Electro-acoustic stimulation parameter adjustment

Abstract

A system and a method are presented herein, the method comprising: obtaining one or more electrophysiological measurements of acoustic hearing associated with a hearing device recipient; obtaining one or more hearing performance metrics of the recipient; analyzing the one or more electrophysiological measures relative to the one or more hearing performance measures; and generating an output based on the analysis of the one or more electrophysiological measures relative to the one or more hearing performance measures.

Description

background Technical Field

[0001] This invention generally relates to the adjustment of electroacoustic stimulation parameters. Background Technology

[0002] Over the past few decades, medical devices have provided a wide range of therapeutic benefits to recipients. Medical devices can include internal or implantable components / devices, external or wearable components / devices, or combinations thereof (e.g., devices having an external component that communicates with the implantable component). Medical devices, such as conventional 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 for many years in performing life-saving and / or lifestyle improvement functions and / or recipient monitoring.

[0003] Over the years, the types of medical devices and the range of functions they perform have increased. For example, many medical devices, sometimes referred to as “implantable medical devices,” now typically include one or more instruments, devices, sensors, processors, controllers, or other functional mechanical or electrical components that are permanently or temporarily implanted into a recipient’s body. These functional devices are typically used to diagnose, prevent, monitor, treat, or manage diseases / injuries or their symptoms, or to study, replace, or modify anatomical structures or physiological processes. Many of these functional devices utilize power and / or data received from an external device that is part of or operates in conjunction with the implantable component. Summary of the Invention

[0004] In one aspect, a first method is provided. The first method includes: obtaining one or more electrophysiological measures of acoustic hearing associated with a recipient of a hearing device; obtaining one or more hearing performance measures of the recipient; analyzing the one or more electrophysiological measures relative to the one or more hearing performance measures; and generating an output based on the analysis of the one or more electrophysiological measures relative to the one or more hearing performance measures.

[0005] In another aspect, a second method is provided. The second method includes: performing one or more electrophysiological tests to obtain one or more evoked potentials or one or more impedances from a hearing device recipient; correlating the one or more evoked potentials or impedances with one or more hearing performance measures; and automatically providing programmed adjustments of one or more electroacoustic stimuli (EAS) for the hearing device based on the correlation.

[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 include instructions that, when executed by a processor, cause the processor to: obtain one or more electrophysiological measurements of acoustic hearing associated with a recipient of the hearing device; obtain one or more hearing performance measurements of the recipient; analyze the one or more electrophysiological measurements relative to the one or more hearing performance measurements; and initiate at least one adjustment to the operation of the hearing device based on the analysis of the one or more electrophysiological measurements relative to the one or more hearing performance measurements.

[0007] In another aspect, a hearing device system is provided. The hearing device system includes: one or more sensors; a memory storing computer-readable instructions; and a processor configured to execute the computer-readable instructions to: perform one or more electrophysiological tests to obtain one or more evoked potentials or one or more impedances from a recipient of the hearing device; correlate the one or more evoked potentials or the one or more impedances with one or more hearing performance measures; and automatically provide programmed adjustments of one or more electroacoustic stimulations (EAS) for the hearing device based on the correlation. Attached Figure Description

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

[0009] Figure 1A This is a schematic diagram illustrating an electroacoustic hearing device according to certain embodiments presented herein;

[0010] Figure 1B yes Figure 1A A block diagram of an electroacoustic hearing device;

[0011] Figure 1C This is a block diagram of a receiver device, such as a mobile phone or computer, on which stimulus modulation logic can be implemented according to certain embodiments presented herein.

[0012] Figure 2 An exemplary graphical receiver interface (GUI) screen of a computing device for collecting receiver input about subjective hearing ability using ecological instantaneous assessment (EMA) technology, according to an exemplary embodiment, is shown.

[0013] Figure 3A This is a schematic diagram showing the initial electroacoustic stimulation (EAS) programming parameters set for acoustic stimulation and electrical stimulation, respectively;

[0014] Figure 3BThis is a schematic diagram illustrating the change from acoustic stimulation to electrical stimulation only in the affected region where a threshold change is detected, according to an exemplary embodiment.

[0015] Figure 3C This is a schematic diagram illustrating an increased acoustic stimulus in the affected region where a threshold change is detected, according to an exemplary embodiment.

[0016] Figure 3D This is a schematic diagram illustrating, according to an exemplary embodiment, the change to electroacoustic stimulation (wherein optionally the acoustic stimulation is increased) in the affected region where a threshold change is detected;

[0017] Figure 4 This is a conceptual diagram illustrating an exemplary system according to one or more aspects of this disclosure;

[0018] Figure 5 This is an operation flowchart of a first method for implementing automated electroacoustic stimulation programming adjustment according to an exemplary embodiment; and

[0019] Figure 6 This is an operational flowchart of a second method for implementing automated electroacoustic stimulation programming adjustment according to an exemplary embodiment. Detailed Implementation

[0020] Overview

[0021] Individuals suffer from different types of hearing loss (e.g., conductive and / or sensorineural) and / or varying degrees / severities of hearing loss. However, it is now common for many recipients to retain some residual natural hearing ability (residual hearing) after receiving hearing devices. For example, progressive improvements in the design of cochlear electrode arrays (stimulation components), surgical implantation techniques, tools, etc., have enabled non-invasive surgery that preserves at least some of the recipient's delicate inner ear structures (e.g., cochlear hair cells) and natural cochlear function, particularly in the lower frequency regions of the cochlea.

[0022] The number of recipients of electroacoustic stimulation hearing devices (e.g., devices that deliver electrical stimulation and acoustic or mechanical stimulation, sometimes referred to herein as electroacoustic stimulation (EAS)) has been expanding, at least in part due to the ability to preserve residual low-frequency acoustic hearing during cochlear implantation surgery. Typically, due to limitations in residual hearing, acoustic stimulation is used to present the sound signal components corresponding to the lower frequencies of the sound signal, while electrical stimulation is used to present the sound signal components corresponding to the higher frequencies of the sound signal.

[0023] Compared to purely electrical hearing devices, electroacoustic stimulation hearing devices offer significant benefits by preserving interaural time differences and temporal fine structure cues, thereby improving sound localization, speech performance in noise, speech naturalness, and music perception. In other words, recipients with residual hearing typically benefit from acoustic stimulation in addition to electrical stimulation because acoustic stimulation adds a more “natural” sound to their auditory perception compared to an ear receiving only electrical stimulation. Specifically, the temporal coding of auditory signals is particularly important for low frequencies and is optimally achieved through acoustic stimulation. Thus, combined electroacoustic stimulation particularly benefits from this low-frequency acoustic coding. For example, adding acoustic stimulation can provide improved pitch and music perception and / or appreciation because acoustic signals can contain lower frequency (e.g., fundamental pitch F0) representations that may be more significant than those from electrical stimulation. Other benefits of residual hearing can include, for example, improved sound localization, binaural demasking release, and the ability to distinguish acoustic signals in noisy environments.

[0024] On average, cochlear implantation results in a 20-30 dB shift in low-frequency acoustic hearing threshold. However, the changes in acoustic hearing experienced vary greatly among recipients, and recipients may experience anything from complete acoustic hearing loss to no acoustic hearing loss. Not only is the degree of acoustic hearing loss highly variable among recipients, but it may also occur at different times and at different rates. When the acoustic hearing threshold shifts, the parameters of the electroacoustic stimulation hearing device (sometimes referred to in this paper as electroacoustic (EAS) programming or electroacoustic stimulation parameters) should also be adjusted to provide optimal electroacoustic input.

[0025] In a standard setup, adjusting electroacoustic stimulation parameters typically involves frequent in-person clinic visits, where professionals perform pure-tone audiometry and manually adjust these parameters. However, due to factors such as cost, lack of insurance coverage, shortage of trained audiologists, and long distances from clinics, patients often find it inconvenient to visit clinics. Therefore, for some patients, the need to visit multiple clinics for electroacoustic stimulation parameter adjustments can not only be costly but may also require them to live with inappropriate sound perceptions for a considerable period of time (potentially unknowingly).

[0026] Therefore, this paper presents a technique for adjusting the electroacoustic stimulation parameters of a medical device, such as an electroacoustic hearing aid, based on electrophysiological measurements of acoustic hearing associated with the recipient of the medical device, combined with one or more hearing performance measurements of the recipient. One or more electrophysiological measurements are analyzed relative to one or more hearing performance measurements to program and adjust one or more electroacoustic stimuli (EAS) of the hearing device based on relevant settings, adjustments, determinations, etc. (collectively and generally referred to as "settings" in this paper).

