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WO2025133849A1 - Architecture de simulation pour l'administration de thérapies - Google Patents

Architecture de simulation pour l'administration de thérapies Download PDF

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Publication number
WO2025133849A1
WO2025133849A1 PCT/IB2024/062559 IB2024062559W WO2025133849A1 WO 2025133849 A1 WO2025133849 A1 WO 2025133849A1 IB 2024062559 W IB2024062559 W IB 2024062559W WO 2025133849 A1 WO2025133849 A1 WO 2025133849A1
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WO
WIPO (PCT)
Prior art keywords
model
simulation
patient
stimulation
input
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/IB2024/062559
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English (en)
Inventor
Leonid M. Litvak
Andrew J. Cleland
Abigail L. SKERKER
Malgorzata M. STRAKA
Andrew L. Schmeling
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.)
Medtronic Inc
Original Assignee
Medtronic Inc
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 Medtronic Inc filed Critical Medtronic Inc
Publication of WO2025133849A1 publication Critical patent/WO2025133849A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B23/00—Models for scientific, medical, or mathematical purposes, e.g. full-sized devices for demonstration purposes
    • G09B23/28—Models for scientific, medical, or mathematical purposes, e.g. full-sized devices for demonstration purposes for medicine
    • 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
    • A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
    • A61N1/3606—Implantable neurostimulators for stimulating central or peripheral nerve system adapted for a particular treatment
    • A61N1/36062—Spinal stimulation
    • 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
    • A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
    • A61N1/3606—Implantable neurostimulators for stimulating central or peripheral nerve system adapted for a particular treatment
    • A61N1/36071—Pain
    • 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
    • A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
    • A61N1/36128—Control systems
    • A61N1/36132—Control systems using patient feedback
    • 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
    • A61N1/372—Arrangements in connection with the implantation of stimulators
    • A61N1/37211—Means for communicating with stimulators
    • A61N1/37235—Aspects of the external programmer
    • A61N1/37247—User interfaces, e.g. input or presentation means

Definitions

  • the present disclosure is generally directed to training tools and relates more particularly to a simulation tool for simulating stimulation procedures, such as spinal cord stimulation procedures and brain stimulation procedures.
  • Implantable electrical stimulators may be used to deliver electrical stimulation therapy to patients to treat a variety of symptoms or conditions such as chronic pain, tremor, Parkinson's disease, epilepsy, urinary or fecal incontinence, sexual dysfunction, obesity, or gastroparesis.
  • an implantable stimulator delivers neurostimulation therapy in the form of electrical pulses.
  • An implantable stimulator may deliver neurostimulation therapy via one or more leads that include electrodes located proximate to target tissues of the brain, the spinal cord, pelvic nerves, peripheral nerves, or the stomach of a patient.
  • stimulation may be used in different therapeutic applications, such as deep brain stimulation (DBS), spinal cord stimulation (SCS), pelvic stimulation, gastric stimulation, or peripheral nerve stimulation.
  • Stimulation also may be used for muscle stimulation, e.g., functional electrical stimulation (FES) to promote muscle movement or prevent atrophy.
  • DBS deep brain stimulation
  • SCS spinal cord stimulation
  • FES functional electrical stimulation
  • a clinician may select values for a number of programmable parameters to define the electrical stimulation therapy to be delivered by the implantable stimulator to a patient.
  • the clinician may select one or more electrodes for delivery of the stimulation, a polarity of each selected electrode, a voltage or current pulse amplitude, a pulse width, and a pulse frequency as stimulation parameters.
  • a set of parameters such as a set including electrode combination, electrode polarity, amplitude, pulse width, and pulse rate, may be referred to as a program in the sense that they define the electrical stimulation therapy to be delivered to the patient.
  • Clinicians develop a sense of the appropriate program to apply to a patient over years of experience and trial-and-error with the patient’s input. It would be desirable to lead the clinician to an appropriate program initially or to minimize the amount of back and forth required between the patient and the clinician.
  • Embodiments of the present disclosure provide a simulation interface that enables the clinician to simulate various scenarios (e.g., different programs/parameter settings) with a patient or a virtual patient, thereby allowing the clinician to learn by trial-and-error without subjecting the patient to undue pain, stress, or in-person visits with their clinician.
  • the simulations conducted by the clinician on the simulation interface may help the clinician select the appropriate or optimal program for a patient without requiring any actual patient interactions.
  • the clinician may have a patient model that is developed to represent a particular patient (e.g., a patient- specific model). The clinician may interact with the patient model in the proposed simulation interface to determine which types of parameters or settings are appropriate for a particular patient.
  • the settings may be defined based on the clinician’s interactions with the patient model.
  • Example aspects of the present disclosure include a simulation system having one or more processors, a memory, and one or more programs stored in the memory, where the one or more programs are executed by the processors, the one or more programs including instructions for: generating a first model representing a patient; generating a second model representing a stimulation device; and causing the first model and second model to interact based on one or more simulation inputs related to a stimulation therapy, thereby causing one or more simulation outputs in the first model, second model, or both.
  • the simulation input includes one or more stimulation waveforms from the second model.
  • the simulation outputs from the first model include one or more of: a response waveform in response to the one or more simulation input from the second model and a paresthesia output responsive to the one or more simulation inputs.
  • the simulation inputs include a preprogrammed input, user input, or both.
  • the paresthesia output is displayed on a user interface providing a simulation environment for the first model and the second model.
  • an interaction between the first model and the second model is adjustable by switching between an open-loop simulation configuration and a closed-loop simulation configuration.
  • the first model provides a waveform output to the second model when operating in the closed-loop simulation setting.
  • the waveform output comprises an Evoked Compound Action Potential (ECAP) waveform.
  • ECAP Evoked Compound Action Potential
  • the first model is adjustable to simulate different distances between an electrode that delivers the one or more simulated signals and a spinal cord of the patient.
  • the first model is adjustable based on different patient postures that affect the distance between the electrode that delivers the one or more simulated signals and the spinal cord of the patient.
  • the first model comprises an Artificial Intelligence (Al) model that processes at least one input received at the first model.
  • Al Artificial Intelligence
  • the second model comprises one or more Artificial Intelligence (Al) models that process the one or more simulated electrical signals.
  • Al Artificial Intelligence
  • the one or more Al models comprises a plurality of models interconnected with one another.
  • the simulation system further includes a user interface that enables a user to interact with at least one of the first model and the second model and to adjust an operating parameter thereof.
  • the user interface provides a display of the one or more simulated electrical signals.
  • the user interface further displays a reaction produced by the first model in response to processing the one or more simulated electrical signals.
  • the first model is stored on one or more servers and made available to a user device via a communication network.
  • the second model is stored on the one or more servers and made available to the user device via the communication network.
