Optimal Adaptive Filter Design of M-wave Elimination for Treating Tooth Grinding

When tooth grinding occurs, electrical stimulation is given at the same time, and tooth grinding stops on such stimulation. Electromyography signals are used as control signals of electrical stimulation to disturb tooth grinding. However because of the electrical stimulation, the M-waves are generated and mixed with spontaneous electromyogram. In this study, we designed an optimal filter to remove M-wave and conserve spontaneous electromyogram simultaneously. The inverse power method (IPM) showed that the optimal filter coefficient is the eigenvector corresponding to the minimum eigenvalue of the input covariance matrix. In order to evaluate the performance of the optimal filter, we compared using a conventional band pass filter and adaptive filter using least mean square algorithm. The experimental results show that the optimal filter can effectively remove the M-wave compared to the previously studied prediction error filter it in practice because of cost and difficulties in the examination method. We can define the EMG signal generated at the beginning of ejaculation as a voluntary EMG signal. There is a signals. It is shown that the optimization process with two constraints is the same as that of a typical eigenfilter design. Finally, the optimal filter has an eigenvector corresponding to the minimum eigenvalue of the input covariance matrix as a coefficient. We also propose a method for adaptively implementing the proposed optimal filter using inverse power method (IPM). And we verify the optimization of the proposed method through experimental process using simulation data.