Objective: Enhanced spatiotemporal ventricular repolarization variability has been associated with ventricular arrhythmias and sudden cardiac death, but the involved mechanisms remain elusive. In this paper, a methodology for estimation of parameters and state variables of stochastic human ventricular cell models from input voltage data is proposed for investigation of repolarization variability. Methods: The proposed methodology formulates state–space representations based on developed stochastic cell models and uses the unscented Kalman filter to perform joint parameter and state estimation. Evaluation over synthetic and experimental data is presented. Results: Results on synthetically generated data show the ability of the methodology to: first, filter out measurement noise from action potential (AP) traces; second, identify model parameters and state variables from each of those individual AP traces, thus allowing robust characterization of cell-to-cell variability; and, third, replicate statistical population's distributions of input AP-based markers, including dynamic markers quantifying beat-to-beat variability. Application onto experimental data demonstrates the ability of the methodology to match input AP traces while concomitantly inferring the characteristics of underlying stochastic cell models. Conclusion: A novel methodology is presented for estimation of parameters and hidden variables of stochastic cardiac computational models, with the advantage of providing a one-to-one match between each individual AP trace and a corresponding set of model characteristics. Significance: The proposed methodology can greatly help in the characterization of temporal (beat-to-beat) and spatial (cell-to-cell) variability in human ventricular repolarization and in ascertaining the corresponding underlying mechanisms, particularly in scenarios with limited available experimental data.

中文翻译：

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数据驱动的随机模型参数和状态变量的识别：在心跳搏动变异性研究中的应用

目的：时空性室性复极化的增强与室性心律失常和心源性猝死有关，但所涉及的机制仍然难以捉摸。本文提出了一种从输入电压数据估计随机人心室细胞模型的参数和状态变量的方法，以研究复极化的可变性。方法：所提出的方法基于已开发的随机细胞模型来制定状态-空间表示，并使用无味的卡尔曼滤波器执行联合参数和状态估计。提出了对合成和实验数据的评估。结果：综合生成的数据的结果表明该方法具有以下能力：首先，从动作电位（AP）迹线中滤除测量噪声；第二，从每个单独的AP迹线中识别模型参数和状态变量，从而可以可靠地表征细胞间的变异性；第三，复制基于AP的输入标记的统计种群分布，包括量化逐次变异性的动态标记。在实验数据上的应用证明了该方法能够匹配输入的AP迹线，并同时推断出基础随机细胞模型的特征。结论：提出了一种新颖的方法来估计随机心脏计算模型的参数和隐藏变量，其优点是在每个单独的AP迹线和相应的模型特征集之间提供一对一的匹配。意义：