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WO2025177122A1 - Personnalisation de surveillance de la santé de la cochlée - Google Patents

Personnalisation de surveillance de la santé de la cochlée

Info

Publication number
WO2025177122A1
WO2025177122A1 PCT/IB2025/051534 IB2025051534W WO2025177122A1 WO 2025177122 A1 WO2025177122 A1 WO 2025177122A1 IB 2025051534 W IB2025051534 W IB 2025051534W WO 2025177122 A1 WO2025177122 A1 WO 2025177122A1
Authority
WO
WIPO (PCT)
Prior art keywords
recipient
monitoring
electrophysiological measurements
impedance
condition
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/IB2025/051534
Other languages
English (en)
Inventor
Joerg Pesch
Filiep J. Vanpoucke
Dorien HUYSMANS
Wolfram Frederik DUECK
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Cochlear Ltd
Original Assignee
Cochlear Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Cochlear Ltd filed Critical Cochlear Ltd
Publication of WO2025177122A1 publication Critical patent/WO2025177122A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/36036—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation of the outer, middle or inner ear
    • A61N1/36038—Cochlear stimulation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
    • A61B5/053—Measuring electrical impedance or conductance of a portion of the body
    • A61B5/0538—Measuring electrical impedance or conductance of a portion of the body invasively, e.g. using a catheter
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/12—Audiometering
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7282—Event detection, e.g. detecting unique waveforms indicative of a medical condition
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/02—Details
    • A61N1/04—Electrodes
    • A61N1/05—Electrodes for implantation or insertion into the body, e.g. heart electrode
    • A61N1/0526—Head electrodes
    • A61N1/0541—Cochlear electrodes

