F\u00f8lgende tre indl\u00e6g blev pr\u00e6senteret p\u00e5 The International Hearing Aid Research Conference (IHCON) p\u00e5 Granlibakken Conference Center, Tahoe City, Californien, USA den 15-19 august 2018.<\/p>\n
Data-driven auditory profiling as a tool for defining Better hEAring Rehabilitation (BEAR)<\/strong><\/p>\n Raul Sanchez-Lopez1<\/sup>, Michal Fereczkowski1<\/sup>, Federica Bianchi1<\/sup>,\u00a0S\u00e9bastien Santurette1,2<\/sup>, Torsten Dau1<\/sup><\/strong> Background<\/strong>: While the audiogram still stands as the main tool for selecting hearing-aid compensation strategies\u00a0in audiological clinics, there is ample evidence that loss of hearing sensitivity cannot fully\u00a0 account\u00a0for common difficulties encountered by people with sensorineural hearing loss, such as\u00a0understanding speech in noisy environments. Forty years after R. Plomp proposed his attenuation distortion\u00a0model of hearing impairment, it remains a challenge to address the distortion component,\u00a0mainly related to supra-threshold deficits, via adequate clinical diagnostics and corresponding\u00a0hearing-aid compensation strategies. Inspired by the different auditory profiling approaches used in\u00a0the literature, a major aim of the Better hEAring Rehabilitation (BEAR) project is to define a new\u00a0clinical profiling tool, a test battery, for individualized hearing loss characterization. Auditory tests for characterizing individual hearing deficits: The BEAR test battery<\/strong><\/p>\n Raul Sanchez-Lopez1<\/sup>, Michal Fereczkowski1<\/sup>, Federica Bianchi2<\/sup>, Mouhamad El-Haj-Ali3<\/sup>, Tobias Neher3<\/sup>, Torsten Dau1<\/sup> and S\u00e9bastien Santurette1,4<\/sup><\/strong><\/em> Background:<\/strong> The Better hEAring Rehabilitation (BEAR) project seeks to develop and assess new clinically feasible strategies for individualized hearing-loss diagnosis and hearing-aid fitting. The aim is to improve current clinical practice, where the fitting process relies on the pure-tone audiogram and trial-and-error methods. These usually result in inconsistent practices and patient dissatisfaction and inefficient service. Existing evidence suggests that the audiogram does not sufficiently describe supra-threshold performance of hearing-impaired listeners. Detailed characterization of hearing deficits can be complex. Therefore, one aim of the BEAR project is to design a hearing test battery for classification of listeners into a small number of auditory profiles. If successful, this BEAR test battery may be refined and reduced to form the basis for improved profile-based hearing-aid fitting protocols. Hearing aid processing strategies for listeners with different auditory profiles: Insights from the BEAR project<\/strong><\/p>\n Mengfan Wu1<\/sup>, Mouhamad El-Haj-Ali1<\/sup>, Raul Sanchez-Lopez2<\/sup>, Michal Fereczkowski2<\/sup>, Federica Bianchi2<\/sup>, Torsten Dau2<\/sup>, S\u00e9bastien Santurette2,3<\/sup>, Tobias Neher1<\/sup><\/em><\/strong> Background:<\/strong> The Better hEAring Rehabilitation (BEAR) project pursues the development and assessment of new clinically feasible strategies for individual hearing loss diagnosis and hearing aid fitting. Two essential elements of this research are the design of a new diagnostic test battery for identifying different auditory profiles and linking those profiles to hearing aid processing strategies. The current study focused on establishing links between four auditory profiles and benefit from six hearing aid processing strategies. F\u00f8lgende tre indl\u00e6g blev pr\u00e6senteret p\u00e5 The International Hearing Aid Research Conference (IHCON) p\u00e5 Granlibakken Conference Center, Tahoe City, Californien, USA den 15-19 august 2018. Data-driven auditory profiling as a …<\/p>\n","protected":false},"author":4,"featured_media":2277,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[4],"tags":[],"_links":{"self":[{"href":"https:\/\/bear-hearing.dk\/da\/wp-json\/wp\/v2\/posts\/2275"}],"collection":[{"href":"https:\/\/bear-hearing.dk\/da\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/bear-hearing.dk\/da\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/bear-hearing.dk\/da\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/bear-hearing.dk\/da\/wp-json\/wp\/v2\/comments?post=2275"}],"version-history":[{"count":21,"href":"https:\/\/bear-hearing.dk\/da\/wp-json\/wp\/v2\/posts\/2275\/revisions"}],"predecessor-version":[{"id":2676,"href":"https:\/\/bear-hearing.dk\/da\/wp-json\/wp\/v2\/posts\/2275\/revisions\/2676"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/bear-hearing.dk\/da\/wp-json\/wp\/v2\/media\/2277"}],"wp:attachment":[{"href":"https:\/\/bear-hearing.dk\/da\/wp-json\/wp\/v2\/media?parent=2275"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/bear-hearing.dk\/da\/wp-json\/wp\/v2\/categories?post=2275"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/bear-hearing.dk\/da\/wp-json\/wp\/v2\/tags?post=2275"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\n1<\/sup>Hearing Systems Group, Department of Electrical Engineering, Technical University of Denmark,\u00a0Kgs. Lyngby, Denmark
\n2<\/sup> Department of Otorhinolaryngology, Head and Neck Surgery and Audiology, Rigshopitalet, Copenhagen, Denmark<\/em><\/p>\n
\nMethods<\/strong>: The proposed BEAR approach is based on the hypothesis that any listener\u2019s hearing can be\u00a0characterized along two dimensions reflecting largely independent types of perceptual distortions.\u00a0In order to keep the approach as neutral as possible, no a priori assumption was made about the\u00a0nature of the two distortion types. Instead, a statistical analysis method, combining unsupervised\u00a0and supervised learning, was applied to existing data. The aim was to provide a tool to help define\u00a0the two distortion types, such that potentially relevant tests for classifying listeners into different auditory profiles could be identified. So far, the data from two auditory profiling studies were reanalyzed based on this approach. First, an unsupervised-learning technique including archetypal analysis was used to identify extreme patterns in the data, forming the basis for different auditory profiles. Next, a decision tree was determined to classify the listeners into one of the profiles.