[0027] For example, in some examples, electrophysiological measures (e.g., one or more evoked potentials or one or more impedances) obtained via one or more electrophysiological tests are correlated with one or more hearing performance measures to automatically set one or more electroacoustic stimulation parameters of the hearing device. In some examples, the techniques presented herein include machine learning clinical tools configured to predict acoustic hearing loss. In some examples, the techniques presented herein monitor device malfunctions based on relative analysis (e.g., comparison) of expected acoustic and electroevoked responses.

[0028] According to the techniques presented herein, electrophysiological measurements (e.g., various evoked potentials, impedance, etc.) that serve as relevant measures of both the "acoustic hearing threshold" and "hearing performance measures" (e.g., objective / behavioral performance, in situ audiometry, subjective hearing ability, listening effort, and conversational participation) can be obtained by implantable medical devices (including but not limited to cochlear implants). These electrophysiological measurements (e.g., via evoked potentials and impedance) serve as relevant measures of both the "acoustic hearing threshold" and "hearing performance measures" (e.g., objective performance, in situ audiometry, subjective hearing ability, listening effort, and conversational participation) based on recipient input, automatically providing appropriate electroacoustic stimulation programming adjustments without in-person clinic visits. As a result, the system and techniques described herein represent an improvement over conventional techniques that require frequent in-person clinic visits to optimize electroacoustic stimulation programming.

[0029] According to one aspect, the systems and techniques described herein enable automated alterations to electroacoustic stimulation programming by: (1) monitoring electrophysiological / related measures of acoustic hearing (e.g., evoked potentials and impedances) and measures of hearing performance (e.g., measures of objective performance, in situ audiometry, subjective hearing ability, listening effort, and / or conversational engagement); (2) applying one or more of these measures via machine learning models to infer changes in acoustic hearing thresholds and / or device defects; and (3) automatically adjusting electroacoustic stimulation programming parameters (and / or notifying professionals of potential changes in hearing / device functionality so that corresponding adjustments can be made). For example, monitoring subjective hearing ability may involve obtaining recipient input via ecological transient assessment (EMA), as further explained below.

[0030] In this way, the aspects of the technology presented in this paper provide automated clinical applications of these electrophysiological measurements in recipients of electroacoustic stimulation to detect changes in acoustic hearing or device defects and to determine programmed adjustments without the need for frequent in-person clinic visits, or to provide a justification for such visits.

[0031] It should be understood that several different types of electronic devices exist in which / utilizing them can implement the techniques presented herein. For ease of description only, the techniques presented herein are described primarily with reference to specific electronic devices in the form of cochlear implant systems. However, it should be understood that the techniques presented herein can also be implemented, in part or entirely, by any of several different types of devices, including consumer electronics devices (e.g., mobile / wireless devices, wearable devices, computing devices, televisions, home appliances / white goods, Internet of Things (IoT) devices, audio devices, etc.), computing systems (e.g., servers in data centers), various types of software systems, such as databases, machine learning and artificial intelligence systems, other medical devices, diagnostic devices, etc. For example, the techniques presented herein can be in or used with hearing devices, various implantable medical devices, such as vestibular devices (e.g., vestibular implants), 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.

[0032] As used herein, the term "hearing device" will be broadly interpreted as any device that acts on an individual's actual or potential auditory perception (including to improve the perception of sound signals, to reduce the perception of sound signals, etc.). Specifically, a hearing device is capable of delivering sound signals to a user in any form, including as acoustic stimulation, mechanical stimulation, electrical stimulation, etc., and / or is operable to suppress all or some sound signals. Thus, a hearing device can be a device for use by people with hearing impairments (e.g., hearing aids, middle ear prostheses, bone conduction devices, direct acoustic stimulators, electroacoustic hearing devices, auditory brainstem stimulators, bimodal hearing prostheses, bilateral hearing prostheses, dedicated tinnitus treatment devices, tinnitus treatment device systems, combinations or variations thereof, etc.) or a device for use by people with normal hearing (e.g., consumer devices providing audio streaming, consumer headphones, headphones and other listening devices), hearing protection devices, etc.

[0033] As previously stated, the techniques presented herein can be used with several different types of hearing devices that deliver electrical stimulation (current signals) alone or in combination with acoustic or mechanical stimulation, such as cochlear implants, auditory brainstem stimulators, tinnitus stimulators, bimodal hearing prostheses, electroacoustic hearing devices, etc. Therefore, as used herein, “acoustic stimulation” can be directed to deliver an aided / amplified acoustic signal to the cochlea or to deliver an unaided (natural) acoustic signal to the cochlea (i.e., relying on natural hearing in the outer, middle, and / or inner ear).

[0034] As mentioned above, for ease of illustration only, this document primarily refers to one specific type of hearing device, namely an electroacoustic hearing device comprising a cochlear implant portion and a hearing aid portion, to describe the embodiments. Similarly, the techniques presented herein can be used with other types of hearing devices having different types of output devices.

[0035] Exemplary systems and components

[0036] In order to deliver both electrical and acoustic (electroacoustic) stimulation to the recipient, the recipient can be equipped with medical devices, such as electroacoustic hearing devices. Figure 1A This is a schematic diagram of such a medical device in the form of an exemplary electroacoustic hearing device 100 configured to implement various embodiments of the present invention, while Figure 1B This is a block diagram of the electroacoustic hearing device 100. For ease of explanation, it will be described together. Figure 1A and Figure 1B .

[0037] The electroacoustic hearing device 100 includes an external component 102 and an internal / implantable component 104. The external component 102 is attached directly or indirectly to the recipient's body and includes a sound processing unit 110, an external coil 106, and a magnet typically fixed relative to the external coil 106. Figure 1A (Not shown in the document). External coil 106 is connected to sound processing unit 110 via cable 134. Sound processing unit 110 includes one or more sound input devices 108 (e.g., microphone, audio input port, cable port, pickup coil, wireless transceiver, etc.), sound processor 112, external transceiver unit (transceiver) 114, and power supply 116. External component 102 and / or implantable component 104, or both, may include one or more functional components that can be used to implement the techniques described herein. For example, implantable component 104 includes monitoring component 145 that can be configured to capture electrophysiological measurements as described elsewhere herein. External component 102 includes components for forwarding the captured electrophysiological measurements to another device (e.g., Figure 1C The functionality of the user device 150 shown (e.g., wireless interface 147).

[0038] exist Figure 1A and 1B In the example, sound processing unit 110 is a behind-the-ear (BTE) sound processing unit. However, in other embodiments, sound processing unit 110 may be a wearable sound processing unit, a button-type sound processing unit, an in-the-ear (ITE) unit, etc. Connected to sound processing unit 110 (e.g., via cable 135 or wireless interface 147) is a component configured to deliver acoustic stimuli to a recipient, sometimes referred to as hearing aid 141. For this purpose, hearing aid 141 includes a receiver 142 ( Figure 1B The receiver can be positioned, for example, in or near the recipient's outer ear. Receiver 142 is an acoustic transducer configured to deliver acoustic signals (acoustic stimuli) to the recipient via the recipient's ear canal and middle ear.

[0039] Figure 1A and 1B The illustration shows the use of receiver 142 to deliver acoustic stimuli to a recipient. However, as described above, it should be understood that acoustic stimuli can be delivered in several other ways. For example, other embodiments may include an external or implantable vibrator configured to deliver acoustic stimuli to a recipient. In yet other embodiments, hearing aid 141 may be omitted, and the recipient's cochlea may be acoustically stimulated using unassisted acoustic signals delivered via the natural hearing pathway (i.e., via the functional outer and middle ear). Acoustic stimuli may also be delivered using an in-ear hearing aid, controlled by sound processor 112.

[0040] like Figure 1B As shown, the implantable component 104 includes an implant body (main module) 122, a lead area 124, and an elongated cochlear stimulation assembly 126. The implant body 122 generally includes an airtight housing 128, in which an internal transceiver unit (transceiver) 130 and a stimulator unit 132 are disposed. The implant body 122 also includes an internal / implantable coil 136, which is typically outside the housing 128 but connected via an airtight feedthrough (…). Figure 1B (Not shown) is connected to transceiver 130. The implantable coil 136 is typically a wire antenna coil composed of multi-turn electrically insulated single or multi-strand platinum or gold wire. The electrical insulation of the implantable coil 136 is provided by a flexible molding (e.g., a silicone molding), which is not... Figure 1B As shown in the diagram. Typically, the magnet is fixed relative to the implantable coil 136.

