Respiratory Rate Extraction Via an Autoregressive Model Using the Optimal Parameter Search Criterion

Abstract

We present an autoregressive model-based method which enables accurate respiratory rate extraction from pulse oximeter recordings over a wide range: 12-48 breaths/min. The method uses the optimal parameter search (OPS) technique to estimate accurate AR parameters which are then factorized into multiple pole terms. The pole with the highest magnitude is shown to correspond to the respiratory rate. The performance of the proposed method to extract respiratory rate is compared to the widely used Burg algorithm using both simulation examples and pulse oximeter recordings. In a previous study, we demonstrated several nonparametric time-frequency approaches that were more accurate than Burg's algorithm when the data length was 1min [Chon, K. H., S. Dash, and K. Ju. IEEE Trans. Biomed. Eng. 56(8):2054-2063, 2009]. One of the key advantages of the AR method is that a shorter data length can be used. Thus, in this study, we reduced the data length to 30s and applied our OPS algorithm to examine if accurate respiratory rates can be extracted directly from pulse oximeter recordings. It was found that our proposed method's accuracy was consistently better with smaller variance than Burg's method. In particular, our proposed method's accuracy was significantly greater when respiratory rates were lower than 24 breaths/min.