Breathing rate estimation based on multiple linear regression

Abstract

The breathing rate is a key clinical parameter that can now be estimated using photoplethysmographic methods. Here, we present an indirect method of breathing rate estimation that does not require bulky and uncomfortable sensors. Breathing modulates a pulsed wave; we extracted the maximum and minimum values, and first-order derivatives thereof, to measure breathing amplitude, frequency, and baseline drift. Demodulation was used to obtain multiple breathing waveforms, from which peak values are extracted to obtain breathing rates. Multiple linear regression was used to combine the breathing rates of different feature points. We used a breathing dataset for 53 subjects, and divided the data into training and test sets when calculating the regression coefficients. We also assessed the generalizability of our linear model. We found that breathing rate estimation was more accurate when using a multivariate signal method with multiple versus a single feature point. The mean absolute error, mean error, and standard deviation of the error were 1.28, −0.07, and 1.60 breaths per minute, respectively.