Article

# 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

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**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.Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2011; 2011:8444-7. - [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.01/2010; - [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.Frontiers in Physiology 01/2012; 3:4.

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