WO2025210451A1

WO2025210451A1 - Data-derived device parameter determination

Info

Publication number: WO2025210451A1
Application number: PCT/IB2025/053208
Authority: WO
Inventors: Christopher Bennett; Zachary Mark Smith; Bastiaan Van Dijk
Original assignee: Cochlear Ltd
Current assignee: Cochlear Ltd
Priority date: 2024-04-02
Filing date: 2025-03-26
Publication date: 2025-10-09
Anticipated expiration: 2026-10-02

Abstract

Presented herein are methods and systems for configuring one or more operational settings or parameters of an implantable medical device using a data-derived analysis of electrical field propagation measurements. Electrical field propagation measurements captured via the implantable medical device are processed by feature extraction techniques to generate a plurality of features. The data-derived model processes the plurality of features to determine one or more operational parameters of the implantable medical device. The implantable medical device can then be configured using the one or more operational parameters.

Description

DATA-DERIVED DEVICE PARAMETER DETERMINATION

BACKGROUND

Field of the Invention

[oooi] The present invention relates generally to configuring one or more operational parameters of an implantable medical device based on analysis of electrical field propagation measurements.

Related Art

[0002] Medical devices have provided a wide range of therapeutic benefits to recipients over recent decades. Medical devices can include internal or implantable components/devices, external or wearable components/devices, or combinations thereof (e.g., a device having an external component communicating with an implantable component). Medical devices, such as traditional hearing aids, partially or fully-implantable hearing prostheses (e.g., bone conduction devices, mechanical stimulators, cochlear implants, etc.), pacemakers, defibrillators, functional electrical stimulation devices, and other medical devices have been successful in performing lifesaving and/or lifestyle enhancement functions and/or recipient monitoring for a number of years.

[0003] The types of medical devices and the ranges of functions performed thereby have increased over the years. For example, many medical devices, sometimes referred to as “implantable medical devices,” now often include one or more instruments, apparatus, sensors, processors, controllers or other functional mechanical or electrical components that are permanently or temporarily implanted in a recipient. These functional devices are typically used to diagnose, prevent, monitor, treat, or manage a disease/injury or symptom thereof, or to investigate, replace or modify the anatomy or a physiological process. Many of these functional devices utilize power and/or data received from external devices that are part of, or operate in conjunction with, implantable components.

SUMMARY

[0004] In one aspect, a method is provided. The method comprises: obtaining a plurality of electrical field propagation measurements captured via an implantable medical device configured to be implanted in a recipient; extracting a plurality of features from the plurality of electrical field propagation measurements; analyzing the plurality of features with a data- derived model to determine one or more operational parameters of the implantable medical device; and configuring the implantable medical device with the one or more operational parameters.

[0005] In another aspect, a second method is provided. The second method comprises: obtaining a plurality of electrical field propagation measurements from a body region of a recipient; extracting a plurality of features from the plurality of electrical field propagation measurements; and processing the plurality of features with a model to generate one or more estimated stimulation parameters for a recipient device.

[0006] In another aspect, one or more non-transitory computer readable storage media are provided. The one or more non-transitory computer readable storage media comprising instructions that, when executed by a processor, cause the processor to: obtain a plurality of electrical field propagation measurements captured via an implantable medical device; analyze a plurality of features of the plurality of electrical field propagation measurements with a data- derived model to determine one or more operational parameters of the implantable medical device; and configure the implantable medical device with the one or more operational parameters.

[0007] In another aspect, a system is provided. The system comprises: a memory; and at least one processor operable coupled to the memory, wherein the at least one processor is configured to: obtain a plurality of electrical field propagation measurements from a body region of a recipient; analyze a plurality of features of the plurality of electrical field propagation measurements with a data-derived model to generate one or more estimated stimulation parameters for a recipient device; and configure the recipient device with the one or more estimated stimulation parameters.

BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Embodiments of the present invention are described herein in conjunction with the accompanying drawings, in which:

[0009] FIG. 1A is a schematic diagram illustrating a cochlear implant system with which aspects of the techniques presented herein can be implemented;

[ooio] FIG. IB is a side view of a recipient wearing a sound processing unit of the cochlear implant system of FIG. 1A;

[ooii] FIG. 1C is a schematic view of components of the cochlear implant system of FIG. 1 A; [0012] FIG. ID is a block diagram of the cochlear implant system of FIG. 1A;

[0013] FIG. IE is a schematic diagram illustrating a computing device with which aspects of the techniques presented herein can be implemented;

[0014] FIG. 2A is a functional block diagram illustrating a system for configuring an implantable medical device with one or more operational parameters;

[0015] FIG. 2B is a functional block diagram illustrating another embodiment of a system for configuring an implantable medical device with one or more operational parameters;

[0016] FIG. 2C is a functional block diagram illustrating another embodiment of a system for configuring an implantable medical device with one or more operational parameters;

[0017] FIG. 2D is a functional block diagram illustrating another embodiment of a system for configuring an implantable medical device with one or more operational parameters;

[0018] FIG. 2E is a functional block diagram illustrating another embodiment of a system for configuring an implantable medical device with one or more operational parameters;

[0019] FIG. 2F is a functional block of a system that implements a data-derived model;

[0020] FIG. 2G is functional block view of a system that implements a data-derived model;

[0021] FIG. 3A is a flowchart illustrating details of a method for configuring an implantable medical device with one or more validated parameters;

[0022] FIG. 3B is a flowchart illustrating details of the validation process described in FIG. 3A;

[0023] FIG. 3C is a flowchart illustrating details of parameter refinement described in FIG. 3B;

[0024] FIG. 4 is a flowchart illustrating a method for configuring an implantable medical device with one or more operational parameters;

[0025] FIG. 5 is a flowchart illustrating a method for generating one or more estimated stimulation parameters for a recipient device;

[0026] FIG. 6 is a flowchart illustrating operations for configuring an implantable medical device with one or more operational parameters, wherein the operations are performed by a processor executing instructions stored in one or more non-transitory computer readable storage media; [0027] FIG. 7 is a flowchart illustrating operations performed by a system comprising a memory and at least one processor operable coupled to the memory, wherein the at least one processor is configured to perform the operations;

[0028] FIG. 8 is a schematic diagram illustrating a vestibular stimulator system with which aspects of the techniques presented herein can be implemented; and

[0029] FIG. 9 is a schematic diagram illustrating a retinal prosthesis system with which aspects of the techniques presented herein can be implemented.

DETAILED DESCRIPTION

[0030] Presented herein are techniques for configuring one or more operational settings or parameters, such as stimulation settings/parameters (e.g., threshold levels (“T-levels”), comfort levels (“C-levels”), a dynamic range, etc.), of an implantable medical device (e.g., auditory prosthesis) using a data-derived analysis of electrical field propagation measurements. More specifically, in accordance with certain embodiments presented herein, features are extracted from electrical field propagation measurements captured via the implantable medical device. A data-derived model processes the extracted features to determine one or more operational parameters for use by the implantable medical device. The implantable medical device can then be configured using the one or more operational parameters (e.g., the one or more operational parameters can be instantiated/installed in the implantable medical device for subsequent operational use). In one example, the one or more operational parameters are validated based on user feedback.