[0041] An elongated stimulation component 126 is configured to be at least partially implanted in the cochlea 120 of a recipient and includes a plurality of longitudinally spaced intracochlear electrical stimulation contacts (electrodes) 138, which together form a contact array 140 for delivering electrical stimulation (current signals) to the recipient's cochlea. In some arrangements, in addition to the electrodes 138, the contact array 140 may also include other types of stimulation contacts, such as optical stimulation contacts.

[0042] The elongated stimulation component 126 extends through an opening 121 in the cochlea (e.g., cochlear window, round window, etc.) and has a lead area 124 and an airtight feedthrough ( Figure 1B (Not shown) is connected to the proximal end of the stimulator unit 132. The lead region 124 includes a plurality of conductors (wires) that electrically couple the electrode 138 to the stimulator unit 132.

[0043] Returning to external component 102, the plurality of sound input devices 108 are configured to detect / receive sound signals and generate electrical output signals therefrom. Sound processor 112 is configured to perform sound processing that converts the output signals received from the plurality of sound input devices into coded data signals representing acoustic and / or electrical stimulation for delivery to a receiver. That is, as previously described, the electroacoustic hearing device 100 operates to evoke in the receiver the perception of sound signals received by the plurality of sound input devices 108 by delivering one or both of electrical stimulation signals and acoustic stimulation signals to the receiver. Therefore, according to the current operating settings (sometimes referred to as the operating “diagram”), sound processor 112 is configured to convert the output signals received from the plurality of sound input devices into a first set of output signals representing electrical stimulation and / or into a second set of output signals representing acoustic stimulation. The output signals representing electrical stimulation are... Figure 1B Arrow 115 indicates that the output signal of the acoustic stimulus is in Figure 1B The middle part is indicated by arrow 117.

[0044] Output signal 115 is provided to transceiver 114. Transceiver 114 is configured to transmit the output signal 115 in an encoded manner percutaneously to implantable component 104 via external coil 106. More specifically, magnets fixed relative to external coil 106 and implantable coil 136 facilitate operational alignment of external coil 106 and implantable coil 136. This operational alignment of the coils enables external coil 106 to transmit the encoded output signal 115 and electrical signals received from power supply 116 to implantable coil 136. In some examples, external coil 106 transmits the encoded output signal 115 to implantable coil 136 via a radio frequency (RF) link. However, various other types of energy transfer (e.g., infrared (IR), electromagnetic, capacitive, and inductive transfer) can be used to transfer power and / or data from external components to the electroacoustic hearing device, and therefore, Figure 1B Only one exemplary arrangement is shown.

[0045] Generally, the encoded output signal 115 is received at transceiver 130 and provided to stimulator unit 132. Stimulator unit 132 is configured to generate electrical stimulation (e.g., a current signal) using the output signal 115 to be delivered to the recipient's cochlea via one or more stimulation contacts 138. In this way, the electroacoustic hearing device 100 electrically stimulates the recipient's auditory nerve cells in a manner that allows the recipient to perceive one or more components of the received sound signal, thereby bypassing the missing or defective hair cells that normally translate acoustic vibrations into neural activity.

[0046] Figure 1BThe diagram also shows an EAS adjustment module 118. As will be explained in more detail below, the EAS adjustment module 118 is operable to adjust the electroacoustic stimulation parameters (EAS programming) of the electroacoustic hearing device 100. In some examples, the EAS adjustment module 118 is controlled from a separate user device 150 (…). Figure 1C The user device 150 receives data / instructions and is configured to send communication signals or commands to adjust, for example, the amplitude / magnitude, frequency range, etc., of the electrical and acoustic stimuli delivered to the receiver via the electroacoustic hearing device 100. It is noteworthy that, within certain parameter or frequency boundaries, such adjustments can be performed by the receiver without input from a hearing expert or other hearing professionals, or with limited input from such experts or professionals. According to embodiments, the receiver can adjust selected parameters of the electroacoustic hearing device 100 themselves using intuitive controls on the user device 150. For example, in some embodiments, the receiver can control the loudness of the acoustic stimulus, the mixing of the delivered electrical and acoustic stimuli, etc., via the user device 150 and the EAS adjustment module 118.

[0047] Figure 1A and 1B The diagram illustrates an arrangement of the electroacoustic hearing device 100 including an external component 102. However, it should be understood that embodiments of the invention can be implemented in hearing devices with alternative arrangements. For example, embodiments of the invention can be implemented in fully implantable devices, such as fully implantable auditory prostheses. A fully implantable auditory prosthesis is an auditory prosthesis in which all components are configured to be implanted under the recipient's skin / tissue. Because all components are implantable, a fully implantable auditory prosthesis is configured to operate without an external device for at least a limited period of time. However, an external device can be used, for example, to charge the internal power source (battery) of the fully implantable auditory prosthesis.

[0048] As mentioned above, recipients typically retain at least some of their normal hearing function (i.e., retain at least some residual hearing). Therefore, when an aided (or potentially unaided) acoustic signal is delivered to the recipient's outer ear, the recipient's cochlea may be acoustically stimulated. Figure 1A and 1B In this example, receiver 142 is used to assist the recipient's residual hearing. More specifically, an output signal 117 (i.e., a signal representing an acoustic stimulus) is provided to receiver 142. Receiver 142 is configured to use the output signal 117 to generate an acoustic stimulus signal provided to the recipient. In other words, receiver 142 is used to amplify and / or amplify a sound signal delivered to the cochlea via the middle ear bone and elliptical window, thereby generating a pressure wave in the perilymph within the cochlea.

[0049] In this way, Figure 1A and 1BThe electroacoustic hearing device 100 is configured to deliver both acoustic stimulation and electrical stimulation (current signal) to a recipient. The combination of acoustic and electrical stimulation is sometimes referred to herein as electroacoustic stimulation. The electrical stimulation is generated from at least a first portion / segment (i.e., frequency or frequency range) of the sound signal, while the acoustic stimulation signal is generated from at least a second portion of the sound signal. The recipient's operating settings, determined and set during the fitting process, dictate how the electroacoustic hearing device 100 operates to convert the sound signal into acoustic and / or electrical stimulation.

[0050] Figure 1C This is a block diagram of a user device 150 (e.g., a mobile phone or computer) on which EAS monitoring logic 155 can be hosted, according to certain embodiments presented herein. EAS monitoring logic 155 can be configured to present a graphical receiver interface to a receiver and communicate with (e.g., send commands to) the electroacoustic hearing device 100 via the EAS adjustment module 118 and possibly associated controllers.

[0051] Generally, user device 150 may be a computing device including processor 152 or controller, memory 154, communication interface 156, and receiver interface 158. Memory 154 may store EAS monitoring logic 155, the function of which is described below. Each of these components may communicate with each other via a bus (not shown).

[0052] Processor 152 or controller is, for example, a microprocessor or microcontroller that executes instructions for EAS monitoring logic 155. Processor 152 can execute EAS monitoring logic 155 to, for example, generate a user interface for the receiver of user device 150, determine optimal EAS programming for electroacoustic hearing device 100, generate signals representing changes in the EAS programming of electroacoustic hearing device 100 for execution by EAS adjustment module 118, etc. (e.g., execute reference...) Figure 5 And / or 6 (the methods 500 and / or 600 described). It should be understood that the memory 154 may include, for ease of illustration, methods already described in the diagram. Figure 1C Other logic elements omitted.

[0053] Memory 154 may include read-only memory (ROM), random access memory (RAM), disk storage media, optical storage media, flash memory, electrical, optical, or other physical / tangible storage devices. Therefore, generally, memory 154 may include one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (processor 152), it is operable to perform the operations described herein.

[0054] Communication interface 156 may include any combination of, for example, a network port (e.g., an Ethernet port), a wireless network interface, a Universal Serial Bus (USB) port, an IEEE 1394 interface, a PS / 2 port, etc. Figure 1B and 1C In the example, communication interface 156 is connected via wireless interface 147 ( Figure 1B The communication interface 156 can be directly connected to the electroacoustic hearing device 100 or connected to an external device that communicates with the electroacoustic hearing device 100. The communication interface 156 can be configured to communicate with the electroacoustic hearing device 100 via a wired or wireless connection 160 (e.g., to provide instructions or commands for EAS programming changes via the EAS adjustment module 118, such as a desired offset of the mixed stimulus window and / or acoustic stimulus volume).

