Motor unit number estimation (MUNE) is an electrodiagnostic procedure used to estimate the number of MUs in a muscle. In this thesis, a new MUNE technique, called Automated MPS, has been developed to overcome the shortcomings of two current techniques, namely MPS and MUESA. This method can be summarized as follows. First, a muscle is stimulated with a train of constant intensity current pulses. Depending on various factors, one to three MUs activate probabilistically after each pulse, and several responses are collected. These collected responses should be divided into up to 2^n clusters, such that each cluster represents one possible combination of n Surface-detected Motor Unit Potentials (SMUPs). After clustering the collected responses, the average response of each cluster is calculated, the outliers are excluded, and similar groups are merged together. Then, depending on the number of response set groups, a decomposition technique is applied to the response clusters to obtain the $n$ constituent SMUPs. To estimate the number of MUs, the aforementioned process is repeated several times until enough SMUPs to calculate a reliable mean-SMUP are acquired. The number of MUs can then be determined by dividing the maximal compound muscle action potential (CMAP) size by the mean-SMUP size. The focus of this thesis was on using pattern recognition techniques to detect n SMUPs from a collected set of waveforms. Several experiments were performed using both simulated and real data to evaluate the ability of Automated MPS in finding the constituent SMUPs of a response set. Our experiments showed that performing Automated MPS needs less experience compared with MPS. Moreover, it can deal with more difficult situations and detect more accurate SMUPs compared with MUESA.

Motor unit number estimation (MUNE) is an electrodiagnostic procedure used to estimate the number of MUs in a muscle. In this thesis, a new MUNE technique, called Automated MPS, has been developed to overcome the shortcomings of two current techniques, namely MPS and MUESA. This method can be summarized as follows. First, a muscle is stimulated with a train of constant intensity current pulses. Depending on various factors, one to three MUs activate probabilistically after each pulse, and several responses are collected. These collected responses should be divided into up to 2^n clusters, such that each cluster represents one possible combination of n Surface-detected Motor Unit Potentials (SMUPs). After clustering the collected responses, the average response of each cluster is calculated, the outliers are excluded, and similar groups are merged together. Then, depending on the number of response set groups, a decomposition technique is applied to the response clusters to obtain the $n$ constituent SMUPs. To estimate the number of MUs, the aforementioned process is repeated several times until enough SMUPs to calculate a reliable mean-SMUP are acquired. The number of MUs can then be determined by dividing the maximal compound muscle action potential (CMAP) size by the mean-SMUP size. The focus of this thesis was on using pattern recognition techniques to detect n SMUPs from a collected set of waveforms. Several experiments were performed using both simulated and real data to evaluate the ability of Automated MPS in finding the constituent SMUPs of a response set. Our experiments showed that performing Automated MPS needs less experience compared with MPS. Moreover, it can deal with more difficult situations and detect more accurate SMUPs compared with MUESA.