[0031] Certain implantable medical devices operate by delivering, such as cochlear implants, electro-acoustic hearing prosthesis, auditory brainstem implants, etc., operate by converting at least a portion of received sound signals into electrical stimulation signals (current signals) for delivery to a recipient’s auditory system. The window/range of electrical amplitudes (current levels) at which electrical stimulation signals may be delivered to the recipient’s auditory system is limited. In particular, if the amplitude of the electrical stimulation signals is too low, then the associated sounds used to generate the electrical stimulation signals will not be perceived by the recipient (i.e., the stimulation signals will either not evoke a neural response in the cochlea or evoke a neural response that cannot be perceived by the recipient). Conversely, if the amplitude of the electrical stimulation signals is too high, then the associated sounds used to generate the electrical stimulation signals will be perceived as too loud or uncomfortable by the recipient. As such, electrical stimulation signals are generally delivered between a lower limit, referred to herein as a “threshold level,” at which the associated sound signals are barely audible to the recipient, and an upper limit, referred to herein as a “comfort level,” above which the associated sound signals are uncomfortably loud to the recipient. The difference in electrical amplitudes between the threshold level and the comfort level is referred to herein as the “dynamic range.” In general, the term “stimulation parameters” herein can include the threshold level, the comfort level, the dynamic range, or other attributes (e.g., rate) of the electrical stimulation signals to be delivered to a recipient, regardless of whether or not the electrical stimulation signals are generated based on sound signals.

[0032] In the specific example of cochlear implants, due to a recipient’s specific anatomical features, the insertion depth of a given electrode, or other variables, the dynamic range may be different for different electrodes implanted in a recipient. That is, different electrodes implanted in a recipient may have different associated threshold and comfort levels. The range in acoustic amplitudes of sound signals received by a cochlear implant (or other auditory prosthesis) is considerably larger than the dynamic range associated with an electrode. As such, the conversion of the received sound signals into electrical stimulation signals for delivery to the recipient includes, among other operations, mapping (compression) of the acoustic amplitudes into electrical amplitudes within the dynamic range of the corresponding electrode(s) (i.e., the stimulating contact(s) at which the electrical stimulation is delivered to the recipient).

[0033] Current methods for determining operational parameters of an implantable medical device require a recipient to report feedback to a clinician providing stimulation in a traditional fitting session. This process can be time-consuming and inconvenient for the recipient. However, the techniques presented herein leverage a data-derived model to estimate/determine one or more operational parameters without requiring the recipient to be present, therefore increasing the efficiency of the fitting process. Moreover, the techniques presented herein include iteratively refining one or more estimated operational parameters based on a clinician’s feedback, thus resulting in continuous improvement of predictions generated by the data- derived model. Further, utilizing neural response measurements as additional inputs to the data-derived model provides information on how neural health affects the one or more operational parameters of the implantable medical device.

[0034] There are a number of different types of devices in/with which embodiments of the present invention may be implemented. Merely for ease of description, the techniques presented herein are primarily described with reference to a specific device in the form of a cochlear implant system. However, it is to be appreciated that the techniques presented herein may also be partially or fully implemented by any of a number of different types of devices, including consumer electronic device (e.g., mobile phones), wearable devices (e.g., smartwatches), hearing devices, implantable medical devices, consumer electronic devices, etc. As used herein, the term “hearing device” is to be broadly construed as any device that acts on an acoustical perception of an individual, including to improve perception of sound signals, to reduce perception of sound signals, etc. In particular, a hearing device can deliver sound signals to a user in any form, including in the form of acoustical stimulation, mechanical stimulation, electrical stimulation, etc., and/or can operate to suppress all or some sound signals. As such, a hearing device can be a device for use by a hearing-impaired person (e.g., hearing aids, middle ear auditory prostheses, bone conduction devices, direct acoustic stimulators, electro-acoustic hearing prostheses, auditory brainstem stimulators, bimodal hearing prostheses, bilateral hearing prostheses, dedicated tinnitus therapy devices, tinnitus therapy device systems, combinations or variations thereof, etc.), a device for use by a person with normal hearing (e.g., consumer devices that provide audio streaming, consumer headphones, earphones, and other listening devices), a hearing protection device, etc. In other examples, the techniques presented herein can be implemented by, or used in conjunction with, various implantable medical devices, such as visual devices (i.e., bionic eyes), sensors, pacemakers, drug delivery systems, defibrillators, functional electrical stimulation devices, catheters, seizure devices (e.g., devices for monitoring and/or treating epileptic events), sleep apnea devices, electroporation devices, etc.

[0035] FIGs. 1A-1D illustrate an example cochlear implant system 102 with which aspects of the techniques presented herein can be implemented. The cochlear implant system 102 comprises an external component 104 that is configured to be directly or indirectly attached to the body of the user, and an intemal/implantable component 112 that is configured to be implanted in or worn on the head of the user. In the examples of FIGs. 1A-1D, the implantable component 112 is sometimes referred to as a “cochlear implant.” FIG. 1A illustrates the cochlear implant 112 implanted in the head 154 of a user, while FIG. IB is a schematic drawing of the external component 104 worn on the head 154 of the user. FIG. 1C is another schematic view of the cochlear implant system 102, while FIG. ID illustrates further details of the cochlear implant system 102. For ease of description, FIGs. 1A-1D will generally be described together. [0036] In the examples of FIGs. 1A-1D, the external component 104 comprises a sound processing unit 106, an external coil 108, and generally, a magnet fixed relative to the external coil 108. The cochlear implant 112 includes an implantable coil 114, an implant body 134, and an elongate stimulating assembly 116 configured to be implanted in the user’s cochlea. In one example, the sound processing unit 106 is an off-the-ear (OTE) sound processing unit, sometimes referred to herein as an OTE component, that is configured to send data and power to the implantable component 112. In general, an OTE sound processing unit is a component having a generally cylindrically shaped housing 111 and which is configured to be magnetically coupled to the user’s head 154 (e.g., includes an integrated external magnet 150 configured to be magnetically coupled to an intemal/implantable magnet 152 in the implantable component 112). The OTE sound processing unit 106 also includes an integrated external (headpiece) coil 108 (the external coil 108) that is configured to be inductively coupled to the implantable coil 114.

[0037] It is to be appreciated that the OTE sound processing unit 106 is merely illustrative of the external devices that could operate with implantable component 112. For example, in alternative examples, the external component 104 may comprise a behind-the-ear (BTE) sound processing unit configured to be attached to, and worn adjacent to, the recipient’s ear. A BTE sound processing unit comprises a housing that is shaped to be worn on the outer ear of the user. In certain examples, the BTE is connected to a separate external coil assembly via a cable, where the external coil assembly is configured to be magnetically and inductively coupled to the implantable coil 114, while in other embodiments the BTE includes a coil disposed in or on the housing worn on the outer ear of the user. It is also to be appreciated that alternative external components could be located in the user’s ear canal, worn on the body, etc.

[0038] Although the cochlear implant system 102 includes the sound processing unit 106 and the cochlear implant 112, as described below, the cochlear implant 112 can operate independently from the sound processing unit 106, for at least a period, to stimulate the user. For example, the cochlear implant 112 can operate in a first general mode, sometimes referred to as an “external hearing mode,” in which the sound processing unit 106 captures sound signals which are then used as the basis for delivering stimulation signals to the user. The cochlear implant 112 can also operate in a second general mode, sometimes referred as an “invisible hearing” mode, in which the sound processing unit 106 is unable to provide sound signals to the cochlear implant 112 (e.g., the sound processing unit 106 is not present, the sound processing unit 106 is powered-off, the sound processing unit 106 is malfunctioning, etc.). As such, in the invisible hearing mode, the cochlear implant 112 captures sound signals itself via implantable sound sensors and then uses those sound signals as the basis for delivering stimulation signals to the user. Further details regarding operation of the cochlear implant 112 in the external hearing mode are provided below, followed by details regarding operation of the cochlear implant 112 in the invisible hearing mode. It is to be appreciated that reference to the external hearing mode and the invisible hearing mode is merely illustrative and that the cochlear implant 112 could also operate in alternative modes.

