Time domain BRS estimation: Least squares versus quantile regression

The BRS can be quantified as the slope between SBP and RR values identified in baroreflex events, estimated by ordinary least squares (OLS) minimization. Quantile regression (QR) is a more robust procedure than OLS and allows a more complete characterization of the data, by estimating conditional functions for different quantiles of interest. In this work, OLS and QR for BRS estimation are compared regarding slope estimates and dispersion. The EuroBaVar results indicate that OLS slope and QR slopes at different quantiles do not exhibit significant differences. Also, OLS and QR slopes require similar number of beats to achieve a given BRS precision in stationary recordings. Finally, BRS estimated with OLS exhibit relative dispersion lower than 10% and 5% when computed from stationary recordings of approximately 3 and 9 minutes length, respectively.