Department of Radiation Sciences, Biomedical Engineering, Umeå University, 901 87, Umeå, Sweden.
Department of Mathematics and Mathematical Statistics, Umeå University, 901 87, Umeå, Sweden.
BMC Res Notes. 2022 Jun 15;15(1):207. doi: 10.1186/s13104-022-06093-1.
In this study, the aim was to compare the performance of four spatiotemporal decomposition algorithms (stICA, stJADE, stSOBI, and sPCA) and parameters for identifying single motor units in human skeletal muscle under voluntary isometric contractions in ultrafast ultrasound image sequences as an extension of a previous study. The performance was quantified using two measures: (1) the similarity of components' temporal characteristics against gold standard needle electromyography recordings and (2) the agreement of detected sets of components between the different algorithms.
We found that out of these four algorithms, no algorithm significantly improved the motor unit identification success compared to stICA using spatial information, which was the best together with stSOBI using either spatial or temporal information. Moreover, there was a strong agreement of detected sets of components between the different algorithms. However, stJADE (using temporal information) provided with complementary successful detections. These results suggest that the choice of decomposition algorithm is not critical, but there may be a methodological improvement potential to detect more motor units.
在这项研究中，我们旨在比较四种时空分解算法（stICA、stJADE、stSOBI 和 sPCA）和参数在超快速超声图像序列中识别人类骨骼肌中单个运动单位的性能，这是之前研究的扩展。使用两个度量来量化性能：（1）与金标准针状肌电图记录相比，组件时间特征的相似性；（2）不同算法之间检测到的组件集的一致性。
我们发现，在这四种算法中，没有一种算法能够显著提高基于空间信息的 stICA 对运动单位识别的成功率，而 stICA 与使用空间或时间信息的 stSOBI 相结合效果最佳。此外，不同算法之间检测到的组件集具有很强的一致性。然而，stJADE（使用时间信息）提供了互补的成功检测。这些结果表明，分解算法的选择并不关键，但在检测更多运动单位方面可能存在改进方法的潜力。
