发表机构
Stanford University; Massachusetts General Hospital（斯坦福大学; 麻省总医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于伽马广义线性模型的状态空间心跳动力学模型，利用凸优化高效求解状态估计，能捕捉心率瞬变并量化器官间耦合，为心血管健康评估提供新工具。
AI 中文摘要
目的：本工作提出了一种心跳动力学的动态状态空间统计模型，其最优状态估计可以通过凸优化高效求解，并用于评估自主神经和心血管健康。方法：该状态空间模型将高斯-马尔可夫过程先验与使用对数链接函数的伽马发射相结合。参数使用模型的静态版本进行估计，并采用交替方向乘子法高效地找到最优的最大后验潜在状态估计。模型选择通过使用偏自相关函数在模型规模和模型拟合之间寻找权衡来完成。结果：我们在合成生成的心跳中证明，该模型能够紧密恢复潜在的潜在权重。然后，我们使用倾斜台数据集表明，该统计模型能够比传统的局部平均方法更好地捕捉心率模式中的急剧瞬变。此外，将心跳时间与来自其他器官（如呼吸相位）的信息相结合，我们展示了我们的统计建模框架能够提取信息论度量，以量化器官间耦合，如呼吸性窦性心律失常。结论：这种用于建模心跳动力学的统计框架提供了一种高效框架，能够提供生理学上有意义的见解。意义：本工作拓宽了可用于评估心跳动力学的潜在工具。
英文摘要
Objective: This work proposes a dynamic state-space statistical model of heartbeat dynamics whose optimal state estimate can be solved efficiently with convex optimization and used to assess autonomic and cardiovascular health. Methods: This state-space model combines a Gauss-Markov process prior with gamma emissions using a log-link function. Parameters are estimated using a static version of the model, and alternating direction method of multipliers is used to efficiently find the optimal maximum a priori latent state estimate. Model selection is performed by finding the tradeoff between model size and model fit using the partial autocorrelation function. Results: We demonstrate in synthetically generated heartbeats that this model is able to closely recover the underlying latent weights. Then, we show using a tilt table dataset that this statistical model is able to better capture sharp transients in heart rate patterns than traditional local averaging methods. In addition, combining heart beat timings with information from other organs such as the respiratory phase, we show that our statistical modeling framework enables the extraction of information-theoretic measures to quantify inter-organ coupling such as respiratory sinus arrythmia. Conclusion: This statistical framework for modeling heartbeat dynamics provides an efficient framework that can provide physiologically meaningful insights. Significance: This work broadens potential tools that can be used to assess heartbeat dynamics.
Comments16 pages, 5 figures
