Systems and methods for simulating spinal cord stimulation

Abstract

Systems, methods, and user devices are described as including generating a simulated electrical signal, applying the simulated electrical signal as a stimulation input to a patient model based on a selected scenario, and displaying a visualization of a response of the patient model to the stimulation input. The systems and methods include providing a testing tool for users to simulate a spinal cord stimulation procedure based on a variety of different patient models and scenarios.

Description

SYSTEMS AND METHODS FOR SIMULATING SPINAL CORD STIMULATIONCROSS-REFERENCE TO RELATED APPLICATION(S)

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 612,911, filed December 20, 2023 and U.S. Provisional Patent Application No. 63 / 612,917, filed December 20, 2023, which is incorporated herein by reference in its entirety.FIELD

[0002] The present disclosure is generally directed to training tools and relates more particularly to a user training tool for simulating spinal cord stimulation procedures.BACKGROUND

[0003] Neuromodulation therapy, such as spinal cord stimulation (SCS), may be carried out by sending an electrical signal generated by a device (e.g., a pulse generator) to a stimulation target (e.g., nerves, non-neuronal cells, etc.), which may provide a desired electrophysiologic, biochemical, or genetic response in the stimulation target. Neuromodulation therapy systems may be used to deliver electrical stimulation for providing chronic pain treatment to a patient.BRIEF SUMMARY

[0004] Example aspects of the present disclosure include a method for simulating an SCS procedure, the method comprising: generating a simulated electrical signal; applying the simulated electrical signal to a patient model based upon a selected scenario; and displaying a response of the patient model to the simulated electrical signal.

[0005] Example aspects of the present disclosure include a system for simulating a spinal cord stimulation procedure, the system comprising: a processor; and memory storing instructions which, when executed by the processor, cause the system to: generate a simulated electrical signal; apply the simulated electrical signal to a patient model based on a selected scenario; and display a response of the patient model to the .simulated electrical signal.

[0006] Example aspects of the present disclosure include a user device for training a user to conduct a spinal cord stimulation procedure, the user device comprising: a processor; and memory storing instructions which, when executed by the processor, cause the user device to: generate asimulated electrical signal; apply the simulated electrical signal to a patient model based on a selected scenario; and display a response of the patient model to the simulated electrical signal.

[0007] Aspects of the above method, system, and / or user device include wherein the patient model is selected based on a user input.

[0008] Aspects of the above method, system, and / or user device include wherein the scenario is selected based on an interaction with a graphical user interface (GUI).

[0009] Aspects of the above method, system, and / or user device include wherein the simulated electrical signal is generated by a pulse generation simulator.

[0010] Aspects of the above method, system, and / or user device include wherein the selected scenario indicates one or more positions of one or more electrodes in relation to the patient’s spinal cord.

[0011] Aspects of the above method, system, and / or user device include wherein the one or more positions affect a distance variable in the patient model.

[0012] Aspects of the above method, system, and / or user device include wherein the applying of the simulated electrical signal to the patient model comprises simulating a reaction of physiological qualities associated with the patient model to the simulated electrical signal.

[0013] Aspects of the above method, system, and / or user device include wherein the displaying of the response of the patient model comprises displaying one or more of a patient’s reaction, a location of pain, or a location of paresthesia.

[0014] Aspects of the above method, system, and / or user device include wherein the displaying the response of the patient model comprises generating and displaying an assessment.

[0015] Aspects of the above method, system, and / or user device include the assessment indicates one or more of: a closed loop status, an open loop status, a paresthesia level score, an aggressor management score, or an average current level score.

[0016] Aspects of the above method, system, and / or user device include the assessment indicating a closed-loop status, wherein the closed-loop status indicates whether a closed loop feature was enabled or disabled during the application of the simulated electrical signal to the patient model.

[0017] Aspects of the above method, system, and / or user device include the assessment indicating a paresthesia level score, wherein the paresthesia level score indicates whether a level of paresthesia was within an acceptable zone based on the patient model during the application of the simulated electrical signal to the patient model.