  • At least one of the first model and the second model are provided on a user device.
  • a system which may include: a processor; and memory storing a first model representing a patient, where the first model enables the processor to: receive an input from a second model representing a stimulation device, wherein the input comprises one or more simulated signals generated in accordance with a simulation scenario; pass the input through one or more processing components; and produce one or more outputs representing a simulated patient response to the input.
  • the first model comprises one of a plurality of patient models and wherein the first model is selected based on a user input.
  • the one or more processing components comprises a medium processing component that simulates an amount of current that reaches a spinal cord of the patient from the one or more simulated signals.
  • the one or more processing components comprises a neural excitation component that models an amount of neural excitation.
  • the one or more processing components comprises an Evoked Compound Action Potential (ECAP) component that generates a response waveform generated in response to the one or more simulated signals.
  • ECAP Evoked Compound Action Potential
  • the one or more processing components comprises a central processing component that generates an integration in time of neural excitation responsive to the one or more simulated signals.
  • the one or more processing components comprises a perception component that generates a paresthesia output responsive to the one or more simulated signals.
  • the processor is separated from the memory by a communication network.
  • the processor and memory are provided on a common device.
  • the one or more outputs comprises an Evoked Compound Action Potential (ECAP) signal that is provided back to the second model as a feedback to the second model.
  • ECAP Evoked Compound Action Potential
  • the one or more outputs comprises a paresthesia output responsive to the one or more simulated signals.
  • the one or more outputs comprises a response waveform.
  • the first model is configurable to operate in an open-loop simulation state where the one or more outputs are provided to a user interface and wherein the first model is further configurable to operate in a closed-loop simulation state where the one or more outputs are also provided back to the second model.
  • Other aspects of the present disclosure provide a method that includes: receiving, at a patient model, an input comprising one or more simulated signals generated in accordance with a simulation scenario; passing the input through one or more processing components of the patient model that simulate a patient response to the input; and producing one or more outputs with the patient model based on the one or more processing components processing the input.
  • the one or more outputs are provided as feedback from the patient model to a second model representing a stimulation device.
  • the input is received from a physical stimulation device.
  • the input is received from a model representing a stimulation device.
  • the method further includes: enabling the patient model to switch between an open-loop simulation configuration where the one or more outputs are provided to a user interface and a closed-loop simulation configuration where the one or more outputs are also provided back to a second model representing a stimulation device.
  • Other aspects of the present disclosure provide a method that includes: generating one or more simulated signals in accordance with a simulation scenario; providing the one or more simulated signals to at least one of a patient and a patient model; and receiving a feedback generated by the at least one of the patient and the patient model responsive to the one or more simulated signals.
  • the feedback comprises at least one of an Evoked Compound Action Potential (ECAP) signal, an ECAP waveform, evoked resonant neural activity (ERNA), local field potential (LFP) biomarkers, information describing cramping, information describing heaviness in the legs, information describing changes in movements, information describing muscle activity, information describing muscle tightness, information describing headaches, information describing dizziness, information describing numbness, information describing trouble with speech or balance, information describing changes with gait, information describing mood changes, information describing fatigue, information describing pain, and information describing discomfort.
  • ECAP Evoked Compound Action Potential
  • ERNA evoked resonant neural activity
  • LFP local field potential
  • the one or more simulated signals are provided to the patient and wherein the feedback is received from the patient.
  • the one or more simulated signals are provided to the patient model and wherein the feedback is received from the patient model.
  • the one or more simulated signals are converted into actual stimulation signals and provided to the patient via one or more electrodes and wherein the feedback is received via one or more additional electrodes.
  • Example aspects of the present disclosure include any of the above aspects in combination with any one or more other aspects.
  • Example aspects of the present disclosure include any one or more of the features disclosed herein.
  • Example aspects of the present disclosure include any one or more of the features as substantially disclosed herein.
  • Example aspects of the present disclosure include any one or more of the features as substantially disclosed herein in combination with any one or more other features as substantially disclosed herein.
  • Example aspects of the present disclosure include any one of the aspects/features/embodiments in combination with any one or more other aspects/ features/ embodiments .
  • Example aspects of the present disclosure include the use of any one or more of the aspects or features as disclosed herein.
  • each of the expressions “at least one of A, B and C”, “at least one of A, B, or C”, “one or more of A, B, and C”, “one or more of A, B, or C” and “A, B, and/or C” means A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B and C together.
  • each one of A, B, and C in the above expressions refers to an element, such as X, Y, and Z, or class of elements, such as XI -Xn, Yl-Ym, and Zl-Zo
  • the phrase is intended to refer to a single element selected from X, Y, and Z, a combination of elements selected from the same class (e.g., XI and X2) as well as a combination of elements selected from two or more classes (e.g., Y1 and Zo).
  • FIG. 1 A is a diagram of a system in a first arrangement according to at least one embodiment of the present disclosure
  • Fig. IB is a diagram of a system in a second arrangement according to at least one embodiment of the present disclosure.
  • FIG. 1C is a diagram of a system in a third arrangement according to at least one embodiment of the present disclosure.
  • Fig. ID is a diagram of a system in a fourth arrangement according to at least one embodiment of the present disclosure.
  • Fig. IE is a diagram of a system in a fifth arrangement according to at least one embodiment of the present disclosure.
  • Fig. 2 is a block diagram of system components according to at least one embodiment of the present disclosure
  • FIG. 3 is a block diagram of simulation system components according to at least one embodiment of the present disclosure.
  • FIG. 4 is a flow diagram illustrating a first method according to at least one embodiment of the present disclosure
  • Fig. 5 is a flow diagram illustrating a second method according to at least one embodiment of the present disclosure.
  • Fig. 6 is a flow diagram illustrating a third method according to at least one embodiment of the present disclosure.
  • Fig. 7 is a flow diagram illustrating a fourth method according to at least one embodiment of the present disclosure.
  • the described methods, processes, and techniques may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. Alternatively, or additionally, functions may be implemented using machine learning models, neural networks, artificial neural networks, or combinations thereof (alone or in combination with instructions).
  • Computer- readable media may include non-transitory computer-readable media, which corresponds to a tangible medium such as data storage media (e.g., random-access memory (RAM), read-only memory (ROM), electrically erasable programmable ROM (EEPROM), flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer).
  • data storage media e.g., random-access memory (RAM), read-only memory (ROM), electrically erasable programmable ROM (EEPROM), flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer).
