MATHEMATICAL MODELS OF COMPLEX CARDIAC ARRHYTHMIAS

复杂心律失常的数学模型

• 批准号: 7366527
• 负责人: Leon M. GLASS
• 金额: $ 0.81万
• 依托单位: BETH ISRAEL DEACONESS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-03-01 至 2007-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7366527
• 关键词: MATHEMATICAL; MODELS; COMPLEX; CARDIAC; ARRHYTHMIAS

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.

该子项目是利用NIH/NCRR资助的中心赠款提供的资源的许多研究子项目之一。子项目和研究者（PI）可能从另一个NIH来源获得了主要资金，因此可以在其他CRISP条目中表示。所列机构为中心，不一定是研究者所在机构。患有心律失常的个体，其中异常心跳与正常心跳穿插，可能处于心源性猝死的高风险中。这项工作的重点是分析和建模心电图数据收集超过24小时的时间内，在患者频繁的心室异位。我们将异常心跳的统计特性可视化在“心纹”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，是在一个固定的时间间隔后产生的前一个正常的心跳，或由一个独立的振荡器，可能会或可能不会与正常的心跳相互作用。虽然，我们已经发现，这是很难再现所有的统计特征的记录使用这些简单的模型，在一种情况下，有密切的对应关系的模型和广泛的统计特征的数据。我们正在分析经历过心脏性猝死的患者的霍尔特记录，以描述心脏骤停前活动的统计模式。这项工作提请注意心脏的复杂节律，并介绍了可用于对其进行分类的方法。