The quantitative analysis of decomposed electromyographic (EMG) signals reveals information for diagnosing and characterizing neuromuscular disorders. Neuromuscular jitter is an important measure that reflects the stability of the operation of a neuromuscular junction. It is conventionally measured using single fiber electromyographic (SFEMG) techniques. SFEMG techniques require substantial physician dexterity and subject cooperation. Furthermore, SFEMG needles are expensive, and their re-use increases the risk of possible transmission of infectious agents. Using disposable concentric needle (CN) electrodes and automating the measurment of neuromuscular jitter would greatly facilitate the study of neuromuscular disorders. An improved automated jitter measurment system based on the decomposition of CN detected EMG signals is developed and evaluated in this thesis. Neuromuscular jitter is defined as the variability of time intervals between two muscle fiber potentials (MFPs). Given the candidate motor unit potentials (MUPs) of a decomposed EMG signal, which is represented by a motor unit potential train (MUPT), the automated jitter measurement system designed in this thesis can be summarized as a three-step procedure: 1) identify isolated motor unit potentials in a MUPT, 2) detect the significant MFPs of each isolated MUP, 3) track significant MFPs generated by the same muscle fiber across all isolated MUPs, select typical MFP pairs, and calculate jitter. In Step one, a minimal spanning tree-based 2-phase clustering algorithm was developed for identifying isolated MUPs in a train. For the second step, a pattern recognition system was designed to classify detected MFP peaks. At last, the neuromuscular jitter is calculated based on the tracked and selected MFP pairs in the third step. These three steps were simulated and evaluated using synthetic EMG signals independently, and the whole system is preliminary implemented and evaluated using a small simulated data base. Compared to previous work in this area, the algorithms in this thesis showed better performance and great robustness across a variety of EMG signals, so that they can be applied widely to similar scenarios. The whole system developed in this thesis can be implemented in a large EMG signal decomposition system and validated using real data.

The quantitative analysis of decomposed electromyographic (EMG) signals reveals information for diagnosing and characterizing neuromuscular disorders. Neuromuscular jitter is an important measure that reflects the stability of the operation of a neuromuscular junction. It is conventionally measured using single fiber electromyographic (SFEMG) techniques. SFEMG techniques require substantial physician dexterity and subject cooperation. Furthermore, SFEMG needles are expensive, and their re-use increases the risk of possible transmission of infectious agents. Using disposable concentric needle (CN) electrodes and automating the measurment of neuromuscular jitter would greatly facilitate the study of neuromuscular disorders. An improved automated jitter measurment system based on the decomposition of CN detected EMG signals is developed and evaluated in this thesis. Neuromuscular jitter is defined as the variability of time intervals between two muscle fiber potentials (MFPs). Given the candidate motor unit potentials (MUPs) of a decomposed EMG signal, which is represented by a motor unit potential train (MUPT), the automated jitter measurement system designed in this thesis can be summarized as a three-step procedure: 1) identify isolated motor unit potentials in a MUPT, 2) detect the significant MFPs of each isolated MUP, 3) track significant MFPs generated by the same muscle fiber across all isolated MUPs, select typical MFP pairs, and calculate jitter. In Step one, a minimal spanning tree-based 2-phase clustering algorithm was developed for identifying isolated MUPs in a train. For the second step, a pattern recognition system was designed to classify detected MFP peaks. At last, the neuromuscular jitter is calculated based on the tracked and selected MFP pairs in the third step. These three steps were simulated and evaluated using synthetic EMG signals independently, and the whole system is preliminary implemented and evaluated using a small simulated data base. Compared to previous work in this area, the algorithms in this thesis showed better performance and great robustness across a variety of EMG signals, so that they can be applied widely to similar scenarios. The whole system developed in this thesis can be implemented in a large EMG signal decomposition system and validated using real data.

<p>Signal processing within the neurophysiological field is challenging and requires short processing time and reliable results. In this thesis, three main problems are considered.</p><p>First, a modified line source model for simulation of muscle action potentials (APs) is presented. It is formulated in continuous-time as a convolution of a muscle-fiber dependent transmembrane current and an electrode dependent weighting (impedance) function. In the discretization of the model, the Nyquist criterion is addressed. By applying anti-aliasing filtering, it is possible to decrease the discretization frequency while retaining the accuracy. Finite length muscle fibers are incorporated in the model through a simple transformation of the weighting function. The presented model is suitable for modeling large motor units.</p><p>Second, the possibility of discerning the individual AP components of the concentric needle electromyogram (EMG) is explored. Simulated motor unit APs (MUAPs) are prefiltered using Wiener filtering. The mean fiber concentration (MFC) and jitter are estimated from the prefiltered MUAPs. The results indicate that the assessment of the MFC may well benefit from the presented approach and that the jitter may be estimated from the concentric needle EMG with an accuracy comparable with traditional single fiber EMG.</p><p>Third, automatic, rather than manual, detection and discrimination of recorded C-fiber APs is addressed. The algorithm, detects the Aps reliably using a matched filter. Then, the detected APs are discriminated using multiple hypothesis tracking combined with Kalman filtering which identifies the APs originating from the same C-fiber. To improve the performance, an amplitude estimate is incorporated into the tracking algorithm. Several years of use show that the performance of the algorithm is excellent with minimal need for audit.</p>

Signal processing within the neurophysiological field is challenging and requires short processing time and reliable results. In this thesis, three main problems are considered. First, a modified line source model for simulation of muscle action potentials (APs) is presented. It is formulated in continuous-time as a convolution of a muscle-fiber dependent transmembrane current and an electrode dependent weighting (impedance) function. In the discretization of the model, the Nyquist criterion is addressed. By applying anti-aliasing filtering, it is possible to decrease the discretization frequency while retaining the accuracy. Finite length muscle fibers are incorporated in the model through a simple transformation of the weighting function. The presented model is suitable for modeling large motor units. Second, the possibility of discerning the individual AP components of the concentric needle electromyogram (EMG) is explored. Simulated motor unit APs (MUAPs) are prefiltered using Wiener filtering. The mean fiber concentration (MFC) and jitter are estimated from the prefiltered MUAPs. The results indicate that the assessment of the MFC may well benefit from the presented approach and that the jitter may be estimated from the concentric needle EMG with an accuracy comparable with traditional single fiber EMG. Third, automatic, rather than manual, detection and discrimination of recorded C-fiber APs is addressed. The algorithm, detects the Aps reliably using a matched filter. Then, the detected APs are discriminated using multiple hypothesis tracking combined with Kalman filtering which identifies the APs originating from the same C-fiber. To improve the performance, an amplitude estimate is incorporated into the tracking algorithm. Several years of use show that the performance of the algorithm is excellent with minimal need for audit.