Tarvainen Mika P, Georgiadis Stefanos D, Ranta-Aho Perttu O, Karjalainen Pasi A
Department of Applied Physics, University of Kuopio, Finland.
Physiol Meas. 2006 Mar;27(3):225-39. doi: 10.1088/0967-3334/27/3/002. Epub 2006 Jan 13.
A time-varying parametric spectrum estimation method for analysing non-stationary heart rate variability signals is presented. As a case study, the dynamics of heart rate variability during an orthostatic test is examined. In this method, the non-stationary signal is first modelled with a time-varying autoregressive model and the model parameters are estimated recursively with a Kalman smoother algorithm. The benefit of using the Kalman smoother is that the lag error present in a Kalman filter, as well as in all other adaptive filters, can be avoided. The spectrum estimates for each time instant 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. By using the presented method, high resolution time-varying spectrum estimates with no lag error can be produced. Other benefits of the method are the straightforward procedure for evaluating the statistics of the spectrum estimates and the option of spectral decomposition.
提出了一种用于分析非平稳心率变异性信号的时变参数谱估计方法。作为一个案例研究，对直立倾斜试验期间心率变异性的动态进行了研究。在该方法中，首先用时变自回归模型对非平稳信号进行建模，并使用卡尔曼平滑算法递归估计模型参数。使用卡尔曼平滑器的好处是可以避免卡尔曼滤波器以及所有其他自适应滤波器中存在的滞后误差。然后从估计的模型参数中获得每个时刻的谱估计。利用误差传播原理推导所获得谱估计的统计量。所获得的谱估计可以进一步分解为单独的分量，从而可以分别检查心率变异性低频和高频分量的时间变化。通过使用所提出的方法，可以产生无滞后误差的高分辨率时变谱估计。该方法的其他优点是评估谱估计统计量的过程简单，以及具有谱分解的选项。