  • processors such as one or more digital signal processors (DSPs), general purpose microprocessors (e.g., Intel Core i3, i5, i7, or i9 processors; Intel Celeron processors; Intel Xeon processors; Intel Pentium processors; AMD Ryzen processors; AMD Athlon processors; AMD Phenom processors; Apple A10 or 10X Fusion processors; Apple Al l, A12, A12X, A12Z, or Al 3 Bionic processors; or any other general purpose microprocessors), graphics processing units (e.g., Nvidia GeForce RTX 2000-series processors, Nvidia GeForce RTX 3000-series processors, AMD Radeon RX 5000-series processors, AMD Radeon RX 6000-series processors, or any other graphics processing units), application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuit
  • DSPs digital signal processors
  • ECAPs Evoked Compound Action Potentials
  • a simulated electrical signal applied to a patient model simulates an SCS procedure.
  • the patient model outputs a simulated body signal and simulated patient feedback.
  • the simulation may be in the form of a training tool which may be used by a user. The user may be enabled by interacting with the tool to select a patient model from a list and to select a particular scenario of arrangement of virtual contacts to apply the simulated electrical signal.
  • Figs. 1A-1E depict various arrangements of a system 100 according to at least some embodiments of the present disclosure. While the various arrangements of the system 100 will be described with respect to five particular examples, it should be appreciated that additional or different arrangements or configurations of the system 100 may be implemented without departing from the scope of the present disclosure. For instance, aspects of one arrangement (e.g., an arrangement as illustrated in Fig. 1A) may be combined with aspects of another arrangement (e.g., an arrangement as illustrated in Fig. IE). The examples depicted and described herein are not intended to limit the claims and should not be construed as such.
  • the system 100 may be configured to provide a simulation interface 126 in which a user, such as a clinician, doctor, care provider, physician’s assistant, etc. is able to simulate, test, and/or modify stimulation settings.
  • a user such as a clinician, doctor, care provider, physician’s assistant, etc.
  • the user may be allowed to define parameters of a stimulation program, then test the stimulation program, within the simulation environment, using a patient model 112 and/or a stimulation device model 114.
  • the system 100 may provide a simulation interface 126 that enables simulation of various types of stimulation, such as DBS, SCS, pelvic stimulation, gastric stimulation, peripheral nerve stimulation, or FES.
  • the system 100 is shown to include a server 102, a user device 124, and a communication network 122.
  • the server 102 is configured to communicate with the user device 124 via the communication network 122.
  • the server 102 in some embodiments, may correspond to one or multiple servers. Alternatively or additionally, the server 102 may be represented as cloud-based memory and/or processing resources.
  • the illustration of a server 102 is intended to show a computing resource or set of computing resources that are configured to provide a simulation service to the user device 124.
  • the server 102 is illustrated to include a processor 104, a memory 106, and a communication interface 108.
  • the user device 124 is shown to include a simulation interface 126, which may correspond to a set of Graphical User Interface (GUI) elements that are presented via a user interface of the user device 124.
  • GUI Graphical User Interface
  • the simulation interface 126 may correspond to a simulation environment (e.g., a web-based environment, an applicationbased environment, or the like) that interacts with the server 102 and presents a user of the user device 124 with the ability to define simulation settings, test simulation settings, adjust simulation settings, and control various aspects of a simulation scenario such that different configurations of a stimulation device are tested before being applied to a patient.
  • the user device 124 may enable a user to access some or all of the components of the server 102 to facilitate a simulation scenario.
  • Computing devices may comprise more or fewer components than the server 102.
  • the system 100 may provide a single device with the components of the server 102 and the components of the user device 124.
  • a single computing device e.g., a user device 1214
  • a server 102 may be provided with a user interface and/or simulation interface 126 without departing from the scope of the present disclosure.
  • the processor 104 may correspond to any processor described herein or any similar processing unit or processing resource.
  • the processor 104 may be configured to execute or process data (e.g., instructions, Artificial Intelligence (Al) models, neural networks, etc.) stored in the memory 106.
  • data e.g., instructions, Artificial Intelligence (Al) models, neural networks, etc.
  • the processor 104 may carry out one or more computing steps utilizing the patient model 112, the stimulation device model 114, simulation data 116, scenario data 118, and/or based on data received from the communication interface 108, or the simulation interface 126.
  • the processor 104 may include or correspond to one or more Central Processing Units (CPUs), one or more Graphics Processing Units (GPUs), one or more Data Processing Units (DPUs), one or more ASICs, one or more FPGAs, one or more processing circuits, one or more Integrated Circuit (IC) chips, combinations thereof, and the like.
  • CPUs Central Processing Units
  • GPUs Graphics Processing Units
  • DPUs Data Processing Units
  • ASICs application specific integrated circuits
  • FPGAs Integrated Circuit
  • Memory 106 may store data used to perform systems and methods described herein. Such data may include, for example, and as described in greater detail below, a patient model 112, a stimulation device model 114, simulation data 116, scenario data 118, and display instructions 120.
  • the memory 106 may be or comprise RAM, DRAM, SDRAM, other solid-state memory, any memory described herein, or any other tangible, non-transitory memory for storing computer- readable data and/or instructions.
  • the memory 106 may store information or data useful for completing, for example, any steps of the methods depicted and described herein, or of any other methods.
  • the memory 106 may store, for example, instructions and/or machine learning models that support the creation, management, and/or delivery of a simulation interface 126 to a user.
  • the data stored in memory 106 may enable one or more functions of the simulation of electrical signals and the application of simulated electrical signals to patient/s and/or patient models 112.
  • the memory 106 may store content (e.g., instructions and/or machine learning models) that, when executed by the processor 104, cause the processor 104 to simulate an electrical signal being applied to a target anatomical element such as a nerve, such as to block or regulate chronic pain.
  • Content stored in the memory 106 may, in some embodiments, be organized into one or more applications, modules, packages, layers, or engines. Alternatively, or additionally, the memory 106 may store other types of content or data (e.g., machine learning models, artificial neural networks, deep neural networks, etc.) that can be processed by the processor 104 to carry out the various method and features described herein.
  • content or data e.g., machine learning models, artificial neural networks, deep neural networks, etc.
  • the data, algorithms, and/or instructions may cause the processor 104 to manipulate data stored in the memory 106 and/or received from or via the communication interface 108 (e.g., via the communication network 122).
  • the communication interface 108 may be used for receiving data from an external source (such as the user device 124, the communication network 122, a database (not illustrated), and/or any other system or component not part of the system 100), and/or for transmitting instructions, images, or other information to an external system or device (e.g., another computing device, the user device 124, the communication network 122, a database, and/or any other system or component not part of the system 100).
  • an external source such as the user device 124, the communication network 122, a database (not illustrated), and/or any other system or component not part of the system 100
  • an external system or device e.g., another computing device, the user device 124, the communication network 122, a database, and/or any other system or component not part of the system 100.
  • the communication interface 108 may comprise one or more wired interfaces (e.g., a USB port, an Ethernet port, a Firewire port) and/or one or more wireless transceivers or interfaces (configured, for example, to transmit and/or receive information via one or more wireless communication protocols such as 802.11a/b/g/n, Bluetooth, NFC, ZigBee, and so forth).
