This study presents a novel algorithm for the automatic detection of motor unit (MU) fractions within the motor unit potential (MUP) scans derived from multiscanning EMG recordings. MU fractions are spatially distinct regions identified in the MUP scans that reflect the distribution of muscle fibres within each MU. Multiscanning EMG allows recording multiple MUPs simultaneously in a single recording, improving efficiency and reducing patient discomfort. The algorithm combines amplitude thresholding, morphological operations, and connected component analysis to identify MU fractions. Algorithm performance was evaluated using MUP scans from tibialis anterior muscles of five healthy individuals. The analysis was performed in two ways: the first included all the fractions detected automatically, and the second included only those fractions detected in both the automatic and the ground truth. Additionally, the association between muscle depth, number of MU fractions, and signal-to-noise ratio (SNR) of the recorded signals was analysed. T-tests showed no statistically significant difference between the algorithm and ground truth for both start and end markers. ANOVA indicated that muscle depth did not affect the signal-to-noise ratio ( f = 1.06, p = 0.35). Overall, the algorithm reliably identified MU fractions. The proposed automatic method accurately detects MU fractions, providing a valuable tool for analysing motor unit activity in clinical and research settings.
Muraleedharan et al. (Wed,) studied this question.