Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:748-751. doi: 10.1109/EMBC48229.2022.9871578.
Muscle force production is the result of a sequence of electromechanical events that translate the neural drive issued to the motor units (MUs) into tensile forces on the tendon. Current technology allows this phenomenon to be investigated non-invasively. Single MU excitation and its mechanical response can be studied through high-density surface electromyography (HDsEMG) and ultrafast ultrasound (US) imaging respectively. In this study, we propose a method to integrate these two techniques to identify anatomical characteristics of single MUs. Specifically, we tested two algorithms, combining the tissue velocity sequence (TVS, obtained from ultrafast US images), and the MU firings (extracted from HDsEMG decomposition). The first is the Spike Triggered Averaging (STA) of the TVS based on the occurrences of individual MU firings, while the second relies on the correlation between the MU firing patterns and the TVS spatio-temporal independent components (STICA). A simulation model of the muscle contraction was adapted to test the algorithms at different degrees of neural excitation (number of active MUs) and MU synchronization. The performances of the two algorithms were quantified through the comparison between the simulated and the estimated characteristics of MU territories (size, location). Results show that both approaches are negatively affected by the number of active MU and synchronization levels. However, STICA provides a more robust MU territory estimation, outperforming STA in all the tested conditions. Our results suggest that spatio-temporal independent component decomposition of TVS is a suitable approach for anatomical and mechanical characterization of single MUs using a combined HDsEMG and ultrafast US approach.
肌肉力量的产生是一系列机电事件的结果，这些事件将神经驱动传递到运动单位（MUs）转化为肌腱上的拉伸力。目前的技术允许对这种现象进行非侵入性研究。通过高密度表面肌电图（HDsEMG）和超快超声（US）成像，可以分别研究单个 MU 的兴奋及其机械响应。在这项研究中，我们提出了一种将这两种技术结合起来识别单个 MU 的解剖学特征的方法。具体来说，我们测试了两种算法，将组织速度序列（TVS，从超快 US 图像中获得）和 MU 发射（从 HDsEMG 分解中提取）结合起来。第一种是基于单个 MU 发射的 TVS 的尖峰触发平均（STA），第二种则依赖于 MU 发射模式和 TVS 时空独立成分（STICA）之间的相关性。我们适应了肌肉收缩的模拟模型来测试在不同神经兴奋程度（活跃 MU 的数量）和 MU 同步水平下的算法。通过比较模拟和估计的 MU 区域特征（大小、位置）来量化两种算法的性能。结果表明，这两种方法都受到活跃 MU 的数量和同步水平的负面影响。然而，STICA 提供了更稳健的 MU 区域估计，在所有测试条件下都优于 STA。我们的结果表明，TVS 的时空独立成分分解是一种使用 HDsEMG 和超快 US 相结合的方法对单个 MU 进行解剖学和力学特征描述的合适方法。
