Bayesian analysis of trinomial data in behavioral experiments and its application to human studies of general anesthesia
Name
Brown_Bayesian analysis.pdf
Size
2.17 MB
Format
Adobe PDF
Checksum (MD5)
622f43466784efbf7b3e099ab4ec7bdf
Author(s)
Purdon, Patrick Lee
Brown, Emery N.
Wong, Kin Foon Kevin
Smith, Anne C.
Pierce, Eric T.
Harrell, P. Grace
Walsh, John L.
Salazar, Andres Felipe
Tavares, Casie L.
Cimenser, Aylin
Date Issued
August 2011
Journal
Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Wong, K. F. K., A. C. Smith, E. T. Pierce, P. G. Harrell, J. L. Walsh, A. F. Salazar, C. L. Tavares, et al. “Bayesian Analysis of Trinomial Data in Behavioral Experiments and Its Application to Human Studies of General Anesthesia.” 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (n.d.).
Version
Author's final manuscript
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.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/IEMBS.2011.6091165