  • the communication interface 108 may be useful for enabling the server 102 to communicate with one or more other processors 104 or computing devices (e.g., the user device 124 or another server 102), whether to reduce the time needed to accomplish a computingintensive task or for any other reason.
  • data such as the patient model 112, stimulation device model 114, simulation data 114, scenario data 116, and display instructions 120 described herein may be obtained in whole or in part by the server 102 from one or more databases and/or cloud computing resources via communications exchanged over the communication network 122.
  • the communication network 122 may be or represent the Internet or any other wide area network.
  • the server 102 may be connected to the communication network 122 via the communication interface 108, using a wired connection, a wireless connection, or both.
  • the server 102 may communicate with a database and/or an external device (e.g., a user device 124) via the communication network 122.
  • the patient model 112 may correspond to an instruction set, Al model, neural network, or the like that is capable of receiving one or more inputs, processing the one or more inputs in a way that simulates a patient processing the one or more inputs, and then generated one or more outputs that represent or simulate a patient response.
  • the patient model 112 may provide an engine for simulating a patient that is affected by stimulation inputs provided by an actual stimulation device or by the stimulation device model 114.
  • the patient model 112 may have varying degrees of complexity depending upon a simulation scenario being analyzed in the simulation interface 126.
  • the patient model 112 may correspond to one of multiple patient models that are available for testing within the simulation interface 126.
  • the patient model 112 used for a particular simulation may be selected by the user of the user device 124 when defining a simulation scenario. Different patient models 112 may be selected to test different impacts of a common simulation scenario of different patient types. Alternatively or additionally, the same patient model 112 may be subjected to multiple different simulation scenarios to determine how a single type of patient might respond to various stimulation programs.
  • the patient model 112 may correspond to a generic patient model (e.g., representing a common or typical patient) or the patient model 112 may correspond to a patient-specific model that is developed to approximate a specific patient’s response to a simulation scenario.
  • the user of the user device 124 may employ the simulation interface 126 to select one or more patient models 112 to be tested under a simulation scenario.
  • one or more different simulation scenarios may be selected.
  • Examples of different stimulation parameters and/or stimulation algorithms that may be selected for a particular simulation scenario include, without limitation, a number of electrodes to deliver a stimulation signal, an electrode combination to deliver a stimulation signal, electrode polarity, stimulation signal amplitude, stimulation signal pulse width, stimulation signal pulse rate, therapy duration, implant location, electrode placement, closed-loop versus open-loop, etc. Adjusting one or more of theses different parameters or algorithms may relate to selecting a different simulation scenario, which ultimately may help the clinician to select a therapeutic programs to be used on the patient.
  • simulation scenarios may be deployed to simulate or emulate a potential therapeutic program to be delivered to a patient.
  • the stimulation device model 114 may be configured, based on user selection, to implement a control algorithm of a closed-loop stimulation device. Alternatively or additionally, the stimulation device model 114 may be configured, based on user selection, to implement an openloop stimulation device. In some embodiments, the stimulation device model 114 may generate one or more simulated electrical signals in accordance with a simulation scenario, then deliver the one or more simulated electrical signals to the patient model 112. As an example, the stimulation device model 114 may output a simulated electrical signal having a stimulation amplitude that varies over time. The simulated electrical signal may also be delivered by different electrodes to the patient model 112 (e.g., at different distances from a target anatomical element).
  • the stimulation device model 114 may implement a mapping between ECAPs over time (e.g., ECAP(t)) and stimulation amplitude over time (e.g., StimulationAmplitude(t)) for ping and governed based on device settings.
  • ECAPs over time e.g., ECAP(t)
  • stimulation amplitude over time e.g., StimulationAmplitude(t)
  • Example settings include, without limitation, Programmed Pl current, Programmed P2 current, reaction and recovery thresholds, attack and release times, averaging of waveforms, artifact cancellation methods, etc.
  • Simulations may be executed using scenario data 118 in the simulation interface 126.
  • the scenario data 118 may correspond to the various parameters and algorithms selected for applying a simulation scenario within the simulation interface.
  • simulation data 116 may be generated.
  • the simulation data 116 may include a description of inputs being provided to the patient model 112 and/or stimulation device model 114.
  • the simulation data 116 may also include a description of outputs being generated at the patient model 112 and/or stimulation device model 114.
  • the inputs and/or outputs generated by the various models 112, 114 during execution of a simulation scenario may be displayed to a user via the simulation interface 126.
  • the display instructions 120 may be executed by the processor 104 to determine how to present the simulation data 116 and/or scenario data 118 to the user via the simulation interface 126. To the extent that the display size of a user interface may be limited, the amount of data and the manner in which the data is presented via the simulation interface 126 may be controlled by the display instructions 120.
  • the display instructions 120 may also be responsible for changing a display of the simulation interface 126 based on user inputs and/or based on changes in the simulation scenario.
  • the system 100 of Fig. IB is shown to provide a user device 124 with the components needed to facilitate execution of a simulation scenario.
  • the user device 124 is shown to include many of the components previously illustrated as being included in the server 102. It should be appreciated, however, that the user device 124 of Fig. IB may be similar or identical to the user device 124 of Fig. 1A.
  • the user device 124 may correspond to a personal computing device or collection of computing devices.
  • Non-limiting examples of a suitable user device 124 include a Personal Computer (PC), a laptop, a tablet, a smartphone, a Personal Digital Assistant (PDA), or the like.
  • Fig. IB The arrangement of Fig. IB is not shown to include a communication network 122, but it should be appreciated that the user device 124 is connectable to a communication network 122. In some embodiments, the user device 124 may be capable of interacting with a server 102 that also has components as illustrated in Fig. 1A.
  • the simulation interface 126 may be rendered/displayed to a user via the user interface 110 of the user device 124.
  • the user interface 110 may be or comprise a keyboard, mouse, trackball, monitor, television, screen, touchscreen, and/or any other device for receiving information from a user and/or for providing information to a user.
  • the user interface 110 may be used, for example, to receive a user selection or other user input regarding any step of any method described herein.
  • the user interface 110 may be used to select one or more parameters for the virtual electrodes used in scenarios including, but not limited to, selecting a size and/or location of an electrode.
  • the user interface 110 may receive input prior to a simulation, such as to select a patient model and/or scenario, and may receive input during a simulation, such as to adjust settings.
  • the user interface 110 may also include user output capabilities, as mentioned above, thereby facilitating a visual display and/or audible presentation of the simulation interface 126. Notwithstanding the foregoing, any required input for any step of any method described herein may be generated automatically by the system 100 (e.g., by the processor 104 or another component of the system 100) or received by the system 100 from a source external to the system 100.
