Auto-regression-driven, reallocative particle filtering approaches in PPG-based respiration rate estimation

Interest towards respiratory state assessment with non-obtrusive instrumentation has led to the design of novel algorithmic solutions. Notably, respiratory behavior has been observed to cause modulative changes in two discreetly measurable physiological signals, PPG and ECG. The potential to integrate respiratory rate measurements in widely used instrumentation with no additional cost has made the research of suitable signal processing methods attractive. We have studied and compared auto-regressive (AR) model order optimization and coefficient extraction methods combined with a reallocative particle filtering approach for respiration rate estimation from finger PPG signal. The evaluated coefficient extraction methods were Yule-Walker, Burg, and Least-square. Considered model order optimization methods were Akaike’s information criteria (AIC) and Minimum description length. Methods were evaluated with a publicly available dataset comprised of approximately 10-minute measurements from 39 healthy subjects at rest. From the evaluated AR model parameter extraction methods, Burg's method combined AIC performed the best. We obtained the mean absolute error of 2.7 and bias of -0.4 respirations per minute with this combination.