# US 12,714,856 B2 · Implantable electrical stimulation (IES) system and a method thereof > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,714,856 B2 - Document ID: US12714856B2 - Status: Granted - Kind: B2 - Application number: 18/198,294 - Filed: 2023-05-17 - Published: 2024-01-11 - Granted: 2026-08-25 - Earliest claimed: 2022-07-11 - Applicants: Charles Tak Ming Choi - Inventors: Charles Tak Ming Choi - Web page: https://patentgrep.com/patent/US12714856B2 ## CPC - A61N 1/36039: {fitting procedures} - A61N 1/36139: {with automatic adjustment} - A61N 1/36185: {Selection of the electrode configuration} - A61N 1/0541: {Cochlear electrodes} - A61N: ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY (measurement of bioelectric currents A61B; surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body A61B18/00; anaesthetic apparatus in general A61M; incandescent lamps H01K; infrared radiators for heating H05B) ## Abstract An implantable electrical stimulation (IES) system and a method thereof, is disclosed. The implantable electrical stimulation (IES) system may comprise an implantable electrode array having a plurality of electrode contacts, an electrical stimulus means, a sensor and a processing circuitry. The electrical stimulus means may be configured for applying the electrical stimulus to at least one stimulating electrode contact of the implantable electrode array. The sensor may be configured for sensing and determining a measured ECAP of the at least one stimulating electrode to generate data for determining a plurality of parameters for the at least one stimulating electrode of the implantable electrode array. The processing circuitry may be configured for processing the measured ECAP and plurality of parameters of the at least one stimulating electrode of the implantable electrode array to compute the corresponding parameters for the remaining electrode contacts of the implantable electrode array. ## Claims ### Claim 1 An implantable electrical stimulation (IES) system, comprising: an implantable electrode array having a plurality of electrode contacts; an electrical stimulus means for applying the electrical stimulus to at least one stimulating electrode contact of the implantable electrode array; a sensor for sensing and determining a measured evoked compound action potential (ECAP) of the at least one stimulating electrode contact to generate data for determining a plurality of parameters for the at least one stimulating electrode contact of the implantable electrode array; and a processing circuitry configured for: creating an implantable electrical stimulation (IES) model and adjusting an electrode position until a root mean square (RMS) error is a minimal fixed error or less than 5μ V to calculate a spread of electrical current, calculating a simulated evoked compound action potential (ECAP) based on the spread of electrical current, processing the measured ECAP and the simulated ECAP to calculate a calibration factor (CF) of each electrode to minimize a root mean square of the fit error between the simulated ECAP and the measured ECAP using N levels of the electrical current for calibrating stimulation parameters of the electrode array, and processing the measured ECAP, the simulated ECAP, the calibration factor (CF), and the plurality of parameters of the at least one stimulating electrode contact of the implantable electrode array to compute the corresponding parameters for the remaining electrode contacts of the implantable electrode array. ### Claim 2 (depends on 1) The implantable electrical stimulation (IES) system of claim 1, wherein the sensor is configured for sensing and determining the measured ECAP of the at least one stimulating electrode contact during a prescribed time period of 0.05-2 milliseconds after the application of the electrical stimulus and lasts for approximately 0.3-10 milliseconds thereafter. ### Claim 3 (depends on 1) The implantable electrical stimulation (IES) system of claim 1, wherein the processing circuitry is configured to perform the following processing steps: creating the IES model and adjusting the electrode position based on a numerical method selected from a group consisting of a finite element model, a boundary element model, or any other numerical model to calculate the spread of electrical current. ### Claim 4 (depends on 3) The implantable electrical stimulation (IES) system of claim 3, wherein the accuracy of the spread of electrical current is improved by incorporating electrode-tissue interface impedance values computed by using an electric field imaging (EFI) or using a current steering technique in the electrical stimulation which allows an activation of neurons of the at least one stimulating electrode contact. ### Claim 5 (depends on 3) The implantable electrical stimulation (IES) system of claim 3, wherein the processing circuitry is configured for providing the spread of electrical current to more than one neuron model or to a second analytical formula to calculate the simulated ECAP. ### Claim 6 (depends on 1) The implantable electrical stimulation (IES) system of claim 1, wherein the plurality of parameters comprises a maximum electrical current level, a most comfortable stimulation current (M) level, a comfortable stimulation current (C) level, and a threshold (T) stimulation current level, and wherein the processing circuitry is configured to estimate the activated equivalent number of neurons at the most comfortable stimulation current (M) level, the comfortable stimulation current (C) level, and the threshold (T) stimulation current level of the at least one stimulating