Literature DB >> 30998547

Understanding Variability in Individual Response to Hearing Aid Signal Processing in Wearable Hearing Aids.

Pamela Souza1, Kathryn Arehart2, Tim Schoof3, Melinda Anderson4, Dorina Strori5,6, Lauren Balmert7.   

Abstract

OBJECTIVES: Previous work has suggested that individual characteristics, including amount of hearing loss, age, and working memory ability, may affect response to hearing aid signal processing. The present study aims to extend work using metrics to quantify cumulative signal modifications under simulated conditions to real hearing aids worn in everyday listening environments. Specifically, the goal was to determine whether individual factors such as working memory, age, and degree of hearing loss play a role in explaining how listeners respond to signal modifications caused by signal processing in real hearing aids, worn in the listener's everyday environment, over a period of time.
DESIGN: Participants were older adults (age range 54-90 years) with symmetrical mild-to-moderate sensorineural hearing loss. We contrasted two distinct hearing aid fittings: one designated as mild signal processing and one as strong signal processing. Forty-nine older adults were enrolled in the study and 35 participants had valid outcome data for both hearing aid fittings. The difference between the two settings related to the wide dynamic range compression and frequency compression features. Order of fittings was randomly assigned for each participant. Each fitting was worn in the listener's everyday environments for approximately 5 weeks before outcome measurements. The trial was double blind, with neither the participant nor the tester aware of the specific fitting at the time of the outcome testing. Baseline measures included a full audiometric evaluation as well as working memory and spectral and temporal resolution. The outcome was aided speech recognition in noise.
RESULTS: The two hearing aid fittings resulted in different amounts of signal modification, with significantly less modification for the mild signal processing fitting. The effect of signal processing on speech intelligibility depended on an individual's age, working memory capacity, and degree of hearing loss. Speech recognition with the strong signal processing decreased with increasing age. Working memory interacted with signal processing, with individuals with lower working memory demonstrating low speech intelligibility in noise with both processing conditions, and individuals with higher working memory demonstrating better speech intelligibility in noise with the mild signal processing fitting. Amount of hearing loss interacted with signal processing, but the effects were small. Individual spectral and temporal resolution did not contribute significantly to the variance in the speech intelligibility score.
CONCLUSIONS: When the consequences of a specific set of hearing aid signal processing characteristics were quantified in terms of overall signal modification, there was a relationship between participant characteristics and recognition of speech at different levels of signal modification. Because the hearing aid fittings used were constrained to specific fitting parameters that represent the extremes of the signal modification that might occur in clinical fittings, future work should focus on similar relationships with more diverse types of signal processing parameters.

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Mesh:

Year:  2019        PMID: 30998547      PMCID: PMC6786927          DOI: 10.1097/AUD.0000000000000717

Source DB:  PubMed          Journal:  Ear Hear        ISSN: 0196-0202            Impact factor:   3.570


  49 in total

1.  The Practical Hearing Aids Skills Test-Revised.

Authors:  Karen A Doherty; Jamie L Desjardins
Journal:  Am J Audiol       Date:  2012-02-21       Impact factor: 1.493

2.  Spectral peak resolution and speech recognition in quiet: normal hearing, hearing impaired, and cochlear implant listeners.

Authors:  Belinda A Henry; Christopher W Turner; Amy Behrens
Journal:  J Acoust Soc Am       Date:  2005-08       Impact factor: 1.840

3.  Interactions between cognition, compression, and listening conditions: effects on speech-in-noise performance in a two-channel hearing aid.

Authors:  Thomas Lunner; Elisabet Sundewall-Thorén
Journal:  J Am Acad Audiol       Date:  2007 Jul-Aug       Impact factor: 1.664

4.  Cognition counts: a working memory system for ease of language understanding (ELU).

Authors:  Jerker Rönnberg; Mary Rudner; Catharina Foo; Thomas Lunner
Journal:  Int J Audiol       Date:  2008-11       Impact factor: 2.117

5.  Tympanometric measures in older adults.

Authors:  T L Wiley; K J Cruickshanks; D M Nondahl; T S Tweed; R Klein; B E Klein
Journal:  J Am Acad Audiol       Date:  1996-08       Impact factor: 1.664

6.  Assessment of hearing aid algorithms using a master hearing aid: the influence of hearing aid experience on the relationship between speech recognition and cognitive capacity.

