Time-Varying Analysis of Heart Rate Variability with Kalman Smoother Algorithm

A time-varying parametric spectrum estimation method for analyzing nonstationary heart rate variability signals is presented. In the method, the nonstationary signal is first modeled with time-varying autoregressive model and the model parameters are estimated recursively with a Kalman smoother algorithm. The spectrum estimates for each time are then obtained from the estimated model parameters. Statistics of the obtained spectrum estimates are derived using the error propagation principle. The obtained spectrum estimates can further be decomposed into separate components and, thus, the time-variation of low and high frequency components of heart rate variability can be examined separately