• Conf Proc IEEE Eng Med Biol Soc · Jan 2011

    Bayesian analysis of trinomial data in behavioral experiments and its application to human studies of general anesthesia.

    • Kin Foon Kevin Wong, Anne C Smith, Eric T Pierce, P Grace Harrell, John L Walsh, Andrés Felipe Salazar, Casie L Tavares, Aylin Cimenser, Michael J Prerau, Eran A Mukamel, Aaron Sampson, Patrick L Purdon, and Emery N Brown.
    • Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA. kfwong@partners.org
    • Conf Proc IEEE Eng Med Biol Soc. 2011 Jan 1; 2011: 4705-8.

    AbstractAccurate 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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