[0018] Aspects of the above method, system, and / or user device include the assessment indicating an aggressor management score and an average current level score, wherein the aggressormanagement score indicates whether aggressors occurring during the application of the simulated electrical signal to the patient model were managed by a user and the average current level score indicates whether an average amount of current of the simulated electrical signal applied to the patient model was within an acceptable zone based on the patient model during the application of the simulated electrical signal to the patient model.

[0019] Aspects of the above method, system, and / or user device include further comprising receiving input from a user during the application of the simulated electrical signal to the patient model.

[0020] Aspects of the above method, system, and / or user device include wherein the input from the user comprises one or more of a change in an amperage and a voltage of the simulated electrical signal.

[0021] Aspects of the above method, system, and / or user device include wherein the input from the user comprises an adjustment to the patient model.

[0022] Aspects of the above method, system, and / or user device include wherein the adjustment to the patient model includes one or more of an aggressor and a position of the patient model.

[0023] Aspects of the above method, system, and / or user device include wherein displaying the response of the patient model to the electrical signal comprises displaying an open closed loop electrical stimulation and a closed loop electrical stimulation.

[0024] Aspects of the above method, system, and / or user device include wherein the response of the patient model provides an indication of a tingling sensation.

[0025] Aspects of the above method, system, and / or user device include wherein the response of the patient model comprises a measurement of evoked compound action potentials (ECAPs).

[0026] Aspects of the above method, system, and / or user device include wherein the patient model comprises one or more of a digital patient and a real patient.

[0027] Aspects of the above method, system, and / or user device include wherein the response of the patient model comprises an indication of paresthesia.

[0028] Aspects of the above method, system, and / or user device include wherein the displayed response includes a graph of a simulated ECAP signal magnitude over time.

[0029] Aspects of the above method, system, and / or user device include wherein the displayed response comprises an ECAP waveform.

[0030] Aspects of the above method, system, and / or user device include wherein the simulated electrical signal is generated based on a real spinal cord stimulation device.

[0031] Example aspects of the present disclosure include any of the above aspects in combination with any one or more other aspects.

[0032] Example aspects of the present disclosure include any one or more of the features disclosed herein.

[0033] Example aspects of the present disclosure include any one or more of the features as substantially disclosed herein.

[0034] 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.

[0035] Example aspects of the present disclosure include any one of the aspects, features, and / or embodiments in combination with any one or more other aspects, features, and / or embodiments.

[0036] Example aspects of the present disclosure include the use of any one or more of the aspects or features as disclosed herein.

[0037] It is to be appreciated that any feature described herein can be claimed in combination with any other feature(s) as described herein, regardless of whether the features come from the same described embodiment.

[0038] The details of one or more aspects of the disclosure are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the techniques described in this disclosure will be apparent from the description and drawings, and from the claims.

[0039] The phrases “at least one,” “one or more,” and “and / or” are open-ended expressions that are both conjunctive and disjunctive in operation. For example, 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. When 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).

[0040] The term “a” or “an” entity refers to one or more of that entity. As such, the terms “a” (or “an”), “one or more” and “at least one” can be used interchangeably herein. It is also to be noted that the terms “comprising,” “including,” and “having” can be used interchangeably.

[0041] The preceding is a simplified summary of the disclosure to provide an understanding of some aspects of the disclosure. This summary is neither an extensive nor exhaustive overview of thedisclosure and its various aspects, embodiments, and configurations. It is intended neither to identify key or critical elements of the disclosure nor to delineate the scope of the disclosure but to present selected concepts of the disclosure in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other aspects, embodiments, and configurations of the disclosure are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.

