Arnold M, Miltner W H, Witte H, Bauer R, Braun C
Institute of Medical Statistics, Computer Sciences and Documentation, Friedrich Schiller University Jena, Germany.
IEEE Trans Biomed Eng. 1998 May;45(5):553-62. doi: 10.1109/10.668741.
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.
提出了一种自适应在线程序，用于通过卡尔曼滤波对非平稳多元时间序列进行自回归（AR）建模。估计的时变模型的参数可用于计算线性相关性的瞬时度量。在两个示例中讨论了该程序在生理信号分析中的实用性：第一，在呼吸运动、心率波动和血压分析中；第二，在多通道脑电图（EEG）信号分析中。首次表明，在完整动物中，从常氧状态转变为低氧状态需要自主心脏呼吸控制进行巨大的短期重新调整。一项使用实验性EEG数据的应用支持了以下观察结果：大脑细胞集合之间相关性的发展是联想学习或条件作用的基本要素。