electrode contact of the implantable electrode array based on the calibration factors (CF). ### Claim 7 (depends on 6) The implantable electrical stimulation (IES) system of claim 6, wherein the processing circuitry is configured to use the estimated activated equivalent number of neurons to compute or predict the most comfortable stimulation current (M) level, the comfortable stimulation current (C) level, and the threshold (T) stimulation current level for the remaining electrode contacts of the implantable electrode array. ### Claim 8 (depends on 3) The implantable electrical stimulation (IES) system of claim 3, wherein the processing circuitry is configured to train a first machine learning (ML) model, a first neural network (NN), a first deep neural network (DNN) model, a first generative adversarial network (GAN) or a first Artificial Intelligence (AI) based method for computing the electrical current spread using the said numerical method. ### Claim 9 (depends on 8) The implantable electrical stimulation (IES) system of claim 8, wherein the processing circuitry is configured to provide an output of the first machine learning (ML) model, the first neural network (NN), the first deep neural network (DNN) model, the first generative adversarial network (GAN) or the first Artificial Intelligence (AI) based method to a second machine learning (ML) model, a second neural network (NN), a second deep neural network (DNN) model, a second generative adversarial network (GAN) or a second Artificial Intelligence (AI) based method to calculate the number of neurons being activated to compute or predict the most comfortable stimulation current (M) level, the comfortable stimulation current (C) level, and the threshold (T) stimulation current level for the remaining electrode contacts of the implantable electrode array. ### Claim 10 A method for an implantable electrical stimulation (IES), comprising: providing, an implantable electrode array having a plurality of electrode contacts; applying, via an electrical stimulus means, the electrical stimulus to at least one stimulating electrode contact of the implantable electrode array; sensing and determining, via a sensor, a measured evoked compound action potential (ECAP) of the at least one stimulating electrode contact to generate data for determining a plurality of parameters for the at least one stimulating electrode contact of the implantable electrode array; and creating, via a processing circuitry, an implantable electrical stimulation (IES) model and adjusting an electrode position until a root mean square (RMS) error is a minimal fixed error or less than 5μ V to calculate a spread of electrical current; calculating, via the processing circuitry, a simulated evoked compound action potential (ECAP) based on the spread of electrical current; processing, via the processing circuitry, the measured ECAP and the simulated ECAP to calculate a calibration factor (CF) of each electrode to minimize a root mean square of the fit error between the simulated ECAP and the measured ECAP using N levels of the electrical current for calibrating stimulation parameters of the electrode array; and processing, via the processing circuitry, the measured ECAP, the simulated ECAP, the calibration factor (CF), and the plurality of parameters of the at least one stimulating electrode contact of the implantable electrode array to compute the corresponding parameters for the remaining electrode contacts of the implantable electrode array. ### Claim 11 (depends on 10) The method of claim 10, wherein the sensing and determining the measured ECAP of the at least one stimulating electrode contact is performed during a prescribed time period of 0.05-2 milliseconds following the application of the electrical stimulus and lasts for approximately 0.3-10 milliseconds thereafter. ### Claim 12 (depends on 10) The method of claim 10, wherein the method comprises: creating, via the processing circuitry, the IES model and adjusting the electrode position based on numerical methods consisting of a finite element model, a boundary element model or any other numerical model to calculate the spread of electrical current. ### Claim 13 (depends on 12) The method of claim 12, wherein the accuracy of the spread of electrical current is improved by incorporating electrode-tissue interface impedance values computed by using an electric field imaging (EFI) or using a current steering technique in the electrical stimulation which allows an activation of neurons through the at least one stimulating electrode contact. ### Claim 14 (depends on 12) The method of claim 12, wherein the method comprises a step for providing the spread of electrical current to more than one neuron model or to a second analytical formula to calculate the simulated ECAP. ### Claim 15 (depends on 10) The method of claim 10, wherein the plurality of parameters comprises a maximum electrical current level, a most comfortable stimulation current (M) level, a comfortable stimulation current (C) level, and a threshold (T) stimulation current level, and wherein the processing circuitry is configured to estimate the activated equivalent number of neurons at the most comfortable stimulation current (M) level, the comfortable stimulation current (C) level, and the threshold (T) stimulation current level of the at least one stimulating electrode contact of the implantable electrode array based on the calibration factors (CF). ### Claim 16 (depends on 15) The method of claim 15, wherein the method comprises a step for computing or predicting the most comfortable stimulation current (M) level, the