Continuous Venous Oxygen Saturation Estimation via Population-Informed Personalized Gaussian Sum Extended Kalman Filtering

Mixed venous oxygen saturation (SvO2) can play a pivotal role for patient monitoring and treatment in critical care and cardiopulmonary medicine. Unfortunately, its continuous measurement requires the use of invasive pulmonary artery catheters. This letter presents a novel population-informed personalized Gaussian sum extended Kalman filtering (PI-P-GSEKF) approach to continuous SvO2 estimation from arterial oxygen saturation (SpO2) measurement. The main challenge in SvO2 estimation is large inter-individual variability in the cardiopulmonary dynamics, which seriously deteriorates the efficacy of standard EKF. To cope with this challenge, we employ the GSEKF in which individual EKFs are designed using a mathematical model of cardiopulmonary dynamics whose operating points are selected from (i) population-level generative sampling (thus population-informed ) and (ii) Markov chain Monte Carlo (MCMC) sampling based on a one-time SpO2-SvO2 measurement (thus personalized ). Using the experimental data collected from 8 hypoxia trials in 4 large animals, we showed the ability of the PI-P-GSEKF to estimate SvO2 from SpO2 in comparison with its PIEKF (EKF with population-level generative sampling as the source of process noise) and PI-GSEKF (GSEKF with population-level generative sampling alone) counterparts (average SvO2 root-mean-squared error: PI-EKF 4.7%, PIGSEKF 4.3%, PI-P-GSEKF 3.0%). We also showed that population-level generative sampling and MCMC sampling both had respective roles in improving SvO2 estimation accuracy. In sum, the PI-P-GSEKF demonstrated its proof-of-principle to enable non-invasive continuous SvO2 estimation.