A Hybrid Classifier Fusion Approach for Motor Unit Potential Classification During EMG Signal Decomposition

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 (CC_r) (6.1%) and reduced error rate (E_r) (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 CC_r (6.2%) and reduced E_r (0.9%). For real signals, the hybrid classifier fusion system had on average an improved CC_r (7.5%) and reduced E_r (1.7%).