A MULTIPLE MODEL ALGORITHM FOR ESTIMATING MOTOR UNIT FIRING PATTERN STATISTICS

Abstract

The firing patterns and recruitment behavior of motor units (MUs) during a muscle contraction can be used in diagnosing neuromuscular disorder, studying motor control, improving the performance of electromyography (EMG) signal decomposition, and assessing the validity of MU potential trains extracted by an EMG decomposition algorithm. However, MU firing patterns extracted via EMG decomposition might contain several missed or erroneous that can lead to misleading conclusion. In this paper, we presented a multiple model estimation system (MMES) for estimating the mean ([Formula: see text]) and standard deviation ([Formula: see text]) of inter-discharge intervals (IDIs) of a MU. The presented MMES aggregates an existing error-filtered estimation (EFE) algorithm and a multiple linear regression model to estimate both [Formula: see text] and [Formula: see text]. The MMES estimates these two parameters using 10 features extracted from given IDIs and initial estimation of [Formula: see text] and [Formula: see text] values provided by EFE algorithm. Evaluation results using both simulated and real IDIs revealed that MMES performed better than EFE algorithm in estimating both [Formula: see text] and [Formula: see text] values in terms of root mean square error (RMSE), estimating variance and the range of estimated values. The RMSE values for MMES in estimating [Formula: see text] for simulated and real IDIs, respectively, were [Formula: see text] and [Formula: see text] that are statistically lower than that of EFE algorithm with RMSE [Formula: see text] and [Formula: see text]. In estimating [Formula: see text] in simulated and real IDIs, RMSE values of MMES, respectively were [Formula: see text] and [Formula: see text] that was significantly lower than those values obtained for EFE [Formula: see text] (for simulated IDIs) and [Formula: see text] (for real IDIs). More importantly, MMES outperformed EFE algorithm for real train with right-skewed IDI distribution. Consequently, MMES is more accurate, reliable and consistent than EFE algorithm for estimating IDI mean and standard deviation.