Establishing a valid, reliable, and objective method for determining the number of functioning motor units in a muscle is important clinically, as it would provide a quantitative means of documenting changes in neuromuscular health over time. This thesis addressed the reliability of motor unit number estimates (MUNEs) derived using decomposition-based quantitative electromyography (DQEMG) from the extensor digitorum brevis (EDB) and abductor hallucis (AH) muscles. Additionally, the effect of the mean surface motor unit potential (SMUP) parameter averaging method (arithmetic/ensemble), the size-related parameter used to calculate MUNE (amplitude/area), and the type of SMUP marker editing (automatic/manual) was investigated in terms of MUNE values. Two separate analyses on a single data set collected from twenty healthy subjects on two occasions were conducted. MUNEs were calculated by dividing a size-related parameter (amplitude/area) of the compound muscle action potential (CMAP) by the same size-related parameter of a representative mean SMUP. First, paired t-tests investigated differences in MUNEs calculated using arithmetic and ensemble averaged SMUP parameters. Within- and between-day reliability of the two measurements was established using intra-class correlation coefficients (ICCs), coefficients of variation (CV), mean absolute differences (MAD), and Bland Altman limits of agreement (LOA). Second, MUNEs (using both parameters) derived from automated and manually edited SMUPs were compared. The effect of the size-related parameter and editing type was identified using a two-factor, repeated measures analysis of variance. Reliability was determined as described above. Arithmetic averaged SMUP parameters produced smaller MUNEs than those derived from ensemble averaging (p<0.001). SMUP area produced higher MUNEs than SMUP amplitude (p<0.05), except when using arithmetic averaged parameters in AH. Interaction effects between editing type and size parameter were present in both muscles (F>6.68, p<0.001). Between-day MUNEs had lower CVs and MADs, higher ICCs, and narrower LOAs than within-day MUNEs. MUNEs derived from arithmetic averaged SMUP parameters showed the highest reliability (ICCs>0.91). MUNEs calculated from automated SMUP marker placements were highly correlated (r>0.86) and displayed comparable reliabilities to those derived from manual marker placement (ICCs>0.90). To optimize the reproducibility of MUNEs calculated using DQEMG, while minimizing processing time, between-day automated estimates using arithmetic averaged SMUP amplitude is recommended. / Thesis (Master, Rehabilitation Science) -- Queen's University, 2012-08-30 08:32:06.141

Functional recovery following peripheral nerve injuries is poor due to muscle atrophy and fibrosis being major contributing factors. Electrical muscle stimulation has been used for decades in some capacity to treat denervation related muscular changes. The research presented in this thesis explores a new stimulation paradigm and its effects on short and long term muscle denervation. The first part of this work describes the new stimulation paradigm and the design and development of the stimulator used to deliver this paradigm. The paradigm involved daily 1-hour stimulation sessions featuring 600 contractions at high stimulus frequencies (100 Hz) and low pulse durations (200 μs). To test the device and paradigm, a pilot study involving muscle stimulation throughout a one month period of denervation in rat lower limb muscles was carried out. The results showed that this short but intense stimulus session significantly reduced the rate of muscle atrophy compared to animals that did not receive stimulation. Furthermore, muscle weight and consequently muscle force were also significantly greater. The stimulus paradigm was then used to investigate muscle that was denervated and immediately repaired. Ideally, immediate nerve repair following nerve injuries produces the best outcome. One month of electrical muscle stimulation following nerve repair enhanced this outcome through significant increases in muscle weight and force. Additionally, contrary to many previous studies, the stimulus paradigm had no negative effects on reinnervation. Taken together, electrical muscle stimulation can provide significant improvements over the best case scenario of immediate nerve repair. The third part of this work investigated the use of chronic electrical muscle stimulation throughout three months of denervation and the impact on reinnervation. Results showed that reinnervation in chronically stimulated animals were no different than animals that were denervated and immediately repaired. The last part of this work combined the use of electrical muscle stimulation with sensory protection in chronically denervated muscle. Sensory protection involves suturing a sensory nerve to protect a muscle during denervation and was shown in previous studies to reduce muscle atrophy, preserve muscle spindles and the structure of the distal nerve stump. The results showed significantly greater muscle weights and force in the combined treatment compared to the individual treatments alone. Reinnervation in these animals was as good as those that were immediately repaired. This suggests that contractile support combined with sensory protection may provide superior functional outcomes in chronically denervated muscle. The findings presented in this thesis provide new evidence for the use of short duration daily electrical muscle stimulation immediately following nerve repair or throughout long term denervation. Evidence for a new therapy, muscle stimulation with sensory protection, is also presented and shown to provide superior functional outcomes compared to either therapy alone. The contributions made in this body of work may provide clinicians with evidence to pursue clinical use of the outlined strategies and ultimately help patients optimally recover from peripheral nerve injuries. / Doctor of Philosophy (PhD)