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