Automatic Decomposition System Based On Clinical Needle EMG Signal

A myoelectric signal (MES) detected with needle electrodes is a sum of the motor unit action potentials (MUAPs) trains of all recruited motor units (MUs) and additive noise, including background instrumental noise, Gaussian white noise, and sometimes power line interference (PLI). Electromyograph (EMG) decomposition is the process of resolving a composite MES into its constituent MUAP trains. Diognostic application and the commercial success of these techniques have been lagging behind despite the enthusiasm of researchers and the number, quality and significance of scientific publication. Decomposition algorithm are being developed and published.The presented algorithm for EMG decomposition includes the following steps: 1) Removing noises; 2) MUAPs extraction and EMG signal segmentation ; 3) MUAP clustering; 4) MUAP classification. Our work mainly includes the following points:Firstly, removing noise and effective MUAP peak detection is the first important step in EMG decomposition. We first combined independent component analysis and wavelet filtering to remove power line interference, and then applied a wavelet filtering method and threshold estimation calculated using wavelet transform to suppress background noise and Gaussian white noise. In contrast to existing methods based on amplitude single-threshold filtering of the original myoelectric signal or a conventional digitally filtered signal, our technique is fast and robust.Secondly, in this thesis we first take the original data, the morphological feature data and the wavelet coefficients at third to sixth level after wavelet transform at sixth level as feature space. Otherwise, we utilized the wavelet packet coefficients of the optimal decomposition based on linear discriminative analysis and fuzzy clustering. Among all these feature extraction methods, the wavelet coefficients is the most consuming, and the optimal wavelet packet coefficients is the most effective in reducing the dimensionality.Thirdly, we arranged these single MUAPs to their constituent MUAPTs based on the single-linkage hierarchical clustering algorithm. The supervised classifier we used is based on the minimum distance classifier to classify non-overlapping but un-classified active segments by clustering. Morever, we used the fuzzy K-means clustering to optimize the classification program and to improve the classification accuracy.Finally, a very important aspect in this field is the evaluation of performance. In order to obtain a reference decomposition result, different methods have been proposed: 1) synthetic signals; 2) real signal decomposed manually; 3) recordings from the same MU at different locations and the results compared. Our methods are focused on the former two methods. The EMG signals were generated on the basis of a model of a normal intramuscular EMG recording, proposed by the former researcher. All of our artificial recordings were corrupted with random white noise at various signal-to-noise ratios (SNR) and with a PLI signal. The generation of the firing pattern was based on three statistical characteristics of the pattern: regular firing, double-discharge firing, and random firing. A regular firing pattern was introduced by use of the mean inter-pulse interval (IPI), which follows a size principle. Otherwise, we developed a new EMG manually decomposition program based on the new pattern classification and digital signal processing technique.The technique of our EMG signal decomposition is fast and robust, which has been evaluated through synthetic EMG signals and real EMG signals. Because the speed of our EMG decomposition program was improved greatly and the performance of our method is very robust our technique may be suitable for on-line analysis. Therefore, our decomposition technique only clustered and classified single MUAPs. Certainly, the decomposition results are incomplete because of the restriction of time and the limitation of our data collection. Currently, in order to finish the complete EMG decomposition, we're going to do the further research and investigation related to new EMG signal decomposition method. The next study will include but nor limited points as follows: (1) extracting new effective MUAP features such as non-linear feature parameters; (2) trying to finish complete EMG signal decomposition through resolving superimposed action potentials.