[0039] In FIGs. 1A and 1C, the cochlear implant system 102 is shown with an external device 110, configured to implement aspects of the techniques presented. The external device 110, which is shown in greater detail in FIG, 1 E, is a computing device, such as a personal computer (e.g., laptop, desktop, tablet), a mobile phone (e.g., smartphone), a remote control unit, etc. The external device 110 and the cochlear implant system 102 (e.g., sound processing unit 106 or the cochlear implant 112) wirelessly communicate via a bi-directional communication link 126. The bi-directional communication link 126 may comprise, for example, a short-range communication, such as Bluetooth link, Bluetooth Low Energy (BLE) link, a proprietary link, etc.

[0040] Returning to the example of FIGs. 1A-1D, the sound processing unit 106 of the external component 104 also comprises one or more input devices configured to capture and/or receive input signals (e.g., sound or data signals) at the sound processing unit 106. The one or more input devices include, for example, one or more sound input devices 118 (e.g., one or more external microphones, audio input ports, telecoils, etc.), one or more auxiliary input devices 128 (e.g., audio ports, such as a Direct Audio Input (DAI), data ports, such as a Universal Serial Bus (USB) port, cable port, etc.), and a short-range wireless transmitter/receiver (wireless transceiver) 120 (e.g., for communication with the external device 110), each located in, on or near the sound processing unit 106. However, it is to be appreciated that one or more input devices may include additional types of input devices and/or less input devices (e.g., the short- range wireless transceiver 120 and/or one or more auxiliary input devices 128 could be omitted).

[0041] The sound processing unit 106 also comprises the external coil 108, a charging coil 130, a closely-coupled radio frequency transmitter/receiver (RF transceiver) 122, at least one battery 132, and an external processing module 124. The external processing module 124 can be configured to perform a number of operations that are represented in FIG. ID by an electrical field propagation measurements module 131, a sound processor 133, and a monitoring module 135. Each of the electrical field propagation measurements module 131, the sound processor 133, and the monitoring module 135 can be formed by one or more processors (e.g., one or more Digital Signal Processors (DSPs), one or more uC cores, etc.), firmware, software, etc. arranged to perform operations described herein. That is, the electrical field propagation measurements module 131, the sound processor 133, and the monitoring module 135 can each be implemented as firmware elements, partially or fully implemented with digital logic gates in one or more application-specific integrated circuits (ASICs), partially or fully in software, etc. Although FIG. ID illustrates the electrical field propagation measurements module 131, a sound processor 133, and a monitoring module 135 as being implemented/performed at the external processing module 124, it is to be appreciated that these elements (e.g., functional operations) could also or alternatively be implemented/performed as part of the internal processing module 158, as part of the external device 110, etc. As such, the electrical field propagation measurements module 131, the sound processor 133, and the monitoring module 135 are each shown using dashed lines in FIGs. ID and IE.

[0042] Returning to the example of FIGs. 1A-1D, the implantable component 112 comprises an implant body (main module) 134, a lead region 136, and the stimulating assembly 116, all configured to be implanted under the skin (tissue) 115 of the user. The implant body 134 generally comprises a hermetically-sealed housing 138 that includes, in certain examples, at least one power source 125 (e.g., one or more batteries, one or more capacitors, etc.), in which the RF interface circuitry 140 and a stimulator unit 142 are disposed. The implant body 134 also includes the intemal/implantable coil 114 that is generally external to the housing 138, but which is connected to the RF interface circuitry 140 via a hermetic feedthrough (not shown in FIG. ID).

[0043] As noted, the stimulating assembly 116 is configured to be at least partially implanted in the user’s cochlea. The stimulating assembly 116 includes a plurality of longitudinally spaced intra-cochlear electrical electrodes (stimulating contacts) 144 that collectively form a contact array (electrode array) 146 for delivery of electrical stimulation (current) to the recipient’s cochlea. The stimulating assembly 116 extends through an opening in the recipient’s cochlea (e.g., cochleostomy, the round window, etc.) and has a proximal end connected to stimulator unit 142 via lead region 136 and a hermetic feedthrough (not shown in FIG. ID). Lead region 136 includes a plurality of conductors (wires) that electrically couple the electrodes 144 to the stimulator unit 142. The implantable component 112 also includes an electrode outside of the cochlea, sometimes referred to as the extra-cochlear electrode (ECE) 139.

[0044] As noted, the cochlear implant system 102 includes the external coil 108 and the implantable coil 114. The external magnet 150 is fixed relative to the external coil 108 and the intemal/implantable magnet 152 is fixed relative to the implantable coil 114. The external magnet 150 and the intemal/implantable magnet 152 fixed relative to the external coil 108 and the intemal/implantable coil 114, respectively, facilitate the operational alignment of the external coil 108 with the implantable coil 114. This operational alignment of the coils enables the external component 104 to transmit data and power to the implantable component 112 via a closely-coupled wireless link 148 formed between the external coil 108 with the implantable coil 114. In certain examples, the closely-coupled wireless link 148 is an RF link. However, various other types of energy transfer, such as infrared (IR), electromagnetic, capacitive and inductive transfer, may be used to transfer the power and/or data from an external component to an implantable component and, as such, FIG. ID illustrates only one example arrangement.

[0045] As noted above, the sound processing unit 106 includes the external processing module 124. The external processing module 124 is configured to process the received input audio signals (received at one or more of the input devices, such as sound input devices 118 and/or auxiliary input devices 128) and convert the received input audio signals into output control signals for use in stimulating a first ear of a recipient or user (i.e., the external processing module 124 is configured to perform sound processing on input signals received at the sound processing unit 106). Stated differently, the one or more processors (e.g., processing element(s) implementing firmware, software, etc.) in the external processing module 124 are configured to execute sound processing logic in memory to convert the received input audio signals into output control signals (stimulation signals) that represent electrical stimulation for delivery to the recipient.

[0046] As noted, FIG. ID illustrates an embodiment in which the external processing module 124 in the sound processing unit 106 generates the output control signals. In an alternative embodiment, the sound processing unit 106 can send less processed information (e.g., audio data) to the implantable component 112, and the sound processing operations (e.g., conversion of input sounds to output control signals 156) can be performed by a processor within the implantable component 112. [0047] In FIG. ID, according to an example embodiment, output control signals (stimulation signals) are provided to the RF transceiver 122, which transcutaneously transfers the output control signals (e.g., in an encoded manner) to the implantable component 112 via the external coil 108 and the implantable coil 114. That is, the output control signals (stimulation signals) are received at the RF interface circuitry 140 via the implantable coil 114 and provided to the stimulator unit 142. The stimulator unit 142 is configured to utilize the output control signals to generate electrical stimulation signals (e.g., current signals) for delivery to the user’s cochlea via one or more of the stimulating contacts 144. In this way, cochlear implant system 102 electrically stimulates the user’s auditory nerve cells, bypassing absent or defective hair cells that normally transduce acoustic vibrations into neural activity, in a manner that causes the recipient to perceive one or more components of the input audio signals (the received sound signals).

[0048] As detailed above, in the external hearing mode, the cochlear implant 112 receives processed sound signals from the sound processing unit 106. However, in the invisible hearing mode, the cochlear implant 112 is configured to capture and process sound signals for use in electrically stimulating the user’s auditory nerve cells. In particular, as shown in FIG. ID, an example embodiment of the cochlear implant 112 can include a plurality of implantable sound sensors 165(1), 165(2) that collectively form a sensor array 160, and an internal processing module 158. Similar to the external processing module 124, the internal processing module 158 may comprise, for example, one or more processors and a memory device (memory) that includes sound processing logic. The memory device may comprise any one or more of: Non- Volatile Memory (NVM), Ferroelectric Random Access Memory (FRAM), read only memory (ROM), random access memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. The one or more processors are, for example, microprocessors or microcontrollers that execute instructions for the sound processing logic stored in memory device.

