Time-varying spectrum estimation of heart rate variability signals with Kalman smoother algorithm

A time-varying parametric spectrum estimation method for analyzing dynamics of heart rate variability (HRV) signals is presented. In the method, HRV signal is first modeled with a time-varying autoregressive model and the model parameters are solved recursively with a Kalman smoother algorithm. Time-varying spectrum estimates are then obtained from the estimated model parameters. The obtained spectrum can be further decomposed into separate components, which is especially advantageous in HRV applications where low frequency (LF) and high frequency (HF) components are generally aimed to be distinguished. As case studies, the dynamics of HRV signals recorded during 1) orthostatic test, 2) exercise test and 3) simulated driving task are analyzed.