Rasheed Sarbast, Stashuk Daniel W, Kamel Mohamed S
Department of Systems Design Engineering, University of Waterloo, 508-G Sunnydale Place, Waterloo, ON N2L 3G1, Canada.
IEEE Trans Biomed Eng. 2007 Sep;54(9):1715-21. doi: 10.1109/TBME.2007.892922.
In this paper, we propose a hybrid classifier fusion scheme for motor unit potential classification during electromyographic (EMG) signal decomposition. The scheme uses an aggregator module consisting of two stages of classifier fusion: the first at the abstract level using class labels and the second at the measurement level using confidence values. Performance of the developed system was evaluated using one set of real signals and two sets of simulated signals and was compared with the performance of the constituent base classifiers and the performance of a one-stage classifier fusion approach. Across the EMG signal data sets used and relative to the performance of base classifiers, the hybrid approach had better average classification performance overall. For the set of simulated signals of varying intensity, the hybrid classifier fusion system had on average an improved correct classification rate (CCr) (6.1%) and reduced error rate (Er) (0.4%). For the set of simulated signals of varying amounts of shape and/or firing pattern variability, the hybrid classifier fusion system had on average an improved CCr (6.2%) and reduced Er (0.9%). For real signals, the hybrid classifier fusion system had on average an improved CCr (7.5%) and reduced Er (1.7%).
在本文中，我们提出了一种用于肌电图（EMG）信号分解过程中运动单位电位分类的混合分类器融合方案。该方案使用一个聚合模块，该模块由两个阶段的分类器融合组成：第一阶段在抽象层面使用类别标签，第二阶段在测量层面使用置信度值。使用一组真实信号和两组模拟信号对所开发系统的性能进行了评估，并与组成该系统的基础分类器的性能以及单阶段分类器融合方法的性能进行了比较。在所使用的EMG信号数据集范围内，相对于基础分类器的性能，混合方法总体上具有更好的平均分类性能。对于强度不同的模拟信号集，混合分类器融合系统平均而言具有更高的正确分类率（CCr）（提高了6.1%）和更低的错误率（Er）（降低了0.4%）。对于形状和/或放电模式变化量不同的模拟信号集，混合分类器融合系统平均而言具有更高的CCr（提高了6.2%）和更低的Er（降低了0.9%）。对于真实信号，混合分类器融合系统平均而言具有更高的CCr（提高了7.5%）和更低的Er（降低了1.7%）。