  • the user interface 110 may be useful to allow a user or other user to modify instructions to be executed by the processor 110 according to one or more embodiments of the present disclosure, and/or to modify or adjust a setting of other information displayed on the user interface 110 or corresponding thereto.
  • the user interface 110 is shown as part of the user device 124, in some embodiments, the user device 124 may utilize a user interface 110 that is housed separately from one or more remaining components of the user device 124. In some embodiments, the user interface 110 may be located proximate one or more other components of the user device 124, while in other embodiments, the user interface 110 may be located remotely from one or more other components of the user device 124.
  • FIG. 1C provides a first computing device 128 and a second computing device 130.
  • the first computing device 128 and/or second computing device 130 may be configured similarly or identically to the server 102 and/or to the user device 124.
  • components described as being included in the server 102 and/or the user device 124 may be provided in the first computing device 128 and/or second computing device 130.
  • the first computing device 128 is shown to include the processor 104, the memory 106, the communication interface 108, and the user interface 110.
  • the memory 106 of the first computing device is shown to include the patient model 112, simulation data 116, and scenario data 118.
  • the second computing device 130 is shown to also include the processor 104, the memory 106, the communication interface 108, and the user interface 110.
  • the memory 106 of the second computing device 130 is shown to include the stimulation device model 114, simulation data 116, scenario data 118, and display instructions 120.
  • the arrangement of Fig. 1C deploys one computing device (e.g., the first computing device 128) to operate a patient side of a simulation and another computing device (e.g., the second computing device 130) to operate a device side of a simulation.
  • the first computing device 128 may communicate with the second communication device 130 directly (e.g., via a wired connection or wireless connection established between the communication interfaces 108 of the devices 128, 130). Alternatively or additionally, the first computing device 128 may communication with the second communication device 130 through the communication network 122.
  • the processor 104 of the first computing device 128 may be configured to execute the patient model 112.
  • the processor 104 of the second computing device 128 may be configured to execute the stimulation device model 114.
  • the second computing device 130 may generate one or more simulated electrical signals in accordance with the simulation scenario, then the second computing device may transmit the one or more simulated electrical signals to the first computing device 128.
  • the processor 104 of the first computing device 128 may execute the patient model 112, which takes the inputs from the stimulation device model 114 and produces one or more outputs representing a simulated patient response to the one or more simulated electrical signals.
  • the output(s) of the patient model 112 may be displayed in a simulation interface 126 via the user interface 110 of the first computing device 128 or via the user interface 110 of the second computing device 130.
  • the output(s) of the patient model 112 may be provided back to the second computing device 130 for processing by the display instructions 120.
  • the display instruction 120 may cause the simulation interface 126 to display the simulation data 116, the scenario data 118, the one or more simulated electrical signals, and the one or more outputs via the user interface 110. It should be appreciated that the display instructions 120 may alternatively or additionally be provided in the first computing device 128 without departing from the scope of the present disclosure.
  • the system 100 is shown to include the first computing device 128, which may be similar or identical to the first computing device 128 of Fig. 1C.
  • the system 100 is also shown to include a stimulation device 132 in place of the second computing device 130.
  • the stimulation device 132 may correspond to any suitable type of physical device that generates an actual stimulation signal and delivers the stimulation signal to a patient.
  • the stimulation device 132 may correspond to an implantable stimulation device.
  • the stimulation device 132 may not be implantable, but rather may be configured to deliver a stimulation signal to a patient via one or more implanted electrodes.
  • Nonlimiting examples of stimulation devices 132 that may be utilized include the devices depicted and described in U.S. Patent Nos. 7,313,445; 7,761,985; 9,757,555; and 10,953,221, the disclosures of which are hereby incorporated herein by reference in their entirety.
  • the stimulation device 132 is shown to include a processor 104 and a communication interface 108.
  • the communication interface 108 may facilitate communications with the first computing device 128 during a simulation involving the stimulation device 132.
  • the first computing device 128 may present the simulation interface 126 to a user via the user interface 110.
  • Simulation data 116 and/or scenario data 118 may be shared between the first computing device 128 and the stimulation device 132 via the communication interfaces 108 of each device.
  • the stimulation device 132 is shown to include one or more signal generation circuits 134 and one or more signal delivery circuits 136.
  • the signal generation circuit(s) 134 may be configured to generate a stimulation signal or series of stimulation signals based on scenario data 118 received from the first computing device 128.
  • the signal generation circuit(s) 134 may cooperate with the signal delivery circuit(s) 136 to deliver the stimulation signal(s) to a patient.
  • the stimulation signal(s) may be delivered to the first computing device 128 as inputs to the patient model 112. This arrangement may allow the testing/simulation of an actual stimulation device 132 with a virtual patient (e.g., via the patient model 112).
  • the system 100 is shown to include a stimulation delivery device 138 and electrodes 140 in communication with the second computing device 130.
  • the stimulation delivery device 138 may be similar or identical to stimulation device 132, meaning that components of the stimulation device 132 may be provided in the stimulation delivery device 138.
  • the stimulation delivery device 138 may be configured, in some embodiments, to deliver stimulation to a patient 142 via one or more electrodes 140 according to simulation data 116 and/or scenario data 118.
  • the second computing device 130 may operate as a controller for the stimulation delivery device 138.
  • the stimulation delivery device 138 may then be configured to deliver physical stimulation signals to a patient 142 via the electrode(s) 140. While delivering stimulation signals to a patient 142 may not be the same as implementing a simulation scenario as described herein, the second computing device 130 may still be configured to store simulation data 116 and scenario data 118 from previously-run simulation scenarios. Based on those scenarios, the second computing device 130 may then be used to control a stimulation delivery device 140, which delivers stimulation signals to the patient 142 according to a program developed with the help of the simulation scenarios run in the second computing device 130.
  • the stimulation delivery device 138 and/or electrode(s) 140 may be implantable in the patient 142. It should be appreciated, however, that the stimulation delivery device 138 and/or electrode(s) 140 do not necessarily need to be implantable or remain implanted in the patient 142.
  • the communication between the second computing device 130 and the stimulation delivery device 138 may be achieved via a wired communication link (e.g., with a wired communication interface 108) or via a wireless communication link (e.g., with a wireless communication interface 108).
  • the various arrangements of the system 100 depicted and described herein may be configured to provide a simulation environment or testing tool that enables a clinician to simulate and test various scenarios.
  • testing tool 200 may comprise components of the system 100 or that the system 100 may include components of the testing tool 200 without departing from the scope of the present disclosure. In other words, the testing tool 200 may be deployed within the context of the system 100 (e.g., within the simulation interface 126).
  • a testing tool 200 may include applying an electrical signal simulation 204 (e.g., one or more simulated electrical signals) to a patient model 112 to output a patient model response 208.
