Citi Luca, Valenza Gaetano, Purdon Patrick L, Brown Emery N, Barbieri Riccardo
Neuroscience Statistics Research Laboratory, Harvard Medical School, Massachusetts General Hospital, Boston, MA 02114, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:3124-7. doi: 10.1109/EMBC.2012.6346626.
We present a novel methodology for instantaneous estimation of quantitative correlates of depth of Anesthesia from noninvasive electrocardiographic recordings. The analysis is based on a point process model of heartbeat dynamics that allows for continuous tracking of linear and nonlinear HRV indices, including a novel instantaneous assessment of the Lyapunov Spectrum by using a cubic autoregressive formulation. The effective estimation of the model parameters is ensured by the Laguerre expansion of the Wiener-Volterra kernels along with the maximum local log-likelihood procedure. We apply the proposed assessment to experimental recordings from healthy subjects during propofol anesthesia. The new assessment reveals novel time-varying complex heartbeat dynamics that underlie the quasi-periodic heartbeat fluctuations elicited by the sympatho-vagal balance. Results suggest that such quantification provides important information which is independent from the standard autonomic assessment and significantly correlated with loss of consciousness. Further investigation will focus on evolving our mathematical approach towards a promising monitoring tool for an accurate, noninvasive assessment of general anesthesia.
我们提出了一种新颖的方法，可从无创心电图记录中即时估计麻醉深度的定量相关指标。该分析基于心跳动力学的点过程模型，该模型允许连续跟踪线性和非线性心率变异性（HRV）指标，包括通过使用三次自回归公式对李雅普诺夫谱进行新颖的即时评估。通过维纳 - 沃尔泰拉核的拉盖尔展开以及最大局部对数似然程序，确保了模型参数的有效估计。我们将所提出的评估方法应用于健康受试者在丙泊酚麻醉期间的实验记录。新的评估揭示了由交感 - 迷走神经平衡引起的准周期性心跳波动背后的新颖时变复杂心跳动力学。结果表明，这种量化提供了重要信息，该信息独立于标准自主神经评估，并且与意识丧失显著相关。进一步的研究将集中于发展我们的数学方法，以形成一种有前景的监测工具，用于准确、无创地评估全身麻醉。