Definitions

  • 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.
  • a first method comprises: obtaining one or more current electrophysiological measurements associated with a recipient of a medical device; and monitoring a condition associated with the recipient based on the one or more current electrophysiological measurements and a history of electrophysiological measurements associated with the recipient.
  • a second method is provided.
  • the one or more non-transitory computer readable storage media comprise instructions that when executed by a processor, cause the processor to: obtain one or more electrophysiological measurements associated with a cochlea of a recipient; obtain historical data associated with prior electrophysiological measurements obtained from the cochlea; and analyze the one or more electrophysiological measurements based on the historical data.
  • a system includes: a memory; at least one processor operable coupled to the display screen and the memory, wherein the at least one processor is configured to: obtain one or more electrophysiological measurements associated with a cochlea of a recipient; obtain historical data associated with prior electrophysiological measurements obtained from the cochlea; analyze the one or more electrophysiological measurements based on the historical data.
  • FIG. 1A is a schematic diagram illustrating a cochlear implant system with which aspects of the techniques presented herein can be implemented; [0010] FIG.
  • FIG. 1B is a side view of a recipient wearing a sound processing unit of the cochlear implant system of FIG.1A;
  • FIG.1C is a schematic view of components of the cochlear implant system of FIG.1A;
  • FIG.1D is a block diagram of the cochlear implant system of FIG.1A;
  • FIG.1E is a schematic diagram illustrating a computing device with which aspects of the techniques presented herein can be implemented;
  • FIG. 2A is an exemplary graph illustrating impedance fluctuations associated with a first recipient of a medical device, according to embodiments described herein; Atty. Docket No.3065.0755i Client Ref. No.
  • FIG. 3 is a diagram illustrating the generation of a recipient-dependent anomaly detection model, according to embodiments described herein;
  • FIG. 4 is a block diagram illustrating using a recipient-dependent model to classify a datapoint associated with a recipient as normal or anomalous, according to embodiments described herein;
  • FIG.5 is a flow diagram illustrating a method of monitoring a condition of a recipient of a medical device, according to embodiments described herein; [0023] FIG.
  • FIG. 6 is a flow diagram of a method for monitoring a condition associated with a recipient based on a model, according to embodiments described herein;
  • FIG. 7 is a flow diagram illustrating a method of analyzing one or more electrophysiological measurements based on historical data associated with prior electrophysiological measurements associated with a recipient, according to embodiments described herein;
  • FIG. 8 is a schematic diagram illustrating a vestibular stimulator system with which aspects of the techniques presented herein can be implemented;
  • FIG.9 is a schematic diagram illustrating a retinal prosthesis system with which aspects of the techniques presented herein can be implemented.
  • DETAILED DESCRIPTION [0027] Presented herein are techniques for personalized monitoring of a recipient of an implantable medical device.
  • the personalized recipient monitoring which can include Atty. Docket No.3065.0755i Client Ref. No. CID03755WOPC1 monitoring of a condition of a recipient (e.g., cochlea health), monitoring of a status of the implantable medical device (e.g., auditory prosthesis), etc., is based on both current data associated (e.g., obtained from) the recipient, as well as historical data associated with the recipient.
  • a model is trained based on historical data associated with the recipient (e.g., electrophysiological measurement data, such as impedance measurement data, over a period of time) and the model is used to analyze similar current data associated with the recipient and, accordingly, monitor the recipient.
  • monitoring a “condition” or “status” of a recipient includes monitoring a health condition of a recipient, monitoring a condition or status of an implantable medical device implanted in or worn by the recipient, etc.
  • Current methods for monitoring of cochlea health look at absolute values of change in extracted biomarkers (e.g., electrophysiological measurement data, such impedance measurement data, etc.).
  • biomarkers e.g., electrophysiological measurement data, such impedance measurement data, etc.
  • Techniques described herein provide for an alarm system that can be adjusted to the individual norm of impedance values for a recipient to avoid false alarms in subjects with a larger range of impedances while still being sensitive to changes in subjects with a very narrow range of impedances.
  • techniques described herein provide for separating impedance into access resistance and polarization components, which indicate different temporal patterns that represent different biological processes.
  • techniques described herein provide for an alarm system in which an abnormal variation is both recipient-dependent and impedance component-dependent.
  • Atty. Docket No.3065.0755i Client Ref. No. CID03755WOPC1 There are a number of different types of devices in/with which embodiments of the present invention can be implemented.
  • 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 can 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.
  • consumer electronic device e.g., mobile phones
  • wearable devices e.g., smartwatches
  • hearing devices e.g., implantable medical devices, consumer electronic devices, etc.
  • 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.
  • 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.
  • 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.
  • a hearing-impaired person e.g., hearing aids, middle ear auditory prostheses, bone conduction devices, direct acoustic stimulators, electro
  • FIGs.1A-1D illustrates 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 internal/implantable component 112 that is configured to be implanted in or worn on the head of the user.
  • 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
  • FIG.1B 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
  • FIG. 1D illustrates further details of the Atty.
  • 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.
  • DAI Direct Audio Input
  • USB Universal Serial Bus
  • wireless transceiver wireless transceiver
  • 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 rechargeable battery 132, and an external sound processing module 124.
  • the external sound processing module 124 can be configured to perform a number of operations which are Atty. Docket No.3065.0755i Client Ref. No.
  • the external sound 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 sound processing module 124 is configured to perform sound processing on input signals received at the sound processing unit 106).
  • the one or more processors e.g., processing element(s) implementing firmware, software, etc.
  • the external sound 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.
  • 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.
  • electrical stimulation signals e.g., current signals
  • the cochlear implant system 102 could operate differently in different embodiments.
  • 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.
  • external sound processing module 124 can also include an inertial measurement unit (IMU) 170.
  • the inertial measurement unit 170 is configured to measure the inertia of the user's head, that is, motion of the user's head.
  • inertial measurement unit 170 comprises one or more sensors 175 each configured to sense one or more of rectilinear or rotatory motion in the same or different axes.
  • sensors 175 that can be used as part of inertial measurement unit 170 include accelerometers, gyroscopes, inclinometers, compasses, and the like.
