A Fast Respiratory Rate Estimation Method using Joint Sparse Signal Reconstruction based on Regularized Sparsity Adaptive Matching Pursuit.

Abstract

Many algorithms have been used to estimate respiratory rate (RR) from Photoplethysmography (PPG) recently. However, the accuracy and time consumption are still a challenging issue. In this paper, we propose a novel algorithm for RR estimation using Joint Sparse Signal Reconstruction (JSSR) based on Regularized Sparsity Adaptive Matching Pursuit (RSAMP) in a real-time fashion. The algorithm has been tested on Capnobase dataset and the results showed that the mean absolute error (MAE) and root mean squared error between estimates and references are 1.09 breaths per minute (bpm) and 2.44 bpm, respectively. And our method only costs 0.54 seconds for calculation.