[0055] The receiver interface 158 may include one or more output devices, such as a liquid crystal display (LCD) and a speaker, for presenting visual or auditory information to a clinician, audiologist, or more relevantly to a receiver. The receiver interface 158 may also include one or more input devices, such as a keypad, keyboard, mouse, touchscreen, etc.

[0056] It should be understood that Figure 1C The arrangement of user device 150 shown is illustrative, and the embodiments presented herein may include any combination of hardware, software, and firmware configured to perform the functions described herein. For example, user device 150 may be a personal computer, a handheld device (e.g., a tablet computer), a mobile device (e.g., a mobile phone), and / or any other electronic device.

[0057] According to certain embodiments presented herein, EAS monitoring logic 155 is configured to generate a user interface that can be presented to a receiver on a user device 150 and receive input from the receiver. Figure 2 An example of this type of user interface is shown in the image.

[0058] Exemplary embodiments

[0059] As previously mentioned, low-frequency acoustic hearing can be successfully preserved after cochlear implantation surgery, thus allowing for electroacoustic stimulation (EAS). Also as noted above, compared to electrical hearing alone, EAS offers significant benefits by preserving interaural time differences and temporal fine structural cues, thereby improving sound localization, speech performance in noise, speech naturalness, and music perception. However, optimal EAS programming for hearing device recipients depends on the recipient's available postoperative acoustic hearing (e.g., cochlear implantation may result in an average 20-30 dB shift in the low-frequency threshold, and the degree and rate of loss are highly variable among recipients).

[0060] To address the aforementioned and other needs, exemplary embodiments of this disclosure provide systems and techniques for recipient-manipulated, automated and / or partially automated adjustments (i.e., adjustments to EAS programming) of parameters of an electroacoustic hearing device. That is, as previously described, an electroacoustic hearing device such as electroacoustic hearing device 100 delivers both acoustic and electrical stimulation. For lower frequencies, only acoustic stimulation may be delivered to the recipient, and for higher frequencies, only electrical stimulation may be delivered to the recipient. More specifically, electrical stimulation is not delivered below a first frequency referred to as a “high frequency” or “electrical” cutoff frequency, and acoustic stimulation is not delivered above a second frequency referred to as a “low frequency” or “acoustic cutoff” frequency. The region between the high-frequency cutoff frequency and the low-frequency cutoff frequency, i.e., the region where both acoustic and electrical stimulation can be delivered simultaneously, may be referred to as the overlap region. In some cases, it may be desirable to have a minimal overlap region in order to minimize the overlap between electrical and acoustic stimulation. However, to avoid blank areas in treatment, transitions may be implemented to provide a smooth, monotonous transition in loudness perception. In other words, the acoustic stimulation level and / or electrical stimulation level can be gradually reduced as the frequency changes between the high-frequency cutoff frequency and the low-frequency cutoff frequency, and vice versa.

[0061] The frequency at which the acoustic and electrical stimulation levels intersect is called the "cross frequency," denoted as frequency Fcross. The cross frequency does not need to be equidistant from frequencies F1 and F2, but it can be. The cross frequency actually and ultimately depends on the receiver's hearing capacity or perception within the frequency range processed by the electroacoustic hearing device 100.

[0062] As used herein, adjustments to the parameters of an electroacoustic stimulation hearing device (i.e., adjustments to the EAS programming) can refer to adjustments to the crossover frequency, the properties of the overlapping region, the amplitude / magnitude of the electrical and acoustic stimulation, and / or other parameters.

[0063] A. Electrophysiological measurements

[0064] The system described herein is configured to assess electrophysiological measures associated with acoustic hearing thresholds. These electrophysiological measures may include, but are not limited to, evoked potentials and / or impedances. Evoked potentials may include neural responses such as acoustically evoked neural responses / potentials obtained via, for example, a telemetry system, such as an electrocochleography (ECochG) response, or an electrically evoked neural response / potential. For example, an ECochG response is an acoustically evoked complex action potential (acoustic evoked potential) measured using intracochlear electrodes of a cochlear implant to record the response of the distal portion of the auditory nerve to frequency-specific acoustic stimuli presented via the acoustic components of the system. Frequency-specific ECochG responses can be evoked by changing the frequency of the presented acoustic signal. This threshold response can be analyzed to represent cochlear microphonic potentials (CMs) in the outer hair cells, which have been shown to correlate with hearing measurement thresholds.

[0065] Electroneural responses (evoked potentials) are electrically evoked compound action potentials measured using intracochlear electrodes of a cochlear implant to record the response of the distal portion of the auditory nerve to electrical stimulation from a given intracochlear electrode. Frequency-specific responses can be evoked by stimulation from different intracochlear electrodes at varying locations (i.e., more basal electrodes corresponding to higher frequencies and more apical electrodes corresponding to lower frequencies). Different measures of neural responses (e.g., threshold, growth function slope, suprathreshold amplitude, etc.) have not been shown to be directly related to behavioral thresholds; however, it has been hypothesized that neural responses may be more useful when used in conjunction with other objective measures.

[0066] Impedance can refer to, for example, common-ground impedance, unipolar impedance (e.g., MP1, MP2), four-point impedance, or time-varying impedance (e.g., transimpedance matrix (TIM)). Generally, cochlear implant impedance represents the resistance to current flow between the two electrodes or sets of electrodes. The system presented in this paper allows for simultaneous stimulation and recording in various configurations detailed below. Impedance is derived from the known applied current (I) and measured voltage (V) using Ohm's law (V = I x R).

[0067] "Common-ground impedance": In common-ground mode, a current is applied and the voltage between a single cochlear electrode and all other cochlear electrodes shorted together is measured. An increase in the average common-ground impedance between the electrodes occurs simultaneously with a delayed shift in the hearing threshold, while a stable average common-ground impedance is associated with a stable hearing threshold. Common-ground impedance can be measured along the electrode array to provide frequency-specific information reflecting tissue growth and health along the cochlea.

[0068] "Monopole Impedance" (MP1 or MP2): This impedance measurement mode involves stimulation and recording from ground to two external cochlear grounds (i.e., pin ground and shell ground) of an individual intracochlear electrode. The measurement is taken at a single point in time at the end of the stimulation pulse, summarizing all elements of the impedance. It can be measured for each individual electrode, providing insights into frequency-specific variations in the acoustic hearing threshold.

[0069] Four-point impedance: Four-point impedance is measured by applying a current between two external electrodes using four adjacent internal cochlear electrodes, while simultaneously measuring the voltage difference between the two internal electrodes. The four adjacent electrodes extend from the base to the apex of the electrode array to provide frequency-specific electrophysiological information along the cochlea. Increased total four-point impedance is associated with cochlear hemorrhage / inflammation and the development of fibrosis that may lead to a delayed increase in hearing threshold after cochlear implantation. Generally, four-point impedance has been found to increase within 24 hours of cochlear implantation and within 3 months post-operation, particularly in the basal region, consistent with the natural timeline of acute and chronic inflammatory responses. The duration of the inflammatory response may vary between individual recipients. Therefore, postoperative increases in four-point impedance may occur before or concurrently with an increase in acoustic hearing threshold.

[0070] Time-varying impedance (TIM): The transimpedance matrix is ​​measured in the same mode as the monopole (MP1+2) described above. It expands the collected information by assessing impedance at numerous time points during the pulse. This allows the impedance metric to be analyzed into sub-components, “access impedance” and “polarization impedance.” Increased access impedance and stable polarization impedance are associated with changes in hearing threshold, suggesting that this metric can be used as a biomarker for changes in acoustic hearing.

[0071] Recording of electrophysiological measurements can begin during surgery and can continue postoperatively. Postoperative electrophysiological measurements can be manually activated (e.g., by the recipient pressing a button or remotely by a clinician), can be triggered by events (e.g., decreased hearing performance or wear time), or can occur at standard or customized intervals (e.g., at specific times each day). Specific electrophysiological events / changes relative to the timing of cochlear implantation can represent different changes in intracochlear and behavioral thresholds.

[0072] B. Hearing performance measurement

[0073] In addition, the system described herein is configured to evaluate one or more hearing performance measures (“performance measures”) in relation to acoustic hearing thresholds. For example, hearing performance measures may include, but are not limited to: objective or behavioral performance, in situ audiometry, subjective hearing ability, listening effort and / or conversational engagement.