  • an electrical signal simulation 204 e.g., one or more simulated electrical signals
  • user inputs 202, configuration settings, and other variables may affect one or both of the signal simulation 204 and the patient model 112.
  • a user may select a patient model 112, select a simulation scenario, adjust a position of the patient, and/or adjust other settings.
  • other user inputs 206 such as the adjustment of thresholds, patient position, program variables, aggressors, and settings may affect one or both of the signal simulation 204 and the patient model 112.
  • live feedback visualizations 210 may be generated and/or displayed on a user interface 110 and/or an assessment 212 may be generated and/or displayed as part of the simulation interface 126.
  • the simulation of the ECAP signal may depend at least in part on user input and configuration settings. For example, a user may select or design a scenario as described in greater detail below.
  • the signal simulation 204 may be performed by the system 100 to replicate realistic functionality of a stimulation device 132 and/or stimulation delivery device 138 in an open-loop configuration or a closed-loop configuration.
  • the testing tool 200 helps test multiple device configurations (e.g., open-loop configuration and/or closed-loop configuration) prior to administering treatment to a patient.
  • the patient model 112 may not necessarily change, but the amount of feedback provided from the patient model 112 to a stimulation device model 114 may change. For instance, in a closed-loop configuration, output(s) from the patient model 112 may be provided back to the stimulation device model 114.
  • the feedback may include ECAP signals, ECAP waveforms, or any other simulated response generated by the patient model 112 based on processing the signal simulation 204.
  • the feedback may also include patient feedback, which may include but is not limited to jolts/shocks, cramping, heaviness in the legs, changes in movements, muscle activity, muscle tightness, headaches, dizziness, numbness, trouble with speech or balance, changes with gait, mood changes, fatigue, pain, discomfort, etc.
  • the electrical signal simulation 204 may correspond to a process executed by the processor 104 to simulate an electrical signal as which may be used for electrical stimulation and/or nerve blocking. Generating the electrical signal may be achieved by adjusting one or more of a signal frequency, a signal type (e.g., square wave, sinusoidal wave, triangle wave, etc.), a duty cycle, etc.
  • An electrical signal simulation 204 may be out from a virtual device or application process (e.g., the stimulation device model 114) which produces simulated electrical stimulation.
  • a patient model 112 as described herein may be a simulation of a patient that is capable of producing response signals based on the electrical signal simulation 204.
  • a distance between the electrical contact applying the electrical signal and the spinal cord may change, causing the resulting response to stimulation to be weaker or stronger based on the change in the distance.
  • the testing tool 200 determines a change in the ECAP. If the testing tool 200 is implementing a simulation of a closed-loop configuration, then the change in ECAP may result in a change to the electrical signal simulation 204.
  • Outputs of the patient model 112 e.g., the patient model response 208) may then be provided as feedback to the stimulation device model 114 in addition to being output as live feedback visualization 210 and assessment data 212.
  • a user may be enabled to selectively switch the simulation from the simulation of an open-loop configuration (in which no feedback applies to the electrical signal) to a closed-loop configuration (in which feedback applies to the electrical signal).
  • FIG. 3 additional details of a virtual patient 302 and virtual stimulation device 304, 306 will be described in accordance with at least some embodiments of the present disclosure.
  • the virtual patient 302 may be similar or identical to the patient model 112.
  • the virtual device 304 and virtual stimulation 306 may be collectively embodied as the patient model 114.
  • the diagram of Fig. 3 is intended to show more detailed interactions between the various components of the models 112, 114.
  • the virtual stimulation device 304 and virtual stimulation 306 may collectively operate like the stimulation device model 114.
  • the virtual stimulation device 304 and virtual stimulation 306 may be configured, via the testing tool 200, to generate and provide one or more simulated electrical signals 320 to the virtual patient 302.
  • the simulated electrical signal(s) 320 may be generated in accordance with a simulation scenario, which may correspond to a default simulation scenario or a user-selected simulation scenario.
  • the virtual patient 302 and components thereof may be configured to process the simulated electrical signal(s) 320 and generate simulated patient responses thereto.
  • the virtual patient 302 may generate a paresthesia output 322 and a response 324 to the simulated electrical signal(s) 320.
  • Examples of the response 324 include, without limitation, an ECAP signal, an ECAP waveform, and the like.
  • the response 324 may selectively be provided back to the virtual device 304 as a simulated ECAP response. Both outputs 322, 324 may be displayed to a user via a simulation interface 126.
  • the outputs 322, 324 may be generated by one or more processing components of the virtual patient 302.
  • the processing components include, without limitation, a first medium processing component 308, a neural excitation processing component 310, a central processing component 312, a perception processing component 314, a second medium processing component 316, and an ECAPs processing component 318.
  • the components of the virtual patient 302 may be provided as Al models, neural networks, and/or instruction sets. It should be appreciated that some components of the virtual patient 302 may be provided as Al models/neural networks whereas other components of the virtual patient 302 may be provided as instruction sets.
  • the first and/or second medium processing components 308, 316 may be configured to simulate an amount of current that reaches a neuron of the patient from the one or more simulated electrical signals. In some embodiments, the first and/or second medium processing components 308, 316 may determine how much current gets to a neuron based on a simulated electrode configuration, a simulated distance from the electrode to the neuron, and/or patient characteristics.
  • Examples of patient characteristics that may be modeled by the processing components 308, 316 include, without limitation, thickness of cerebrospinal fluid (CSF), phase of heartbeat (e.g., because CSF and the spinal cord change slightly with each heartbeat), location of various anatomical structures (e.g., vertebrae), patient posture (e.g., sitting, standing, supine, walking, bending, etc.), presence of aggressors, and the like. Any patient parameter which may impact an amount of current that reaches a neuron may impact the way that the medium processing components 308, 316 process the inputs to the virtual patient 302 (e.g., the one or more simulated electrical signals 320).
  • the medium processing components 308, 316 may be provided as homogenous models or more complex models.
  • the distance D(t) can be generalized or individualized on a per-patient basis (e.g., based on inputs received from MRI scans, CT scans, or other imaging of a patient).
  • the neural excitation processing component 310 may be configured to model an amount of neural excitation based on an output of the first medium processing component 308.
  • the neural excitation processing component 310 may provide a linear model or a more complex model to simulate a neural excitation based on the distance output by the first medium processing component 308.
  • the ECAPs processing component 318 may be configured to generate a response 324 (e.g., an ECAP waveform or ECAP signal) in response to the one or more simulated electrical signals.
  • a response 324 e.g., an ECAP waveform or ECAP signal
  • the ECAPs processing component 318 generates a waveform that can be recorded and displayed on the simulation interface 126.
  • the ECAP generated in response to stimulation signals may propogate back through the medium so is affected by the parameter d, which is computed by the second medium processing component 316.
