Methods to Estimate Respiratory Rate Using the Photoplethysmography Signal

Abstract

: Academia and industry have devoted significant effort to the research and development of smart wearable devices applied to health monitoring. The photoplethysmography (PPG) sensor is widely used for monitoring biosignals, such as heart and respiratory rate (RR), which are influenced by the cardiovascular system. This work focuses on analyzing methods for RR estimation regarding the effect of breathing on the PPG signal variation. This work describes, implements, and analyzes four methods for estimating RR. These methods are based on capturing RR using Fast Fourier Transform, median, and extracting physiological characteristics induced by respiration in the PPG signal. The most efficient method merges three RR calculations analyzed on the same signal, achieving nearly 93% of efficacy in the best scenario. The method efficacies were calculated using PPG signals from the BIDMC and CapnoBase databases collected from patients during hospital care. The analysis allows for understanding and mitigating the RR estimation challenges and evaluating the most efficacy method for a wearable device monitoring scenario.