[0074] "Objective or behavioral performance": Objective hearing performance measures / metrics can be collected through self-managed automated tests (e.g., digital trill tests managed via smartphone apps).

[0075] "In situ audiometry": In situ audiometry can be performed using acoustic or electrical stimulation presented by a cochlear implant device through a self-managed automated test, such as using a smartphone app.

[0076] "Subjective listening ability": Figure 2 Exemplary graphical receiver interface (GUI) screens 210 and 220 are illustrated on a receiver interface 158 of a user device 150 used for collecting receiver input regarding subjective changes in hearing (GUI screen 210) and / or subjective hearing ability (GUI screen 220) using an ecological transient assessment (EMA), according to an exemplary embodiment. In one example, GUI screen 210 displays output / prompt 212 (e.g., “Have you noticed a decline in your hearing over the past month?” etc.) and input / response 214 (e.g., “Yes” or “No”, etc.). In another example, GUI screen 220 displays output / prompt 222 (e.g., “Please rate your overall hearing ability over the past week”, etc.) and input / response 224 (e.g., “Excellent,” “Good,” “Acceptable,” “Poor,” “Very Poor,” etc.).

[0077] Figure 2 The exemplary GUI screens 210 and 220 are illustrative in nature and non-limiting, and various other suitable outputs / prompts and / or inputs / responses are possible within the scope of this disclosure. In some other examples, EMA responses (inputs) can be collected from the recipient via push notifications (outputs) delivered through smart accessories such as smartphones, smartwatches, smart appliances, etc. In some other examples, EMA questions or prompts (outputs) can be presented to an individual in an audible manner, and the individual can then verbally record their EMA responses (inputs). For example, subjective ratings can be performed using Likert scales, visual analog scales, free text fields, or similar methods.

[0078] “Listening effort”: Listening effort can also be a measure of hearing performance. Listening effort can be monitored during device use using EMA (as described above for subjective hearing ability) or via psychophysiological biomarkers. The system can physiologically monitor listening effort biomarkers during speech using a sensor package consisting of one or more of the following sensors: (multiple) microphones, (multiple) photoplethysmography (PPG) sensors, (multiple) electrooculography (EOG) sensors, (multiple) electrocardiography (ECG) sensors, (multiple) temperature sensors, (multiple) electromyography (EMG) sensors, (multiple) inertial measurement unit (IMU) sensors, (multiple) electroencephalography (EEG) sensors, (multiple) functional near-infrared spectroscopy (fNIRS) sensors, (multiple) blood pressure sensors, (multiple) respiratory rate sensors, and / or (multiple) skin conductance response (GSR) sensors.

[0079] The following physiological changes can indicate an acute stress response consistent with increased listening effort (multiple sensors used to detect changes in biomarkers are given in parentheses): increased fundamental frequency and speech rate of the human voice (i.e., increased high-frequency modulation amplitude and / or root mean square amplitude) (microphone combined with self-voice detection); increased respiratory rate (ECG, PPG, respiratory rate sensor); decreased heart rate variability (ECG, PPG); increased heart rate (ECG, PPG); increased skin conductivity (GSR); increased blood pressure (ECG, PPG, blood pressure sensor); increased prefrontal cortex oxygenation (fNIRS); increased pupillary dilation (EOG); increased core temperature (temperature sensor); decreased alpha oscillation (~8-13 Hz) power (EEG); changes in movement such as those caused by stress habits (IMU, EMG); etc., any of which can be implemented as monitoring components 145 ( Figure 1B ( ) part.

[0080] "Conversational Engagement": Conversational engagement (i.e., whether the receiver can hear and actively participate in the conversation) can be another measure of auditory performance. The system can detect when the receiver is in a conversational environment based on speech detected using automatic environment classification techniques (e.g., speech in quiet or speech in noise). In these environments, the system can further use, for example, self-voice detection (OVD) to detect the receiver's conversational engagement. The microphone's OVD input can be compared with non-OVD speech input to check whether the receiver is engaging in normal conversational turns. In an exemplary embodiment, linguistic analysis of the OVD signal can be additionally applied to verify, for example, by asking for repetition or saying, for example, "What?", that the receiver's speech is content-based rather than non-content-based.

[0081] A decline in hearing performance over time (e.g., objective performance decline, increased in situ audiometry, subjective hearing decline, increased listening effort, and / or decreased conversational engagement) can be used to trigger the execution of (multiple) machine learning models to detect hearing changes or device malfunctions, and / or hearing performance metrics can be input into (multiple) machine learning models, as further described below. Recipient input regarding subjective hearing performance can also be a valuable rehabilitation tool for recipients and healthcare professionals.

[0082] C. Machine Learning Models

[0083] In some exemplary embodiments, the system may utilize machine learning models to classify acoustic hearing and / or device defects. The machine learning model may be, for example... Figure 1C The user device 150 is part of the EAS monitoring logic 155.

[0084] Acoustic hearing can be classified: one or more of these electrophysiological measures (including their timing relative to cochlear implantation) and one or more of these hearing performance measures (i.e., objective performance, in situ audiometry, subjective hearing, listening effort, and / or conversational participation) can be fed into a machine learning classification model (e.g., deep neural networks, k-means clustering models, support vector machines) or another type of machine learning model to infer changes in frequency-specific acoustic hearing thresholds.

[0085] In one exemplary embodiment, the initial or default machine learning classification model may be based on a single population dataset of hundreds, thousands, or more. In an alternative exemplary embodiment, the classification model may begin with a characteristic-specific or population-specific classification model that includes recipient data that can inform the model, such recipient data including, but not limited to, age, sex, ethnicity, duration of hearing loss, duration of severe to profound hearing loss, onset of hearing loss, audiometry configuration, preoperative acoustic thresholds, etiology, electrode array, comorbidities, hearing aid use, insertion method, steroid therapy, and / or tympanic scala position. The classification model acquires electrophysiological measurements and / or hearing performance measurements and classifies whether the recipient has experienced acoustic threshold shift, multiple affected frequency regions, and / or the degree of acoustic threshold shift.

[0086] Furthermore, this machine learning algorithm can be further improved over time based on the confirmation of changes in acoustic thresholds from pure-tone audiometry measurements performed during clinic visits. For example, changes in frequency-specific acoustic thresholds collected at the clinic over 3 months can be used to inform what previous electrophysiological measurements indicated. This information can be added to the training dataset for improving initial or default machine learning classification models or their own personalized / customized models for other recipients.

[0087] Classifying Device Defects: Machine learning can also be used to classify the functionality of acoustic components. Acoustic components may experience device defects, such as earwax blockage or receiver demagnetization, which can prevent the acoustic component from providing appropriate acoustic stimulation. In this case, the electrophysiological response to acoustic stimulation (i.e., ECochG) will be eliminated or attenuated, while the electrophysiological response to electrical stimulation will still be present and inconsistent with the acoustic response.

[0088] Device defects affecting the output of acoustic components can also induce changes in hearing performance (e.g., decreased objective performance, increased in situ audiometry, decreased subjective hearing, increased listening effort, and / or decreased conversational engagement). Classification models incorporate factors from electrophysiological and / or hearing performance measurements into pattern recognition / matching algorithms to detect whether a recipient may be experiencing device defects in acoustic components.

[0089] Combining neural responses (e.g., acoustically evoked neural responses / potentials, electrically evoked potentials) and / or multiple other physiological signals (e.g., impedance data) with machine learning approaches will provide a more robust estimate of hearing ability, improving the accuracy of the system in detecting hearing changes. Machine learning approaches can achieve greater resolution (e.g., more than three severity ratings, such as mild / moderate / severe) by using machine learning and training the model with specified frequency-specific variations. Furthermore, combining neural response information (e.g., evoked potentials and / or impedance data) with receiver-input-based measures of hearing performance (e.g., receiver input via EMA) can further improve system accuracy by enhancing or improving the system's ability to detect changes in hearing thresholds and / or device defects.

[0090] D. Electroacoustic Stimulation (EAS) Programming Adjustment

[0091] If the system detects a change in the frequency-specific acoustic hearing threshold, it can automatically adjust the output of the electroacoustic stimulation, the high-frequency cutoff parameter, and / or the low-frequency cutoff parameter. In some examples, the system may provide a mechanism for determining the threshold levels of the acoustic and / or electrical stimulation and / or determining the upper limit and crossover frequency of the EAS.

[0092] Figure 3A This is a schematic diagram 310 showing the initial EAS programming parameters set for the acoustic stimulation 312 and electrical stimulation 314 of the electrode array 140. Figure 3B-3D Different EAS programming adjustments for electrode array 140 are shown according to various exemplary embodiments described herein.

