Linear and Nonlinear Quantification of Respiratory Sinus Arrhythmia during Propofol General Anesthesia

Neuroscience Statistics Research Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2009; 2009:5336-9. DOI: 10.1109/IEMBS.2009.5332693
Source: PubMed


Quantitative evaluation of respiratory sinus arrhythmia (RSA) may provide important information in clinical practice of anesthesia and postoperative care. In this paper, we apply a point process method to assess dynamic RSA during propofol general anesthesia. Specifically, an 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 respiratory measures. The estimated second-order bilinear interaction allows us to evaluate the nonlinear component of the RSA. The instantaneous RSA gain and phase can be estimated with an adaptive point process filter. The algorithm's ability to track non-stationary dynamics is demonstrated using one clinical recording. Our proposed statistical indices provide a valuable quantitative assessment of instantaneous cardiorespiratory control and heart rate variability (HRV) during general anesthesia.

Download full-text


Available from: Zhe Chen, Aug 21, 2014
  • Source
    • "To provide an exemplary application, we employ the proposed point process methods to analyze experimental recordings from healthy subjects during administration of propofol to induce controlled states of general anesthesia (Purdon et al., 2009). To this extent, as reviewed in this article, our recent investigations have reported promising results in monitoring cardiovascular regulation under induction of anesthesia (Chen et al., 2009b, 2010b, 2011a). "
    [Show abstract] [Hide abstract]
    ABSTRACT: In recent years, time-varying inhomogeneous point process models have been introduced for assessment of instantaneous heartbeat dynamics as well as 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 (baroreflex) sensitivity (BRS), are derived within a parametric framework and instantaneously updated with adaptive and local maximum likelihood estimation algorithms. Inclusion of second-order non-linearities, with subsequent bispectral quantification in the frequency domain, further allows for definition of instantaneous metrics of non-linearity. We here present a comprehensive review of the devised methods as applied to experimental recordings from healthy subjects during propofol anesthesia. Collective results reveal interesting dynamic trends across the different pharmacological interventions operated within each anesthesia session, 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, non-invasive assessment in clinical practice. We also discuss the limitations and other alternative modeling strategies of our point process approach.
    Full-text · Article · Feb 2012 · Frontiers in Physiology
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Human heartbeat intervals are known to have nonlinear and nonstationary dynamics. In this paper, we propose a model of R-R interval dynamics based on a nonlinear Volterra-Wiener expansion within a point process framework. Inclusion of second-order nonlinearities into the heartbeat model allows us to estimate instantaneous heart rate (HR) and heart rate variability (HRV) indexes, as well as the dynamic bispectrum characterizing higher order statistics of the nonstationary non-gaussian time series. The proposed point process probability heartbeat interval model was tested with synthetic simulations and two experimental heartbeat interval datasets. Results show that our model is useful in characterizing and tracking the inherent nonlinearity of heartbeat dynamics. As a feature, the fine temporal resolution allows us to compute instantaneous nonlinearity indexes, thus sidestepping the uneven spacing problem. In comparison to other nonlinear modeling approaches, the point process probability model is useful in revealing nonlinear heartbeat dynamics at a fine timescale and with only short duration recordings.
    Full-text · Article · Feb 2010 · IEEE transactions on bio-medical engineering
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Anesthesia is a vital and important part of any surgical practice and allows doctors to operate safely and painlessly on a patients. The wide variety of available anesthetics allows anesthesiologists to select the most suitable type of anesthesia and anesthetic drug for a patient. Providing balanced anesthesia by testing the depth of anesthesia (DOA) is a way to know sudden awareness or increasing level of anesthesia during a surgery. In this paper, we present several methods to analyze the results of our experiments performed on 33 patients under coronary vessel surgery. In the first method, by applying the wavelet transform on EEG signal, a new index (namely WAI) is obtained, which shows the conscious level of the patient. In the second method, 10 features related to EEG signal during 10 second windows, such as, edge frequency, and beta ratio, are extracted and used by neural and neuro-fuzzy networks as inputs. Then, the value of DOA is calculated for each of the used algorithms. The correlation value of these methods, which is a criterion of the accuracy, is shown by the BIS monitor output. Simulation results show that the highest amount of correlation is achieved using neural networks with respect to BIS index.
    Full-text · Article · Oct 2010
Show more