  • Such sensors can be implemented in, for example, micro electromechanical systems (MEMS) or with other technology suitable for the particular application.
  • MEMS micro electromechanical systems
  • 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.
  • the memory 184 can include random access memory (RAM), read only memory (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.
  • the memory 184 can include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media or combinations thereof.
  • 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 (Radiofrequency), among others.
  • the network adapter 186 Atty. Docket No.3065.0755i Client Ref. No. CID03755WOPC1 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.
  • changes in extracted biomarkers obtained from a recipient of a medical device (e.g., hearing device) recipient can be used to monitor a “condition” or “status” of the recipient.
  • monitoring a “condition” or “status” of a recipient includes monitoring a health Atty. Docket No.3065.0755i Client Ref. No. CID03755WOPC1 condition of a recipient, monitoring a condition or status of medical device implanted in or worn by the recipient, etc.
  • impedance values associated with a recipient can be measured and analyzed to monitor a condition associated with the recipient. For example, changes in impedance data (impedance measurements) above or below threshold values can indicate a clinical event.
  • FIG. 2A is a graph 210 illustrating impedance fluctuations associated with a first recipient of a medical device. As illustrated in graph 210, the impedances associated with the first recipient are low and steady around the median impedance value. In addition, the impedances jump in the morning. Impedance peaks are common overnight when a recipient is not using a medical device.
  • FIG. 2B is a graph 220 illustrating impedance fluctuations associated with a second recipient of a medical device. As illustrated in graph 220, the impedance values associated with the second recipient are low and steady around the median impedance value. Similar to the first recipient, the impedances of the second recipient jump in the morning.
  • FIG. 2C is a graph 230 illustrating impedance fluctuations associated with a third recipient of a medical device. As illustrated in graph 230, the impedance values associated with the third recipient are high and steady around the median impedance value. In this case, there are no morning impedance jumps or peaks. Instead, in this example, the third recipient experiences impedance fluctuations during the day.
  • FIG. 2D is a graph 240 illustrating impedance fluctuations associated with a fourth recipient of a medical device.
  • Graph 240 illustrates that the impedances are high and peak in the mornings. Unlike in graphs 210, 220, and 230, the impedance values in graph 240 are not steady around the median. Instead, in addition to the daily impedance fluctuations, the impedance values also change over a longer time.
  • FIG. 2A-2D illustrate “normal” impedance variations for four different recipients. In other words, none of the recipients experienced any clinical events or reported issues with hearing or cochlea health during the analyzed time periods. Instead, each recipient has a different level of “normal” impedance values and impedance variations. As can be seen, impedance levels and degree of impedance fluctuation can vary drastically from recipient to recipient, even in the absence of a clinical event. [0061] FIG.
  • 2E is a graph 250 illustrating the range of impedances values occurring per recipient within a day for 20 recipients of medical devices.
  • graph 250 illustrates the daily impedance delta per day per recipient.
  • the values for each recipient indicate the difference between the highest impedance value measured in a day and the lowest impedance value measured in the same day per recipient.
  • Each box plot in graph 250 illustrates the ranges of impedance values for all electrodes for each day over 14 days. When looking at the range of impedances values occurring per subject within a day, ranges of well above 1 kOhm are seen in 4 out of the 20 subjects with maximum values of ⁇ 6kOhm.
  • box plots 252, 254, 256, and 258 illustrate much higher ranges in the daily impedance deltas than the other box plots in graph 250.
  • box plot 252 shows a maximum value of greater than 6 kOhm.
  • Atty. Docket No.3065.0755i Client Ref. No. CID03755WOPC1 [0062] Although the daily fluctuations for the recipients in graph 250 varied greatly from recipient to recipient, none of these changes are associated with a clinically relevant event. In other words, none of the recipients reported any problems with hearing or cochlea health during the time periods illustrated in the graphs. It can be seen that “normal” impedance values can differ from recipient to recipient.
  • FIG. 2F illustrates a graph 260 in which the impedance values are separated into a resistive part and a capacitive part.
  • plot 262 shows the impedance values for a recipient
  • plot 264 shows the resistive portion of the impedance
  • plot 266 shows the capacitive/polarization portion of the impedance.
  • the daily fluctuations of the polarization portion align with the daily fluctuations of the impedance values. Therefore, fluctuations in the different portions of the impedance can account for the fluctuations in the impedance values over different time periods (e.g., fluctuations per day versus fluctuations per week, month, etc.).
  • the personalized alarm system described herein accounts for fluctuations in the different components of the impedance. [0065] Presented herein is a framework to scan multi-dimensional diagnostic data for abnormal events that can develop at different possible time scales (e.g., over a day, over a week, over a month, etc.).
  • the system presented herein learns from a recipient’s own history to personalize the analysis of measured data (e.g., impedance values) based on the individual’s reference of normal variations (e.g., in women during the monthly cycle or other “benign” fluctuations) and based on other personal data.
  • measured data e.g., impedance values
  • the benefit of the system is that the personalized alarm system is more robust and avoids false alarms. Any false alarm will have a negative impact on the user and will lower the compliance in case of a true alarm.
  • the system also can be capable of re-calibrating itself based on additional data, such as a permanent change in a user’s conditions.
  • the additional data can include a non-permanent change.
  • FIG. 3 is a diagram illustrating the generation of a recipient-dependent anomaly detection model.
  • the general mechanism of the recipient-dependent anomaly detection model consists of model creation, classification, and retraining.
  • averaged recipient data 304 from other recipients of medical devices is fed into machine learning model 302.
  • the averaged recipient data 304 is based on of an averaged model of all existing recipient models (e.g., from prior datasets or ongoing study data). For example, the models for other recipients of medical devices are averaged and inputted into machine learning model 302 to determine a baseline model.
  • Recipient measurements 306 associated with a recipient of a medical device are additionally fed into machine learning model 302.
  • Recipient measurements 306 can include, for example, an impedance dataset associated with a recipient.
  • the impedance data can be continuously collected and stored at an external storage (e.g., at a remote storage in the cloud).
  • the impedance data can be collected at multiple time points during a pulse to allow decomposition (e.g., into resistive and polarization components).
  • the impedance dataset can Atty. Docket No.3065.0755i Client Ref. No. CID03755WOPC1 be multi-dimensional.
  • each measurement instance can contain a combination of different measurements (e.g., complex impedance measurements, transimpedance measurements, bipolar 4-point impedances, etc.).
  • measurements are repeated (e.g., multiple times per day over several months), thus generating longitudinal data.