[0049] In the invisible hearing mode, the implantable sound sensors 165(1), 165(2) of the sensor array 160 are configured to detect/capture input sound signals 166 (e.g., acoustic sound signals, vibrations, etc.), which are provided to the internal processing module 158. The internal processing module 158 is configured to convert received input sound signals 166 (received at one or more of the implantable sound sensors 165(1), 165(2)) into output control signals 156 for use in stimulating the first ear of a recipient or user (i.e., the internal processing module 158 is configured to perform sound processing operations). Stated differently, the one or more processors (e.g., processing element(s) implementing firmware, software, etc.) in the internal processing module 158 are configured to execute sound processing logic in memory to convert the received input sound signals 166 into output control signals 156 that are provided to the stimulator unit 142. The stimulator unit 142 is configured to utilize the output control signals 156 to generate electrical stimulation signals (e.g., current signals) for delivery to the user’s cochlea, thereby bypassing the absent or defective hair cells that normally transduce acoustic vibrations into neural activity.

[0050] It is to be appreciated that the above description of the so-called external hearing mode and the so-called invisible hearing mode are merely illustrative and that the cochlear implant system 102 could operate differently in different embodiments. For example, in one alternative implementation of the external hearing mode, the cochlear implant 112 could use signals captured by the sound input devices 118 and the implantable sound sensors 165(1), 165(2) of sensor array 160 in generating stimulation signals for delivery to the user.

[0051] FIG. IE is a block diagram illustrating one example arrangement for an external computing device 110 configured to perform one or more operations in accordance with certain embodiments presented herein. As shown in FIG. IE, in its most basic configuration, the external computing device 110 includes at least one processing unit 183 and a memory 184. The processing unit 183 includes one or more hardware or software processors (e.g., Central Processing Units) that can obtain and execute instructions. The processing unit 183 can communicate with and control the performance of other components of the external computing device 110. The memory 184 is one or more software or hardware-based computer-readable storage media operable to store information accessible by the processing unit 183. The memory 184 can store, among other things, instructions executable by the processing unit 183 to implement applications or cause performance of operations described herein, as well as other data. The memory 184 can be volatile memory (e.g., RAM), non-volatile memory (e.g., ROM), or combinations thereof. The memory 184 can include transitory memory or non-transitory memory. The memory 184 can also include one or more removable or non-removable storage devices. In examples, the memory 184 can include RAM, ROM) EEPROM (Electronically- Erasable Programmable Read-Only Memory), flash memory, optical disc storage, magnetic storage, solid state storage, or any other memory media usable to store information for later access. By way of example, and not limitation, the memory 184 can include wired media, such as a wired network or direct-wired connection, and wireless media, such as acoustic, RF, infrared, other wireless media, or combinations thereof. As noted, in certain embodiments, the memory 184 comprises the electrical field propagation measurements module 131, the sound processor 133, and the monitoring module 135 that, when executed, enables the processing unit 183 to perform aspects of the techniques presented.

[0052] In the illustrated example of FIG. IE, the external computing device 110 further includes a network adapter 186, one or more input devices 187, and one or more output devices 188. The external computing device 110 can include other components, such as a system bus, component interfaces, a graphics system, a power source (e.g., a battery), among other components. The network adapter 186 is a component of the external computing device 110 that provides network access (e.g., access to at least one network 189). The network adapter

186 can provide wired or wireless network access and can support one or more of a variety of communication technologies and protocols, such as Ethernet, cellular, Bluetooth, near-field communication, and RF, among others. The network adapter 186 can include one or more antennas and associated components configured for wireless communication according to one or more wireless communication technologies and protocols. The one or more input devices

187 are devices over which the external computing device 110 receives input from a user. The one or more input devices 187 can include physically-actuatable user-interface elements (e.g., buttons, switches, or dials), a keypad, keyboard, mouse, touchscreen, and voice input devices, among other input devices that can accept user input. The one or more output devices 188 are devices by which the external computing device 110 is able to provide output to a user. The output devices 188 can include a display 190 (e.g., a liquid crystal display (LCD)) and one or more speakers 191, among other output devices for presentation of visual or audible information to the recipient, a clinician, an audiologist, or other user.

[0053] It is to be appreciated that the arrangement for the external computing device 110 shown in FIG. IE is merely illustrative and that aspects of the techniques presented herein can be implemented at a number of different types of systems/devices including any combination of hardware, software, and/or firmware configured to perform the functions described herein. For example, the external computing device 110 can be a personal computer (e.g., a desktop or laptop computer), a hand-held device (e.g., a tablet computer), a mobile device (e.g., a smartphone), a surgical system, and/or any other electronic device having the capabilities to perform the associated operations described elsewhere herein.

[0054] As noted, in accordance with embodiments presented herein, an implantable medical device is configured to capture electrical field propagation measurements from a tissue/body region of a recipient in which the device is implanted. In certain embodiments, the implantable medical device is configured to capture the electrical field propagation measurements from a fluidically-sealed body chamber/cavity of the recipient, such as the inner ear (e.g., cochlea, vestibular system, etc.) of the recipient. The electrical field propagation measurements can comprise, for example, impedance measurements, such as transimpedance measurements.

[0055] In operation, the electrical field propagation measurements captured by an implantable medical device are analyzed by a data-derived model to estimate/determine one or more operational parameters for the implantable medical device. More specifically, features of the electrical field propagation measurements are extracted and utilized as input to the data-derived model. The data-derived model is configured to analyze the extracted features and output the one or more operational parameters, such as stimulation parameters, for the implantable medical device.

[0056] In certain embodiments, as described further below, the implantable medical device is also configured to capture neural response measurements from the body region. These neural response measurements can also be input to the data-derived model such that the data-derived model determines one or more operational parameters based on the electrical field propagation measurements and the neural response measurements.

[0057] In certain embodiments, also as described further below, the one or more operational parameters are compared to reference values in a validation process. If the one or more operational parameters do not fall within a predetermined range of the reference values, the one or more operational parameters are rejected. A user can be notified of the rejection and, in certain examples, feedback from the user is requested. In such examples, the one or more operational parameters are refined based on user feedback.

[0058] FIG. 2A is a functional block diagram illustrating a system 200A for configuring an implantable medical 206 with one or more operational parameters, in accordance with certain embodiments presented herein. The implantable medical device 206 can be, for example, a cochlear implant (e.g., cochlear implant 112), or another device.

[0059] As illustrated in FIG. 2A, electrical field propagation measurements 201 are captured from the implantable medical device 206 and provided to a computing device 210. The computing device 210 can be, for example, a personal computer (e.g., a desktop or laptop computer), a hand-held device (e.g., a tablet computer), a mobile device (e.g., a smartphone), a surgical system, a cloud server, and/or any other electronic device having the capabilities to perform the associated operations described herein.

[0060] In operation, the computing system 210 comprises logic (when executed by one or more processors) is configured to perform feature extraction 202 and to implement a data-derived model 204A. Feature extraction 202 is applied to extract one or more features 203 from the electrical field propagation measurements 201. The data-derived model 204A processes the one or more features 203 to determine one or more operational parameters 205 of the implantable medical device 206. The implantable medical device 206 is then configured with the one or more operational parameters 205. That is, the one or more one or more operational parameters 205 can be instantiated/installed in the implantable medical device 206 for subsequent operational use. Further details of the feature extraction 202 and the use of the data-derived model 204A are provided below.

