Surface Emg Action Potential Conduction Velocity Estimates

The electromyography (EMG) signal is a kind of non-stationary biological signal, it can be regarded as the superposed result of the various noise and the motor unit action potential (MUAP) trains of the activated motor units. Compared with the intramuscular EMG (IEMG) signal which is recorded by inserting into the muscle tissue using the indwelling electrodes, the surface EMG (SEMG) signal is recorded by placing on the skin surface using the surface electrodes, and it has the advantage of non-invasive detection characteristic. The SEMG signal can be applied in the clinical medicine, sports medicine and rehabilitation engineering.The action potential conduction velocity of SEMG signal can be used to interpret the functional characteristic information of the neuromuscular system, especially, the variational trend of conduction velocity can be utilized to describe the muscular fatigue during the muscle contraction processes. In this paper, the estimation method of action potential conduction velocity was computed by the time-delay estimation, the cross-correlation technique was tested by the quantitative analysis of the simulated SEMG signal, and then was applied for the experimental analysis of the real SEMG signal.The main work and achievement of this paper could be described as the following:1. Using the designed parameter of inter-electrode distance (IED) for the multi-channel SEMG signal acquisition, the estimated value of conduction velocity could be acquired by the relationship between the IED and time-delay. The three kinds of time-delay estimation methods, cross-correlation technique, bispectrum analysis and maximum likelihood algorithm, were compared in our work. After considering computational complexity and precision of the different method, the time-delay estimation method based on the cross-correlation technique was chose.2. A physiological model of SEMG signal was applied for quantitative analysis of the action potential conduction velocity estimation. This model could simulate the physiological generation process of SEMG signal effectively, and could provide the prior knowledge for the signal generation process, but the real SEMG signal could not. Using this model, the action potential conduction velocity of the different conditions could be analyzed in detail, such as the sampling rate, electrode location, electrode configuration and SEMG signal, and so on. The simulation analysis facilitated the performance evaluation, and the experimental results showed that the cross-correlation technique worked well for the action potential conduction velocity estimation.3. Because of the noise influence, in order to improve the signal quality, the SEMG signal was needed to do the preprocessing of noise reduction. After the processing by the band-pass filter, the algorithms of wavelet transform and spectrum interpolation were used to suppress the random noise and power line component. Then, the action potential conduction velocity of real SEMG signal was acquired by the time-delay estimation of cross-correlation technique, the experimental analysis under the different contraction levels were also accomplished. The results provided the quantitative description for the transmission process of action potentials in the real SEMG signal.