In situ validation of a parametric model of electrical field distribution in an implanted cochlea

Abstract : Cochlear implants have been proved to be an effective treatment for patients with sensorineural hearing loss. Among all the approaches that have been developed to design better cochlear implants, 3D model-based simulation stands out due to its detailed description of the electric field which helps reveal the electrophysiological phenomena inside the cochlea. With the advances in the cochlear implant manufacturing technology, the requirement on simulation accuracy increases. Improving the simulation accuracy relies on two aspects: 1) a better geometrical description of the cochlea that is able to distinguish the subtle differences across patients; 2) a comprehensive and reliable validation of the created 3D model. In this paper, targeting at high precision simulation, we propose a parametric cochlea model which uses micro-CT images to adapt to different cochlea geometries, then demonstrate its validation process with multi-channel stimulation data measured from a implanted cochlea. Comparisons between the simulation and validation data show a good match under a variety of stimulation configurations. The results suggest that the electric field distribution is affected by the geometric characteristics of each individual cochlea. These differences can be correctly reflected by simulations based on a 3D model tuned with personalized data.
Complete list of metadatas

Cited literature [18 references]  Display  Hide  Download

https://hal.inria.fr/hal-01242020
Contributor : Kai Dang <>
Submitted on : Friday, December 11, 2015 - 12:23:34 PM
Last modification on : Thursday, January 11, 2018 - 4:48:51 PM

File

ner_2015.pdf
Files produced by the author(s)

Licence


Copyright

Identifiers

Collections

Citation

Kai Dang, Maureen Clerc, Clair Vandersteen, Nicolas Guevara, Dan Gnansia. In situ validation of a parametric model of electrical field distribution in an implanted cochlea. 7th International IEEE EMBS Conference on Neural Engineering, Apr 2015, Montpellier, France, France. ⟨10.1109/NER.2015.7146711⟩. ⟨hal-01242020⟩

Share

Metrics

Record views

850

Files downloads

231