A New Approach to Time Dependent AR Modeling of Signals and its Application to Analysis of the Fourth Heart Sound

In this paper, we present a new method to estimate spectrum transition between short-length signals of the succeeding frames in low SNR cases. If the transition pattern is complex and/or there are large differences in the transition patterns among the individual sets of multiframe signals, it is difficult to estimate the transition pattern stably by the previously proposed time-varying AR modeling because the results are considerably dependent on the choice of the basic functions to be used. We propose a new approach of modeling to estimate the spectrum transition of the multiframe signals by using a linear algorithm without any basic functions. Instead of basic functions we use the spectrum transition constraint and the SVD-based technique is applied on the proposed method to obtain more accurate estimates. By applying this method to the analysis of multiframe signals of the fourth heart sounds obtained during the stress test, significant differences of the transition patterns are clearly detected in the spectra between patients with myocardial infarction and normal persons. The significant characteristics of these transition patterns may be applied to acoustic diagnosis of heart diseases.