Assessment of Baroreflex Control of Heart Rate During General Anesthesia Point Process Method

Evaluation of baroreflex control of heart rate (HR) has important implications in clinical practice of anesthesia and postoperative care. In this paper, we present a point process method to assess the dynamic baroreflex gain using a closed-loop model of the cardiovascular system. Specifically, the i...

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Bibliographic Details
Main Authors: Pierce, Eric T., Harrell, P. Grace, Chen, Zhe, Purdon, Patrick Lee, Brown, Emery N., Barbieri, Riccardo
Other Authors: Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Format: Article
Language:en_US
Published: Institute of Electrical and Electronics Engineers 2010
Subjects:
Online Access:http://hdl.handle.net/1721.1/58953
https://orcid.org/0000-0001-5651-5060
https://orcid.org/0000-0003-2668-7819
https://orcid.org/0000-0002-6166-448X
Description
Summary:Evaluation of baroreflex control of heart rate (HR) has important implications in clinical practice of anesthesia and postoperative care. In this paper, we present a point process method to assess the dynamic baroreflex gain using a closed-loop model of the cardiovascular system. Specifically, the inverse Gaussian probability distribution is used to model the heartbeat interval, whereas the instantaneous mean is identified by a linear or bilinear bivariate regression on the previous R-R intervals and blood pressure (BP) measures. The instantaneous baroreflex gain is estimated in the feedback loop with a point process filter, while the RRrarrBP feedforward frequency response is estimated by a Kalman filter. In addition, the instantaneous cross-spectrum and cross-bispectrum (as well as their ratio) can also be estimated. All statistical indices provide a valuable quantitative assessment of the interaction between heartbeat dynamics and hemodynamics during general anesthesia.Zhe Chen et al. “Assessment of baroreflex control of heart rate during general anesthesia using a point process method.” Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on. 2009. 333-336. Web.