Point Process Models of Human Heart Beat Interval Dynamics

人体心跳间隔动力学的点过程模型

• 批准号: 7568161
• 负责人: RICCARDO BARBIERI
• 金额: $ 39.38万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-02-01 至 2012-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7568161
• 关键词: Address; Agreement; Algorithms; Anesthesia procedures; Area; Autonomic nervous system; Biological; Brain; Cardiovascular Diseases; Cardiovascular system; Circadian Rhythms; Clinical Research; Computer software; Congestive Heart Failure; Data; Data Set; Detection; Diagnosis; Electrocardiogram; Event; Fetus; Frequencies; Functional Magnetic Resonance Imaging; Functional disorder; Generations; Health; Heart; Heart Rate; Hospitals; Human; Intensive Care Units; Measurement; Measures; Meditation; Methods; Modeling; Monitor; Operative Surgical Procedures; Patients; Physical Examination; Physiological; Physiology; Process; Regulation; Research; Research Personnel; Series; Simulate; Sleep; Statistical Methods; Structure; Testing; Time; Treatment Efficacy; Woman; base; clinical practice; design; heart rate variability; research study; theories; web site

DESCRIPTION (provided by applicant): Heart rate is a moment-to-moment indicator of cardiovascular integrity measured on every physical examination. Heart rate is also monitored continuously in patients under anesthesia, during surgery, in those treated in an intensive care unit and in fetuses during labor. Heart rate variability is an important quantitative marker of cardiovascular regulation by the autonomic nervous system that is widely used in research studies, as well as in clinical practice to diagnose both cardiovascular and non-cardiovascular diseases to track its progression and to assess the efficacy of therapies. The measurement and interpretation of heart rate and heart rate variability depend critically on how these quantities are computed from the time-series of R-wave events on the electrocardiogram. While the design of algorithms to compute heart rate and to assess heart rate variability is an active area of research, none of the current approaches considers the natural point process structure of human heart beats, together with the physiology underlying the generation of the discrete, biological events. To address these issues, the first four specific aims of this project are to test the hypotheses that: 1) Human heart beats can be accurately characterized by using a statistical framework based on point process models of the R-R intervals and that this framework can be used to establish new definitions of heart rate and heart rate variability. 2) We can develop local maximum likelihood and point process adaptive filtering algorithms to track in real-time heart rate and heart rate variability and goodness- of-fit methods based on the theory of point processes can be used to assess the agreement between human heart beat series and model estimates of these series derived from the algorithms. 3) The algorithms developed in Specific Aim 2 can be used to construct time domain and frequency domain measures of heart rate variability and to detect and correct, ectopic, erroneous and missed beats in heart beat series. 4) The analysis paradigm developed in Specific Aims 1 to 3 can be used to characterize cardiovascular and autonomic function in tilt-table and autonomic blockade assessments of the cardiovascular system, pathophysiology assessment in congestive heart failure, functional magnetic resonance imaging studies of the brain during meditation, and studies of circadian and sleep physiology. Specific Aim 5 is to provide on our website software to implement the statistical methods developed to address Specific Aims 1 to 4. This will facilitate the research of investigators wishing to characterize heart rate and heart rate variability as part of their physiological studies. The broad, long-term objectives of the project are to provide researchers with a coherent statistical paradigm to characterize cardiovascular control through analysis of heart beat interval dynamics. The health implications of this project are a more accurate characterization of cardiovascular control in research and clinical studies of both normal and pathological conditions.

描述(由申请者提供)：心率是每次体检时测量的心血管完整性的时刻指标。麻醉下的病人、手术中的病人、在重症监护病房接受治疗的病人以及分娩期间的胎儿的心率也被持续监测。心率变异性是自主神经系统调节心血管功能的一个重要的定量指标，在研究和临床实践中被广泛用于诊断心血管和非心血管疾病、跟踪其进展和评估治疗的有效性。心率和心率变异性的测量和解释关键取决于如何从心电图上的R波事件的时间序列计算这些量。虽然计算心率和评估心率变异性的算法设计是一个活跃的研究领域，但目前的方法都没有考虑到人类心跳的自然点过程结构，以及产生离散的生物事件的生理学基础。为了解决这些问题，本项目的前四个具体目标是测试以下假设：1)通过使用基于R-R间期的点过程模型的统计框架可以准确地表征人类心率，并且该框架可以用于建立心率和心率变异性的新定义。2)我们可以开发局部最大似然和点过程自适应滤波算法来实时跟踪心率和心率变异性，基于点过程理论的拟合优度方法可以用来评估人体心跳序列与由算法得到的这些序列的模型估计之间的一致性。3)本文提出的算法可用于构建心率变异性的时域和频域度量，并可用于检测和纠正心跳序列中的异位、错跳和漏搏。4)在具体目标1至3中开发的分析范式可用于表征心血管系统的倾斜和自主神经阻断评估、充血性心力衰竭的病理生理学评估、冥想期间大脑的功能磁共振成像研究以及昼夜节律和睡眠生理学的研究。具体目标5是在我们的网站上提供软件，以实施为解决具体目标1至4而开发的统计方法。这将促进希望将心率和心率变异性作为其生理研究的一部分的研究人员的研究。该项目的广泛和长期目标是为研究人员提供一个连贯的统计范式，通过分析心跳间期动力学来表征心血管控制。该项目的健康影响是在研究和临床研究中更准确地描述心血管控制的正常和病理情况。