Nonlinear Analysis of Heart Rate Variability

描述（由申请人提供）：心率变异性（HRV）作为心脏自主神经功能障碍的简单无创标志物越来越受到关注，最近的临床试验表明，HRV降低是充血性心力衰竭（CHF）患者预后不良的有力预测因素。尽管有这样的临床兴趣，HRV的机制仍然知之甚少。此外，基于普通统计或非线性动力学技术的常规HRV分析受到这些技术在区分HRV的确定性分量与模糊潜在机制的伴随随机分量方面的一般困难的阻碍。为了克服这些障碍，BCST项目将建立在非线性系统理论的最新突破之上，这些理论允许对HRV进行可靠的分析。该项目的目标1将开发基于先进的非线性系统识别方法的分析技术，这些方法允许在面对伴随的测量噪声和生理噪声分量时对HRV的混沌分量进行可靠的定量评估。这些使能技术将以综合计算工具包的形式提供给生物医学界，供一般研究应用。该项目的目标2将应用这些分析技术，对来自一项已建立的临床试验（UK-HEART）的大量非卧床CHF患者（N > 500）的心率混乱降低进行彻底的重新评估，并对心率混乱降低作为这些患者死亡和死亡模式预测因子的临床评价进行为期6年的随访。目标1-2的结果将为心脏自主调节的计算模型的开发（目标3）提供强有力的分析和实验基础，该计算模型解释了健康和CHF中HRV的不同确定性和随机性成分。这些临床和建模研究反过来将展示所提出的分析技术，从而为其应用于其他复杂的生物医学建模和分析问题铺平了道路。

DESCRIPTION (provided by applicant): Heart rate variability (HRV) has received increasing attention as a simple noninvasive marker of cardiac-autonomic dysfunction, and recent clinical trials have shown that decreased HRV is a strong predictor of poor prognosis in patients with congestive heart failure (CHF). Despite such clinical interests, the mechanism of HRV remains poorly understood. Moreover, conventional HRV analysis based on ordinary statistical or nonlinear dynamics techniques are hampered by a general difficulty of these techniques in differentiating the deterministic components of HRV from the attendant stochastic components that obscure the underlying mechanism. To overcome these hurdles, this BCST project will build upon recent breakthoughs in nonlinear systems theory that allow reliable analysis of HRV. Aim 1 of the project will develop analytical techniques based on advanced nonlinear systems identification approaches that allow reliable quantitative assessment of the chaotic components of HRV in the face of attendant measurement noise and physiologic noise components. These enabling technologies will be made available to the biomedical community in the form of a comprehensive computational tool kit for general research applications. Aim 2 of the project will apply these analytical techniques to a thorough reappraisal of the decreased heart-rate chaos in a large population (N > 500) of ambulatory CHF patients from an established clinical trial (UK-HEART) and to the clinical evaluation of decreased heart-rate chaos as a predictor of death and mode of death in these patients followed over a six-year period. The results from Aims 1-2 will provide strong analytical and experimental bases for the development (in Aim 3) of a computational model of cardiac-autonomic regulation that accounts for the varying deterministic and stochastic components of HRV in health and in CHF. These clinical and modeling studies in turn will showcase the proposed analytical techniques thereby paving the way for their application to other complex biomedical modeling and analysis problems in general.