  • the trajectories of impedance data and components can be described in a time series analysis (e.g., seasonal analysis) to disentangle changes on different timescales related to different processes (e.g., passivation overnight, long term changes due to fibrous tissue growth, mid-term changes due to hormonal changes, etc.).
  • Parameter extraction is performed on the impedance dataset to decompose the complex impedance into subcomponents, such as access resistance and polarization impedance.
  • transimpedance data can be decomposed into components that can include, for example, far- and near-field components, estimation of abnormal current paths, estimation of abnormal distribution of tissue impedances, etc.
  • Each component can capture different inner- ear processes on different timescales.
  • Each parameter is represented as a timeseries and inputted to the machine learning model 302.
  • Additional data 312 can be inputted to machine learning model 302 to improve the prediction accuracy.
  • Additional data 312 can include sensor data available from fitness trackers or smartwatches (e.g., skin temperature measurements, heart rate, skin conductance, menstrual cycle information, etc.).
  • additional data 312 can include medical device information from data logs, such as the device usage and applied charge. Additional data 312 can also include personal information, such as medication intake, changes of vertigo or tinnitus levels, hearing test scores, etc.
  • machine learning model 302 can generate recipient-dependent model 308.
  • the recipient-dependent model 308 can be used to monitor the condition (e.g., cochlea health, status of medical device, etc.) of a recipient.
  • the recipient-dependent model 308 is generated for a specific recipient of a medical device based on data associated with the recipient. Because the recipient-dependent model 308 is generated based on recipient-specific data, fewer false alarms will be generated than with a Atty. Docket No.3065.0755i Client Ref. No. CID03755WOPC1 conventional alarm system. For example, the recipient-dependent model 308 is generated to account for regular fluctuations over a day, week, month, etc.
  • the recipient-dependent model 308 is generated based on the subcomponents of the measured data. Since the system is trained based on measurements from the recipient, the recipient-dependent model 308 determines whether current measurements are out of range of the normal for the day, week, month, etc. or for a particular sub-component of a measurement. In other words, the model is generated based on personalized baselines over different time frames/periods and impedance changes are evaluated based on the personalized baselines. [0074] The recipient-dependent model 308 can be updated based on updated measurements 310 associated with the recipient. In this way, the system can readjust itself and periodically adapt the recipient-dependent model 308 based on changes associated with the recipient.
  • impedance values associated with the recipient can change.
  • the system can update the recipient-dependent model 308 based on the fibrosis growth while continuing to monitor the longer-term changes due to fibrous tissue growth.
  • the recipient can also experience different kinds of permanent changes that can affect “normal” impedance values.
  • the recipient-dependent model 308 can continue to adapt and become more accurate, which decreases the chances of false alarms.
  • the system can continue to adapt as more measurements associated with the recipient are taken.
  • FIG.4 is a block diagram illustrating using recipient-dependent model 308 to classify a datapoint associated with a recipient as normal or anomalous.
  • recipient measurements 402 can be inputted into recipient-dependent model 308.
  • Recipient measurements 402 can include electrophysiological data (e.g., impedance data) associated with the recipient. Similar to the recipient measurements 306 described above, recipient measurement 402 can include a multi-dimensional dataset and can be decomposed into subcomponents.
  • Recipient measurements 402 can additionally include sensor data associated with the recipient (e.g., temperature measurements, heart rate, skin conductance, menstrual cycle information, etc.).
  • Additional information 404 can be inputted to the recipient-dependent model 308.
  • the additional information 404 can include, for example, information associated with the recipient’s medical device (e.g., device usage, applied charge, etc.) and personal information Atty. Docket No.3065.0755i Client Ref. No. CID03755WOPC1 associated with the recipient (e.g., medication intake, changes of vertigo or tinnitus levels, hearing test scores, etc.).
  • additional information 404 can include a stimulation history associated with the recipient’s medical device. Since it is known that electric stimulation has a conditioning effect on the electrode impedances, the stimulation history can be inputted into recipient-dependent model 308 during the analysis of impedance changes.
  • impedances are expected to increase overnight when no stimulation is applied for several hours. Furthermore, during live mode stimulation, not all electrodes are selected in each stimulation frame and the stimulation level depends on the incoming signal. Therefore, the conditioning effect depends on the overall device use of the medical device and the incoming sound. [0078] In other words, the stimulation history of an electrode array can affect impedance measurements associated with a recipient. If unusually high impedances are measured, but the electrodes have not been stimulated for a period of time, the high impedance values are likely due to the lack of stimulation of the electrodes and not due to a critical event. Therefore, taking the stimulation history into account can decrease the likelihood of false alarms.
  • the model generates a forecast of the next impedance measurement with a margin.
  • the model can predict what will happen in the next few days or in a few months based on the observation of current trends.
  • the length of the predicted period can be variable.
  • the new datapoint is identified as an anomaly if the difference exceeds the forecast margin.
  • the analysis can additionally include clustering.
  • the input parameters can be clustered using spectral clustering, which does not assume spherical distributions of the clusters such as k-means.
  • 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 internal/implantable coil 814 that is generally external to the housing 838, but which is connected to the transceiver via a hermetic feedthrough (not shown).
  • a hermetic feedthrough not shown.
  • the stimulating assembly 816 comprises a plurality of electrodes 844(1)-(3) disposed in a carrier member (e.g., a flexible silicone body).
  • 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.
  • 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 can be used with stimulating assemblies having different numbers of stimulation electrodes, stimulating assemblies having different lengths, etc.
  • 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.
  • 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.
  • sensory inputs e.g., photons entering the eye
  • 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.
  • 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 which extends through surgical incision 989 formed in the eye wall.
  • processing module Atty. Docket No.3065.0755i Client Ref. No. CID03755WOPC1 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 processing module 925 can be implanted in the recipient and function by communicating with the external device 910, such as a behind-the-ear 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, which sensor-stimulator is implanted in the recipient.
  • an external light / image capture device e.g., located in / on a behind-the-ear device or a pair of glasses, etc.
  • the sensor-stimulator 990 captures light / images, which sensor-stimulator is implanted in the recipient.
  • 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.

