US 11,990,222 B2
Patient customized electro-neural interface models for model-based cochlear implant programming and applications of same
Jack H. Noble, Nashville, TN (US); Ahmet Cakir, Nashville, TN (US); Benoit M. Dawant, Nashville, TN (US); Robert F. Labadie, Nashville, TN (US); and Rene H. Gifford, Franklin, TN (US)
Assigned to VANDERBILT UNIVERSITY, Nashville, TN (US)
Appl. No. 17/637,875
Filed by VANDERBILT UNIVERSITY, Nashville, TN (US)
PCT Filed Aug. 26, 2020, PCT No. PCT/US2020/047884
§ 371(c)(1), (2) Date Feb. 24, 2022,
PCT Pub. No. WO2021/041466, PCT Pub. Date Mar. 4, 2021.
Claims priority of provisional application 62/891,480, filed on Aug. 26, 2019.
Prior Publication US 2022/0285005 A1, Sep. 8, 2022
Int. Cl. G16H 20/40 (2018.01); A61B 6/03 (2006.01); A61B 6/12 (2006.01); A61B 6/50 (2024.01); A61N 1/05 (2006.01); G06F 30/23 (2020.01); G06T 7/33 (2017.01); G06T 7/55 (2017.01); G06T 7/73 (2017.01); A61B 34/10 (2016.01)
CPC G16H 20/40 (2018.01) [A61B 6/032 (2013.01); A61B 6/12 (2013.01); A61B 6/501 (2013.01); A61B 6/506 (2013.01); A61N 1/0541 (2013.01); G06F 30/23 (2020.01); G06T 7/33 (2017.01); G06T 7/55 (2017.01); G06T 7/73 (2017.01); A61B 2034/102 (2016.02); A61B 2034/105 (2016.02); G06T 2207/10081 (2013.01); G06T 2207/30052 (2013.01); G06T 2207/30172 (2013.01)] 29 Claims
OG exemplary drawing
 
1. A method for performing model-based cochlear implant programming (MOCIP) on a living subject with a cochlear implant (CI) to determine stimulation settings of a patient-customized electro-neural interface (ENI) model, comprising:
localizing an electrode array of the CI and intracochlear structures of the living subject to determine patient-specific electrode positions of the CI and a patient-specific anatomy shape;
generating a CI electric field model based on the patient-specific electrodes positions of the CI and the patient-specific anatomy shape; and
establishing an auditory nerve fiber (ANF) bundle model using the CI electric field model, and estimating neural health of the living subject using the ANF bundle model, wherein the estimating the neural health of the living subject comprises:
establishing the ANF bundle model with a plurality of ANF bundles, wherein each of the ANF bundles includes a plurality of fibers;
simulating electrically evoked compound action potentials (eCAPs) in each of the fibers of the ANF bundles, wherein the eCAPs are measured by amplitude growth functions (AGFs), spread of excitation (SOE) functions, and refractory recovery functions (RRFs); and
estimating, for the electrodes of the CI, the ANF bundles activated by each of the electrodes in response to a given stimulus; and
performing validation of the ANF bundle model by:
training the ANF bundle model using the eCAPs measured by one of the AGFs, SOE functions and RRFs; and
estimating the neural health of the living subject using the trained ANF bundle model by simulating the eCAPs measured by a different one of the AGFs, SOE functions and RRFs.