A Mother Wavelet Selection Algorithm for Respiratory Rate Estimation from Photoplethysmogram

In recent decades, a new attempt at estimating respiratory rate (RR) from Photoplethysmogram (PPG) becomes an active area. It can be implemented by different algorithms among of which wavelet based methods are commonly used with good performances achieved. However, the study on the reason why different mother wavelets have different performances on RR estimation as well as how a suitable wavelet can be easily selected is insufficient. In this paper, a mother wavelet selection algorithm is proposed for RR estimation from PPG signal. Six popular mother wavelets, namely db10, db12, sym8, bior6.8, rbio6.8 and coif5, are compared in terms of the sum of decomposition coefficient magnitudes and the one with maximum value is chosen for RR information extraction and reconstruction. In the experiments, the proposed algorithm is compared with the related six mother wavelets working separately. Two evaluation tools, root mean squared normalized error (RMSNE) and Bland & Altman plot, are adopted. The evaluation results demonstrate the better performance of the proposed algorithm. In addition, the above finding reveals that the mother wavelet with a larger sum of coefficient magnitudes has a better performance on RR estimation from PPG signal which can be used as wavelet selection criteria in this area.