Mainardi L T, Bianchi A M, Baselli G, Cerutti S
Department of Biomedical Engineering, Polytechnic University, Milano, Italy.
IEEE Trans Biomed Eng. 1995 Mar;42(3):250-9. doi: 10.1109/10.364511.
Various algorithms of autoregressive (AR) recursive identification make it possible to evaluate power spectral distribution in correspondence with each sample of a time series, and time-variant spectral parameters can be calculated through the evaluation of the pole positions in the complex z-plane. In traditional analysis, the poles are obtained by zeroing the denominator of the model transfer function, expressed as a function of the AR coefficients. In this paper, two algorithms for the direct updating and tracking of movements of poles of an AR time-variant model on the basis of the innovation given to the coefficients are presented and investigated. The introduced algorithms are based upon 1) the classical linearization method and 2) a recursive method to compute the roots of a polynomial, respectively. In the present paper, applications in the field of heart rate variability (HRV) signal analysis are presented and efficient tools are proposed for quantitative extraction of spectral parameters (power and frequency of the low-frequency (LF) and high-frequency (HF) components) for the monitoring of the action of the autonomic nervous system in transient patho-physiological events. These computational methods seem to be very attractive for HRV applications, as they inherit the peculiarity of recursive time-variant identification, and provide a more immediate comprehension of the spectral process characteristics when expressed in terms of poles and AR spectral components.
各种自回归（AR）递归识别算法使得能够根据时间序列的每个样本评估功率谱分布，并且可以通过评估复z平面中的极点位置来计算时变谱参数。在传统分析中，通过将模型传递函数的分母设为零来获得极点，该模型传递函数表示为AR系数的函数。本文提出并研究了两种基于系数创新的直接更新和跟踪AR时变模型极点运动的算法。所引入的算法分别基于1）经典线性化方法和2）计算多项式根的递归方法。在本文中，介绍了心率变异性（HRV）信号分析领域的应用，并提出了有效的工具，用于定量提取频谱参数（低频（LF）和高频（HF）分量的功率和频率），以监测自主神经系统在瞬态病理生理事件中的作用。这些计算方法对于HRV应用似乎非常有吸引力，因为它们继承了递归时变识别的特性，并且当用极点和AR频谱分量表示时，能更直接地理解频谱过程特征。