[0061] In certain embodiments, feature extraction 202 is implemented using dimensionality reduction techniques. Dimensionality reduction techniques include any technique that reduces the dimensionality of data, including principal component analysis (PCA). The data-derived model 204A can be, for example, a statistical model, a probabilistic model, or a machine learning model. Moreover, the data-derived model 204A can be an unsupervised, semisupervised, or supervised model. Statistical models can be implemented using regression techniques and PCA. Probabilistic models can be implemented using Bayesian techniques. Machine learning models can include neural networks, K-nearest neighbors, support vector machines, etc. Neural networks can be deep neural networks, including convolutional neural networks.

[0062] As noted, the electrical field propagation measurements 201 are captured by the implantable medical device 206. To this end, the implantable medical device 206 includes one or more electrodes 244 and a measurement circuit 245 (e.g., one or more amplifiers, etc.) used to capture the electrical field propagation measurements 201. In certain embodiments, the electrical field propagation measurements 201 can include, for example, transimpedance measurements, electrical voltage tomography measurements, or electrical impedance tomography measurements. In examples, in which the electrical field propagation measurements 201 are transimpedance measurements, the measurements can be represented by a transimpedance matrix (TIM) that represents the propagation of electrical field(s) in the cochlea in response to electrical stimulation. [0063] Also as noted above, the feature extraction 202 extracts one or more features 203 from the electrical field propagation measurements 201. In certain embodiments, feature extraction 202 extracts one or more features 203 that represent one or more characteristics of at least one distance measurement. In such embodiments, a distance measurement can indicate a distance between one of a plurality of electrodes and tissue (e.g., modiolus tissue) of the recipient. Table 1, below, provides a list of example features 203 that can be extracted from electrical field propagation measurements 201 in accordance with certain embodiments presented herein.

Table 1- Features and Descriptions

[0064] As noted above, the data-derived model 204A processes the one or more features 203 to determine the one or more operational parameters 205. In certain embodiments, the data- derived model 204A is developed using PCA. More specifically, a plurality sets of one or more features 203 are extracted from electrical field propagation measurements 201. For each set of one or more features 203, a first PCA model is trained and applied to transform the set of one or more features 203. Further, a second PCA model is trained using the one or more operational parameters 205 such as T-levels, C-levels, or dynamic range. The one or more operational parameters 205 are then transformed using the second trained PCA model. Alternatively, a separate PCA model may be trained for each type of operational parameter. For example, one PCA model is trained using T-levels, while another PCA model is trained using C-levels. Weights of the resulting principal components from the PCA models are estimated using statistical modeling, probabilistic modeling, or machine learning. A feature selection algorithm is applied to the plurality sets of one or more features 203 to select an optimized set of features as input to data-derived model 204A. The data-derived model 204A is implemented based on the optimized set of one or more features 203 and the one or more weights.

[0065] FIG. 2B is a functional block diagram illustrating another embodiment of system 200A for configuring the implantable medical device 206 with one or more operational parameters 205. The arrangement of FIG. 2B is substantially similar to that of FIG. 2A except that the computing system 210 obtains the electrical field propagation measurements 201 from another computing system 246, rather than directly from the implantable medical device 206.

[0066] For example, in certain embodiments, the implantable medical device 206 is configured to capture electrical field propagation measurements 201 in an intraoperative setting when a recipient undergoes cochlear implant surgery. During or after the surgery, the captured electrical field propagation measurements 201 are stored in the computing system 246 for subsequent use by the computing system 210. In certain embodiments, the computing system 246 can be a cloud server. In such embodiments, a user can upload the electrical field propagation measurements 201 captured by the implantable medical device 206 to the computing system 246. Alternatively, the electrical field propagation measurements 201 captured by the implantable medical device 206 can be automatically uploaded to the computing system 246. In certain embodiments, the computing system 246 and the computing system 210 are the same device.

[0067] FIG. 2C is a functional block diagram illustrating another embodiment of system 200A for configuring the implantable medical 206 with one or more operational parameters in accordance with certain embodiments presented herein. The arrangement of FIG. 2C is substantially similar to that of FIG. 2A except that that, in FIG. 2C, validation 207 is applied to the one or more operational parameters 205 determined by the data-derived model 204A.

[0068] More specifically, in this example, the computing system 210 comprises logic (when executed by one or more processors) configured to perform validation 207 on the one or more operational parameters 205 to generate one or more validated operational parameters 208. The implantable medical device 206 is then configured with the one or more validated operational parameters 208. That is, the one or more validated operational parameters 208 can be instantiated/installed in the implantable medical device 206 for subsequent operational use. Details of the feature extraction 202 and the use of the data-derived model 204A are provided above in the description of FIG. 2A. Further details of the validation 207 are provided below in FIGS. 3A-3C. Although validation 207 is described with respect to the data-derived model 204A, validation 207 is applicable to any of the data-derived models discussed herein.

[0069] FIG. 2D is a functional block diagram illustrating a system 200B configuring implantable medical device 206 with one or more operational parameters. As illustrated in FIG. 2D, neural response measurements 209 are captured by the implantable medical device 206 and provided to the computing device 210. In certain embodiments, neural response measurements 209 can include electrically evoked compound action potential (eCAP) measurements.

[0070] In operation, the computing system 210 comprises logic (when executed by one or more processors) is configured to perform feature extraction 202 and to implement a data-derived model 204. Feature extraction 202 is applied to extract one or more features 203 from the electrical field propagation measurements 201. The data-derived model 204B processes the one or more features 203 and the neural response measurements 209 to determine one or more operational parameters 205 of the implantable medical device 206. The implantable medical device 206 is then configured with the one or more operational parameters 205. That is, the one or more operational parameters 205 can be instantiated/installed in the implantable medical device 206 for subsequent operational use.

[0071] FIG. 2E is a functional block diagram illustrating a system 200E configuring implantable medical device 206 with one or more operational parameters. In certain embodiments, in addition to the one or more extracted features 203, one or more predictors 247 can be input to the data-derived model 204C. The one or more predictors 247 can include, for example, one or more placement characteristics associated with the placement of one or more electrode arrays inside the cochlea. For example, the one or more placement characteristics can include the depth of insertion of the one or more electrode arrays, the distance from the modiolar axis to an electrode, the distance from the mid-modiolar axis to an electrode, or the distance from the modiolus to an electrode. In such embodiments, the one or more placement characteristics can be measured from medical imaging, such as computed tomography (CT) scans of the cochlea, using annotation tools or automated processing algorithms. In certain embodiments, the CT scans of a recipient’s cochlea can be obtained after the recipient undergoes cochlear implant surgery.

[0072] In operation, the computing system 210 comprises logic (when executed by one or more processors) is configured to perform feature extraction 202 and to implement a data-derived model 204C. Feature extraction 202 is applied to extract one or more features 203 from the electrical field propagation measurements 201. The data-derived model 204C processes the one or more features 203 and the one or more predictors 247 to determine one or more operational parameters 205 of the implantable medical device 206. The implantable medical device 206 is then configured with the one or more operational parameters 205. That is, the one or more one or more operational parameters 205 can be instantiated/installed in the implantable medical device 206 for subsequent operational use.

[0073] FIG. 2F is a schematic view of a system 200D that implements a data-derived model 204D utilizing one or more inputs, one or more processing techniques, and one or more modeling techniques. In certain embodiments, the one or more operational parameters 205 processed by dimensionality reduction 250 are inputs to one or more modeling techniques. [0074] In certain embodiments, feature extraction 202 is applied to electrical field propagation measurements 201. Then, dimensionality reduction 250 is applied to the extracted features to generate one or more inputs to the one or more modeling techniques. In certain embodiments, the electrical field propagation measurements 201 do not undergo feature extraction 202. Instead, the electrical field propagation measurements 201 are processed directly by dimensionality reduction 250 to generate inputs for the one or more modeling techniques.

