In this paper, we establish a surface electromyography(sEMG) signal model and study the signal decomposition method from noisy background. Firstly, single fiber action potential (SFAP), motor unit action potential (MU...In this paper, we establish a surface electromyography(sEMG) signal model and study the signal decomposition method from noisy background. Firstly, single fiber action potential (SFAP), motor unit action potential (MUAP) and motor unit action potential train(MUAPT) are simulated based on the tripolar signal source model, and then the sEMG is obtained; secondly, the simulated sEMG signal is extracted from the mixed signals that consists of white noises, power frequency interference signal and electrocardio signal by independent component analysis (ICA) algorithms; lastly, the spikes corresponding to each motor unit action potential from the simulated sEMG signals were detected by applying the wavelet transform (WT) method. Simulation results showed that sEMG model could describe the physiological process of sEMG, ICA and WT methods could extract the sEMG signal and its features, which will lay a foundation for further classifying the MUAP.展开更多
基金The open project of the State Key Laboratory of Robotics and System(HIT)the open project of the State Key Laboratory of Cognitive Neuroscience and Learning and the Natural science fund for colleges and universities in Jiangsu Province+2 种基金 grant number: 10KJB510003the natural science fund in Changzhou City grant number: CJ20110023
文摘In this paper, we establish a surface electromyography(sEMG) signal model and study the signal decomposition method from noisy background. Firstly, single fiber action potential (SFAP), motor unit action potential (MUAP) and motor unit action potential train(MUAPT) are simulated based on the tripolar signal source model, and then the sEMG is obtained; secondly, the simulated sEMG signal is extracted from the mixed signals that consists of white noises, power frequency interference signal and electrocardio signal by independent component analysis (ICA) algorithms; lastly, the spikes corresponding to each motor unit action potential from the simulated sEMG signals were detected by applying the wavelet transform (WT) method. Simulation results showed that sEMG model could describe the physiological process of sEMG, ICA and WT methods could extract the sEMG signal and its features, which will lay a foundation for further classifying the MUAP.
