To gain insights into cardiac arrhythmias, researchers have developed and employed various measurement techniques, such as electrocardiography, optical mapping, and patch clamping. However, there are no measurement methods that allow simultaneous recording of all cellular quantities, including intracellular ionic concentrations and gating states, that may play an important role in arrhythmia formation. To help address this shortcoming, we applied observability analysis, a method from control theory, to the Luo Rudy dynamic (LRd) model of a cardiac ventricular myocyte. After linearizing the time-integrated LRd model about selected periodic orbits, we computed the observability properties of the model to determine whether past system states could be reconstructed from different hypothetical sets of measurements. Under the simplifying assumption that only one dynamical variable could be measured periodically, we found that intracellular potassium concentration generally yielded the largest observability values and thus contained the most information about the dominant modes of the system. The impacts on observability of measurement timings, inter-stimulus interval length, and an alternans-promoting parameter shift were also studied. Pole-placement state observer algorithms were designed and tested in simulations for several scenarios, and we found that it is possible to infer unmeasured variables from potassium-concentration measurements, and to an extent from membrane-potential measurements, both for longer periods that represent normal rhythms and shorter periods associated with tachyarrhythmias. Our results could lead to improved data assimilation algorithms that combine model predictions with measurements to estimate quantities that are difficult or impossible to measure during in vitro experiments.

为了深入了解心律不齐，研究人员开发并采用了多种测量技术，例如心电图，光学标测和膜片钳。但是，没有可以同时记录可能在心律不齐形成中起重要作用的所有细胞数量（包括细胞内离子浓度和门控状态）的测量方法。为了帮助解决此缺点，我们将可观察性分析（一种来自控制理论的方法）应用于心脏心室肌细胞的Lud Rudy动态（LRd）模型。在对选定周期轨道的时间积分LRd模型进行线性化之后，我们计算了模型的可观察性，以确定是否可以从不同的假设测量集中重建过去的系统状态。在简化的假设下，只有一个动态变量可以定期测量，我们发现细胞内钾浓度通常产生最大的可观察性值，因此包含有关系统主要模式的最多信息。还研究了对测量时间的可观察性，刺激间的时间间隔长度和交替促进参数漂移的影响。在几种情况下模拟设计并测试了杆位状态观测器算法，我们发现可以从钾浓度测量值和膜电位测量值中推断出未测量的变量，这两者都代表了较长的正常时间。与快速性心律失常相关的节律和较短的时期。体外实验。