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Validation of a Cochlear Implant Patient-Specific Model of the Voltage Distribution in a Clinical Setting

View Article: PubMed Central - PubMed

ABSTRACT

Cochlear Implants (CIs) are medical implantable devices that can restore the sense of hearing in people with profound hearing loss. Clinical trials assessing speech intelligibility in CI users have found large intersubject variability. One possibility to explain the variability is the individual differences in the interface created between electrodes of the CI and the auditory nerve. In order to understand the variability, models of the voltage distribution of the electrically stimulated cochlea may be useful. With this purpose in mind, we developed a parametric model that can be adapted to each CI user based on landmarks from individual cone beam computed tomography (CBCT) scans of the cochlea before and after implantation. The conductivity values of each cochlea compartment as well as the weighting factors of different grounding modes have also been parameterized. Simulations were performed modeling the cochlea and electrode positions of 12 CI users. Three models were compared with different levels of detail: a homogeneous model (HM), a non-patient-specific model (NPSM), and a patient-specific model (PSM). The model simulations were compared with voltage distribution measurements obtained from the backward telemetry of the 12 CI users. Results show that the PSM produces the lowest error when predicting individual voltage distributions. Given a patient-specific geometry and electrode positions, we show an example on how to optimize the parameters of the model and how to couple it to an auditory nerve model. The model here presented may help to understand speech performance variability and support the development of new sound coding strategies for CIs.

No MeSH data available.


(A) FEM simulation of the normalized intra-cochlear voltage for the NPSM model using ground Vg3 when the most basal electrode is stimulated. The colors indicate Volt units. The red lines are current density streamlines. The current exits the cochlea through the modiolus, the basal end of the cochlea, and the cochlea walls. (B) Single auditory nerve fiber with k = 10 sections.
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Figure 6: (A) FEM simulation of the normalized intra-cochlear voltage for the NPSM model using ground Vg3 when the most basal electrode is stimulated. The colors indicate Volt units. The red lines are current density streamlines. The current exits the cochlea through the modiolus, the basal end of the cochlea, and the cochlea walls. (B) Single auditory nerve fiber with k = 10 sections.

Mentions: Figure 6 presents the 3D model simulation for a standard cochlea geometry not adapted specifically to a CI user. Current density streamlines were used to estimate the direction of current flow from the stimulating electrode surface to the return electrode (Tran et al., 2015). The estimated percentage of current passing through the basal end, modiolus, and cochlea walls was 20, 24, and 56%, respectively. These values are in agreement with the simulations performed by Tran et al. (2015).


Validation of a Cochlear Implant Patient-Specific Model of the Voltage Distribution in a Clinical Setting
(A) FEM simulation of the normalized intra-cochlear voltage for the NPSM model using ground Vg3 when the most basal electrode is stimulated. The colors indicate Volt units. The red lines are current density streamlines. The current exits the cochlea through the modiolus, the basal end of the cochlea, and the cochlea walls. (B) Single auditory nerve fiber with k = 10 sections.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5120131&req=5

Figure 6: (A) FEM simulation of the normalized intra-cochlear voltage for the NPSM model using ground Vg3 when the most basal electrode is stimulated. The colors indicate Volt units. The red lines are current density streamlines. The current exits the cochlea through the modiolus, the basal end of the cochlea, and the cochlea walls. (B) Single auditory nerve fiber with k = 10 sections.
Mentions: Figure 6 presents the 3D model simulation for a standard cochlea geometry not adapted specifically to a CI user. Current density streamlines were used to estimate the direction of current flow from the stimulating electrode surface to the return electrode (Tran et al., 2015). The estimated percentage of current passing through the basal end, modiolus, and cochlea walls was 20, 24, and 56%, respectively. These values are in agreement with the simulations performed by Tran et al. (2015).

View Article: PubMed Central - PubMed

ABSTRACT

Cochlear Implants (CIs) are medical implantable devices that can restore the sense of hearing in people with profound hearing loss. Clinical trials assessing speech intelligibility in CI users have found large intersubject variability. One possibility to explain the variability is the individual differences in the interface created between electrodes of the CI and the auditory nerve. In order to understand the variability, models of the voltage distribution of the electrically stimulated cochlea may be useful. With this purpose in mind, we developed a parametric model that can be adapted to each CI user based on landmarks from individual cone beam computed tomography (CBCT) scans of the cochlea before and after implantation. The conductivity values of each cochlea compartment as well as the weighting factors of different grounding modes have also been parameterized. Simulations were performed modeling the cochlea and electrode positions of 12 CI users. Three models were compared with different levels of detail: a homogeneous model (HM), a non-patient-specific model (NPSM), and a patient-specific model (PSM). The model simulations were compared with voltage distribution measurements obtained from the backward telemetry of the 12 CI users. Results show that the PSM produces the lowest error when predicting individual voltage distributions. Given a patient-specific geometry and electrode positions, we show an example on how to optimize the parameters of the model and how to couple it to an auditory nerve model. The model here presented may help to understand speech performance variability and support the development of new sound coding strategies for CIs.

No MeSH data available.