Tarvainen M P, Georgiadis S, Lipponen J A, Hakkarainen M, Karjalainen P A
Department of Physics, University of Kuopio, Kuopio, Finland.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:1-4. doi: 10.1109/IEMBS.2009.5332678.
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.
提出了一种用于分析心率变异性（HRV）信号动态变化的时变参数谱估计方法。在该方法中，首先用时变自回归模型对HRV信号进行建模，然后使用卡尔曼平滑算法递归求解模型参数。接着从估计得到的模型参数中获得时变谱估计。所得到的谱可以进一步分解为单独的成分，这在HRV应用中特别有利，因为在HRV应用中通常旨在区分低频（LF）和高频（HF）成分。作为案例研究，分析了在1）直立倾斜试验、2）运动试验和3）模拟驾驶任务期间记录的HRV信号的动态变化。
Zotero 插件