Model-based sensor fusion of multimodal cardiorespiratory signals using an unscented Kalman filter

Abstract

Based on a model of three coupled oscillators describing the influence of respiration, namely respiratory sinus arrhythmia (RSA), and so-called Mayer waves on the heart rate, an unscented Kalman filter (UKF) is designed to perform sensor fusion of multimodal cardiorespiratory sensor signals. The aim is to implicitly use redundancy between the sensor signals to improve the estimated heart rate while utilizing model knowledge. The effectiveness of the approach is shown by estimations of heart rate variability on synthesized data as well as multimodal patient data, which provide different numbers of sensor channels.