Masking in Audiometry: A Clinical Evaluation of Three Methods

Sanders, Jay W.; Rintelmann, William F.

doi:10.1001/archotol.1964.00750040555008

Cited by 15 publications

(5 citation statements)

References 15 publications

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“…The results obtained in the study are in closer agreement with those of Hood et al (1968) and Kirikae (1959), than with those of Hurley and Berger (1970) shown in Table I. The view (Feldman, 1961;Sanders and Rintelmann, 1964;Glorig, 1965) that no transcranial attenuation exists below 1 kHz. was not supported in this study.…”

Section: Methods Of Statistical Treatmentsupporting

confidence: 91%

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Transcranial attenuation in bone conduction audiometry

Nolan

Lyon

1981

J. Laryngol. Otol.

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Transcranial attenuation for bone conduction as a function of frequency (0.25 to 4 kHz.) was measured on a group of unilaterally deaf (n 15) and a group of normal (n = 35) ears. The results indicate a positive mean attenuation for all frequencies with a peak value of 13 db. for both groups at 2 kHz. Inter-subject variability was extremely large. This is considered to be a result of variability in skull bone thickness.

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Section: Methods Of Statistical Treatmentsupporting

confidence: 91%

“…According to Feldman (1961), Sanders and Rintelmann (1964) and Glorig (1965), the intensity is equal in both cochleas, whichever ear is stimulated.…”

Section: Transcranial Attenuation In Bone Conduction Audiometrymentioning

confidence: 99%

Transcranial attenuation in bone conduction audiometry

Nolan

Lyon

1981

J. Laryngol. Otol.

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“…Clinically, masked AMLAG offers several potential benefits compared to manual HWAG. As this study showed, masked AMLAG provides an opportunity for the standardization of masking, a challenging procedure with multiple variations that are frequently implemented incorrectly (Sanders & Rintelmann 1964;Yacullo 2015;Gumus et al 2016;Hamil 2016;Valente 2009). Uniformity of clinical procedures is imperative in order to reduce interclinician variability and ensure that best practices are being achieved.…”

Section: Discussionmentioning

confidence: 95%

“…While only a subset of the general population, for them masking is an essential component of hearing assessment, aiding in differential diagnosis and hearing loss management decisions. Unfortunately, masking is a time-consuming process and is often cited as one of the most challenging procedures for audiologists to learn (Sanders & Rintelmann 1964;Ho et al 2009;Valente 2009;Yacullo 2015;Gumus et al 2016;Hamil 2016). No universally accepted masking standard or guideline exists.…”

Section: Introductionmentioning

confidence: 99%

Dynamically Masked Audiograms With Machine Learning Audiometry

et al. 2020

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Objectives: When one ear of an individual can hear significantly better than the other ear, evaluating the worse ear with loud probe tones may require delivering masking noise to the better ear in order to prevent the probe tones from inadvertently being heard by the better ear. Current masking protocols are confusing, laborious and time consuming. Adding a standardized masking protocol to an active machine learning audiogram procedure could potentially alleviate all of these drawbacks by dynamically adapting the masking as needed for each individual. The goal of this study is to determine the accuracy and efficiency of automated machine learning masking for obtaining true hearing thresholds.Design: Dynamically masked automated audiograms were collected for 29 participants between the ages of 21 and 83 (mean 43, SD 20) with a wide range of hearing abilities. Normal hearing listeners were given unmasked and masked machine learning audiogram tests. Listeners with hearing loss were given a standard audiogram test by an audiologist, with masking stimuli added as clinically determined, followed by a masked machine learning audiogram test. The hearing thresholds estimated for each pair of techniques were compared at standard audiogram frequencies (i.e., 0.25, 0.5, 1, 2, 4, 8 kHz).Results: Masked and unmasked machine learning audiogram threshold estimates matched each other well in normal hearing listeners, with a mean absolute difference between threshold estimates of 3.4 dB. Masked machine learning audiogram thresholds also matched well the thresholds determined by a conventional masking procedure, with a mean absolute difference between threshold estimates for listeners with low asymmetry and high asymmetry between the ears, respectively, of 4.9 dB and 2.6 dB. Notably, out of 6200 masked machine learning audiogram

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“…so called ‘far-advanced’ otosclerosis 17 . The difficulty encountered in assessing cochlear reserve in these patients depends upon a series of factors, the most important of which is probably represented by the problem of masking 18 . The limited output of the audiometers and the crossover of the masker noise can lead to an overestimation of bone-conduction level.…”

mentioning

confidence: 99%