ArticleIEEE transactions on bio-medical engineering2022
Real-Time Localization of Cochlear-Implant Electrode Arrays Using Bipolar Impedance Sensing.
Article in IEEE transactions on bio-medical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Radiation-free cochlear implant position estimation in pediatric patients using impedance telemetry.BMC pediatrics · 2025Article
- Cochlear Implantation: Electrode Impedance in Users With Triphasic Pulse Pattern.Laryngoscope investigative otolaryngology · 2025Article
- Tissue-Engineered Cochlear Fibrosis Model Links Complex Impedance to Fibrosis Formation for Cochlear Implant Patients.Advanced healthcare materials · 2023Article
- Robotic pullback technique of a precurved cochlear-implant electrode array using real-time impedance sensing feedback.International journal of computer assisted radiology and surgery · 2023Article
- Insertion Guidance Based on Impedance Measurements of a Cochlear Electrode Array.Frontiers in computational neuroscience · 2022Article
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Authors and funding
4 authors.
Funding
Abstract
objectiveSurgeons have no direct objective feedback on cochlear-implant electrode array (EA) positioning during insertion, yet optimal hearing outcomes are contingent on placing the EA as close as feasible to viable neural endings. This paper describes a system to non-invasively determine intracochlear positioning of an EA, without requiring any modifications to existing commercial EAs themselves.
methodsElectrical impedance has been suggested as a way to measure EA proximity to the inner wall of the cochlea that houses auditory nerve endings-the modiolus. In this paper, we extend prior work and demonstrate for the first time the relationship between bipolar access resistance and proximity of the EA to the modiolus (E-M proximity). We also evaluate two methods for producing direct, real-time estimates of E-M proximity from bipolar impedance measurements.
resultsWe show that bipolar access resistance is highly correlated with E-M proximity and can be approximately modeled by a power law function. This one dimensional model is shown to be capable of producing accurate real-time estimates of E-M proximity, but its simplicity also limits the potential for future improvement. To address this challenge, we propose a new prediction approach based on a recurrent neural network, which generated an overall prediction accuracy of 93.7%.
conclusionBipolar access resistance is highly correlated with E-M proximity, and can be used to estimate EA positioning. SIGNIFICANCE: This work shows how impedance sensing can be used to localize an EA during insertion into the small, enclosed cochlear environment, without requiring any modifications to existing clinically used EAs.
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