Decomposition Of Surface Electromyography Signal And Its Application In Cerebral-Palsy Evaluation

The surface electromyography (sEMG) signals which is recorded by a electrode on the surface of the contracting muscle is the sum of potential contribution of the motor unit action potential trains(MUAPt) and is weak non-stationary signals. The sEMG decomposition is the inversion of it emergeing, and the the MUAP characteristics and the statistic of motor unit firing and recruitment would be achieved in the result of sEMG decomposition. The sEMG decomposition research which is a difficult task has important theoretical significance and application value. The result would be used for basic research of neuromuscular system and diagnosis of neuromuscular diseases.sEMG signals was complex electric signals and the sEMG decomposition was a difficult task. First the sEMG simulation research was study. Then two new methods aimed at the decomposition of sEMG were proposed based on the extensive review of the decomposition methods of needle EMG and sEMG. The feasibilities of these methods were explored through the decomposition experiment on simulated and real sEMG signals. And The Characteristics of neurons fire in Child Cerebral Palsy had been studied by means of surface Electromyography (sEMG), in order to provide an index of the CP patients.The main work and achievement of the dissertation could be presented as follows:1. Study on the simulation of sEMG. According the physiology process model of sEMG, the simulation study was implemented. The simulation sigals would be used to analysis of decomposition algorithm. The location between electrode and muscle fibers and conduct velocity of muscle fiber were emphatically discussed. Then the recruitment model of MU in stable force was proposed in the paper. Following the true circumstance, the simulation of sEMG was divided into two parts that was monopole electrode situation and different electrodes situation.2. Aiming at the problem of solving underdetermined mixing equations in the technique of Blind Source Separation (BSS), a method based on Sparse Component Analysis (SCA) was adopted in this paper and was applied to the decomposition of Surface electromyography (sEMG). Matching Pursuit (MP) algorithm Improved by Genetic Algorithm was used to de-noise and sparse the sEMG signal and the result is effective. Hough transformation was used to estimate clustering axes and solve mixing matrix. Space degenerate method was also adopted to improve Hough transformation to reduce the disturbance of artifacts. Auto clustering algorithm which named subtractive clustering was used to estimate mixing matrix automatically. Experimental simulated results and real sEMG signals demonstrate that the method in the paper is effective in the decomposition of the sEMG signal at the low contraction force, and its separation results were better.3. Study on Multidimensional subspace clustering which was the method of Independent Component Analysis in sEMG. The algorithm was used to find superposition structure of sEMG and estimate mixing matrix by multichannel sEMG signals projection in subspace. Then the MUAPt was achieved. Two source methods were used to prove the accuracy of algorithm. The decomposition results of the simulated sEMG signals based on the BSS algorithm model showed that the simulated signals could be effectively separated by the algorithm.4. The study of the Characteristics of neurons fire in Child Cerebral Palsy based on Motor Unit Action Potential. The evaluation of dyskinesia and rehabilitation for cerebral-palsy patients had important clinical value. The Smoothed Nonlinear Energy Operator (SNEO) had been adopted to estimate number of MUAP in sEMG which was collected from cerebral-palsy (CP) patients. Then, Inter-pulse Interval (IPI) of MUAP had been achieved. The results were verified by docotor diagnosis and early research of mean potential duration of health people. The result of14patients sEMG signals in different level of the CP showed their IPIs relate to severity of the disease, which also expressed levels of CP dyskinesia, with the positive correlation, And had obvious differences at various CP level. Experimental results of real CP sEMG signals demonstrated that the method proposed in this paper was effective and Inter-pulse Interval(IPI) of MUAP could be used to be quantitative index for the evaluation of CP dyskinesia and rehabilitation.The research was supported by the National Natural Science Foundation of China "Surface EMG decomposition based on linear varied force and temporal spacial information from multiple channels"(30870656).