A Remote Respiration Rate Measurement Method for Non-Stationary Subjects Using CEEMDAN and Machine Learning

Respiratory Rate (RR) monitoring can inform healthcare providers of early indicators of critical illnesses. However, the obtrusive nature of contact-based sensors for RR monitoring makes them uncomfortable for extended use and vulnerable to movement-derived noise. Hence, camera-based approaches have attracted considerable attention as they enable contact-free RR monitoring. This paper presents an improved non-contact method for RR monitoring that leverages camera derived remote photoplethysmography (rPPG) to measure RR. Unlike previous work, the proposed method supports subject movement during monitoring. We apply Independent Component Analysis (ICA) on the RGB channels of facial videos to distinguish the source (i.e. PPG signal) from noise. We use the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) scheme to decompose the selected ICA output into its Intrinsic Mode Functions (IMFs). We propose a Machine Learning (ML) algorithm to select the IMF that best reflects the RR. We evaluated the proposed method on 200 facial videos collected from 10 subjects. Our approach decreased the RMSE by at least 39.6% compared to state-of-the-art techniques when subjects were stationary. For subjects in movement, we achieved an RMSE of 2.30 BPM (breaths/min). The proposed method can facilitate non-contact continuous measurement of RR for various clinical and home-based healthcare solutions including the monitoring of infants in neonatal intensive care, elderly individuals in senior care centers, patients in hospital emergency waiting rooms, and prisoners on suicide watch.