comfortable stimulation current (C) level, and the threshold (T) stimulation current level for the remaining electrodes of the implantable electrode array using the estimated activated equivalent number of neurons. ### Claim 17 (depends on 12) The method of claim 12, wherein the method comprises a step for training a first machine learning (ML) model, a first neural network (NN), a first deep neural network (DNN) model, a first generative adversarial network (GAN) or a first Artificial Intelligence (AI) based method for computing the electrical current spread using the numerical methods. ### Claim 18 (depends on 17) The method of claim 17, wherein the method comprises a step for providing an output of the first machine learning (ML) model, the first neural network (NN),the generative adversarial network (GAN), the first deep neural network (DNN) model, the first generative adversarial network (GAN) or the first Artificial Intelligence (AI) based method to a second machine learning (ML) model, a second neural network (NN), a second deep neural network (DNN) model, a second generative adversarial network (GAN) or a second Artificial Intelligence (AI) based method to calculate the number of neurons being activated to compute or predict the most comfortable stimulation current (M) level, the comfortable stimulation current (C) level, and the threshold (T) stimulation current level for the remaining electrode contacts of the implantable electrode array. ### Claim 19 An apparatus enabling an implantable electrical stimulation (IES), comprising: an implantable electrode array having a plurality of electrode contacts; an electrical stimulus means for applying the electrical stimulus to at least one stimulating electrode contact of the implantable electrode array; a sensor for sensing and determining a measured evoked compound action potential (ECAP) of the at least one stimulating electrode contact to generate data for determining a plurality of parameters for the at least one stimulating electrode contact of the implantable electrode array; and a processing circuitry configured for: creating an implantable electrical stimulation (IES) model and adjusting an electrode position until a root mean square (RMS) error is a minimal fixed error or less than 5μ V to calculate a spread of electrical current, calculating a simulated evoked compound action potential (ECAP) based on the spread of electrical current, processing the measured ECAP and the simulated ECAP to calculate a calibration factor (CF) of each electrode to minimize a root mean square of the fit error between the simulated ECAP and the measured ECAP using N levels of the electrical current for calibrating stimulation parameters of the electrode array, and processing the measured ECAP, the simulated ECAP, the calibration factor (CF), and the plurality of parameters of the at least one stimulating electrode contact of the implantable electrode array to compute the corresponding parameters for the remaining electrode contacts of the implantable electrode array. ### Claim 20 (depends on 19) The apparatus of claim 19, wherein the sensing and determining the measured ECAP of the at least one stimulating electrode contact is performed by the sensor during a prescribed time period of 0.05-2 milliseconds following the application of the electrical stimulus and lasts for approximately 0.3-10 milliseconds thereafter. ### Claim 21 (depends on 19) The apparatus of claim 19, wherein the processing circuitry is further configured for: creating the IES model and adjusting the electrode position based on numerical methods consisting of a finite element model, a boundary element model or any other numerical model to calculate the spread of electrical current. ### Claim 22 A non-transitory computer readable medium storing a program enabling an implantable electrical stimulation (IES), the program comprising a plurality of programmed instructions, the plurality of programmed instructions comprising instructions for: receiving a measured evoked compound action potential (ECAP) of at least one stimulating electrode contact of an implantable electrode array from a sensor to generate data for determining a plurality of parameters for the at least one stimulating electrode contact of the implantable electrode array; creating an implantable electrical stimulation (IES) model and adjusting an electrode position until a root mean square (RMS) error is a minimal fixed error or less than 5μ V to calculate a spread of electrical current; calculating a simulated evoked compound action potential (ECAP) based on the spread of electrical current; processing the measured ECAP and the simulated ECAP to calculate a calibration factor (CF) of each electrode to minimize a root mean square of the fit error between the simulated ECAP and the measured ECAP using N levels of the electrical current for calibrating stimulation parameters of the electrode array; and processing the measured ECAP, the simulated ECAP, the calibration factor (CF), and the plurality of parameters of the at least one stimulating electrode contact of the implantable electrode array to compute the corresponding parameters for the remaining electrode contacts of the implantable electrode array. ### Claim 23 (depends on 22) The non-transitory computer readable medium of claim 22, wherein the plurality of programmed instructions comprises instructions for: creating the IES model and adjusting the electrode position based on numerical methods consisting of a finite element model, a boundary element model or any other numerical model to calculate the spread of electrical current.