[0075] System 200D is configured to implement the one or more modeling techniques to generate and output a data-derived model 204D based on one or more inputs generated from the one or more processing techniques discussed herein. For example, the one or more modeling techniques include statistical modeling 221, probabilistic modeling 252, and machine learning 254.

[0076] FIG. 2G is a schematic view of a system 200E that trains a convolutional neural network used to determine one or more operational parameters 205 for configurating the implantable medical device 206. As illustrated in FIG. 2G, dimensionality reduction 250 is applied to one or more operational parameters 205. Then, the processed one or more operational parameters 205 and the electrical field propagation measurements 201 are used to train a convolutional neural network 255. The system 200E is configured to output the trained convolutional neural network 255 as the data-derived model 204E.

[0077] FIG. 3 A is a flowchart illustrating a method 300, in accordance with certain embodiments presented herein. Method 300 begins at 301 where one or more operational parameters generated by a data-derived model are validated to generate one or more validated stimulation parameters. At 302, one or more operational parameters of a recipient device are adjusted based on the one or more validated stimulation parameters. For example, the T-levels and C-levels of a cochlear implant device may be adjusted based on the T-levels and C-levels determined by the data-derived model.

[0078] FIG. 3B is a flowchart illustrating further details of the validation process 301 of FIG. 3A. In this example, at 303, one or more estimated stimulation parameters are compared to one or more reference values. At 304, a determination is made as to whether the one or more estimated stimulation parameters are within a predetermined range of the one or more reference values. If the one or more estimated stimulation parameters are within the predetermined range, then the validation process proceeds to 305 and outputs the one or more operational parameters based on the one or more estimated stimulation parameters. If the one or more estimated stimulation parameters are not within the predetermined range, then the validation process proceeds to 306 to refine the one or more estimated stimulation parameters.

[0079] In certain embodiments, the one or more reference values are derived from population statistics associated with the one or more stimulation parameters. That is, at 303, the one or more estimated stimulation parameters are compared to values derived from population statistics. Further, the predetermined range can be set by a user or other suitable methods. For example, the user may set the predetermined range to be between the 5^th percentile and the 95^th percentile, and estimated stimulation parameters falling outside of this range would result in parameter refinement.

[0080] In certain embodiments, the one or more features 203 are extracted from the electrical field propagation measurements 201 and the one or more features 203 are transformed by one or more PCA models, resulting in outputs that are processed by PCA weight models that estimate the weights of the PCA transforms. Each estimated weight is multiplied with a corresponding principal component, and the corresponding results from the multiplications are summed and stored in a vector. Based on the values in the vector, one or more operational parameters 205 are estimated. In such embodiments, the validation process compares the one or more weights associated with the PCA transforms to a weight distribution of training data used to train the PCA models. If the one or more weights do not fall within a predetermined range, then the estimated one or more stimulation parameters 205 are refined.

[0081] FIG. 3C is a flowchart illustrating details of the parameter refinement process described at 306 of FIG. 3B. More specifically, the operations of FIG. 3C begin at 307 where the one or more estimated stimulation parameters are rejected because they are not within the predetermined range. Upon rejection, at 308, the one or more estimated stimulation parameters are sent to a user with information indicating the reason for rejection. In certain embodiments, the user is a clinician involved in a fitting session or another individual qualified to review parameters for configuring an implantable medical device. In certain embodiments, information on the difference between the one or more estimated stimulation parameters and the one or more reference values is displayed on a graphical display as the reason for rejection.

[0082] At 309, the user’s feedback on refining the one or more estimated stimulation parameters is received. At 310, the one or more estimated stimulation parameters are refined based on the user’s feedback. Upon refinement of the one or more estimated stimulation parameters, the process returns to 304 of FIG. 3B, where the refined estimated stimulation parameters are reevaluated. The operations of 304 and 306 of FIG. 3B proceed iteratively until the one or more stimulated parameters fall within the predetermined range at 304, at which point the process outputs one or more operational parameters based on the one or more estimated stimulation parameters at step 305.

[0083] FIG. 4 is a flowchart illustrating a method 400, in accordance with certain embodiments presented herein. Method 400 begins at 401 where a plurality of electrical field propagation measurements, captured via an implantable medical device configured to be implanted in a recipient, is obtained. At 402, a plurality of features is extracted from the plurality of electrical field propagation measurements. For example, dimensionality reduction techniques such as PCA can be applied to extract the plurality of features. At 403, the extracted features are analyzed with a data-derived model to determine one or more operational parameters of the implantable medical device. At 404, the implantable medical device is configured with the one or more operational parameters determined by the data-derived model. For example, the settings of the implantable medical device are adjusted based on the one or more determined operational parameters such as T-levels, C-levels, or dynamic ranges.

[0084] FIG. 5 is a flowchart illustrating a method 500, in accordance with certain embodiments presented herein. Method 500 beings at 501 where a plurality of electrical field propagation measurements is obtained from a body region of a recipient. For example, the plurality of electrical field propagation measurements is obtained from an inner ear of a recipient. At 502, a plurality of features is extracted from the plurality of electrical field propagation measurements. At 503, the plurality of features is processed with a model to generate one or more estimated stimulation parameters for a recipient device. For example, the estimated stimulation parameters include T-levels, C-levels, or dynamic ranges.

[0085] FIG. 6 is a flowchart illustrating a method 600, in accordance with certain embodiments presented herein, where the operations are performed by a processor executing instructions stored in one or more non-transitory computer readable storage media. At 601 , the processor obtains a plurality of electrical field propagation measurements captured via an implantable medical device. At 602, the processor analyzes a plurality of features of the plurality of electrical field propagation measurements with a data-derived model to determine one or more operational parameters of the implantable medical device. For example, the data- derived model is a statistical model, a probabilistic model, or a machine learning model applied to determine the T-levels, C-levels, or dynamic ranges of an electrically-stimulating hearing prosthesis. At 603, the processor configures the implantable medical device with the one or more operational parameters. For example, the electrically-stimulating hearing prosthesis is configured based on the determined T-levels, C-levels, or dynamic ranges.

[0086] FIG. 7 is a flowchart illustrating a method 700, in accordance with certain embodiments presented herein, where the operations are performed by a system comprising a memory and at least one processor operable coupled to the memory. At 701, the processor is configured to obtain a plurality of electrical field propagation measurements from a body region of a recipient. For example, the plurality of electrical field propagation measurements are captured from an inner ear of a recipient via an implantable medical device. At 702, the processor is configured to analyze a plurality of features of the plurality of electrical field propagation measurements with a data-derived model to generate one or more estimated stimulation parameters for a recipient device. For example, the data-derived model is a statistical model, a probabilistic model, or a machine learning model applied to determine the T-levels, C-levels, or dynamic ranges of an electrically-stimulating hearing prosthesis. At 703, the processor is configured to configure the recipient device with the one or more estimated stimulation parameters. For example, the electrically-stimulating hearing prosthesis is configured based on the determined T-levels, C-levels, or dynamic ranges.

[0087] As previously described, the technology disclosed herein can be applied in any of a variety of circumstances and with a variety of different devices. Example devices that can benefit from technology disclosed herein are described in more detail in FIGS. 8 and 9. The techniques of the present disclosure can be applied to other devices, such as neurostimulators, cardiac pacemakers, cardiac defibrillators, sleep apnea management stimulators, seizure therapy stimulators, tinnitus management stimulators, and vestibular stimulation devices, as well as other medical devices that deliver stimulation to tissue. Further, technology described herein can also be applied to consumer devices. These different systems and devices can benefit from the technology described herein.