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Abstract

L'invention concerne des techniques de surveillance d'un état associé à un receveur d'un dispositif médical. Un modèle est entraîné sur la base de mesures électrophysiologiques historiques associées à un destinataire d'un dispositif médical sur une période de temps. Une ou plusieurs mesures électrophysiologiques actuelles associées au receveur d'un dispositif médical sont obtenues. Une condition associée au destinataire est surveillée sur la base de la ou des mesures électrophysiologiques actuelles et du modèle.
PCT/IB2025/051534 2024-02-22 2025-02-13 Personnalisation de surveillance de la santé de la cochlée Pending WO2025177122A1 (fr)

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Publication number Priority date Publication date Assignee Title
US20150289929A1 (en) * 2012-11-05 2015-10-15 Autonomix Medical, Inc. Systems, methods, and devices for monitoring and treatment of tissues within and/or through a lumen well
US20220095980A1 (en) * 2018-11-09 2022-03-31 Acutus Medical, Inc. Systems and methods for calculating patient information
US20220273951A1 (en) * 2019-09-23 2022-09-01 Cochlear Limited Detection and treatment of neotissue
US20220347475A1 (en) * 2019-08-23 2022-11-03 Advanced Bionics Ag Detection of a positioning state of an electrode lead during a lead insertion procedure
EP4091664A1 (fr) * 2020-01-13 2022-11-23 Zandona Freitas, Ângelo Eustáquio Procédé et système d'activation et de surveillance neuromusculaire artificielle se fondant sur l'intelligence artificielle

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150289929A1 (en) * 2012-11-05 2015-10-15 Autonomix Medical, Inc. Systems, methods, and devices for monitoring and treatment of tissues within and/or through a lumen well
US20220095980A1 (en) * 2018-11-09 2022-03-31 Acutus Medical, Inc. Systems and methods for calculating patient information
US20220347475A1 (en) * 2019-08-23 2022-11-03 Advanced Bionics Ag Detection of a positioning state of an electrode lead during a lead insertion procedure
US20220273951A1 (en) * 2019-09-23 2022-09-01 Cochlear Limited Detection and treatment of neotissue
EP4091664A1 (fr) * 2020-01-13 2022-11-23 Zandona Freitas, Ângelo Eustáquio Procédé et système d'activation et de surveillance neuromusculaire artificielle se fondant sur l'intelligence artificielle

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