The respiratory rate estimation for a patient based on photoplethysmography data

The respiratory rate (RR) is among the first vital signs to change in deteriorating patients, which is reported to be the best individual predictor of cardiac arrest in the general ward. The photoplethysmographic (PPG) signal is obtained by measuring the intensity of light penetrating through or reflected by the skin and its widespread application is the routine noninvasive monitoring of arterial oxygen saturation by pulse oximetry. However, it is generally accepted that it has potential to provide clinically useful information on the cardiovascular and respiratory system and its parametric waveform evaluation is successfully applied to detect sleep apnea. In this paper, we propose a novel algorithm for respiratory rate estimation form PPG signals, which is based on correntropy-based spectral density (CSD) calculation. This method provides an improved spectral resolution as compared to conventional techniques such as a power spectral density (PSD) and shows promise in the detection of modulated patterns of a PPG signal.