PPG derived respiratory rate estimation in daily living conditions

Respiratory rate (RR) derived from photoplethysmogram (PPG) during daily activities can be corrupted due to movement and other artefacts. We have investigated the use of ensemble empirical mode decomposition (EEMD) based smart fusion approach for improving the RR extraction from PPG. PPG was recorded while subjects performed five different activities: sitting, standing, climbing and descending stairs, walking, and running. RR was obtained using EEMD and smart fusion. The median absolute error (AE) of the proposed method is superior, median AE = 3.05 (range 3.01 to 3.18) breath/min in estimating RR during five different activities. Therefore, the proposed method can be implemented for overcoming the artefact problems when recording continuous RR monitoring during activities of daily living.