[0042] Numerous additional features and advantages of the present disclosure will become apparent to those skilled in the art upon consideration of the embodiment descriptions provided hereinbelow.BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0043] The accompanying drawings are incorporated into and form a part of the specification to illustrate several examples of the present disclosure. These drawings, together with the description, explain the principles of the disclosure. The drawings simply illustrate preferred and alternative examples of how the disclosure can be made and used and are not to be construed as limiting the disclosure to only the illustrated and described examples. Further features and advantages will become apparent from the following, more detailed, description of the various aspects, embodiments, and configurations of the disclosure, as illustrated by the drawings referenced below.

[0044] Fig. 1 is a diagram of a system according to at least one embodiment of the present disclosure;

[0045] Fig. 2 is a diagram of a system according to at least one embodiment of the present disclosure;

[0046] Figs. 3A-3C illustrate user interfaces according to at least one embodiment of the present disclosure;

[0047] Fig. 4 illustrates example scenarios according to at least one embodiment of the present disclosure;

[0048] Figs. 5A and-5B illustrate user interfaces according to at least one embodiment of the present disclosure;

[0049] Figs. 6-14 illustrate user interfaces according to at least one embodiment of the present disclosure;

[0050] Fig. 15 illustrates example patient reactions according to at least one embodiment of the present disclosure;

[0051] Fig. 16 illustrates an example assessment according to at least one embodiment of the present disclosure; and

[0052] Figs. 17 and 18 are each flowcharts according to at least one embodiment of the present disclosure.DETAILED DESCRIPTION

[0053] It should be understood that various aspects disclosed herein may be combined in different combinations than the combinations specifically presented in the description and accompanying drawings. It should also be understood that, depending on the example or embodiment, certain acts or events of any of the processes or methods described herein may be performed in a different sequence, and / or may be added, merged, or left out altogether (e.g., all described acts or events may not be necessary to carry out the disclosed techniques according to different embodiments of the present disclosure). In addition, while certain aspects of this disclosure are described as being performed by a single module or unit for purposes of clarity, it should be understood that the techniques of this disclosure may be performed by a combination of units or modules associated with, for example, a computing device and / or a medical device.

[0054] In one or more examples, 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 (ML) models, artificial intelligence (Al) 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).

[0055] Instructions may be executed by one or more 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 circuitry. Accordingly, the term “processor” as used herein may refer to any of the foregoing structure or any other physical structure suitable for implementation of the described techniques. Also, the techniques could be fully implemented in one or more circuits or logic elements.

[0056] Before any embodiments of the disclosure are explained in detail, it is to be understood that the disclosure is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the drawings. The disclosure is capable of other embodiments and of being practiced or of being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of “including,” “comprising,” or “having” and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. Further, the present disclosure may use examples to illustrate one or more aspects thereof. Unless explicitly stated otherwise, the use or listing of one or more examples (which may be denoted by “for example,” “by way of example,” “e.g.,” “such as,” or similar language) is not intended to and does not limit the scope of the present disclosure.

[0057] Measuring Evoked Compound Action Potentials (ECAPs) involves stimulating a group of neurons and then recording a response to the stimulation from a distance away. Described herein are systems and methods of simulation of systems that measure ECAPs by generating a simulated electrical signal and a patient model and applying the simulated electrical signal to the patient model to generate a simulated body signal. In at least one embodiment of the present disclosure, a simulated electrical signal applied to a patient model simulates an SCS therapy. The patient model outputs a simulated body signal and simulated patient feedback. A body signal as described herein may represent an ECAP signal simulated by a patient model in response to an input of a simulated electrical signal. An ECAP signal may be represented by a signal magnitude overtime and may be represented in a graph in the form of an ECAP waveform. In some implementations, 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. By interacting with a user interface, the user may be enabled to view a body signal created using a closed loop and / or an open loop simulation. These and other implementation details are described in greater detail below.While the systems and methods described herein refer to ECAP and SCS, it should be appreciated the same or similar systems and methods may be used to simulate the performance of any type of treatment or procedure. For example, and not to be considered for any limiting purposes, systems and methods as described herein may be used to perform simulations relating to any one or more of ECAPs, SCS, PH, ERNA, LFP biomarkers, and / or DBS.

