Análise de métodos para inferência da taxa de respiração utilizando o sinal de fotopletismografia

Abstract

Academia and industry have dedicated significant effort to the research and development of smart wearable devices applied to health monitoring. These efforts are primarily influenced by rising healthcare costs and are supported by nanotechnology advances. Regarding the wearable device scenario, the photoplethysmography (PPG) sensor is widely used for monitoring biosignals, such as heart and breathing rates, which are directly or indirectly influenced by the cardiovascular system. This work focuses on analyzing methods for estimating the respiratory rate, considering the effect of breathing on the PPG signal variation. We describe, implement, and examine six methods for estimating respiratory rate. These methods are based on capturing the breathing rate using Fast Fourier Transform, Empirical Mode Decomposition, as well as extracting physiological characteristics induced by breathing in the PPG signal, analyzing the modulated breathing in amplitude, frequency, and intensity variations. The efficacies of the methods were calculated using PPG signals available in synthetic databases, constructed by mathematical equations, and real ones collected from patient monitoring during hospital care. The analysis allows us to understand and mitigate the challenges underlying the process of estimating respiratory rate by PPG and evaluate the best method for a wearable device monitoring scenario.