Adaptive AR modeling of nonstationary time series by means of Kalman filtering
Adaptive AR modeling of nonstationary time series by means of Kalman filtering
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DOI：
10.1109/10.668741
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发表时间：
1998-05-01
影响因子：
4.6
通讯作者：
Braun, C
中科院分区：
文献类型：
--
作者：
Arnold, M;Miltner, WHR;Braun, C
提出了一种利用卡尔曼滤波对非平稳多变量时间序列进行自回归(AR)建模的自适应在线方法。估计的时变模型的参数可以用来计算线性相关性的瞬时度量。通过两个例子讨论了这些过程在生理信号分析中的作用：第一，在呼吸运动、心率波动和血压的分析中；第二，在多通道脑电(EEG)信号的分析中。首次表明，在完整的动物中，从常氧状态到低氧状态的转变需要对自主神经的心脏和呼吸控制进行巨大的短期重新调整。一个实验脑电数据的应用支持了这样的观察，即大脑细胞集合之间的一致性的发展是联想学习或条件反射的基本要素。
An adaptive on-line procedure is presented for autoregressive (AR) modeling of nonstationary multivariate time series by means of Kalman filtering. The parameters of the estimated time-varying model can be used to calculate instantaneous measures of linear dependence. The usefulness of the procedures in the analysis of physiological signals is discussed in two examples: First, in the analysis of respiratory movement, heart rate fluctuation, and blood pressure, and second, in the analysis of multichannel electroencephalogram (EEG) signals. It was shown for the first time that in intact animals the transition from a normoxic to a hypoxic state requires tremendous short-term readjustment of the autonomic cardiac-respiratory control. An application with experimental EEG data supported observations that the development of coherences among cell assemblies of the brain is a basic element of associative learning or conditioning.