor unit twitches (unfused tetanus) evoked by the motoneuronal spike train. Currently, the spike train is estimated from the unfused tetanic signal using a Haar wavelet method (HWM). Although this ultrasound technique has great potential to provide comprehensive access to the neural drive to muscles for a large population of motor units simultaneously, the method has a limited identification rate of the active motor units. The estimation of spikes partly explains the limitation. Since the HWM may be sensitive to noise and unfused tetanic signals often are noisy, we must consider alternative methods with at least similar performance and robust against noise, among other factors.
This study aimed to estimate spike trains from simulated and experimental unfused tetani using a convolutive blind source separation (CBSS) algorithm and compare it against HWM. We evaluated the parameters of CBSS using simulations and compared the performance of CBSS against the HWM using simulated and experimental unfused tetanic signals from voluntary contractions of humans and evoked contraction of rats. We found that CBSS had a higher performance than HWM with respect to the simulated firings than HWM (97.5 ± 2.7 vs 96.9 ± 3.3, p < 0.001). In addition, we found that the estimated spike trains from CBSS and HWM highly agreed with the experimental spike trains (98.0% and 96.4%).
This result implies that CBSS can be used to estimate the spike train of an unfused tetanic signal and can be used directly within the current ultrasound-based motor unit identification pipeline. Extending this approach to decomposing ultrasound images into spike trains directly is promising. However, it remains to be investigated in future studies where spatial information is inevitable as a discriminating factor.
基于分离由运动神经元尖峰序列诱发的肌肉运动单位搐动（未融合强直）的超声信号，已经可以对单个运动单位进行成像。目前，使用 Haar 小波方法（HWM）从未融合强直信号中估计尖峰序列。尽管该超声技术具有同时为大量运动单位提供对肌肉神经驱动全面了解的巨大潜力，但该方法对活跃运动单位的识别率有限。尖峰序列的估计部分解释了这种局限性。由于 HWM 可能对噪声敏感，并且未融合强直信号通常噪声较大，因此我们必须考虑替代方法，这些方法在其他因素之外，至少具有类似的性能且对噪声具有鲁棒性。
本研究旨在使用卷积盲源分离（CBSS）算法从模拟和实验性未融合强直中估计尖峰序列，并与 HWM 进行比较。我们使用模拟来评估 CBSS 的参数，并使用来自人类自主收缩和大鼠诱发收缩的模拟和实验性未融合强直信号来比较 CBSS 与 HWM 的性能。我们发现，CBSS 在模拟放电方面的性能优于 HWM（97.5±2.7 比 96.9±3.3，p<0.001）。此外，我们发现，从 CBSS 和 HWM 估计的尖峰序列与实验尖峰序列高度吻合（98.0%和 96.4%）。
该结果表明，CBSS 可用于估计未融合强直信号的尖峰序列，并可直接用于当前基于超声的运动单位识别管道。将这种方法扩展到直接将超声图像分解为尖峰序列是有希望的。然而，在需要空间信息作为区分因素的未来研究中，仍需要进一步研究。
