Gligorijević Ivan, De Vos Maarten, Blok Joleen H, Mijović Bogdan, van Dijk Johannes P, Van Huffel Sabine
Department of Electrical Engineering, Division SCD-SISTA, KatholiekeUniversiteit Leuven, Leuven 3001, Belgium.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:4090-3. doi: 10.1109/IEMBS.2011.6091016.
A new, automated way to obtain signatures of active motor units (MUs) from high density surface EMG recordings during voluntary contractions is presented. It relies on clustering of repetitive shapes corresponding to different MU action potentials (MUAPs) present. The number of clusters and the mean shapes of the MUAPs as observed on the electrode grid, are estimated in a fast way without user interaction. The algorithm is tested on simulated signals mimicking a small muscle. Our results show that at least 8 MUAPs can be reliably reconstructed and their MU mean firing frequencies can be estimated.
提出了一种新的自动化方法，用于在自主收缩期间从高密度表面肌电图记录中获取活跃运动单位（MU）的特征。它依赖于对与不同运动单位动作电位（MUAP）相对应的重复形状进行聚类。无需用户干预，即可快速估计在电极网格上观察到的聚类数量和MUAP的平均形状。该算法在模拟小肌肉的模拟信号上进行了测试。我们的结果表明，至少可以可靠地重建8个MUAP，并估计它们的MU平均放电频率。