  • artWf(ts) is prototype artifact waveform (e.g., decaying exponential with a linear trend). This particular waveform may be posture and/or amplitude dependent.
  • ecapWf(ts) is a prototypical ECAP waveform. Potentially could change with amplitude as well.
  • the central processing component 312 may be configured to generate an integration in time of neural excitation responsive to the one or more simulated electrical signals.
  • the central processing component 312 may work in conjunction with the perception processing component 314, which is configured to generate a paresthesia output 322 responsive to the one or more simulated electrical signals 320. Because there is an integration in time, longer excitation is normally perceived as being stronger (e.g., up to 100 - 200 msec). Thus, the output of the central processing component 312 may be provided as an input to the perception processing component 314 to generate a simulation of the patient perception to the simulated electrical signal(s) 320.
  • dt is a time step (e.g., 20 msec) but may depend on alphalnt and centralGain, where alphalnt is an integration time constant and centralGain is used to translated excitation to a paresthesia strength (e.g., a 0 to 5 score).
  • Paresthesia output 322 can be shown graphically by an area of the body where patient feels it (e.g., have a location component), and by face expression (e.g., have a severity component).
  • the display of the paresthesia output 322 on the simulation interface 126 may be controlled with a paresthesia mapping, which is expressible as ParesthesiaMapping(Paresthesia(t)), and is a patient variable that generates an area of paresthesia based on paresthesia strength.
  • the virtual patient 302 may also receive other inputs that impact a behavior or response to the stimulation input 306.
  • the virtual patient 302 may be provided with an input representing a medication input (e.g., a parameter may be adjusted to simulate how different medications might impact different outputs of the virtual patient 302).
  • a medication input e.g., a parameter may be adjusted to simulate how different medications might impact different outputs of the virtual patient 302.
  • changes in medication e.g., levodopa, isofluorane, general anesthesia, etc.
  • drug e.g., alcohol, nicotine, opioids, gabapentin, etc.
  • Adjustment of medication inputs and/or drug inputs may have a non-trivial impact on the output(s) generated by the virtual patient 302.
  • Figs. 4-7 various methods will be described in accordance with at least some embodiments of the present disclosure.
  • the various methods depicted and described herein may have steps or processes that are interchangeable with one another. Similarly, various methods may be combined and the order of steps or processes therein may be changed.
  • the method 400 begins when an input is received at a patient model 112 (step 404).
  • the input may comprise one or more stimulation signals, which may correspond to simulated stimulation signals generated by a stimulation device model 114 or to actual stimulation signals generated by a physical device (e.g., the stimulation device 132 and/or stimulation delivery device 138).
  • the input is then passed to one or more processing components of the patient model 112 (step 408).
  • the input may be provided to one or more of a medium processing component 308, 316, a neural excitation processing component 310, a central processing component 312, a perception processing component 314, and an ECAPs processing component 318.
  • the patient model 112 may generate one or more outputs (step 412).
  • the one or more outputs may include a paresthesia output 322 and/or a response 324.
  • the response 324 may include an ECAP signal, an ECAP waveform, or the like.
  • the method 400 may continue by delivering the output(s) of the patient model 112 to a simulation interface 126 (step 416).
  • the output(s) may be visually displayed in a GUI and the simulation interface 126 may change over time as the output(s) change.
  • the method 400 may continue by enabling the user to adjust the simulation.
  • the simulation interface 126 presented to the user may enable the user to create changes to the patient model 112, the stimulation device 132, 138, and/or the stimulation device model 114.
  • the changes may be used to change the simulation scenario (e.g., the scenario data 118), which may impact the inputs provided to the patient model 112 and/or behaviors of the various models.
  • the method 400 may also include an optional step of providing at least one patient model output as feedback to a stimulation device 132, 138 and/or the stimulation device model 114 (step 424).
  • feedback may be provided from the patient model 112 in the form of an ECAP signal and/or ECAP waveform.
  • feedback from the patient model 112 may include information describing one or more of ERNA, LFP biomarkers, jolts/shocks, cramping, heaviness in the legs, changes in movements, muscle activity, muscle tightness, headaches, dizziness, numbness, trouble with speech or balance, changes with gait, mood changes, fatigue, pain, discomfort, etc.
  • the method 500 begins by receiving, at a stimulation device model 114, one or more inputs from a user that define a simulation scenario to execute (step 504).
  • the input(s) may be received from the user within the simulation interface 126 and may include inputs defining a type of patient model 112, a type of stimulation device model 114, parameters for a stimulation program to simulate, and a selection of open-loop versus closed-loop configuration to implement.
  • the stimulation device model 114 may generate one or more simulated electrical signals (step 508).
  • the simulated electrical signal(s) may be generated according to the type of stimulation device model 114 selected and according to stimulation parameters selected.
  • the stimulation device model 114 may then provide the simulated electrical signal(s) to a patient model 112 as input thereto (step 512).
  • the simulated electrical signal(s) may be delivered to the patient model 112 as code or instructions. Alternatively or additionally, the simulated electrical signal(s) may be delivered to the patient model 112 via a communication interface.
  • the method 500 may further include receiving a feedback signal from the patient model 112 (step 516).
  • the feedback signal may be generated by the patient model 112 in response to the patient model 112 processing the simulated electrical signal(s) provided thereto.
  • the feedback may include an ECAP signal, an ECAP waveform, or the like.
  • the feedback may also include other outputs of the patient model 112, such as a paresthesia output 322 or other patient feedback as described herein.
  • Information describing the feedback received from the patient model 112 may then be displayed along with information describing the simulated electrical signal(s) (step 520).
  • the simulated electrical signal(s) and the feedback from the patient model 112 may be displayed via a simulation interface 126.
  • the method 500 may include a step of enabling changes to be made to the patient model 112 and/or the stimulation device model 114 (step 524).
  • the changes may be defined based on user input received at the simulation interface 126.
  • a third method 600 will be described in accordance with at least some embodiments of the present disclosure.
  • the method 600 may have multiple similar steps or processes as compared to method 500.
  • the method 600 begins when an input defining a simulation scenario is received at a stimulation device 132, 138 (step 604).
  • This step may be similar to step 504, but different in that the input is received at a stimulation device 132, 138 instead of a stimulation device model 114.
  • the input(s) defining the simulation scenario may be the same as described in connection with step 504 and may be received from a simulation interface 126.
  • the method 600 continues with the stimulation device 132, 138 generating, based on the simulation scenario, one or more simulated electrical signals (step 608).
  • the simulated electrical signals may correspond to actual electrical signals without departing from the scope of the present disclosure.
  • the method 600 continues by providing the simulated electrical signal(s) as an input to a patient model 112 (step 612).
