Application of dynamic point process models to cardiovascular control.
The development of statistical models that accurately describe the stochastic structure of biological signals is a fast growing area in quantitative research. In developing a novel statistical paradigm based on Bayes' theorem applied to point processes, we are focusing our recent research on characterizing the physiological mechanisms involved in cardiovascular control. Results from a tilt table study point at our statistical framework as a valid model for the heart beat, as generated from complex mechanisms underlying cardiovascular control. The point process analysis provides new quantitative indices that could have important implications for research studies of cardiovascular and autonomic regulation and for monitoring of heart rate and heart rate variability measures in clinical settings.
Department of Anesthesia and Critical Care, Massachusetts General Hospital, Harvard Medical School, Jackson 4, 55 Fruit Street, Boston, MA 02114, USA. Barbieri@neurostat.mgh.harvard.edu
Riccardo
R
Barbieri
Emery N
EN
Brown
D002319:Cardiovascular System; D006339:Heart Rate; D006801:Humans; D008955:Models, Cardiovascular