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Assistant Professor of Anesthesia, Harvard Medical School

Research Areas

  • Cardiovascular and Respiratory Physiology and Modeling
  • Physiological Monitoring in Critical Care Medicine
  • Quantitative Assessment of the Autonomic Nervous System
  • Quantitative Assessment of Autonomic Pain
  • Computational Neuroscience

Description of Research

I am a biomedical engineer, expert in signal processing algorithms for analysis and modeling of biological systems. My research interests include: mathematical modeling of biological systems, with emphasis on the cardiovascular system, the central nervous system and the autonomic nervous system; biomedical signal processing, with emphasis on cardiovascular control and cardiovascular variability signals; and signal processing in neurosciences (EEG, Evoked Potentials, single cell neuronal recordings).

Current research focuses on developing and testing novel signal processing algorithms based on point process theory, systems analysis, and state-space estimation in order to provide a more accurate noninvasive assessment and characterization of human cardiovascular control in a broad range of pathophysiological settings. Cardiovascular recordings are available through important collaborations at local, national and international level. Data include tilt protocol studies, recordings under autonomic blockade, meditation studies, as well as sleep studies. Of late, we have applied the point process paradigm to data from subjects under nauseogenic stimuli and/or acupuncture, and then correlated them with simultaneous fMRI recordings. In yet another study, we have meliorated cardiorespiratory assessments by combining several physiological variables in order to predict apnea episodes in premature infants. Further important studies are being pursued for the assessment of depth of anesthesia using heart rate variability and galvanic skin response, of fetal heartbeat, and for automatic quantitative evaluation of human emotional states. All these applications rely on continuing support from a multidisciplinary team of collaborators and consultants who provide additional expertise in engineering, statistics, cardiology, anesthesiology, and psychology. By evolving the proposed techniques towards the practitioner’s needs, and possibly leading to improved patient outcomes by both pathological and well-being assessment, this research is expected to have a broad impact in clinical and translational medicine.


Barbieri R, Matten EC, Alabi AA, Brown EN. A point process model of human heart rate intervals: new definitions of heart rate and heart rate variability. Am J Physiol Heart Circ Physiol. 2005 Jan;288(1):H424-35. Epub 2004 Sep 16. PubMed PMID:15374824. This work was the first to show that heart beats can be characterized using point process models that lead to new definitions of heart rate and heart rate variability.

Napadow V, Dhond R, Conti G, Makris N, Brown EN, Barbieri R. Brain correlates of autonomic modulation: combining heart rate variability with fMRI. Neuroimage. 2008 Aug 1;42(1):169-77. Epub 2008 Apr 30. PubMed PMID:18524629; PubMed Central PMCID: PMC2603289. This work was the first to show that instantaneous point process estimates of autonomic tone could be correlated with fMRI Imaging to provide brain maps of activation of the central autonomic network.

Chen Z, Brown E, Barbieri R. Assessment of Autonomic Control and Respiratory Sinus Arrhythmia Using Point Process Models of Human Heart Beat Dynamics. IEEE Trans Biomed Eng. 2009 Jul;56(7):1791-802. Epub 2009 Mar 4. PubMed PMID:19272971. PubMed Central PMCID:PMC2804879. This work demonstrates that an extended point process model with respiration as covariate can provide an instantaneous quantitative assessment of the autonomic-mediated influence of respiration on heart rate variability.

Chen Z, Brown E, Barbieri R. Characterizing Nonlinear Heartbeat Dynamics within a Point Process Framework. IEEE Trans Biomed Eng. 2010 Jun;57(6):1335-47. Epub 2010 Feb 17. PubMed PMID:20172783. PubMed Central PMCID:PMC2952361. This manuscript first demonstrate that an extended point process model including second-order nonlinear terms can provide an instantaneous quantitative assessment of heart beat nonlinear dynamics.

Chen Z, Purdon PL, Pierce ET, Harrell PG, Walsh J, Salazar AF, Tavares CL, Brown EN, Barbieri R. Dynamic Assessment of Baroreflex Control of Heart Rate During Induction of Propofol Anesthesia Using a Point Process Method. Ann Biomed Eng. 2011 Jan;39(1):260-76. Epub 2010 Oct 13.PubMed PMID: 20945159. PubMed Central PMCID: PMC3010293. PubMed PMID:20945159. PubMed Central PMCID:PMC3010293. This work established an extended point process model with arterial blood pressure as covariate, providing an instantaneous quantitative assessment of the autonomic-mediated influence of the baroreceptor reflex on heart rate variability.