A new method for measurement of motor unit action potential duration based on correlation, a pilot study We present a new, automatic, correlationbased method for measuring the duration of motor unit action potentials (MUAPs). The method seeks to replicate the way an expert elctromyographer uses his or her eyes, calculating the start and end of the MUAP waveform on the basis of the degree of similarity of nonexcluded discharges. We analysed 68 potentials from normal deltoid muscles during slight contraction. For each MUAP, two experienced electromyographers manually determined start and end marker positions, which were used as gold standard duration positions (GSP) in our subsequent tests. The novel method was compared with Nandedkarxe2x80x99s method and a wavelet transformbased method. To compare the three methods, the differences between the automatic marker positions and GSPs were statistically evaluated using onefactor ANOVA, the estimated mean square error, and a Chisquare test on the numbers of automatic marker placements with gross errors. All these parameters showed smaller values for the novel method and in most of the cases were statistically significant. In addition, the parameters of the new method were subjected to a sensitivity study, showing its good performance within a range of clinically useful parameter values. The new automatic method determined start and end markers in a more accurate and reliable manner than both of the acknowledged stateofthe art methods used in our comparison study.

Generalization of a waveletbased algorithm to adaptively detect activation intervals in weak and noisy myoelectric signals Abstract   This study introduces an adaptive implementation of a Continuous Wavelet Transform (CWT) decomposition technique used to estimate the timing of muscular activation in weak and noisy myoelectric signals. The algorithm updates automatically the threshold based on the statistical properties of the EMG data, through an iterative estimation of the SignaltoNoise Ratio (SNR). Moreover, it includes a stopping criterion for the number of CWT decomposition levels, and this allows a relevant decrease of the computational burden. This algorithm was applied to both synthetic and semisynthetic signals, and compared against the original formulation of the CWTbased technique and a common thresholdbased technique for the detection of muscle activations. Performance of these techniques was assessed by using Bias, Relative Timing Error and Accuracy of the detection. Bias values resulted lower than 18xe2x80xafms, Relative Timing Error lower than 5% and Accuracy greater than 97% for all the tested SNR (ranging from xe2x88x922xe2x80xafdB to 10xe2x80xafdB), and with a substantial independence from SNR levels. The performance was shown to hold also if the hypothesis of absence of muscular activation in the reference window cannot be guaranteed. The results show that the proposed approach, which is adaptive, operatorindependent and iterative, performs properly when applied to weak and noisy myoelectric signals, and is thus a valid general solution when dealing with clinical conditions where muscular activity is low, and when recording conditions cannot be entirely controlled.