[0093] Automatic adjustment occurs when the acoustic hearing threshold is detected to be lower than the baseline state. Figure 3A The action is taken when changes occur, and may include, but is not limited to: (1) changing to electrical stimulation only in the affected area ( Figure 3B(2) Increase acoustic stimulation in the affected area ( Figure 3C ), and / or (3) become electroacoustic stimulation in the affected area ( Figure 3D ).

[0094] Figure 3B This is a schematic diagram 320 illustrating, according to an exemplary embodiment, a change from acoustic stimulation 322 to electrical stimulation only 324 in the affected region 326 where an increased detection threshold is detected. (See diagram 320.) Figure 3B As shown, the low-frequency cutoff of electrical stimulation will be reduced to include the affected area to provide electrical stimulation coverage in areas where acoustic hearing may have been lost, and the high-frequency cutoff will be reduced to exclude the affected area to reduce excessive amplification. For example, the audibility and comfort of electrical stimulation parameters in the affected area can be verified by subjective receiver input and / or electrically evoked stapes reflex thresholds. Subjective receiver input can be entered on an accessory device such as a smartphone, manually pressed on a sound processor, or verbally and picked up by the system microphone(s).

[0095] Figure 3C This is a schematic diagram 330 illustrating an acoustic stimulus 332 in an affected region 336 where the acoustic stimulus output 333 increases according to another exemplary embodiment, in which the detection threshold increases (while the parameters of the electrical stimulation 334 remain unchanged). Figure 3C As shown, both the electrical and acoustic frequency cutoffs will be maintained, but the acoustic output in the affected region will increase proportionally to the inferred acoustic threshold shift to maintain the target gain-response match. This will be appropriate when the low-frequency acoustic hearing threshold remains audible and may benefit from amplification (< 80 dB HL) despite the shift. For example, the increased audibility and comfort of the increased acoustic output level in the affected region can be verified by subjective receiver input and / or acoustically evoked stapes reflex thresholds.

[0096] Figure 3D This is a schematic diagram 340 illustrating, according to another exemplary embodiment, that in the affected region 346 where an increased detection threshold is detected, electroacoustic stimulation (both acoustic stimulation 342 and electrical stimulation 344 are present simultaneously), wherein (optionally) the acoustic stimulation output 343 increases (e.g., Figure 3D (Represented by the dashed line in the image). For example... Figure 3D As shown, the low-frequency cutoff of electrical stimulation will decrease to include the affected region, and the high-frequency acoustic cutoff will be maintained to support dual coding in the absence of electrophysiological changes accompanied by changes in the acoustic threshold. In this embodiment, the acoustic output can also be increased in the affected region to compensate for the loss, as previously described. As previously described, the audibility and comfort of the acoustic and electrical output parameters in the affected region can be verified.

[0097] E. Networking of electrophysiological / auditory information

[0098] Figure 4 This is a conceptual diagram illustrating an exemplary system 400 according to one or more aspects of this disclosure. System 400 includes a local portion 420, a remote portion 440, and a data communication network 189 (e.g., a wide area network (WAN), etc.). The local portion 420 includes one or more external components 102(A), 102(B) (e.g., one or more sound processing units 110(A), 110(B) and one or more external coils 106(A), 106(B)), a user device 150 (e.g., a smartphone) to which a hearing device receiver (not shown) can interact, and a wireless gateway / router 422 connected to the data communication network 189 (e.g., a WAN) and configured to provide a local network (e.g., a local area network (LAN), etc.) for user device 150. As previously described, external components 102(A), 102(B) include an EAS adjustment module (controller) 118, and user device 150 can be configured to perform EAS monitoring logic 155 according to the techniques described herein. The remote portion 440 includes a computing device 442 with which a medical professional 444 can interact, and which is also connected to a data communication network 189. The user device 150 in the local portion 420 can communicate with the computing device 442 in the remote portion 440 via the data communication network 189, in one example via a wireless gateway / router 422 (e.g., Wi-Fi connection, Bluetooth connection, etc.), or in another example via a cellular network 430 (e.g., 4G / LTE, 5G, next-generation, etc. connection). Exemplary embodiments are not limited to these devices, network technologies, or connection interfaces; various other examples are also possible.

[0099] In one exemplary embodiment, data regarding electrophysiological measurements, performance metrics, inferred device defects, and / or inferred acoustic threshold variations can be forwarded via a data communication network 189 to a professional 444 (e.g., a hearing expert) via a computing device 442 at a remote portion 440. Figure 4 As shown, a professional 444 can use the computing device 442 to view and interact with the information in real time or retrospectively. In this way, electrophysiological information can be used to justify various other uses, such as in-person clinic visits to confirm changes in acoustic thresholds, device defects, and to identify or perform necessary EAS programming changes.

[0100] F. Exemplary Process

[0101] Figure 5 This is an operation flowchart of a first method 500 for implementing automated electroacoustic stimulation programming adjustment according to an exemplary embodiment. For ease of explanation, reference will be made to... Figure 1A-1B Electroacoustic hearing device 100 Figure 1Cand 2 User device 150, and Figure 4 System 400 description Figure 5 Method 500.

[0102] At operation 510, method 500 includes obtaining one or more electrophysiological measures of acoustic hearing associated with a hearing device recipient. At operation 520, method 500 includes obtaining one or more hearing performance measures of the recipient. At operation 530, method 500 includes analyzing the one or more electrophysiological measures relative to the one or more hearing performance measures. At operation 540, method 500 includes generating output based on the analysis of the one or more electrophysiological measures relative to the one or more hearing performance measures.

[0103] In some examples, obtaining one or more electrophysiological measurements of acoustic hearing associated with a hearing device recipient (operation 510) includes obtaining the results of one or more evoked potential measurements or the results of one or more impedance measurements. In some other examples, both the results of one or more evoked potential measurements and the results of one or more impedance measurements may be obtained at operation 510.

[0104] In some examples, obtaining one or more hearing performance measures of the recipient (operation 520) includes obtaining one or more hearing performance measures of the recipient based on feedback provided by the recipient. In some examples, obtaining one or more hearing performance measures of the recipient (operation 520) includes obtaining one or more objective hearing performance measures of the recipient. In some examples, obtaining one or more hearing performance measures of the recipient (operation 520) includes one or more of the following: determining one or more measures of hearing performance, determining one or more measures via in situ audiometry, determining one or more measures of subjective hearing ability, determining one or more measures of listening effort, or determining one or more measures of conversational participation.

[0105] In some examples, analyzing one or more electrophysiological measures relative to one or more hearing performance measures (operation 530) involves applying a machine learning model to detect potential changes in the recipient's acoustic hearing. In some examples, the machine learning model may also be used at operation 530 to classify potential changes in the recipient's acoustic hearing.

[0106] In some examples, analyzing one or more electrophysiological measures relative to one or more hearing performance measures (operation 530) involves applying a machine learning model to detect potential changes in the operation of the recipient's hearing device. In some examples, the machine learning model may also be used to classify potential changes in the operation of the hearing device at operation 530.

[0107] In some examples, generating output based on analysis of one or more electrophysiological measures relative to one or more hearing performance measures (operation 540) includes generating output representing one or more electroacoustic stimulation (EAS) programmed adjustments for the recipient's hearing device in response to determining, based on the analysis, at least one of potential changes in the recipient's acoustic hearing or potential changes in the operation of the hearing device.

[0108] In some examples, generating output based on analysis of one or more electrophysiological measures relative to one or more hearing performance measures (operation 540) includes automatically outputting a notification in response to determining, based on the analysis, at least one of a potential change in the recipient's acoustic hearing or a potential change in the operation of the hearing device.

[0109] Figure 6 This is an operation flowchart of a second method 600 for implementing automated electroacoustic stimulation programming adjustment according to an exemplary embodiment. For ease of explanation, reference will be made to... Figure 1A-1B Electroacoustic hearing device 100 Figure 1C and 2 User device 150, and Figure 4 System 400 description Figure 6 Method 600.

[0110] At operation 610, method 600 includes performing one or more electrophysiological tests to obtain one or more evoked potentials or one or more impedances from a hearing device recipient. At operation 620, method 600 includes correlating one or more evoked potentials or impedances with one or more hearing performance measures. At operation 630, method 600 includes automatically providing programmed adjustments of one or more electroacoustic stimuli (EAS) for the hearing device based on the correlation.