[0058] Fig. 1 depicts a block diagram of a system 100 according to at least one embodiment of the present disclosure is shown. In some examples, the system 100 may implement aspects of or may be implemented by aspects of Figs. 2-18 as described herein. For example, the system 100 may be used to simulate an implantable pulse generator and / or one or more electrical lead(s) and / or contact(s), and / or carry out a simulation of an SCS procedure in accordance with one or more other aspects of one or more of the methods disclosed herein.

[0059] A computing device 102 is illustrated to include a processor 104, a memory 106, a communication interface 108, and a user interface 110. Computing devices according to other embodiments of the present disclosure may comprise more or fewer components than the computing device 602.

[0060] The processor 104 of the computing device 102 may be any processor described herein or any similar processor. The processor 104 may be configured to execute instructions stored in the memory 106, instructions which may cause the processor 104 to carry out one or more computing steps utilizing patient model data 112, simulation data 114, scenario data 116, and / or based on data received from the communication interface 108, user interface 110, the database 120, and / or the cloud 122.

[0061] 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, patient model data 112, simulation data 114, and scenario data 116.

[0062] 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 1700 and 1800 as described herein, or of any other methods. The memory 106 may store, for example, instructions and / or machine learning models that support one or more functions of the simulation of electrical signals and the application of simulated electrical signals to patient models. For instance, the memory 106 may store content (e.g., instructions and / or machine learning models) that, when executed by the processor 104, causethe processor to simulate an electrical signal being applied to a respective target anatomical element such as a nerve, such as to block or regulate chronic pain.

[0063] Content stored in the memory 106, if provided as in instruction, 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. Thus, although various contents of memory 106 may be described as instructions, it should be appreciated that functionality described herein can be achieved through use of instructions, algorithms, and / or machine learning models. 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, the user interface 110, the database 630, and / or the cloud 634.

[0064] The computing device 102 may also comprise a communication interface 108. The communication interface 108 may be used for receiving data from an external source (such as the user device 118, the database 120, the cloud 122, 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 102, the user device 118, the database 120, the cloud 122, 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). In some embodiments, the communication interface 108 may be useful for enabling the device 102 to communicate with one or more other processors 104 or computing devices 102, whether to reduce the time needed to accomplish a computing-intensive task or for any other reason.

[0065] The computing device 602 may also comprise one or more user interfaces 110. 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. In some embodiments, 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 / orscenario, and may receive input during a simulation, such as to adjust settings. 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. In some embodiments, 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.

[0066] Although the user interface 110 is shown as part of the computing device 102, in some embodiments, the computing device 102 may utilize a user interface 110 that is housed separately from one or more remaining components of the computing device 102. In some embodiments, the user interface 110 may be located proximate one or more other components of the computing device 102, while in other embodiments, the user interface 110 may be located remotely from one or more other components of the computer device 102.

[0067] Though not shown, the system 100 may include a controller, though in some embodiments the system 100 may not include the controller. The controller may be an electronic, a mechanical, or an electro-mechanical controller. The controller may comprise or may be any processor described herein. The controller may comprise a memory storing instructions for executing any of the functions or methods described herein as being carried out by the controller. In some embodiments, the controller may be configured to simply convert signals received from the computing device 102 (e.g., via a communication interface 108) into commands for operating the systems and methods described herein.

[0068] In some implementations, data such as the patient model data 112, simulation data 114, and scenario data 116 described below may be obtained in whole or in part by the computing system 102 from one or more databases 120 and / or cloud computing resources 122.

[0069] The database 120 may store information such as patient model data, scenario data, simulation data, and / or results of simulations, such as assessment scores as described herein. The database 120 may be configured to provide any such information to the computing system 102 or to any other device of the system 100 or external to the system 100, whether directly or via the cloud 122. In some embodiments, the database 120 may be or comprise part of a hospital image storage system, such as a picture archiving and communication system (PACS), a health information system (HIS), and / or another system for collecting, storing, managing, and / or transmitting electronic medical records.

