Literature DB >> 2269733

Classification of audiograms by sequential testing using a dynamic Bayesian procedure.

O Ozdamar1, R E Eilers, E Miskiel, J Widen.   

Abstract

A new method for estimating audiograms using behavioral responses is presented. The method is based upon a modification of the Bayesian probability formula in which an outcome is predicted from a static set of events. In the new method, classification of audiograms by sequential testing (CAST), the probabilities of occurrence of audiogram patterns are dynamically updated according to the outcome of each test trial. Computer simulation using an infant response model suggests that the procedure is efficient, sensitive, and specific.

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Year:  1990        PMID: 2269733     DOI: 10.1121/1.400114

Source DB:  PubMed          Journal:  J Acoust Soc Am        ISSN: 0001-4966            Impact factor:   1.840


  3 in total

1.  Bayesian active probabilistic classification for psychometric field estimation.

Authors:  Xinyu D Song; Kiron A Sukesan; Dennis L Barbour
Journal:  Atten Percept Psychophys       Date:  2018-04       Impact factor: 2.199

2.  Fast, Continuous Audiogram Estimation Using Machine Learning.

Authors:  Xinyu D Song; Brittany M Wallace; Jacob R Gardner; Noah M Ledbetter; Kilian Q Weinberger; Dennis L Barbour
Journal:  Ear Hear       Date:  2015 Nov-Dec       Impact factor: 3.570

3.  Dynamics of infant cortical auditory evoked potentials (CAEPs) for tone and speech tokens.

Authors:  Barbara Cone; Richard Whitaker
Journal:  Int J Pediatr Otorhinolaryngol       Date:  2013-05-27       Impact factor: 1.675

  3 in total

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