Florestal Joël R, Mathieu Pierre A, Plamondon Réjean
Département de Physiologie, Institut de Génie Biomédical, Université de Montréal, Montréal QC H3T 1J4 Canada.
IEEE Trans Biomed Eng. 2007 Dec;54(12):2163-71. doi: 10.1109/tbme.2007.894977.
This paper presents a novel method, which aims at resolving difficult superimpositions of motor unit action potentials (MUAPs) obtained from single-channel intramuscular electromyographic recordings. Resolution is achieved by means of a genetic algorithm (GA) combined with a gradient descent method. This dual optimization scheme has been tested by means of simulations of isolated superimpositions involving two to six MUAPs, along with simulated extended signals of 10-s duration where the density reached 300 MUAPs/s. Of the hundreds of isolated superimpositions tested, more than 90% of the MUAPs were positively identified. With extended signals, identification rates of better than 85% were obtained. The GA alone accounted for up to an 8% improvement over the decomposition conducted using only template matching.
本文提出了一种新颖的方法，旨在解决从单通道肌内肌电图记录中获得的运动单位动作电位（MUAPs）的困难叠加问题。通过将遗传算法（GA）与梯度下降法相结合来实现分辨率。这种双重优化方案已通过对涉及两到六个MUAPs的孤立叠加进行模拟测试，以及对持续时间为10秒、密度达到300个MUAPs/秒的模拟扩展信号进行测试。在测试的数百个孤立叠加中，超过90%的MUAPs被正确识别。对于扩展信号，识别率超过85%。与仅使用模板匹配进行的分解相比，单独使用GA最多可提高8%。