[0070] The cloud 122 may be or represent the Internet or any other wide area network. The computing system 102 may be connected to the cloud 122 via the communication interface 108, using a wired connection, a wireless connection, or both. In some embodiments, the computing system 102 may communicate with the database 122 and / or an external device (e.g., a user device 118) via the cloud 122.

[0071] The system 100 or similar systems may be used, for example, to carry out one or more aspects of any of the methods 1700 and 1800 as described herein. The system 100 or similar systems may also be used for other purposes.

[0072] In some implementations, the computing system 102 may generate simulations which may be displayed on another user device 118 or downloaded to another user device 118.

[0073] The user device 118 may be another computing system 102 as illustrated in Fig. 1 or any type of computing device. In some implementations, the computing system 102 may be a server and the user device 118 may be a tablet device, smartphone, or personal computer.

[0074] The computing system 102 may perform all of the methods and systems described herein or may perform a portion of the methods and systems described herein while a user device 118 performs other methods and systems described herein.

[0075] A computing system 102 as described above may be enabled to perform methods and systems in which users can interact with a simulation of a patient receiving SCS treatment. As described above, the computing system 102 may be a tablet device or a personal computer. The computing system 102 may execute a testing tool 200 which, as illustrated in Fig. 2, may involve applying a signal simulation 204 to a patient model scenario 208. The signal simulation 204 and patient model scenario 208 are described in greater detail below.

[0076] As illustrated in Fig. 2, a testing tool 200 may include applying a simulated electrical signal 204 to a patient model scenario 208 to output a patient model response 210. Before the simulation 204 begins, user inputs 202, configuration settings, and other variables may affect one or both of the signal simulation 204 and the patient model scenario 208. For example, a user may select a patient model, select a scenario, adjust a position of the patient, and / or adjust other settings. During the simulation 204, 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 scenario 208. As the testing tool 200 generates the patient model response 210 by applying the simulated electrical signal 204 to the patient model scenario 208 and responding to any user inputs 206 during the simulation, live feedback visualizations 212 may be generated and / ordisplayed on a display device and / or an assessment 214 may be generated and / or displayed on a display device.

[0077] The simulation of the electrical 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.

[0078] The signal simulation 204 may be performed by the computing system 102 to replicate realistic functionality of a closed-loop ECAPs device. In this way, the computing system 102 may be used to provide experience to patients and clinicians prior to administering treatment to themselves or to a patient.

[0079] Inside the simulated patient, the simulated stimulation signal may be affected by medium before getting to simulated neural tissue (e.g., simulated neural elements). The medium may be characterized by the distance between the neural elements and electrodes, d. The relationship between the parameter d and the coupling from stimulation to neural tissue can be relatively simple (e.g. inverse proportionality, e.g. K / d with K being an appropriate constant) or be more complex (e.g. finite element model of tissue surrounding the spinal cord and the electrode). In the latter case, d might represent the CSF thickness.

[0080] The simulation of electrical signal 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 determining one or more of a signal frequency, a signal type (e.g., square wave, sinusoidal wave, triangle wave, etc.), a duty cycle, pulse width, pulse shape, or pulse amplitude etc.

[0081] A simulation of electrical signal may be accomplished by a virtual device or a computational process which produces simulated electrical stimulation and is capable of measuring modeled biological signals. A patient model as described herein may be a simulation of a patient that is capable of being affected by the stimulation to produce a body signal (ECAP) in response to the simulation. For example, in the simulated patient, number of neural elements may be “activated” (caused to fire) with the number of the elements being proportional by the amount by which the stimulation delivered through the medium exceeds the neural threshold multiplied by a slope which indicates potentially the density of the neural elements. Alternatively, number of neural elements excited could be computed by other means, for example, by utilization of more complex neural models such as the derivatives of the Hudgkin-Huxley model. The number of excited elements (or the amount of excitation) may be a function of time and be represented by a variable Exc(t).

