Ambient noise impact on accuracy of automated hearing assessment

Storey, K.; Muñoz, Karen F.; Nelson, Lauri H.; Larsen, Jeffery B.; White, Karl R.

doi:10.3109/14992027.2014.920110

Cited by 37 publications

(73 citation statements)

References 12 publications

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“…This shows that for approximately 95% of participants in this study the additional variation introduced was within an additional 5 dB absolute difference (±2.5 dB relative difference) of ISO standards. It is also comparable to previous clinical validation studies of the KUDUwave which showed 91% accuracy (Swanepoel et al, 2010) and 92% accuracy (Storey et al, 2014) of obtaining hearing thresholds within a 10 dB absolute difference in sound-treated and 40 dBA multi-talker background noise environments.…”

Section: Accuracysupporting

confidence: 74%

“…Whilst the additional 5 dB variation above the ISO standard may be considered low once the confounding factors are accounted for, the accuracy of automated audiometry in this study is lower than previous studies which have fallen within the ISO standard threshold variation limits Margolis et al, 2011;Storey et al, 2014;Swanepoel et al, 2010). Use of the same KUDUwave audiometer in manual-and automated mode, as in (Swanepoel et al, 2010), eliminates the calibration differences that were introduced in the present study, and could easily account for some of the increased differences in hearing thresholds.…”

Section: Accuracycontrasting

confidence: 48%

“…Participants in this study had their automated audiometry thresholds tested in a room that was not sound-treated, whilst the manual audiometry testing was performed in a sound-treated environment, potentially introducing further accuracy variation from previous studies (Maclennan-Smith et al, 2013;Storey et al, 2014). The placement of the bone-conductor also differed, with mastoid placement used for the reference test and forehead placement used for the index test and ambient noise levels for the reference test also exceeded the recommended ISO 8253-1:2010 standard.…”

Section: Accuracymentioning

confidence: 99%

“…The device used in this study (KUDUwave 5000) has previously been validated in an environment that is not sound-treated using its manual-mode (Maclennan-Smith et al, 2013), and in a controlled noise environment in automated-mode (Storey et al, 2014). The present study therefore aims to address a gap in the evidence-base by combining automated testing in an uncontrolled environment that is not sound-treated, using an unselected clinical population of patients attending otolaryngology and audiology appointments at a tertiary public hospital.…”

Section: Introductionmentioning

confidence: 99%

“…The exclusion of patients with known conductive hearing impairments (e.g. Storey et al, 2014) is also a source of potential bias. These patients represent a significant part of the clinical population and it is just as important to have accuracy estimates in such cases as for patients with sensorineural hearing losses.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Clinical validation of automated audiometry with continuous noise-monitoring in a clinically heterogeneous population outside a sound-treated environment

Brennan‐Jones

Eikelboom

Swanepoel

et al. 2016

International Journal of Audiology

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ABSTRACT:Objective: Examine the accuracy of automated audiometry in a clinically heterogeneous population of adults using the KUDUwave automated audiometer.Design: Prospective accuracy study. Manual audiometry was performed in a sound-treated room and automated audiometry was not conducted in a sound-treated environment.Study Sample: 42 consecutively recruited participants from a tertiary otolaryngology department in Western Australia.Results: Absolute mean differences ranged between 5.12 -9.68 dB (air-conduction) and 8.26 -15.00 dB (bone-conduction). 86.5% of manual and automated 4FAs were within 10 dB (i.e. ±5 dB); 94.8% were within 15 dB. However, there were significant (p<0.05) differences between automated and manual audiometry at 0.25, 0.5, 1 and 2 kHz (air-conduction) and 0.5 and 1 kHz (bone-conduction). The effect of age (≥55 years) on accuracy (p = 0.014) was not significant on linear regression (p>0.05; R 2 = 0.11). The presence of a hearing loss (better ear ≥26 dB) did not significantly affect accuracy (p = 0.604; air-conduction), (p = 0.218; boneconduction). Conclusions:This study provides clinical validation of automated audiometry using the KUDUwave in a clinically heterogeneous population, without the use of a sound-treated environment. Whilst threshold variations were statistically significant, future research is needed to ascertain the clinical significance of such variation.

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Section: Accuracysupporting

confidence: 74%

Section: Accuracycontrasting

confidence: 48%

Section: Accuracymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Clinical validation of automated audiometry with continuous noise-monitoring in a clinically heterogeneous population outside a sound-treated environment

Brennan‐Jones

Eikelboom

Swanepoel

et al. 2016

International Journal of Audiology

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Validity of automated threshold audiometry in school aged children

Govender

Mars

2018

International Journal of Pediatric Otorhinolaryngology

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Adapting Audiology Procedures During the Pandemic: Validity and Efficacy of Testing Outside a Sound Booth

et al. 2022

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Purpose: This investigation aims to provide outcomes from a clinical perspective on the validity and efficacy of a wireless automated audiometer system that could be used in multiple settings when a sound booth is not accessible. Testing was conducted in a clinical setting under modified protocols meeting safety precautions during the COVID-19 pandemic. Method: Four doctoral students in audiology served as examiners. Participants were 69 adults between the ages of 20 and 69 years, with normal hearing (≤ 25 dB HL; n = 110 ears) or hearing loss (> 25 dB HL; n = 25 ears). Two versions of a pure-tone air-conduction threshold test following a modified Hughson-Westlake approach were performed and compared at 500, 1000, 2000, 3000, 4000, 6000, and 8000 Hz (a) in a sound-treated test booth using standard manual audiometry and (b) in a quiet, nonsound-treated clinical room (sound booth free) using automated KUDUwave audiometry. Participants were asked to complete a five-item feedback questionnaire, and examiners were interviewed to report on their experience. Results: Clinical validity to within ±10 dB of standard audiometry was demonstrated for 94.5% of the total thresholds ( n = 937) measured with the sound booth–free approach. Less accuracy (73.3%) was observed using a ±5 dB comparison. When comparing the mean thresholds, there were significant differences ( p < .01) between the mean thresholds at most frequencies, with mean sound booth thresholds being higher than the sound booth–free mean thresholds. A strong threshold correlation (.91–.98) was found between the methods across frequencies. Participant and examiner feedback supported the efficacy of the sound booth–free technology. Conclusions: Findings support sound booth–free, automated software-controlled audiometry with active noise monitoring as a valid and efficient procedure for pure-tone hearing threshold assessment. This method offers an effective alternative when circumstances require more transportable hearing assessment technology or do not allow for standard manual audiometry in a sound booth.

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Ambient noise impact on accuracy of automated hearing assessment

Abstract: Ambient noise typical of that found in a non-sound-treated room, did not affect the accuracy of air conduction hearing thresholds obtained with the KUDUwave. The KUDUwave may be a viable method of testing when a clinical audiometer and sound booth are not available.

Cited by 37 publications

References 12 publications

Clinical validation of automated audiometry with continuous noise-monitoring in a clinically heterogeneous population outside a sound-treated environment

Clinical validation of automated audiometry with continuous noise-monitoring in a clinically heterogeneous population outside a sound-treated environment

Validity of automated threshold audiometry in school aged children

Adapting Audiology Procedures During the Pandemic: Validity and Efficacy of Testing Outside a Sound Booth

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