Automatically Measuring Neuromuscular Jitter
The analysis of electromyographic (EMG) signals detected during muscle contraction provides important information to aid in the diagnosis and characterization of neuromuscular disorders. One important analysis measures neuromuscular jitter, which is the variability of the time intervals between two muscle fibre potentials (MFPs) belonging to the same motor unit over a set of discharges. Conventionally, neuromuscular jitter is measured using single fibre (SF) EMG techniques, which can identify individual MFPs by using a SF needle electrode. However, SF electrodes are expensive, very sensitive to needle movement and not easy to operate in practise. <br /><br /> A method is studied in this thesis for automatically measuring neuromuscular jitter in motor unit potentials (MUP), it measures jitter using routine EMG techniques, which detect MUPs using a concentric needle (CN) electrode. The method is based on the detection of near MFP contributions, which correspond to individual muscle fibre contributions to MUPs, and the identification of individual MFP pairs. The method was evaluated using simulated EMG data. After an EMG signal is decomposed into MUP trains, a second-order differentiator, McGill filter, is applied to detect near MFP contributions to MUPs. Then, using nearest neighbour clustering and minimum spanning tree algorithms, the sets of available filtered MUPs can be selected and individual MFPs can be identified according to the features of their shapes. Finally, individual MFP pairs are selected and neuromuscular jitter is measured. <br /><br /> Using the McGill filter, near MFP contributions to detected CN MUPs can be consistently detected across an ensemble of successive firings of a motor unit. The method is an extension of the work Sheng Ma, compared to previous works, more efficient algorithms are used which have demonstrated acceptable performance, and which can consistently measure neuromuscular jitter in a variety of EMG signals.