Frequency Fitting Optimization Using Evolutionary Algorithm in Cochlear Implant Users with Bimodal Binaural Hearing

Saadoun, Alexis; Schein, Antoine; Péan, Vincent; Legrand, Pierrick; Aho, Ludwig Serge; Grayeli, Alexis Bozorg

doi:10.3390/brainsci12020253

Cited by 4 publications

(3 citation statements)

References 83 publications

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“…Recently, a pilot study was performed in which implanted subjects showed better results on speech discrimination tests with an anatomically based FAT compared to their routine clinical settings ( Di Maro et al, 2022 ). Other strategies to improve CI frequency distributions have also been evaluated, such as evolutionary algorithms and smartphone applications which enable FAT self-adjustment by patients ( Fu et al, 2002 ; Jethanamest et al, 2017 ; Saadoun et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Imaging-based frequency mapping for cochlear implants – Evaluated using a daily randomized controlled trial

et al. 2023

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BackgroundDue to variation in electrode design, insertion depth and cochlear morphology, patients with a cochlear implant (CI) often have to adapt to a substantial mismatch between the characteristic response frequencies of cochlear neurons and the stimulus frequencies assigned to electrode contacts. We introduce an imaging-based fitting intervention, which aimed to reduce frequency-to-place mismatch by aligning frequency mapping with the tonotopic position of electrodes. Results were evaluated in a novel trial set-up where subjects crossed over between intervention and control using a daily within-patient randomized approach, immediately from the start of CI rehabilitation.MethodsFourteen adult participants were included in this single-blinded, daily randomized clinical trial. Based on a fusion of pre-operative imaging and a post-operative cone beam CT scan (CBCT), mapping of electrical input was aligned to natural place-pitch arrangement in the individual cochlea. That is, adjustments to the CI’s frequency allocation table were made so electrical stimulation of frequencies matched as closely as possible with corresponding acoustic locations in the cochlea. For a period of three months, starting at first fit, a scheme was implemented whereby the blinded subject crossed over between the experimental and standard fitting program using a daily randomized wearing schedule, and thus effectively acted as their own control. Speech outcomes (such as speech intelligibility in quiet and noise, sound quality and listening effort) were measured with both settings throughout the study period.ResultsOn a group level, standard fitting obtained subject preference and showed superior results in all outcome measures. In contrast, two out of fourteen subjects preferred the imaging-based fitting and correspondingly had better speech understanding with this setting compared to standard fitting.ConclusionOn average, cochlear implant fitting based on individual tonotopy did not elicit higher speech intelligibility but variability in individual results strengthen the potential for individualized frequency fitting. The novel trial design proved to be a suitable method for evaluation of experimental interventions in a prospective trial setup with cochlear implants.

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Section: Introductionmentioning

confidence: 99%

Imaging-based frequency mapping for cochlear implants – Evaluated using a daily randomized controlled trial

et al. 2023

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“…Acoustic models that drive cochlear implants are constrained by the number and tuning of channels, temporal resolution, and topographical discrepancy between the frequency information provided by the electrode and the natural tuning of the cochlea. Interactive genetic algorithms and evolutionary algorithms can support optimization of acoustic models at a more efficient rate of convergence than natural adaptation [ 259 , 260 ]. Models simulating listener errors can also be used to inform development of such algorithms [ 258 ].…”

Section: Models For Neurorehabilitationmentioning

confidence: 99%

NSF DARE—transforming modeling in neurorehabilitation: a patient-in-the-loop framework

Cashaback,

Allen,

Chou

et al. 2024

J NeuroEngineering Rehabil

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In 2023, the National Science Foundation (NSF) and the National Institute of Health (NIH) brought together engineers, scientists, and clinicians by sponsoring a conference on computational modelling in neurorehabiilitation. To facilitate multidisciplinary collaborations and improve patient care, in this perspective piece we identify where and how computational modelling can support neurorehabilitation. To address the where, we developed a patient-in-the-loop framework that uses multiple and/or continual measurements to update diagnostic and treatment model parameters, treatment type, and treatment prescription, with the goal of maximizing clinically-relevant functional outcomes. This patient-in-the-loop framework has several key features: (i) it includes diagnostic and treatment models, (ii) it is clinically-grounded with the International Classification of Functioning, Disability and Health (ICF) and patient involvement, (iii) it uses multiple or continual data measurements over time, and (iv) it is applicable to a range of neurological and neurodevelopmental conditions. To address the how, we identify state-of-the-art and highlight promising avenues of future research across the realms of sensorimotor adaptation, neuroplasticity, musculoskeletal, and sensory & pain computational modelling. We also discuss both the importance of and how to perform model validation, as well as challenges to overcome when implementing computational models within a clinical setting. The patient-in-the-loop approach offers a unifying framework to guide multidisciplinary collaboration between computational and clinical stakeholders in the field of neurorehabilitation.

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“…Also the usability of evolutionary algorithms to optimize the frequency fitting is currently investigated and shows promising results concerning speech outcome and sound quality, whereas specific binaural benefits are yet unknown ( Saadoun et al, 2022 ).…”

Section: Mismatch Compensation and Side Effectsmentioning

confidence: 99%

Considerations for Fitting Cochlear Implants Bimodally and to the Single-Sided Deaf

et al. 2022

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When listening with a cochlear implant through one ear and acoustically through the other, binaural benefits and spatial hearing abilities are generally poorer than in other bilaterally stimulated configurations. With the working hypothesis that binaural neurons require interaurally matched inputs, we review causes for mismatch, their perceptual consequences, and experimental methods for mismatch measurements. The focus is on the three primary interaural dimensions of latency, frequency, and level. Often, the mismatch is not constant, but rather highly stimulus-dependent. We report on mismatch compensation strategies, taking into consideration the specific needs of the respective patient groups. Practical challenges typically faced by audiologists in the proposed fitting procedure are discussed. While improvement in certain areas (e.g., speaker localization) is definitely achievable, a more comprehensive mismatch compensation is a very ambitious endeavor. Even in the hypothetical ideal fitting case, performance is not expected to exceed that of a good bilateral cochlear implant user.

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Frequency Fitting Optimization Using Evolutionary Algorithm in Cochlear Implant Users with Bimodal Binaural Hearing

Cited by 4 publications

References 83 publications

Imaging-based frequency mapping for cochlear implants – Evaluated using a daily randomized controlled trial

Imaging-based frequency mapping for cochlear implants – Evaluated using a daily randomized controlled trial

NSF DARE—transforming modeling in neurorehabilitation: a patient-in-the-loop framework

Considerations for Fitting Cochlear Implants Bimodally and to the Single-Sided Deaf

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