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Published December 18, 2019 | Version v1.0
Dataset Open

Data from "Auditory tests for characterizing hearing deficits: The BEAR test battery"

  • 1. Hearing Systems Section. Department of Health Technology. Technical University of Denmark, Kgs. Lyngby, Denmark.
  • 2. Institute of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark

Description

This repository contains raw and processed data used and described in:

R. Sanchez-Lopez, S.G. Nielsen, M. El-Haj-Ali, F. Bianchi, M, Fereckzowski, O. Cañete, M. Wu, T. Neher, T. Dau and S. Santurette (under review). ``Auditory tests for characterizing hearing deficits: The BEAR test battery,'' Int. J. of Audiology.

[Preprint available in medRxiv:
https://doi.org/10.1101/2020.02.17.20021949]

One aim of the Better hEAring Rehabilitation (BEAR) project is to define a new clinical profiling tool, a test-battery, for individualized hearing loss characterization. Whereas the loss of sensitivity can be efficiently assessed by pure-tone audiometry, it still remains a challenge to address supra-threshold hearing deficits using appropriate clinical diagnostic tools. In contrast to the classical attenuation-distortion model (Plomp, 1986), the proposed BEAR approach is based on the hypothesis that any listener’s hearing can be characterized along two dimensions reflecting largely independent types of perceptual distortions. Recently, a data-driven approach (Sanchez-Lopez et al., 2018) provided evidence consistent with the existence of two independent sources of distortion, and thus different auditory profiles. Eleven tests were selected for the clinical test battery, based on their feasibility, time efficiency and related evidence from the literature. The proposed tests were divided into five categories: audibility, speech perception, binaural-processing abilities, loudness perception, and spectro-temporal resolution. Seventy-five listeners with symmetric, mild-to-severe sensorineural hearing loss were selected from a clinical population of hearing-aid users. The participants completed all tests in a clinical environment and did not receive systematic training for any of the tasks. The analysis of the results focused on the ability of each test to pinpoint individual differences among the participants, relationships among the different tests, and determining their potential use in clinical settings. The results might be valuable for hearing-aid fitting and clinical auditory profiling.

Please cite this article when using the data

The Dataset BEAR3 has also been used in:

Sanchez-Lopez R, Fereczkowski M, Neher T, Santurette S, Dau T. Robust Data-Driven Auditory Profiling Towards Precision Audiology. Trends in Hearing. January 2020. doi:10.1177/2331216520973539

Sanchez-Lopez, R., Fereczkowski, M., Neher, T., Santurette, S., & Dau, T. (2020). Robust auditory profiling: Improved data-driven method and profile definitions for better hearing rehabilitation. Proceedings of the International Symposium on Auditory and Audiological Research7, 281-288. Retrieved from https://proceedings.isaar.eu/index.php/isaarproc/article/view/2019-32  

and

Sanchez Lopez, R., Nielsen, S. G., Cañete, O., Fereczkowski, M., Wu, M., Neher, T., Dau, T., & Santurette, S. (2019). A clinical test battery for Better hEAring Rehabilitation (BEAR): Towards the prediction of individual auditory deficits and hearing-aid benefit. In Proceedings of the 23rd International Congress on Acoustics (pp. 3841-3848). Deutsche Gesellschaft für Akustik e.V.. https://doi.org/10.18154/RWTH-CONV-239177 

Description of the files:

  • BEAR2.xlsx: Anonymized raw data obtained using the BEAR test battery.
  • BEAR3.xlsx: Anonymized processed data for statistical data analysis.
  • BEAR_Reliability.xlsx: Anonymized raw data similar to BEAR2 for the reliability study.
  • DataParticipants.xlsx: Anonymized basic data associated with the participants: Gender, Age, PTA, etc.
  • TestBatteryMethods_v1.0.pdf: Documentation of the test methods.

* The participant IDs in each of the files has been assigned randomly to ensure the anonymization of the data. The pseudo-anonymized data might be shared under request by direct correspondence with the authors.

Files

TestBatteryMethods_v1.0.pdf

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