[0111] In some examples, obtaining one or more evoked potentials (operation 610) includes obtaining one or more electrocochleography (ECochG) measurements, or obtaining one or more neural response measurements, or a combination thereof. In some examples, obtaining one or more impedances (operation 610) includes obtaining one or more common-ground impedance measurements, one or more unipolar impedance measurements, one or more four-point impedance measurements, one or more time-varying impedance measurements, or a combination thereof.

[0112] In some examples, operation 620 includes obtaining one or more measures of hearing performance based on receiver input received from a receiver using a hearing device. In some examples, operation 620 includes obtaining one or more measures of objective hearing performance, obtaining one or more measures via in-situ audiometry, obtaining one or more measures of subjective hearing ability, obtaining one or more measures of listening effort, obtaining one or more measures of conversational participation, or a combination thereof. In some examples, obtaining one or more measures of subjective hearing ability includes obtaining receiver input related to subjective hearing ability via ecological transient assessment (EMA).

[0113] In some examples, method 600 may also include determining, based on relevant information at operation 620, whether a change in acoustic hearing has occurred, and automatically providing one or more electroacoustic stimulation (EAS) programmed adjustments for the hearing device at operation 630 in response to determining that a change in acoustic hearing has occurred.

[0114] In some examples, method 600 may also include determining, based on relevant information at operation 620, whether a change in device functionality has occurred, and automatically providing one or more electroacoustic stimulation (EAS) programmed adjustments for the hearing device at operation 630 in response to determining that a change in device functionality has occurred.

[0115] In some examples, method 600 may also include determining one or more electroacoustic stimulation (EAS) programming adjustments for a hearing device based on correlating one or more evoked potentials or impedances with one or more acoustic hearing thresholds and with one or more hearing performance measures at operation 620.

[0116] In some examples, relating one or more evoked potentials or impedances to one or more acoustic hearing thresholds and to one or more hearing performance measures (operation 620) includes applying a machine learning model to the one or more evoked potentials or impedances and one or more hearing performance measures to infer changes in one or more acoustic hearing thresholds and / or device defects. In some examples, the machine learning model may also be used to determine one or more electroacoustic stimulation (EAS) programming adjustments for a hearing device in response to inferring changes in one or more acoustic hearing thresholds and / or device defects.

[0117] In some examples, method 600 may also include determining potential changes in acoustic hearing based on correlating one or more evoked potentials or impedances with one or more acoustic hearing thresholds and one or more hearing performance measures at operation 620. In some examples, machine learning models may be used to classify potential changes in acoustic hearing.

[0118] In some examples, method 600 may also include determining potential changes in device functionality based on correlating one or more evoked potentials or impedances at operation 620 with one or more acoustic hearing thresholds and with one or more hearing performance measures. In some examples, machine learning models may be used to classify potential changes in device functionality.

[0119] In some examples, method 600 may also include information based on potential changes in acoustic hearing and / or device functionality provided to relevant notification recipients, clinicians, nurses, or designated medical professionals.

[0120] Therefore, the above reference Figure 1A-1C and Figure 4 The system and reference described in the exemplary embodiments Figure 2 , Figures 3A-3D , Figure 5 and Figure 6 The corresponding techniques described in the exemplary embodiments are configured to combine objective metrics and subjective feedback to adjust settings of an electroacoustic stimulation (EAS) device, including but not limited to adjusting acoustic stimulation, adjusting electrical stimulation, or adjusting both types of stimulation. Some examples provide a self-adjusting device that utilizes a combination of objective metrics (e.g., evoked potentials, impedances) and subjective metrics (e.g., obtained via ecological transient assessment (EMA), etc.) with machine learning to automate the analysis of objective and subjective metrics and adjust electroacoustic stimulation parameters based on machine learning analysis.

[0121] According to the exemplary embodiments described herein, providing automated postoperative hearing and device defect tracking and EAS programming adjustments based on advanced electrophysiological metrics and recipient-input-based performance metrics (i.e., objective performance, in situ audiometry, subjective hearing ability, listening effort, and / or conversational engagement) can help reduce the occurrence of excessive postoperative clinic visits, thereby alleviating the burden on recipients and clinics. This, in turn, will positively impact the adoption of EAS fitting. With advancements in drug-eluting dexamethasone electrode arrays, minimally invasive surgical techniques, and the expansion of hearing testing indications, EAS for cochlear implant recipients who require acoustically preserved hearing is an increasingly important area of ​​growth. This invention can also be a valuable rehabilitation tool for recipients and professionals who avoid EAS fitting due to a reluctance to deal with acoustic components (as acoustic components are prone to device defects / failures).

[0122] Variations and Alternatives

[0123] It should also be understood that aspects of the techniques presented herein can be implemented in several different devices. For example, the presented techniques can be implemented in devices that include an intra-ear (ITE) component operating as a hearing aid and an implant operating as a cochlear implant (e.g., most implantable cochlear implants). In such examples, two processors are in operation, and either or both can be adjusted / set based on the techniques presented herein. Specifically, a processor for use in delivering electrical stimulation is present in the implantable component, and a processor for use in delivering acoustic stimulation is present in the ITE component. A microphone can be provided in the ITE component that sends a microphone signal as input to the acoustic stimulation processor (in the ITE component) and as input to the electrical stimulation processor (in the implant) by sending wireless data to the implant.

[0124] As mentioned above, embodiments of the invention have been described herein with reference to a specific type of hearing device, namely an electroacoustic hearing device comprising a cochlear implant portion and a hearing aid portion. However, it should be understood that the technology presented herein can be used with other types of hearing prostheses, such as bimodal hearing prostheses, electroacoustic hearing devices including other types of output devices (e.g., auditory brainstem stimulators, direct acoustic stimulators, bone conduction devices, etc.), tinnitus stimulators, etc.

[0125] It should be understood that while the specific uses of this technology have been described and discussed above, the disclosed technology can be used with various apparatuses according to many examples of this technology. The foregoing discussion is not intended to suggest that the disclosed technology is only suitable for implementation within systems similar to those shown in the accompanying drawings. In general, the processes and systems described herein can be practiced using additional configurations, and / or some aspects described may be excluded without departing from the processes and systems disclosed herein.

[0126] This disclosure describes some aspects of the present technology with reference to the accompanying drawings, which illustrate only some possible aspects. However, other aspects may be embodied in many different forms and should not be construed as limited to the aspects set forth herein. Rather, these aspects are provided to make this disclosure exhaustive and complete and to fully convey the scope of possible aspects to those skilled in the art.

[0127] It should be understood that the various aspects described herein with respect to the accompanying drawings (e.g., parts, components, etc.) are not intended to limit the system and process to the specific aspects described. Therefore, additional configurations can be used to practice the methods and systems described herein, and / or some aspects can be excluded without departing from the methods and systems disclosed herein.

[0128] According to some aspects, a system and a non-transitory computer-readable storage medium are provided. The system is configured with hardware configured to perform operations similar to those of the present disclosure. One or more non-transitory computer-readable storage media include instructions that, when executed by one or more processors, cause one or more processors to perform operations similar to those of the present disclosure.

[0129] Similarly, where the steps of a process are disclosed, these steps are described for illustrative purposes of the method and system and are not intended to limit this disclosure to a particular sequence of steps. For example, these steps may be performed in a different order, two or more steps may be performed simultaneously, additional steps may be performed, and the disclosed steps may be excluded without departing from this disclosure. Furthermore, the disclosed process may be repeated.

[0130] While specific aspects have been described herein, the scope of this technology is not limited to those specific aspects. Those skilled in the art will recognize other aspects or modifications within the scope of this technology. Therefore, specific structures, actions, or media are disclosed only as illustrative aspects. The scope of this technology is defined by the appended claims and any equivalents thereof.

[0131] It should also be understood that the embodiments presented herein are not mutually exclusive, and various embodiments can be combined with one embodiment in any of a variety of different ways.

Claims

1. A method comprising: obtaining one or more electrophysiological measures of acoustic hearing associated with a hearing device recipient; obtaining one or more hearing performance measures of the recipient; analyzing the one or more electrophysiological measures with respect to the one or more hearing performance measures; and generating an output based on the analyzing the one or more electrophysiological measures with respect to the one or more hearing performance measures.

2. The method of claim 1, wherein obtaining the one or more electrophysiological measures of acoustic hearing associated with the hearing device recipient comprises: obtaining results of one or more evoked potential measurements.

