Work

A differential autoregressive modeling approach within a point process framework for non-stationary heartbeat intervals analysis

Year: 2010

Type: conference-paper

Abstract: Modeling heartbeat variability remains a challenging signal-processing goal in the presence of highly non-stationary cardiovascular control dynamics. We propose a novel differential autoregressive modeling approach within a point process probability framework for analyzing R-R interval and blood pressure variations. We apply the proposed model to both synthetic and experimental heartbeat intervals observed in time-varying conditions. The model is found to be extremely effective in tracking non-stationary heartbeat dynamics, as evidenced by the excellent goodness-of-fit performance. Results further demonstrate the ability of the method to appropriately quantify the non-stationary evolution of baroreflex sensitivity in changing physiological and pharmacological conditions.

Source: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

DOI: 10.1109/iembs.2010.5627462

Authors: Zhe Chen, P L Purdon, E N Brown, R Barbieri

Institutions: Harvard University, Massachusetts General Hospital, Harvard–MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology

Language: English

Topic: Heart Rate Variability and Autonomic Control

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green