[0088] FIG. 8 illustrates an example vestibular stimulator system 802, with which embodiments presented herein can be implemented. As shown, the vestibular stimulator system 802 comprises an implantable component (vestibular stimulator) 812 and an external device/component 804 (e.g., external processing device, battery charger, remote control, etc.). The external device 804 comprises a transceiver unit 860. As such, the external device 804 is configured to transfer data (and potentially power) to the vestibular stimulator 812. [0089] The vestibular stimulator 812 comprises an implant body (main module) 834, a lead region 836, and a stimulating assembly 816, all configured to be implanted under the skin/tissue (tissue) 815 of the recipient. The implant body 834 generally comprises a hermetically-sealed housing 838 in which RF interface circuitry, one or more rechargeable batteries, one or more processors, and a stimulator unit are disposed. The implant body 134 also includes an intemal/implantable coil 814 that is generally external to the housing 838, but which is connected to the transceiver via a hermetic feedthrough (not shown).

[0090] The stimulating assembly 816 comprises a plurality of electrodes 844(l)-(3) disposed in a carrier member (e.g., a flexible silicone body). In this specific example, the stimulating assembly 816 comprises three (3) stimulation electrodes, referred to as stimulation electrodes 844(1), 844(2), and 844(3). The stimulation electrodes 844(1), 844(2), and 844(3) function as an electrical interface for delivery of electrical stimulation signals to the recipient’s vestibular system.

[0091] The stimulating assembly 816 is configured such that a surgeon can implant the stimulating assembly adjacent the recipient’s otolith organs via, for example, the recipient’s oval window. It is to be appreciated that this specific embodiment with three stimulation electrodes is merely illustrative and that the techniques presented herein may be used with stimulating assemblies having different numbers of stimulation electrodes, stimulating assemblies having different lengths, etc.

[0092] In operation, the vestibular stimulator 812, the external device 804, and/or another external device can be configured to implement the techniques presented herein. That is, the vestibular stimulator 812, possibly in combination with the external device 804 and/or another external device, can include an evoked biological response analysis system, as described elsewhere herein.

[0093] FIG. 9 illustrates a retinal prosthesis system 901 that comprises an external device 910 (which can correspond to the wearable device 100) configured to communicate with an implantable retinal prosthesis 900 via signals 951. The retinal prosthesis 900 comprises an implanted processing module 925, and a retinal prosthesis sensor-stimulator 990 is positioned proximate the retina of a recipient. The external device 910 and the processing module 925 can communicate via coils 908, 914.

[0094] In an example, sensory inputs (e.g., photons entering the eye) are absorbed by a microelectronic array of the sensor-stimulator 990 that is hybridized to a glass piece 992 including, for example, an embedded array of microwires. The glass can have a curved surface that conforms to the inner radius of the retina. The sensor-stimulator 990 can include a microelectronic imaging device that can be made of thin silicon containing integrated circuitry that convert the incident photons to an electronic charge.

[0095] The processing module 925 includes an image processor 923 that is in signal communication with the sensor-stimulator 990 via, for example, a lead 988 that extends through surgical incision 989 formed in the eye wall. In other examples, processing module 925 is in wireless communication with the sensor-stimulator 990. The image processor 923 processes the input into the sensor-stimulator 990 and provides control signals back to the sensor-stimulator 990 so the device can provide an output to the optic nerve. That said, in an alternate example, the processing is executed by a component proximate to, or integrated with, the sensor-stimulator 990. The electric charge resulting from the conversion of the incident photons is converted to a proportional amount of electronic current which is input to a nearby retinal cell layer. The cells fire and a signal is sent to the optic nerve, thus inducing a sight perception.

[0096] The processing module 925 can be implanted in the recipient and function by communicating with the external device 910, such as a BTE unit, a pair of eyeglasses, etc . The external device 910 can include an external light/image capture device (e.g., located in/on a behind-the-ear device or a pair of glasses, etc.), while, as noted above, in some examples, the sensor-stimulator 990 captures light/images, in which sensor-stimulator 990 is implanted in the recipient.

[0097] As should be appreciated, while particular uses of the technology have been illustrated and discussed above, the disclosed technology can be used with a variety of devices in accordance with many examples of the technology. The above discussion is not meant to suggest that the disclosed technology is only suitable for implementation within systems akin to that illustrated in the figures. In general, additional configurations can be used to practice the processes and systems herein and/or some aspects described can be excluded without departing from the processes and systems disclosed herein.

[0098] This disclosure described some aspects of the present technology with reference to the accompanying drawings, in which only some of the possible aspects were shown. Other aspects can, however, be embodied in many different forms and should not be construed as limited to the aspects set forth herein. Rather, these aspects were provided so that this disclosure was thorough and complete and fully conveyed the scope of the possible aspects to those skilled in the art.

[0099] As should be appreciated, the various aspects (e.g., portions, components, etc.) described with respect to the figures herein are not intended to limit the systems and processes to the particular aspects described. Accordingly, additional configurations can be used to practice the methods and systems herein and/or some aspects described can be excluded without departing from the methods and systems disclosed herein.

[ooioo] According to certain aspects, systems and non-transitory computer readable storage media are provided. The systems are configured with hardware configured to execute operations analogous to the methods of the present disclosure. The one or more non-transitory computer readable storage media comprise instructions that, when executed by one or more processors, cause the one or more processors to execute operations analogous to the methods of the present disclosure.

[ooioi] Similarly, where steps of a process are disclosed, those steps are described for purposes of illustrating the present methods and systems and are not intended to limit the disclosure to a particular sequence of steps. For example, the steps can be performed in differing order, two or more steps can be performed concurrently, additional steps can be performed, and disclosed steps can be excluded without departing from the present disclosure. Further, the disclosed processes can be repeated.

[00102] Although specific aspects were described herein, the scope of the technology is not limited to those specific aspects. One skilled in the art will recognize other aspects or improvements that are within the scope of the present technology. Therefore, the specific structure, acts, or media are disclosed only as illustrative aspects. The scope of the technology is defined by the following claims and any equivalents therein.

[00103] It is also to be appreciated that the embodiments presented herein are not mutually exclusive and that the various embodiments may be combined with another in any of a number of different manners.

Claims

CLAIMS What is claimed is:

1. A method comprising: obtaining a plurality of electrical field propagation measurements captured via an implantable medical device configured to be implanted in a recipient; extracting a plurality of features from the plurality of electrical field propagation measurements; analyzing the plurality of features with a data-derived model to determine one or more operational parameters of the implantable medical device; and configuring the implantable medical device with the one or more operational parameters.

2. The method of claim 1, wherein the plurality of electrical field propagation measurements comprise one of: transimpedance measurements, electrical voltage tomography measurements, or electrical impedance tomography measurements.

3. The method of claim 1 or 2, wherein extracting the plurality of features from the plurality of electrical field propagation measurements comprises: applying one or more dimensionality reduction techniques to extract the plurality of features.

4. The method of claim 1 or 2, wherein the data-derived model is one of: a supervised statistical model, a probabilistic model, or a machine learning model.

5. The method of claim 1 or 2, wherein the one or more operational parameters comprise one or more stimulation parameters of the implantable medical device.

6. The method of claim 5, wherein the implantable medical device includes a plurality of electrodes, and wherein the one or more stimulation parameters comprise two or more of: threshold levels associated with the plurality of electrodes, comfort levels associated with the plurality of electrodes, or a dynamic range associated with the plurality of electrodes.

7. The method of claim 1 or 2, wherein the plurality of features extracted from the plurality of electrical field propagation measurements represent a plurality of characteristics of a plurality of distance measurements, wherein each distance measurement indicates a distance between one of a plurality of electrodes and a modiolus tissue.

