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.
|Citi, Luca; Ba, Demba; Brown, Emery N et al. (2014) Likelihood methods for point processes with refractoriness. Neural Comput 26:237-63|
|Indic, Premananda; Paydarfar, David; Barbieri, Riccardo (2013) Point process modeling of interbreath interval: a new approach for the assessment of instability of breathing in neonates. IEEE Trans Biomed Eng 60:2858-66|
|Valenza, Gaetano; Citi, Luca; Barbieri, Riccardo (2013) Instantaneous nonlinear assessment of complex cardiovascular dynamics by Laguerre-Volterra point process models. Conf Proc IEEE Eng Med Biol Soc 2013:6131-4|
|Valenza, Gaetano; Citi, Luca; LanatÃ , Antonio et al. (2013) A nonlinear heartbeat dynamics model approach for personalized emotion recognition. Conf Proc IEEE Eng Med Biol Soc 2013:2579-82|
|Napadow, Vitaly; Lee, Jeungchan; Kim, Jieun et al. (2013) Brain correlates of phasic autonomic response to acupuncture stimulation: an event-related fMRI study. Hum Brain Mapp 34:2592-606|
|Citi, Luca; Valenza, Gaetano; Barbieri, Riccardo (2012) Instantaneous estimation of high-order nonlinear heartbeat dynamics by Lyapunov exponents. Conf Proc IEEE Eng Med Biol Soc 2012:13-6|
|Orini, Michele; Citi, Luca; Barbieri, Riccardo (2012) Bivariate point process modeling and joint non-stationary analysis of pulse transit time and heart period. Conf Proc IEEE Eng Med Biol Soc 2012:2831-4|
|Citi, Luca; Valenza, Gaetano; Purdon, Patrick L et al. (2012) Monitoring heartbeat nonlinear dynamics during general anesthesia by using the instantaneous dominant Lyapunov exponent. Conf Proc IEEE Eng Med Biol Soc 2012:3124-7|
|Kodituwakku, Sandun; Lazar, Sara W; Indic, Premananda et al. (2012) Point process time-frequency analysis of dynamic respiratory patterns during meditation practice. Med Biol Eng Comput 50:261-75|
|Citi, Luca; Brown, Emery N; Barbieri, Riccardo (2012) A real-time automated point-process method for the detection and correction of erroneous and ectopic heartbeats. IEEE Trans Biomed Eng 59:2828-37|
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