Nonlinear Analysis of Heart Rate Variability

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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.