Order determination in autoregressive modeling of diastolic heart sounds

Previous studies demonstrated that spectral analysis of diastolic heart sounds may provide valuable information for the detection of coronary artery disease. Although parametric modeling methods were successfully used to achieve this goal, and showed considerable performance, the accuracy and precision of the analysis is strongly dependent on the model order selected. In order to investigate the effects of model order selection on the analysis, diastolic heart sound recorded from both normal and diseased patients were analyzed using the AR modeling, which is computationally the most efficient parametric spectral analysis method. The model order were determined by using four different model order selection criteria. The results showed that the four criteria yielded different order for the same data set. On the other hand, different criteria showed different performance in different measurement conditions. Effect of arbitrary order selection was also discussed. As a result, an optimal AR model order that may be used for every case was determined.