Instantaneous assessment of autonomic cardiovascular control during general anesthesia
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nihms351689.pdf
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Author(s)
Zhe Chen
Citi, L.
Purdon, P. L.
Barbieri, R.
Brown, Emery Neal
Date Issued
December 2011
Journal
2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Zhe Chen et al. “Instantaneous Assessment of Autonomic Cardiovascular Control During General Anesthesia.” 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society August 30 - September 3 2011, Boston, Massachusetts, USA © 2011 Institute of Electrical and Electronics Engineers (IEEE)
Version
Author's final manuscript
Abstract
We present a comprehensive probabilistic point process framework to estimate and monitor the instantaneous heartbeat dynamics as related to specific cardiovascular control mechanisms and hemodynamics. Assessment of the model's statistics is established through the Wiener-Volterra theory and a multivariate autoregressive (AR) structure. A variety of instantaneous cardiovascular metrics, such as heart rate (HR), heart rate variability (HRV), respiratory sinus arrhythmia (RSA), and baroreceptor-cardiac reflex (BRS), can be rigorously derived within a parametric framework and instantaneously updated with an adaptive algorithm. Instantaneous metrics of nonlinearity, such as the bispectrum of heartbeat intervals, can also be derived. We have applied the proposed point process framework to experimental recordings from healthy subjects in order to monitor cardiovascular regulation under propofol anesthesia. Results reveal interesting dynamic trends across different pharmacological interventions, confirming the ability of the algorithm to track important changes in cardiorespiratory elicited interactions, and pointing at our mathematical approach as a promising monitoring tool for an accurate, noninvasive assessment of general anesthesia.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/IEMBS.2011.6092083