3. The method of claim 1, wherein obtaining the one or more electrophysiological measures of acoustic hearing associated with the hearing device recipient comprises: obtaining results of one or more impedance measurements.

4. The method of claim 1, wherein obtaining the one or more electrophysiological measures of acoustic hearing associated with the hearing device recipient comprises: obtaining results of one or more evoked potential measurements; and obtaining results of one or more impedance measurements.

5. The method of claim 1, wherein obtaining the one or more hearing performance measures of the recipient comprises: obtaining one or more hearing performance measures of the recipient based on recipient-provided feedback.

6. The method of claim 1, wherein obtaining the one or more hearing performance measures of the recipient comprises: obtaining one or more objective hearing performance measures of the recipient.

7. The method of claim 1, wherein obtaining the one or more hearing performance measures of the recipient comprises one or more of: determining one or more measures of hearing performance; determining one or more measures via in-situ audiometry; determining one or more measures of subjective hearing ability; determining one or more measures of listening effort; or determining one or more measures of conversational engagement.

8. The method of claim 1, 2, 3, 4, 5, 6, or 7, wherein analyzing the one or more electrophysiological measures with respect to the one or more hearing performance measures comprises: applying a machine learning model to detect a potential change in acoustic hearing of the recipient.

9. The method of claim 8, further comprising: classifying the potential change in acoustic hearing of the recipient with the machine learning model.

10. The method of claim 1, 2, 3, 4, 5, 6, or 7, wherein analyzing the one or more electrophysiological measures with respect to the one or more hearing performance measures comprises: applying a machine learning model to detect a potential change in operation of a hearing device of the recipient.

11. The method of claim 10, further comprising: classifying the potential change in operation of the hearing device with the machine learning model.

12. The method of claim 1, 2, 3, 4, 5, 6, or 7, wherein generating an output based on the analyzing the one or more electrophysiological measures with respect to the one or more hearing performance measures comprises: ​ ​ generating output representing one or more electroacoustic stimulation (EAS) programming adjustments for the hearing device in response to determining at least one of a potential change in the recipient's acoustic hearing or a potential change in operation of the recipient's hearing device based on the analysis.

13. The method of claim 1, 2, 3, 4, 5, 6, or 7, wherein generating output based on the analysis of the one or more electrophysiological measures relative to the one or more hearing performance measures comprises: automatically outputting a notification in response to determining at least one of a potential change in the recipient's acoustic hearing or a potential change in operation of a hearing device based on the analysis.

14. A method comprising: performing one or more electrophysiological tests to obtain one or more evoked potentials or one or more impedances from a recipient of a hearing device; correlating the one or more evoked potentials or impedances with one or more hearing performance measures; and automatically providing one or more electroacoustic stimulation (EAS) programming adjustments for a hearing device based on the correlation.

15. The method of claim 14, wherein obtaining the one or more evoked potentials comprises one or more of: obtaining one or more acoustic evoked potential measurements; or obtaining one or more electric evoked potential measurements.

16. The method of claim 14, wherein obtaining the one or more impedances comprises one or more of: obtaining one or more common ground impedance measurements, obtaining one or more monopolar impedance measurements, obtaining one or more four-point impedance measurements, or obtaining one or more time-varying impedance measurements.

17. The method of claim 14, 15, or 16, further comprising: obtaining the one or more hearing performance measures based on recipient input received from the recipient of the hearing device.

18. The method of claim 17, wherein obtaining the one or more hearing performance measures based on recipient input received from the recipient comprises one or more of: obtaining one or more measures of hearing performance; obtaining one or more measures via in-situ audiometry; obtaining one or more measures of subjective hearing ability; obtaining one or more measures of listening effort; and / or obtaining one or more measures of conversational engagement.

19. The method of claim 18, wherein obtaining one or more measures of subjective hearing ability comprises: obtaining recipient input related to subjective hearing ability via ecological momentary assessment (EMA).

20. The method of claim 14, 15, or 16, further comprising: determining whether a change in acoustic hearing has occurred based on the correlation; and automatically providing the one or more electroacoustic stimulation (EAS) programming adjustments for the hearing device in response to determining that the change in acoustic hearing has occurred.

21. The method of claim 14, 15, or 16, further comprising: determining whether a change in device functionality has occurred based on the correlation; and automatically providing the one or more electroacoustic stimulation (EAS) programming adjustments for the hearing device in response to determining that the change in device functionality has occurred. automatically providing one or more electroacoustic stimulation (EAS) programming adjustments for the hearing device in response to determining that the change in device functionality has occurred.

22. The method of claim 14, 15, or 16, further comprising: determining one or more electroacoustic stimulation (EAS) programming adjustments for the hearing device based on correlating the one or more evoked potentials or impedances with one or more acoustic hearing thresholds and with the one or more measures of hearing performance.

23. The method of claim 14, 15, or 16, wherein correlating the one or more evoked potentials or impedances with one or more acoustic hearing thresholds and with one or more measures of hearing performance comprises: applying a machine learning model to the one or more evoked potentials or impedances and the one or more measures of hearing performance to infer a change in one or more of the acoustic hearing thresholds and / or a device deficiency.

24. The method of claim 23, further comprising: determining one or more electroacoustic stimulation (EAS) programming adjustments for the hearing device using the machine learning model in response to inferring the change in the one or more acoustic hearing thresholds and / or the device deficiency.

25. The method of claim 14, 15, or 16, further comprising: determining that a potential change in acoustic hearing has occurred based on correlating the one or more evoked potentials or impedances with one or more acoustic hearing thresholds and the one or more measures of hearing performance.

26. The method of claim 25, further comprising: classifying the potential change in acoustic hearing using a machine learning model.

27. The method of claim 14, 15, or 16, further comprising: determining that a potential change in device functionality has occurred based on correlating the one or more evoked potentials or impedances with one or more acoustic hearing thresholds and the one or more measures of hearing performance.

28. The method of claim 27, further comprising: classifying the potential change in device functionality using a machine learning model.

29. The method of claim 14, 15, or 16, further comprising: informing the recipient, clinician, caregiver, or designated medical professional about the potential change in acoustic hearing and / or device functionality based on the correlating.

30. One or more non-transitory computer-readable storage media comprising instructions that, when executed by a processor, cause the processor to: obtain, from a hearing device, one or more electrophysiological measures of acoustic hearing associated with a recipient of the hearing device; obtain one or more measures of hearing performance of the recipient; analyze the one or more electrophysiological measures relative to the one or more measures of hearing performance; and initiate at least one adjustment to an operation of the hearing device based on the analyzing the one or more electrophysiological measures relative to the one or more measures of hearing performance.

31. The one or more non-transitory computer-readable storage media of claim 30, wherein the one or more electrophysiological measures comprise one or more evoked potentials.

32. The one or more non-transitory computer-readable storage media of claim 30, wherein the one or more electrophysiological measures comprise one or more impedances.

33. The one or more non-transitory computer-readable storage media of claim 30, 31, or 32, wherein the one or more hearing performance measures are obtained based on feedback provided by a recipient.

34. The one or more non-transitory computer-readable storage media of claim 30, 31, or 32, wherein the one or more hearing performance measures comprise one or more objective hearing performance measures.

35. The one or more non-transitory computer-readable storage media of claim 30, 31, or 32, wherein the instructions that, when executed, cause the processor to analyze the one or more electrophysiological measures with respect to the one or more hearing performance measures comprise instructions that cause the processor to: execute a machine learning model to detect a potential change in acoustic hearing of the recipient.

36. The one or more non-transitory computer-readable storage media of claim 35, wherein the machine learning model is configured to classify the potential change in acoustic hearing of the recipient.

37. The one or more non-transitory computer-readable storage media of claim 30, 31, or 32, wherein the instructions that, when executed, cause the processor to analyze the one or more electrophysiological measures with respect to the one or more hearing performance measures comprise instructions that cause the processor to: execute a machine learning model to detect a potential change in operation of the hearing device.

38. The one or more non-transitory computer-readable storage media of claim 37, wherein the machine learning model is configured to classify the potential change in operation of the hearing device.

39. A hearing device system, comprising: one or more sensors; a memory storing computer-readable instructions; and a processor configured to execute the computer-readable instructions to: execute one or more electrophysiological tests to obtain one or more evoked potentials or one or more impedances from a recipient of a hearing device; correlate the one or more evoked potentials or the one or more impedances with one or more hearing performance measures; and automatically provide one or more electroacoustic stimulation (EAS) programming adjustments for the hearing device based on the correlation. ​

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