Nonlinear Approach to Characterization of Temporomandibular Joint Vibrations
A new approach is proposed to characterize and discriminate temporomandi-bular joint vibrations. It consists of three steps. First, signals recorded during each cycle of mandibular movement are unified into a single time series. Second, this time series is embedded in some multidimensional space. Third, nonlinear analysis methods are applied to extract the pertinent signal characteristics. In this way two groups of signals have been characterized; those in the first group were recorded from patients whose post-treatment results werebad and the ones in the second group were recorded from patients whose post-treatment results were good. But patients in both groups had the same clinical features before treatment. It was shown that the two groups can be discriminated from each other by one parameter of the signals recorded from patients comprising the groups, the coefficient of nonlinear forecasting. It was also found that signals of the bad prognosis group share certain nonlinear characteristics although the patients comprising the group may have different pathologies.
Nonlinear Approach to Characterization of Temporomandibular Joint Vibrations
A new approach is proposed to characterize and discriminate temporomandi-bular joint vibrations. It consists of three steps. First, signals recorded during each cycle of mandibular movement are unified into a single time series. Second, this time series is embedded in some multidimensional space. Third, nonlinear analysis methods are applied to extract the pertinent signal characteristics. In this way two groups of signals have been characterized; those in the first group were recorded from patients whose post-treatment results werebad and the ones in the second group were recorded from patients whose post-treatment results were good. But patients in both groups had the same clinical features before treatment. It was shown that the two groups can be discriminated from each other by one parameter of the signals recorded from patients comprising the groups, the coefficient of nonlinear forecasting. It was also found that signals of the bad prognosis group share certain nonlinear characteristics although the patients comprising the group may have different pathologies.