8. The method of claim 1 or 2, further comprising: validating the one or more operational parameters.

9. The method of claim 1 or 2, further comprising: obtaining one or more neural response measurements captured via the implantable medical device; and analyzing the plurality of features and the one or more neural response measurements with the data-derived model to determine the one or more operational parameters of the implantable medical device.

10. The method of claim 1 or 2, wherein configuring the implantable medical device comprises: configuring an auditory prosthesis with the one or more operational parameters.

11. A method, comprising: obtaining a plurality of electrical field propagation measurements from a body region of a recipient; extracting a plurality of features from the plurality of electrical field propagation measurements; and processing the plurality of features with a model to generate one or more estimated stimulation parameters for a recipient device.

12. The method of claim 11, wherein the plurality of features extracted from the plurality of electrical field propagation measurements comprise a plurality of distance measurements, wherein each distance measurement indicates a distance between one of a plurality of electrodes and a modiolus tissue.

13. The method of claim 11 or 12, further comprising: validating the one or more estimated stimulation parameters to generate validated stimulation parameters; and adjusting one or more operational parameters of the recipient device based on the validated stimulation parameters.

14. The method of claim 13, wherein validating the one or more estimated stimulation parameters comprises: comparing the one or more estimated stimulation parameters to one or more reference values; and rejecting the one or more estimated stimulation parameters if the one or more estimated stimulation parameters are not within a predetermined range of the one or more reference values.

15. The method of claim 14, further comprising: if the one or more estimated stimulation parameters are rejected, sending the one or more estimated stimulation parameters to a user with information indicating a reason for rejection.

16. The method of claim 11 or 12, further comprising: receiving feedback from the user to refine the one or more estimated stimulation parameters.

17. The method of claim 11 or 12, wherein the plurality of electrical field propagation measurements comprise transimpedance measurements.

18. The method of claim 11 or 12, wherein extracting the plurality of features from the plurality of electrical field propagation measurements comprises: applying one or more dimensionality reduction techniques to extract the plurality of features.

19. The method of claim 11 or 12, wherein the model is a data-derived model.

20. The method of claim 19, wherein the data-derived model is one of: a supervised statistical model or a machine learning model.

21. The method of claim 11 or 12, wherein the recipient device includes a plurality of electrodes, and wherein the one or more estimated stimulation parameters comprise two or more of: threshold levels associated with the plurality of electrodes, comfort levels associated with the plurality of electrodes, or a dynamic range associated with the plurality of electrodes.

22. The method of claim 11 or 12, wherein the body region of the recipient is a sealed body chamber.

23. The method of claim 22, wherein the sealed body chamber is an inner ear of the recipient.

24. One or more non-transitory computer readable storage media comprising instructions that, when executed by a processor, cause the processor to: obtain a plurality of electrical field propagation measurements captured via an implantable medical device; analyze a plurality of features of the plurality of electrical field propagation measurements with a data-derived model to determine one or more operational parameters of the implantable medical device; and configure the implantable medical device with the one or more operational parameters.

25. The one or more non-transitory computer readable storage media of claim 24, wherein the plurality of electrical field propagation measurements comprise one of: transimpedance measurements, electrical voltage tomography measurements, or electrical impedance tomography measurements.

26. The one or more non-transitory computer readable storage media of claim 24, further comprising instructions that, when executed by a processor, cause the processor to: extract the plurality of features from the plurality of electrical field propagation measurements.

27. The one or more non-transitory computer readable storage media of claim 26, further comprising instructions that, when executed by a processor, cause the processor to: extract the plurality of features from the plurality of electrical field propagation measurements by applying one or more dimensionality reduction techniques to the plurality of features.

28. The one or more non-transitory computer readable storage media of claim 24, wherein the data-derived model is one of: a supervised statistical model, a probabilistic model, or a machine learning model.

29. The one or more non-transitory computer readable storage media of claim 24, 25, 26, 27, or 28, wherein the one or more operational parameters comprise one or more stimulation parameters of the implantable medical device.

30. The one or more non-transitory computer readable storage media of claim 29, wherein the implantable medical device includes a plurality of electrodes, and wherein the one or more stimulation parameters comprise two or more of: threshold levels associated with the plurality of electrodes, comfort levels associated with the plurality of electrodes, or a dynamic range associated with the plurality of electrodes.

31. The one or more non-transitory computer readable storage media of claim 24, 25, 26, 27, or 28, wherein the plurality of features extracted from the plurality of electrical field propagation measurements represent a plurality of characteristics of a plurality of distance measurements, wherein each distance measurement indicates a distance between one of a plurality of electrodes and a modiolus tissue.

32. The one or more non-transitory computer readable storage media of claim 24, 25, 26, 27, or 28, further comprising instructions that, when executed by a processor, cause the processor to: validate the one or more operational parameters.

33. The one or more non-transitory computer readable storage media of claim 24, 25, 26, 27, or 28 further comprising instructions that, when executed by a processor, cause the processor to: obtain one or more neural response measurements captured via the implantable medical device; and analyze the plurality of features and the one or more neural response measurements with the data-derived model to determine the one or more operational parameters of the implantable medical device.

34. The one or more non-transitory computer readable storage media of claim 24, 25, 26, 27, or 28, further comprising instructions that, when executed by a processor, cause the processor to configure an auditory prosthesis with the one or more operational parameters.

35. A system, comprising: a memory; and at least one processor operable coupled to the memory, wherein the at least one processor is configured to: obtain a plurality of electrical field propagation measurements from a body region of a recipient; analyze a plurality of features of the plurality of electrical field propagation measurements with a data-derived model to generate one or more estimated stimulation parameters for a recipient device; and configure the recipient device with the one or more estimated stimulation parameters.

36. The system of claim 35, wherein the plurality of features extracted from the plurality of electrical field propagation measurements comprise a plurality of distance measurements, wherein each distance measurement indicates a distance between one of a plurality of electrodes and a modiolus tissue.

37. The system of claim 35, wherein the at least one processor is further configured to: validate the one or more estimated stimulation parameters to generate validated stimulation parameters; and adjust one or more operational parameters of the recipient device based on the validated stimulation parameters.

38. The system of claim 35, 36, or 37, wherein the at least one processor is further configured to: compare the one or more estimated stimulation parameters to one or more reference values; and reject the one or more estimated stimulation parameters if the one or more estimated stimulation parameters are not within a predetermined range of the one or more reference values.

39. The system of claim 38, wherein the at least one processor is further configured to: if the one or more estimated stimulation parameters are rejected, send the one or more estimated stimulation parameters to a user with information indicating a reason for rejection.

40. The system of claim 39, wherein the at least one processor is further configured to: receive feedback from the user to refine the one or more estimated stimulation parameters.

41. The system of claim 35, 36, or 37, wherein the plurality of electrical field propagation measurements comprise transimpedance measurements.

42. The system of claim 35, 36, or 37, wherein the at least one processor is further configured to: extract the plurality of features of the plurality of electrical field propagation measurements by applying one or more dimensionality reduction techniques.

43. The system of claim 35, 36, or 37, wherein the data-derived model is one of: a supervised statistical model, a probabilistic model, or a machine learning model.

44. The system of claim 35, 36, or 37, wherein the recipient device includes a plurality of electrodes, and wherein the one or more estimated stimulation parameters comprise two or more of: threshold levels associated with the plurality of electrodes, comfort levels associated with the plurality of electrodes, or a dynamic range associated with the plurality of electrodes.

45. The system of claim 35, 36, or 37, wherein the body region of the recipient is a sealed body chamber.

46. The system of claim 45, wherein the sealed body chamber is an inner ear of the recipient.