[0082] In the model, the neural elements may generate a potential which can be recorded at the simulated recording electrodes of the simulated SCS device. This potential is referred to as an ECAP (evoked compound action potential). The potential propagates back through the medium to the electrode, and thereby is affected by parameter d. In some implementations, generating the ECAP signal simulation may comprise generating an amplitude of the ECAP or a full waveform with various peaks characteristic of the ECAP as well as incorporating stimulation artifact and various noise sources.

[0083] To generate an amplitude, the processor 104 may utilize a formula ECAP(t)=curECAPSlope(t)*Exc(t). In the above equation, curECAPSlope may be a function of variable d, e.g., “ecapSlopeMult / d” with ecapSlopeMult being a constant. In addition, noise may be added to the ECAP may introduce a noise (N(t)*noiseLevel, where N(t) is noise (for example drawn from a random distribution) and "noiseLevel" is an amount of the noise, and the amplitude ECAP(t) can be calculated as the sum of Exc(t)* curECAPSlope and N(t)*noiseLevel. In addition, in some embodiments, noise could be designed to be representative of EMG signals sometimes observed on SCS electrodes. In those cases, the noise signal N(t) might have characteristic behavior (e.g., slow peaks and troughs that are much further in time compared to ECAP signal) and be modulated by simulated patient behavior (e.g., get higher during an aggressor).

[0084] To generate a full waveform, the processor 104 may calculate an EcapWF(t,ts) function, wherein ts is a time of sample. For example, each ECAP waveform may comprise fifty samples. The EcapWF(t,ts) function may be calculated by adding an ECAP waveform scaled by ECAP amplitude ECAP(t) with an artifact waveform, such as a decaying exponential waveform with a linear trend. The artifact waveform may be posture and / or amplitude dependent.

[0085] As should be appreciated, electrical signals may be simulated in any conceivable way such as to replicate functionally the ECAP waveform generation by the patient. In some implementations, the simulated electrical signal may be used to replicate one or both of a closed loop spinal cord stimulation (SCS) system and an open loop SCS system. For example, for a closed loop SCS system, the processor 104 may be enabled to, after applying a first electrical signal to a patient model, receive an output from the patient model (e.g., an ECAP) and apply feedback to the electrical signal to update the electrical signal over time. Such feedback may be used to regulate or adjust the simulated electrical signal. For example, a patient model as described below may update over time, such as in response to user inputs causing events and / or automatically occurring events. Such events may include, for example, simulated physical movements of the patient model, such as, but not limited to, coughing, arching the back, and changing position from upright to supine and vice versa.

[0086] When a patient moves their spinal cord, a distance between the electrical contact applying the electrical signal and the spinal cord (or any target anatomical element) may change, causing the resulting neural activation to be weaker or stronger based on the change in the distance. By measuring an output from the patient model, the computing system 102 may be enabled to determine a change in the ECAP and use the difference to adjust the electrical signal to cause an amplitude of the ECAP to remain within a therapeutic range that is comfortable for the patient. During a simulation, as described below, a user may be enabled to selectively switch the electrical signal from an open loop (in which no feedback applies to the electrical signal) therapy to a closed loop therapy. Other factors which may affect the distance variable d(t), and thereby stimulation and ECAP, may include heart beating, as well as breathing. For example, d(t) may be smaller during the heartbeat and be larger in between heart beats; or vice versa.

[0087] As illustrated in Fig. 3 A, a computing system 102 or a user device 118 may be enabled to display a graphical user interface (GUI) menu 300 enabling a user to interact with one or more GUI elements 309a-d to select a scenario for use in applying a simulated electrical signal to a selected patient model. The GUI menu 300 may include a scenario column 303 and a patient column 306. The scenario column 303 may include a navigable list of scenarios represented by GUI elements 309a-d from which a user may be enabled to select a scenario. A user may also be enabled to select a patient model to which the scenario should be applied. In some implementations, a user may also or alternatively be enabled to create or edit a patient model and / or a scenario.

