Katherine L Heisey1, Alexandra M Walker1,2, Kevin Xie1,3, Jenna M Abrams1,2, Dennis L Barbour1. 1. Department of Biomedical Engineering, Laboratory of Sensory Neuroscience and Neuroengineering, Washington University in St. Louis, St. Louis, Missouri, USA. 2. Program in Audiology and Communication Sciences, Department of Otolaryngology, Washington University School of Medicine, St. Louis, Missouri, USA. 3. Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, Missouri, USA.
Abstract
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 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 and 2.6 dB. Notably, out of 6200 masked machine learning audiogram tone deliveries for this study, no instances of tones detected by the nontest ear were documented. The machine learning methods were also generally faster than the manual methods, and for some listeners, substantially so. CONCLUSIONS: Dynamically masked audiograms achieve accurate true threshold estimates and reduce test time compared with current clinical masking procedures. Dynamic masking is a compelling alternative to the methods currently used to evaluate individuals with highly asymmetric hearing, yet can also be used effectively and efficiently for anyone.
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 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 and 2.6 dB. Notably, out of 6200 masked machine learning audiogram tone deliveries for this study, no instances of tones detected by the nontest ear were documented. The machine learning methods were also generally faster than the manual methods, and for some listeners, substantially so. CONCLUSIONS: Dynamically masked audiograms achieve accurate true threshold estimates and reduce test time compared with current clinical masking procedures. Dynamic masking is a compelling alternative to the methods currently used to evaluate individuals with highly asymmetric hearing, yet can also be used effectively and efficiently for anyone.
Authors: Dennis L Barbour; Rebecca T Howard; Xinyu D Song; Nikki Metzger; Kiron A Sukesan; James C DiLorenzo; Braham R D Snyder; Jeff Y Chen; Eleanor A Degen; Jenna M Buchbinder; Katherine L Heisey Journal: Ear Hear Date: 2019 Jul/Aug Impact factor: 3.570
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