Data-Driven Approach for Auditory Profiling and Characterization of Individual Hearing Loss

Sanchez-Lopez, Raul; Bianchi, Federica; Fereczkowski, Michal; Santurette, Sébastien; Dau, Torsten

doi:10.1177/2331216518807400

Cited by 28 publications

(64 citation statements)

References 30 publications

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“…Recently, Sanchez-Lopez et al (2018) proposed a data-driven method for auditory profiling that was tested and verified by analyzing two data sets from previous experimental studies (Johannesen et al, 2016;Thorup et al, 2016). Thorup et al (2016)'s data set was collected in a clinical setup using listeners with either near-normal audiometric thresholds (26 listeners), obscure dysfunction (4 listeners), or mild-to-moderate high-frequency hearing loss (29 listeners).…”

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confidence: 99%

Robust Data-Driven Auditory Profiling Towards Precision Audiology

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The sources and consequences of a sensorineural hearing loss are diverse. While several approaches have aimed at disentangling the physiological and perceptual consequences of different etiologies, hearing deficit characterization and rehabilitation have been dominated by the results from pure-tone audiometry. Here, we present a novel approach based on data-driven profiling of perceptual auditory deficits that attempts to represent auditory phenomena that are usually hidden by, or entangled with, audibility loss. We hypothesize that the hearing deficits of a given listener, both at hearing threshold and at suprathreshold sound levels, result from two independent types of “auditory distortions.” In this two-dimensional space, four distinct “auditory profiles” can be identified. To test this hypothesis, we gathered a data set consisting of a heterogeneous group of listeners that were evaluated using measures of speech intelligibility, loudness perception, binaural processing abilities, and spectrotemporal resolution. The subsequent analysis revealed that distortion type-I was associated with elevated hearing thresholds at high frequencies and reduced temporal masking release and was significantly correlated with elevated speech reception thresholds in noise. Distortion type-II was associated with low-frequency hearing loss and abnormally steep loudness functions. The auditory profiles represent four robust subpopulations of hearing-impaired listeners that exhibit different degrees of perceptual distortions. The four auditory profiles may provide a valuable basis for improved hearing rehabilitation, for example, through profile-based hearing-aid fitting.

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“…While the diotic condition can be resolved monoaurally, the dichotic condition requires the binaural processing abilities to be sufficiently intact to detect the contour. Previous studies showed that some listeners were unable to detect binaural pitch, regardless of the audiometric configuration (Sanchez-Lopez et al, 2018;Santurette & Dau, 2012). Therefore, it was of interest to compare the results of these two binaural processing tests.…”

Section: Binaural Processing Abilitiesmentioning

confidence: 99%

“…Sanchez-Lopez et al 2020In Denmark, the Better hEAring Rehabilitation (BEAR) project was initiated with the aim of developing new diagnostic tests and hearing-aid compensation strategies for audiological practice. Although the assessment of individual hearing deficits can be complex, new evidence suggests that the perceptual consequences of a hearing loss can be characterized effectively by two types of hearing deficits, defined as "auditory distortions" (Sanchez-Lopez, Bianchi, Fereczkowski, Santurette, & Dau, 2018). By analysing the outcomes of two previous studies (Johannesen et al, 2016;Thorup et al, 2016) with a data-driven approach, Sanchez-Lopez et al (2018) identified highfrequency hearing loss as the main predictor of one of the distortions, whereas the definition of the second type of distortion was inconclusive.…”

Section: Introductionmentioning

confidence: 99%

“…Although the assessment of individual hearing deficits can be complex, new evidence suggests that the perceptual consequences of a hearing loss can be characterized effectively by two types of hearing deficits, defined as "auditory distortions" (Sanchez-Lopez, Bianchi, Fereczkowski, Santurette, & Dau, 2018). By analysing the outcomes of two previous studies (Johannesen et al, 2016;Thorup et al, 2016) with a data-driven approach, Sanchez-Lopez et al (2018) identified highfrequency hearing loss as the main predictor of one of the distortions, whereas the definition of the second type of distortion was inconclusive. The mixed results obtained from these analyses were most likely due to differences between the two studies in terms of hearing loss profiles and outcome measures.…”

Section: Introductionmentioning

confidence: 99%

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Auditory tests for characterizing hearing deficits in listeners with various hearing abilities: The BEAR test battery