Authors:  Sebastian Rählmann; Markus Meis; Michael Schulte; Jürgen Kießling; Martin Walger; Hartmut Meister
Journal:  Int J Audiol       Date:  2017-04-27       Impact factor: 2.117

7.  Exploring the Relationship Between Working Memory, Compressor Speed, and Background Noise Characteristics.

Authors:  Barbara Ohlenforst; Pamela E Souza; Ewen N MacDonald
Journal:  Ear Hear       Date:  2016 Mar-Apr       Impact factor: 3.570

8.  Temporal resolution with a prescriptive fitting formula.

Authors:  Marc A Brennan; Frederick J Gallun; Pamela E Souza; G Christopher Stecker
Journal:  Am J Audiol       Date:  2013-12       Impact factor: 1.493

9.  Relationship Among Signal Fidelity, Hearing Loss, and Working Memory for Digital Noise Suppression.

Authors:  Kathryn Arehart; Pamela Souza; James Kates; Thomas Lunner; Michael Syskind Pedersen
Journal:  Ear Hear       Date:  2015 Sep-Oct       Impact factor: 3.570

Review 10.  Frequency-lowering devices for managing high-frequency hearing loss: a review.

Authors:  Andrea Simpson
Journal:  Trends Amplif       Date:  2009-06
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1.  Quantifying the Range of Signal Modification in Clinically Fit Hearing Aids.

Authors:  Varsha Rallapalli; Melinda Anderson; James Kates; Lauren Balmert; Lynn Sirow; Kathryn Arehart; Pamela Souza
Journal:  Ear Hear       Date:  2020 Mar/Apr       Impact factor: 3.570

2.  Open Speech Platform: Democratizing Hearing Aid Research.

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3.  The Type of Noise Influences Quality Ratings for Noisy Speech in Hearing Aid Users.

Authors:  Emily M H Lundberg; Song Hui Chon; James M Kates; Melinda C Anderson; Kathryn H Arehart
Journal:  J Speech Lang Hear Res       Date:  2020-11-30       Impact factor: 2.297

Review 4.  Wearable Health Devices in Health Care: Narrative Systematic Review.

Authors:  Lin Lu; Jiayao Zhang; Yi Xie; Fei Gao; Song Xu; Xinghuo Wu; Zhewei Ye
Journal:  JMIR Mhealth Uhealth       Date:  2020-11-09       Impact factor: 4.773

5.  Perceived Sound Quality Dimensions Influencing Frequency-Gain Shaping Preferences for Hearing Aid-Amplified Speech and Music.

Authors:  Jonathan M Vaisberg; Steve Beaulac; Danielle Glista; Ewan A Macpherson; Susan D Scollie
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6.  Robust Data-Driven Auditory Profiling Towards Precision Audiology.

Authors:  Raul Sanchez-Lopez; Michal Fereczkowski; Tobias Neher; Sébastien Santurette; Torsten Dau
Journal:  Trends Hear       Date:  2020 Jan-Dec       Impact factor: 3.293

Review 7.  Anatomical and audiological considerations in branchiootorenal syndrome: A systematic review.

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8.  Revisiting Auditory Profiling: Can Cognitive Factors Improve the Prediction of Aided Speech-in-Noise Outcome?

Authors:  Mengfan Wu; Stine Christiansen; Michal Fereczkowski; Tobias Neher
Journal:  Trends Hear       Date:  2022 Jan-Dec       Impact factor: 3.496

Review 9.  The cognitive hearing science perspective on perceiving, understanding, and remembering language: The ELU model.

Authors:  Jerker Rönnberg; Carine Signoret; Josefine Andin; Emil Holmer
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