Breathing Rate Estimation Methods From PPG Signals, on CAPNOBASE Database

Abstract

In the present work, a comparative study of different breathing rate estimation methods from PPG signal is proposed. The aim of this comparative study was to select the best algorithm, for respiratory rate estimation, among those already proposed in literature. The following methods were implemented and tested on the free access CAPNOBASE database, by segmenting the PPG signal in 32s and in 64s windows: empirical mode decomposition (EMD), EMD combined with principal component analysis, wavelets analysis, respiratory-induced intensity variation analysis (RIIV), respiratory-induced amplitude variation analysis (RIAV) and respiratory-induced frequency variation analysis (RIFV). Performances were then compared to six different methods already tested on CAP-NOBASE. The best performances were reached by using respiratory induced signals over the IMFs and wavelets. The RIAV signal exceeded other methods in both 64s and 32s signal segments. Only the algorithm proposed by Khreis et al, using Kalman filtering and a data fusion approach outperformed the presented methods for breathing rate estimation from PPG.