Chen Zhe, Purdon Patrick L, Brown Emery N, Barbieri Riccardo
Neuroscience Statistics Research Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:3567-70. doi: 10.1109/IEMBS.2010.5627462.
Modeling heartbeat variability remains a challenging signal-processing goal in the presence of highly non-stationary cardiovascular control dynamics. We propose a novel differential autoregressive modeling approach within a point process probability framework for analyzing R-R interval and blood pressure variations. We apply the proposed model to both synthetic and experimental heartbeat intervals observed in time-varying conditions. The model is found to be extremely effective in tracking non-stationary heartbeat dynamics, as evidenced by the excellent goodness-of-fit performance. Results further demonstrate the ability of the method to appropriately quantify the non-stationary evolution of baroreflex sensitivity in changing physiological and pharmacological conditions.
在存在高度非平稳心血管控制动力学的情况下，对心跳变异性进行建模仍然是一个具有挑战性的信号处理目标。我们提出了一种在点过程概率框架内的新型差分自回归建模方法，用于分析R-R间期和血压变化。我们将所提出的模型应用于在时变条件下观察到的合成和实验心跳间期。该模型在跟踪非平稳心跳动力学方面被发现极其有效，拟合优度性能优异证明了这一点。结果进一步证明了该方法在变化的生理和药理条件下适当量化压力反射敏感性非平稳演变的能力。
