MATHEMATICAL MODELS OF COMPLEX CARDIAC ARRHYTHMIAS

这个子项目是利用由NIH/NCRR资助的中心拨款提供的资源的许多研究子项目之一。子项目和调查员(PI)可能从另一个NIH来源获得了主要资金，因此可能会出现在其他CRISE条目中。列出的机构是针对中心的，而不一定是针对调查员的机构。心律不齐的个体，异常的心跳和正常的心跳穿插在一起，可能是心脏性猝死的高危人群。这项工作的重点是分析和建模在24小时内收集的频发室异位患者的心电数据。我们将异常心跳的统计特性可视化在“心印”中，显示每个患者的特征模式，并可能提供有关潜在机制的信息。这些模式应该通过适当的非线性模型的模拟来再现。我们正在研究的模型中，异常心跳是随机产生的，在前一个正常心跳之后的固定时间间隔产生，或者由一个独立的振荡器产生，该振荡器可能与正常心跳相互作用，也可能不与正常心跳相互作用。虽然我们发现，使用这些简单的模型很难再现记录的所有统计特征，但在一个案例中，该模型与数据的广泛统计特征之间存在密切的对应关系。我们正在分析经历过心源性猝死的患者的Holter记录，以确定心搏骤停前活动的统计模式。这项工作引起了人们对心脏复杂节律的关注，并介绍了可以用来对它们进行分类的方法。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Individuals with arrhythmia in which abnormal heartbeats are interspersed with normal heartbeats may be at high risk for sudden cardiac death. This work is focused towards analyzing and modeling electrocardiographic data collected over a 24 hour period in patients with frequent ventricular ectopy. We visualize the statistical properties of the abnormal heartbeats in 'heart-prints' that show characteristic patterns for each patient and may give information about the underlying mechanisms. These patterns should be reproduced by a simulation of an appropriate nonlinear model. We are investigating models in which abnormal heartbeats are generated randomly, are generated at a fixed time interval following a preceding normal heartbeat, or are generated by an independent oscillator that may or may not interact with the normal heartbeat. Although, we have found that it is difficult to reproduce all the statistical features of the records using these simple models, in one case there is close correspondence between the model and a wide range of statistical features of the data. We are analyzing Holter recordings of patients who have experienced sudden cardiac death in order to characterize the statistical patterns of activity prior to the arrest. This work draws attention to the complex rhythms of the heart, and introduces methods that can be used to classify them.