Sanchez-Lopez

Nielsen

El-Haj-Ali

et al. 2020

Preprint

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Introduction: The Better hEAring Rehabilitation (BEAR) project aims to provide a new clinical profiling tool, a test battery, for hearing loss characterization. Whereas the loss of sensitivity can be efficiently measured using pure-tone audiometry, the assessment of supra-threshold hearing deficits remains a challenge. In contrast to the classical 'attenuation-distortion' model, the proposed BEAR approach is based on the hypothesis that the hearing abilities of a given listener can be characterized along two dimensions reflecting independent types of perceptual deficits (distortions). A data-driven approach provided evidence for the existence of different auditory profiles with different degrees of distortions. Design: Eleven tests were included in a test battery, based on their clinical feasibility, time efficiency and related evidence from the literature. The tests were divided into six categories: audibility, speech perception, binaural processing abilities, loudness perception, spectro-temporal modulation sensitivity and spectro-temporal resolution. Study sample: Seventy-five listeners with symmetric, mild-to-severe sensorineural hearing loss were selected from a clinical population. Results: The analysis of the results showed interrelations among outcomes related to high-frequency processing and outcome measures related to low-frequency processing abilities. Conclusions: The results showed the ability of the tests to reveal differences among individuals and their potential use in clinical settings.

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“…The decision tree model, as a basic machine learning form, is playing an increasingly important role in healthcare applications. Specifically in the hearing science field, this technique has been used to seek the optimal suprathreshold test battery to classify auditory profiles towards effective hearing loss compensations [31]. More frequent applications of a decision tree analysis were evaluating whether a medical and audiological practice is cost-effective, for example, implanting cochlear prosthesis [32], the pursuit of magnetic resonance imaging (MRI) with or without contrast in the workup of undifferentiated asymmetrical sensorineural hearing loss [33], and universal or selective hearing screening on newborns [34, 35].…”

Section: Introductionmentioning

confidence: 99%

A parsimonious approach for screening moderate-to-profound hearing loss in a community-dwelling geriatric population based on a decision tree analysis

Zhang

et al. 2019

BMC Geriatr

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Background: Hearing loss is one of the most common modifiable factors associated with cognitive and functional decline in geriatric populations. An accurate, easy-to-apply, and inexpensive hearing screening method is needed to detect hearing loss in community-dwelling elderly people, intervene early and reduce the negative consequences and burden of untreated hearing loss on individuals, families and society. However, available hearing screening tools do not adequately meet the need for large-scale geriatric hearing detection due to several barriers, including time, personnel training and equipment costs. This study aimed to propose an efficient method that could potentially satisfy this need. Methods: In total, 1793 participants (≥60 years) were recruited to undertake a standard audiometric air conduction pure tone test at 4 frequencies (0.5-4 kHz). Audiometric data from one community were used to train the decision tree model and generate a pure tone screening rule to classify people with or without moderate or more serious hearing impairment. Audiometric data from another community were used to validate the tree model. Results: In the decision tree analysis, 2 kHz and 0.5 kHz were found to be the most important frequencies for hearing severity classification. The tree model suggested a simple two-step screening procedure in which a 42 dB HL tone at 2 kHz is presented first, followed by a 47 dB HL tone at 0.5 kHz, depending on the individual's response to the first tone. This approach achieved an accuracy of 91.20% (91.92%), a sensitivity of 95.35% (93.50%) and a specificity of 86.85% (90.56%) in the training dataset (testing dataset). Conclusions: A simple two-step screening procedure using the two tones (2 kHz and 0.5 kHz) selected by the decision tree analysis can be applied to screen moderate-to-profound hearing loss in a community-based geriatric population in Shanghai. The decision tree analysis is useful in determining the optimal hearing screening criteria for local elderly populations. Implanting the pair of tones into a well-calibrated sound generator may create a simple, practical and time-efficient screening tool with high accuracy that is readily available at healthcare centers of all levels, thereby facilitating the initiation of extensive nationwide hearing screening in older adults.

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Data-Driven Approach for Auditory Profiling and Characterization of Individual Hearing Loss

Cited by 28 publications

References 30 publications

Robust Data-Driven Auditory Profiling Towards Precision Audiology

Robust Data-Driven Auditory Profiling Towards Precision Audiology

Auditory tests for characterizing hearing deficits in listeners with various hearing abilities: The BEAR test battery

A parsimonious approach for screening moderate-to-profound hearing loss in a community-dwelling geriatric population based on a decision tree analysis

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