[0088] Each patient model may be associated with different characteristics. Such models and characteristics may be stored in memory 118 of the computing system 102 as patient model data 112. Patient model data 112 may include textual data (e.g., metadata) for users to see differences. Such data may be displayed in the GUI menu 300 to aid the user in selecting a patient model. Such textual data may include, for example, a description of recent and / or chronic pain (e.g., back and leg pain), a history of success with trials with various therapies (e.g., differential target multiplexed (DTM), Low Dose (LD)), preference for treatment (such as whether the patient prefers or dislikes tingling), personal characteristics relating to effectiveness of contacts and ECAP signal strength, personal reactions to pain and treatments, and / or hints for the users for learning purposes. The following are example patient model descriptions for illustration purposes:

[0089] Margo Strata: Margo has chronic back and leg pain. She underwent a successful trial with DTM endurance without cycling. She does not like feeling tingling. Electrodes were selected that map appropriately and P2 has been set up. There was a good ECAP signal. Tweak the Pl target amplitude and set up closed-loop thresholds for her.

[0090] Andrew Cleveland: Andrew has chronic back pain and leg pain. He underwent a successful trial with LD. He likes paresthesia when it overlaps his painful area and has P2 set up for that coverage. His right lead has perception thresholds (PTs) over 10 mA. He has rib stim on some of his contacts. Figure out if he needs Pl on the top or bottom of the left lead and set up the target amplitude and closed-loop thresholds.

[0091] Andy Schmelting: Andy has back pain and leg pain. He underwent a successful trial with LD. He likes light paresthesia. His contacts pick up more noise than typical. Electrodes were selected that map appropriately and P2 has been set up. There was a good ECAP signal. Tweak the Pl target amplitude and set up closed-loop thresholds for him.

[0092] Leo Lightsnack: Leo has back pain and leg pain. He underwent a successful trial with LD. He likes light tingling most of the time but wants more at times. Over the phone he was told to turn up the amplitude, but that does not seem to be doing anything. He has come into clinic for a reprogramming. You have copied his therapeutic group and want to set up a Boost group. Tweak the Pl target amplitude and set up closed-loop thresholds for him.

[0093] Justin Murky: Justin has back pain and leg pain. He underwent a successful trial with DTM without cycling. He does not like feeling tingling. The right lead was fractured and cannot be used. On the left lead you have set up P2. See which stim configuration you can use to set up Pl target amplitude and closed-loop thresholds.

[0094] Glenn Butter: Glenn has back and leg pain. He underwent a successful trial with DTM. He is okay with mild tingling. He has poor ECAPs. He was only implanted with one lead. You have set up P2. See which sense configuration allows you to see ECAPs. Set up Pl target amplitude and closed-loop thresholds.

[0095] Shell Knife: Shell has neck and left arm pain. She underwent a successful trial with LD. She is okay with mild to moderate tingling. The Pl electrode selection covers her painful area. Set up the target amplitude and closed-loop thresholds.

[0096] Dave Dankswamp: Dave has back and leg pain. He underwent a successful trial with DTM and was implanted and activated a month ago. He does not like tingling. He has not been getting pain relief. See if you can modify his settings to fix the problem.

[0097] June Hinpali: June has chronic back and leg pain. He underwent a successful trial with Differential Targeted Multiplexed (DTM) stimulation. He is hoping for even better results with permanent Inceptiv implant.

[0098] Reed Bornhurt: Reed has chronic leg pain. During the trial, he got fantastic pain relief from low dose stimulation. He would like to have even stronger paresthesia with good coverage of his painful area.

[0099] Joshua Nederdikovsky: Joshua has chronic back and leg pain. He underwent a successful trial with Differential Targeted Multiplexed (DTM) stimulation. Working with patient feedback and the Body signal stream, adjust Target Amplitudes and Reaction and Recovery thresholds before turning Neuro Sense to Active. Turn on closed loop, and perform assessment.

