Literature DB >> 29860544

Development of Improved Software Intelligent System for Audiological Solutions.

S Rajkumar1, S Muttan2, V Sapthagirivasan3,4, V Jaya5, S S Vignesh5.   

Abstract

Of late, there has been an increase in hearing impairment cases and to provide the most advantageous solutions to them is an uphill task for audiologists. Significant difficulty faced by the audiologists is in effective programming of hearing aids to provide enhanced satisfaction to the users. The main aim of our study was to develop a software intelligent system (SIS): (i) to perform the required audiological investigations for finding the degree and type of hearing loss, and (ii) to suggest appropriate values of hearing aid parameters for enhancing the speech intelligibility and the satisfaction level among the hearing aid users. In this paper, we present a Neuro-Fuzzy based SIS to automatically predict and suggest the hearing-aid parameters such as gain values, compression ratio and threshold knee point, which are needed to be fixed for different octave frequencies of sound inputs during the hearing-aid trial. The test signals for audiological investigations are generated through the standard hardware present in a personal computer system and with the aid of a software algorithm. The proposed system was validated with 243 subjects' data collected at the Government General Hospital, Chennai, India. The calculated sensitivity, specificity and accuracy of the proposed audiometer incorporated in the SIS were 98.6%, 96.4 and 98.2%, respectively, by comparing its interpretations with those of the 'gold standard' audiometers. Furthermore, 91% (221 of 243) of the hearing impaired subjects attained satisfaction in the first hearing aid trials itself with the gain values as recommended by the improved SIS. The proposed system reduced around 75% of the 'trial and error' time spent by audiologists for enhancing satisfactory usage of the hearing aid. Hence, the proposed SIS could be used to find the degree and type of hearing loss and to recommend hearing aid parameters to provide optimal solutions to the hearing aid users.

Entities:  

Keywords:  Artificial intelligence; Compression ratio; Computerized audiometer; Digital in medical; Expert system; Hearing aid; Hearing loss; Neuro-Fuzzy; Prescriptive procedure; Software intelligent system; Sound pressure level; Speech discrimination score; Threshold knee point

Mesh:

Year:  2018        PMID: 29860544     DOI: 10.1007/s10916-018-0978-6

Source DB:  PubMed          Journal:  J Med Syst        ISSN: 0148-5598            Impact factor:   4.460


  22 in total

1.  Threshold measurements by self-fitting hearing aids: feasibility and challenges.

Authors:  Gitte Keidser; Harvey Dillon; Dan Zhou; Lyndal Carter
Journal:  Trends Amplif       Date:  2012-03-07

2.  Hearing screening for school children: comparison of low-cost, computer-based and conventional audiometry.

Authors:  B McPherson; M M S Law; M S M Wong
Journal:  Child Care Health Dev       Date:  2010-05       Impact factor: 2.508

3.  Effect of slow-acting wide dynamic range compression on measures of intelligibility and ratings of speech quality in simulated-loss listeners.

Authors:  Peninah S Rosengard; Karen L Payton; Louis D Braida
Journal:  J Speech Lang Hear Res       Date:  2005-06       Impact factor: 2.297

4.  The effect of compression ratio and release time on the categorical rating of sound quality.

Authors:  A C Neuman; M H Bakke; C Mackersie; S Hellman; H Levitt
Journal:  J Acoust Soc Am       Date:  1998-05       Impact factor: 1.840

5.  Uses and abuses of hearing loss classification.

Authors:  J G Clark
Journal:  ASHA       Date:  1981-07

6.  Evaluation of the Self-Fitting Process with a Commercially Available Hearing Aid.

Authors:  Elizabeth Convery; Gitte Keidser; Mark Seeto; Margot McLelland
Journal:  J Am Acad Audiol       Date:  2017-02       Impact factor: 1.664

7.  A comparison of gain for adults from generic hearing aid prescriptive methods: impacts on predicted loudness, frequency bandwidth, and speech intelligibility.

Authors:  Earl E Johnson; Harvey Dillon
Journal:  J Am Acad Audiol       Date:  2011 Jul-Aug       Impact factor: 1.664

Review 8.  NAL-NL2 empirical adjustments.

Authors:  Gitte Keidser; Harvey Dillon; Lyndal Carter; Anna O'Brien
Journal:  Trends Amplif       Date:  2012-11-30

9.  Identification of conductive hearing loss using air conduction tests alone: reliability and validity of an automatic test battery.

Authors:  Elizabeth Convery; Gitte Keidser; Mark Seeto; Katrina Freeston; Dan Zhou; Harvey Dillon
Journal:  Ear Hear       Date:  2014 Jan-Feb       Impact factor: 3.570

10.  The effects of hearing impairment and aging on spatial processing.

Authors:  Helen Glyde; Sharon Cameron; Harvey Dillon; Louise Hickson; Mark Seeto
Journal:  Ear Hear       Date:  2013 Jan-Feb       Impact factor: 3.570

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  1 in total

1.  EDDAMAP: efficient data-dependent approach for monitoring asymptomatic patient.

Authors:  Daniel Adu-Gyamfi; Fengli Zhang; Albert Kofi Kwansah Ansah
Journal:  BMC Med Inform Decis Mak       Date:  2020-09-29       Impact factor: 2.796

  1 in total

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