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

Neuroscience Statistics Research Lab, Massachusetts General Hospital, Harvard Medical School / Harvard-MIT Division of Health Science and Technology, Boston, MA, USA.
Acoustics, Speech, and Signal Processing, 1988. ICASSP-88., 1988 International Conference on (Impact Factor: 4.63). 05/2009; 2009:333-336. DOI: 10.1109/ICASSP.2009.4959588
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ABSTRACT 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 RR→BP 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.

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    ABSTRACT: In this article, we present a point process method to assess dynamic baroreflex sensitivity (BRS) by estimating the baroreflex gain as focal component of a simplified closed-loop model of the cardiovascular system. Specifically, an inverse Gaussian probability distribution is used to model the heartbeat interval, whereas the instantaneous mean is identified by linear and bilinear bivariate regressions on both the previous R-R intervals (RR) and blood pressure (BP) beat-to-beat measures. The instantaneous baroreflex gain is estimated as the feedback branch of the loop with a point-process filter, while the RR-->BP feedforward transfer function representing heart contractility and vasculature effects is simultaneously estimated by a recursive least-squares filter. These two closed-loop gains provide a direct assessment of baroreflex control of heart rate (HR). In addition, the dynamic coherence, cross bispectrum, and their power ratio can also be estimated. All statistical indices provide a valuable quantitative assessment of the interaction between heartbeat dynamics and hemodynamics. To illustrate the application, we have applied the proposed point process model to experimental recordings from 11 healthy subjects in order to monitor cardiovascular regulation under propofol anesthesia. We present quantitative results during transient periods, as well as statistical analyses on steady-state epochs before and after propofol administration. Our findings validate the ability of the algorithm to provide a reliable and fast-tracking assessment of BRS, and show a clear overall reduction in baroreflex gain from the baseline period to the start of propofol anesthesia, confirming that instantaneous evaluation of arterial baroreflex control of HR may yield important implications in clinical practice, particularly during anesthesia and in postoperative care.
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