Literature DB >> 8652865

Evaluation of a maximum likelihood procedure for measuring pure-tone thresholds under computer control.

C Formby1, L P Sherlock, D M Green.   

Abstract

An adaptive, maximum likelihood (ML) procedure was assessed as an automated tool for estimating audiometric pure-tone thresholds in the clinic under computer control. Pure-tone air-conduction thresholds were measured from 101 workmen who received annual hearing rechecks as part of their employee hearing conservation program. A pure-tone threshold was measured bilaterally for each of the standard audiometric frequencies in a 15-trial block to yield 60 percent correct detection with the ML procedure. The workmen were tested on a modified "yes-no" task. On a trial, the signal was presented in a visually cued 200-msec observation interval. Each workman then had 1000 msec to make a "yes" response. If the workman did not respond during the 1000-msec response period, then the computer assumed a "no" response. After either the "yes" or "no" response, the computer adjusted the signal level for the next trial. The thresholds measured by ML procedure compared favorably with thresholds measured from the same listeners by conventional (CONV) audiometry. The efficiency of the ML procedure was also compared in terms of the time necessary for an experienced audiologist to instruct the listener and perform CONV audiometry. CONV audiometry (3-4 minutes per listener) required about half of the time needed for the ML procedure (6-7 minutes per listener). The relatively longer time associated with measuring an audiogram with the ML procedure was due primarily to more trials being used to estimate threshold.

Mesh:

Year:  1996        PMID: 8652865

Source DB:  PubMed          Journal:  J Am Acad Audiol        ISSN: 1050-0545            Impact factor:   1.664


  3 in total

1.  Use of stimulus-frequency otoacoustic emissions to investigate efferent and cochlear contributions to temporal overshoot.

Authors:  Douglas H Keefe; Kim S Schairer; John C Ellison; Denis F Fitzpatrick; Walt Jesteadt
Journal:  J Acoust Soc Am       Date:  2009-03       Impact factor: 1.840

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.  The new age of play audiometry: prospective validation testing of an iPad-based play audiometer.

Authors:  Jeffrey Yeung; Hedyeh Javidnia; Sophie Heley; Yves Beauregard; Sandra Champagne; Matthew Bromwich
Journal:  J Otolaryngol Head Neck Surg       Date:  2013-03-11
  3 in total

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