\nResults<\/strong>: The data-driven analysis provided consistent evidence for the existence of two independent sources of distortion, and thus different auditory profiles, in the data. The results suggested that the first distortion type was related to loss of sensitivity at high frequencies as well as reduced peripheral compression and frequency selectivity, while the second distortion type was linked to binaural temporal-fine-structure processing abilities as well as low-frequency sensitivity loss. The audiogram was not found to reflect an independent dimension on its own, and the most informative predictors for profile identification beyond the audiogram were related to temporal processing, binaural processing, compressive peripheral nonlinearity, and speech-in-noise perception. The current approach can be used to analyze other existing data sets and may help define an optimal test battery to achieve efficient clinical auditory profiling.<\/p>\n
\n1<\/sup> Hearing Systems, Department of Electrical Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark<\/em>
\n2<\/sup> Oticon Medical, Sm\u00f8rum, Denmark<\/em>
\n3<\/sup> Institute of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark<\/em>
\n4<\/sup> Department of Otorhinolaryngology, Head and Neck Surgery, and Audiology, Rigshospitalet, Copenhagen, Denmark<\/em><\/p>\n
\nMethod:<\/strong> Based on the reanalysis of existing auditory profiling data and on criteria of their feasibility, time efficiency, and evidence from the literature, eleven potential tests for inclusion in a clinical test battery were selected. The proposed tests were divided into six categories: audibility, middle-ear analysis, speech perception, binaural-processing abilities, loudness perception, and spectro-temporal resolution. Thirty hearing-impaired listeners with symmetric mild to severe sensorineural hearing loss were selected from a clinical population of hearing-aid users. All listeners performed every test included in the battery. The participants were tested in a clinical environment and did not receive systematic training on any of the tasks.
\nResults:<\/strong> The considered tests have so far shown potential for auditory profiling. The analysis of the preliminary results will focus on the ability of each test to pinpoint individual differences among the participants, interrelations among the tests, as well as their usability for the target clinical population. Importantly, a parallel study will evaluate the extent to which the outcomes of these tests can be used for hearing-aid fitting. Finally, the current test battery is will be refined for implementation in clinical practice, based on the results of a data-driven analysis for auditory profiling.<\/p>\n
\n1<\/sup> Institute of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark<\/em>
\n2<\/sup> Hearing Systems, Department of Electrical Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark<\/em>
\n3<\/sup> Department of Otorhinolaryngology, Head and Neck Surgery, and Audiology, Rigshospitalet, Copenhagen, Denmark<\/em><\/p>\n
\nMethods:<\/strong> Participants were thirty older listeners with bilateral mild-to-severe sensorineural hearing losses who were selected from a clinical population of hearing aid users. Speech-in-noise stimuli were generated with the help of a hearing aid simulator that included directional processing, noise reduction and dynamic range compression. Stimulus presentation was via headphones. Six hearing aid settings that differed in terms of signal-to-noise ratio (SNR) improvement, temporal and spectral speech distortions were selected for testing based on a comprehensive technical evaluation of different parameterisations of the hearing aid simulator. Speech-in-noise perception was assessed at fixed input SNRs that were selected based on individual speech reception threshold (SRT<\/em>50<\/sub>) measurements. Participants were required to recognize five-word, low-context sentences embedded in two realistic noise scenarios. In addition, overall preference and noise annoyance were assessed using a multiple stimulus comparison paradigm.
\nResults:<\/strong> We hypothesize that the perceptual outcome measures from the six hearing aid settings will differ across listeners with different auditory profiles. More specifically, we expect listeners showing high sensitivity to temporal and spectral differences to perform best and\/or to favour hearing aid settings that preserve those cues. In contrast, we expect listeners showing low sensitivity to temporal and spectral differences to perform best and\/or to favour settings that maximize SNR improvement, independent of any additional speech distortions. Altogether, we anticipate that these findings will provide the basis for more individualized fitting strategies to be implemented in wearable hearing aids.<\/p>","protected":false},"excerpt":{"rendered":"