Adaptive fuzzy k-NN classifier for EMG signal decomposition

Abstract

An adaptive fuzzy k-nearest neighbour classifier (AFNNC) for EMG signal decomposition is presented and evaluated. The developed classifier uses an adaptive assertion-based classification approach for setting a minimum classification threshold. The similarity criterion used for grouping motor unit potentials (MUPs) is based on a combination of MUP shapes and two modes of use of motor unit firing pattern information: passive and active. The performance of the developed classifier was evaluated using synthetic signals with specific properties and experimental signals and compared with the performance of an adaptive template matching classifier, the adaptive certainty classifier (ACC). Across the sets of simulated and experimental EMG signals used for comparison, the AFNNC had better average classification performance overall, but due to the assignment of higher numbers of MUPs it made relatively more errors. Nonetheless, these increased error rates would still be acceptable for most clinical uses of decomposed EMG data. An independent and a related set of simulated signals were used for testing. For the independent simulated signals of varying intensity, the AFNNC had on average an improved correct classification rate (CC r) (8.1%) but an increased error rate ( E r) (1.5%) compared to ACC. For the related simulated signals with varying amounts of shape and/or firing pattern variability, the AFNNC on average had an improved CC r (5%) but a slightly increased E r (0.3%) compared to ACC. For experimental signals, the AFNNC on average had improved CC r (6%) but an increased E r (2.1%) compared to ACC. The greatest gains in AFNNC performance relative to that of the ACC occurred when the variability of MUP shapes within motor unit potential trains was high suggesting that compared to a template matching assignment strategy the NN assignment paradigm is better able to ameliorate the classification problems caused by MUP instability.