  • the patient model 112 may receive the electrical signals (whether actual or simulated) and process the electrical signals with the processing components thereof. As the patient model 112 processes the electrical signals, the patient model 112 will generate one or more outputs, which can be provided to the simulation interface 126 (step 616). In some embodiments, the output from the patient model 112 may be displayed via the simulation interface 126 (step 620). The output from the patient model 112 may also be provided as feedback to the stimulation device (e.g., as an ECAP signal or waveform), thereby enabling the stimulation device 132, 138 to operate in a closed-loop configuration.
  • the stimulation device e.g., as an ECAP signal or waveform
  • the method 600 may include a step of enabling changes to the patient model 112 and/or the stimulation device 132, 138 (step 624).
  • the changes may be definable by a user that is interacting with the simulation interface 126.
  • the method 700 begins by receiving an input from a user defining a simulation scenario to implement (step 704).
  • the input may be received at the simulation interface 126 and may include a selection of model(s) to simulate, a selection of type(s) of models to simulate, a selection of signal parameters to simulate, a selection of a duration for the simulation, and a selection of an open-loop configuration versus a closed-loop configuration to simulate.
  • the inputs received in step 704 may define the simulation scenario, which can be stored as scenario data 118 during the simulation. As the simulation is implemented, outputs of the simulation according to the simulation scenario may be stored as simulation data.
  • the method 700 may continue by generating, based on the simulation scenario, one or more electrical signals (step 708).
  • the electrical signal(s) may correspond to simulated electrical signals (e.g., generated by a stimulation device model 114) or actual electrical signals (e.g., generated by a stimulation device 132, 138).
  • the electrical signal(s) may then be provided as an input to a patient (step 712).
  • the patient receiving the electrical signal(s) may correspond to an actual patient (e.g., a physical patient) or a virtual patient 302.
  • the patient responds to the electrical signal(s) one or more feedback signals or responses may be generated.
  • the feedback may be provided back to the simulation interface 126 (step 716).
  • the method 700 may also include providing the feedback to the source of the electrical signal(s) (e.g., the stimulation device model 114 and/or stimulation device 132, 138) when the simulation scenario corresponds to a closed-loop simulation.
  • the method 700 may also include displaying the one or more electrical signals and the feedback on the simulation interface 126 (step 720). In addition to displaying the results of the simulation (e.g., displaying the simulation data 116), the method 700 may also include enabling changes to the simulation scenario via the simulation interface 126 (step 724). Such changes may be requested during a simulation or after a simulation has completed.
  • Example 1 A simulation system, comprising one or more processors, a memory, and one or more programs stored in the memory, wherein the one or more programs are executed by the processors, the one or more programs including instructions for: generating a first model representing a patient; generating a second model representing a stimulation device; and causing the first model and second model to interact based on one or more simulation inputs related to a stimulation therapy, thereby causing one or more simulation outputs in the first model, second model, or both.
  • Example 2 The simulation system of example 1, wherein the one or more simulation inputs comprise one or more stimulation waveforms output from the second model.
  • Example 3 The stimulation system of example 1, wherein simulation outputs from the first model comprise one or more of: a response waveform in response to the one or more simulation input from the second model; and a paresthesia output responsive to the one or more simulation inputs.
  • Example 4 The simulation system of example 3, wherein the paresthesia output is displayed on a user interface providing a simulation environment for the first model and the second model.
  • Example 5 The simulation system of example 1, wherein the simulation inputs comprise a preprogrammed input, user input, or both.
  • Example 6 The simulation system of example 1, wherein an interaction between the first model and the second model is adjustable by switching between an open-loop simulation configuration and a closed-loop simulation configuration.
  • Example 7 The simulation system of example 6, wherein the first model provides a waveform output to the second model when operating in the closed-loop simulation setting.
  • Example 8 The simulation system of example 7, wherein the waveform output comprises an Evoked Compound Action Potential (ECAP) waveform.
  • ECAP Evoked Compound Action Potential
  • Example 9 The simulation system of example 1, wherein the first model is adjustable to simulate different distances between an electrode that delivers the one or more simulated signals and a spinal cord of the patient.
  • Example 10 The simulation system of example 1, wherein the first model is adjustable based on different patient postures that affect the distance between the electrode that delivers the one or more simulated signals and the spinal cord of the patient.
  • Example 11 The simulation system of example 1, wherein the first model comprises an Artificial Intelligence (Al) model that processes at least one input received at the first model [0159]
  • Example 12 The simulation system of example 1, wherein the second model comprises one or more Artificial Intelligence (Al) models that process the one or more simulated electrical signals.
  • Example 13 The simulation system of example 12, wherein the one or more Al models comprises a plurality of models interconnected with one another.
  • Example 14 The simulation system of example 1, further comprising:
  • a user interface that enables a user to interact with at least one of the first model and the second model and to adjust an operating parameter thereof.
  • Example 15 The simulation system of example 14, wherein the user interface provides a display of the one or more simulated electrical signals.
  • Example 16 The simulation system of example 15, wherein the user interface further displays a reaction produced by the first model in response to processing the one or more simulated electrical signals.
  • Example 17 The simulation system of example 1, further comprising a user interface that enables a user to switch between an open-loop simulation configuration where the one or more simulation outputs in the first model are provided to the user interface and a closed-loop simulation configuration where the one or more outputs in the first model are provided to the second model.
  • Example 18 A system, comprising: a processor; and memory storing a first model representing a patient, wherein the first model enables the processor to: receive an input from a second model representing a stimulation device, wherein the input comprises one or more simulated signals generated in accordance with a simulation scenario; pass the input through one or more processing components; and produce one or more outputs representing a simulated patient response to the input.
  • Example 19 The system of example 18, wherein the first model comprises one of a plurality of patient models and wherein the first model is selected based on a user input.
  • Example 20 A method, comprising: receiving, at a patient model, an input comprising one or more simulated signals generated in accordance with a simulation scenario; passing the input through one or more processing components of the patient model that simulate a patient response to the input; and producing one or more outputs with the patient model based on the one or more processing components processing the input.

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Abstract

L'invention concerne des systèmes et des procédés pour simuler l'administration d'une thérapie, telle qu'une thérapie par stimulation électrique, à un patient. Dans un exemple, un système de simulation est décrit pour comprendre un premier modèle représentant un patient et un second modèle représentant un dispositif de stimulation. Le second modèle interagit avec le premier modèle en fournissant un ou plusieurs signaux électriques simulés générés conformément à un scénario de simulation en tant qu'entrées pour un traitement par le premier modèle. Le scénario de simulation peut être défini et ajusté par un utilisateur du système de simulation.
PCT/IB2024/062559 2023-12-20 2024-12-12 Architecture de simulation pour l'administration de thérapies Pending WO2025133849A1 (fr)

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