EMBC'11 Paper Abstract

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Paper FrA13.1

Wong, Kin Foon Kevin (Massachusetts General Hospital), Smith, Anne (University of California Davis), Pierce, Eric (MGH / Harvard Medical School), Harrell, Priscilla Grace (MGH-Harvard Medical School), Walsh, John (Massachusetts General Hospital), Salazar, Andres F. (Massachussets General Hospital), Tavares, Casie (Massachusetts General Hospital), Cimenser, Aylin (Massachusetts General Hospital), Prerau, Michael (Massachusetts General Hospital), Mukamel, Eran (University of California, San Diego), Sampson, Aaron (Massachusetts General Hospital), Purdon, Patrick L (Massachussetts General Hospital), Emery N, MGH-Harvard Medical School-MIT ()

Bayesian Analysis of Trinomial Data in Behavioral Experiments and Its Application to Human Studies of General Anesthesia

Scheduled for presentation during the Invited Session "Computational Approaches for Studying Neural Mechanisms in Anesthesia and Sleep" (FrA13), Friday, September 2, 2011, 08:00−08:15, Arlington Marriott

33rd Annual International IEEE EMBS Conference, August 30 - September 3, 2011, Boston Marriott Copley Place, Boston, MA, USA

This information is tentative and subject to change. Compiled on April 20, 2014

Keywords Algorithms and techniques for systems modelling, Parameter estimation, Advances in theory of biological networks

Abstract

Accurate quantification of loss of response to external stimuli is essential for understanding the mechanisms of loss of consciousness under general anesthesia. We present a new approach for quantifying three possible outcomes that are encountered in behavioral experiments during general anesthesia: correct responses, incorrect responses and no response. We use a state-space model with two state variables representing a probability of response and a conditional probability of correct response. We show applications of this approach to an example of responses to auditory stimuli at varying levels of propofol anesthesia ranging from light sedation to deep anesthesia in human subjects. The posterior probability densities of model parameters and the response probability are computed within a Bayesian framework using Markov Chain Monte Carlo methods.

 

 

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