Point process time-frequency analysis of respiratory sinus arrhythmia under altered respiration dynamics.
Point process time-frequency analysis of respiratory sinus arrhythmia under altered respiration dynamics.
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DOI：
10.1109/iembs.2010.5626648
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Barbieri R
中科院分区：
文献类型：
--
作者：
Kodituwakku S;Lazar SW;Indic P;Brown EN;Barbieri R
呼吸性窦性心律失常 (RSA) 主要由自主神经系统通过其对心跳的调节影响来介导。我们提出了一种量化瞬时 RSA 的算法，应用于动态呼吸条件下的心跳间隔和呼吸记录。血容量压力衍生的心跳序列（脉搏间隔，PI）被建模为逆高斯点过程，瞬时平均 PI 被建模为二元回归，结合了过去的 PI 和心跳时观察到的呼吸值。采用点过程最大似然算法估计模型参数，并采用频域传递函数方法估计瞬时RSA。该模型使用 Kolmogorov-Smirnov (KS) 拟合优度分析以及独立性检验进行统计验证。该算法应用于从事冥想练习的受试者，从而产生独特的呼吸模式动态。实验结果证实了该算法能够跟踪冥想期间引起的心肺相互作用的重要变化，否则在控制静息状态下不会出现这种变化。
Respiratory sinus arrhythmia (RSA) is largely mediated by the autonomic nervous system through its modulating influence on the heartbeat. We propose an algorithm for quantifying instantaneous RSA as applied to heart beat interval and respiratory recordings under dynamic respiration conditions. The blood volume pressure derived heart beat series (pulse intervals, PI) are modeled as an inverse Gaussian point process, with the instantaneous mean PI modeled as a bivariate regression incorporating both past PI and respiration values observed at the beats. A point process maximum likelihood algorithm is used to estimate the model parameters, and instantaneous RSA is estimated by a frequency domain transfer function approach. The model is statistically validated using Kolmogorov-Smirnov (KS) goodness-of-fit analysis, as well as independence tests. The algorithm is applied to subjects engaged in meditative practice, with distinctive dynamics in the respiration patterns elicited as a result. Experimental results confirm the ability of the algorithm to track important changes in cardiorespiratory interactions elicited during meditation, otherwise not evidenced in control resting states.