[0100] Kumi Miagi: Kumi has chronic back and leg pain. She underwent a successful trial with Differential Targeted Multiplexed (DTM) stimulation. She is programmed on day 1 while still in the hospital, with no clear Evoked Compound Action Potentials (ECAPs) at perception threshold. Set up closed-loop program day 1, avoiding any sensations with aggressors in any position. After one week, compare streams and note that closed loop on protects from discomfort.

[0101] A description of the selected patient may be displayed in a GUI element 304. Once a scenario is selected, a description of the selected scenario may also be displayed in the GUI element 304.

[0102] Patient model data 112 may also include simulation model configurations which may alter how each patient model affects an applied simulated electrical signal. For example, each patient model may be enabled to simulate, on a patient- specific basis, how different tissues, bones, organs, and other body parts respond to electrical stimuli.

[0103] Patient model data 112 may further include simulation model configurations which alter how each patient model reacts or responds to an applied stimulated electrical signal. For example, each patient model may be enabled to respond differently to various levels of simulated electrical stimulation.

[0104] In this way, each patient model may be configured to alter an applied simulated signal and to respond to an applied simulated signal in a variety of ways based on a particular voltage and / or amperage of the applied simulated signal when the signal is simulated as being applied to contacts at particular points on the patient model.

[0105] Furthermore, each characteristic of a patient model, from the effect on the electrical signal and the response to the electrical signal, can vary based on whether the patient is upright or supine. For this reason, a different patient model may be created for upright patients and supine patients. Users may be enabled to switch the position of the patient model from upright to supine during a simulated scenario as described below which may cause the model patient to which the simulated signal is applied to change.

[0106] In some implementations, patient models may be created by testing real human responses and / or through the use of one or more machine learning (ML) algorithms and / or neural networks processing of patient data.

[0107] By varying the patient model data 112 for each patient model to a scenario selectable by a user in the GUI menu 300, the user may be enabled to conduct testing using a variety of patients of different types with such variety as may be experienced with real patients in a non-simulated environment. For example, different patient models may have different baseline cerebrospinal fluid (CSF) thickness, respond differently to different levels of current, different postures, the existence of aggressors, heart-beat phases, and / or spinal cord movements and / or thicknesses, etc. Different patients may also have different amounts of sensitivity to paresthesia, as well as differences in density of neural elements (e.g., how fast neural elements are recruited with increasing current). The patient models may be configured to replicate such real-world variations.

[0108] Furthermore, a patient model may be created based on a real patient, or a patient model which is relatively similar to a real patient may be selected. As a result, the testing tool may be used to test different testing programs for a real patient and / or to train a user to apply treatment to the patient, or to train the patient to apply the treatment to themselves. As one example, after patient model is derived, machine learning may be utilized to determine optimal stimulation parameters for the patient (e.g., with right levels of paresthesia, which do not generate overly strong aggressors and that are comfortable for the patient in various postures) and then recommended as suggested settings for this patient.

[0109] While the systems and methods described herein refer to the use of a digital or virtual patient in the form of a patient model, in some implementations the systems and methods described herein may be performed using a real patient. The same user interfaces may be used to control and monitor a real electrical signal applied to a real patient.

[0110] Similarly, systems and methods described herein may be used to apply a real stimulation signal provided by a real device, such as an SCS device, to a patient model. An output of the patient model, such as in the form of a simulated ECAP signal, in response to the real stimulation may be recorded. For example, a computing system as described herein may be capable of receiving a signal output from a real device, processing the signal, and applying the signal to a patient model as a simulated signal. In this way, a user such as a patient or practitioner may be enabled to test a real SCS device on a patient model to avoid potentially causing harm or pain to a user.[oni] In some implementations, users may also be able, such as through interacting with a GUI menu 300, to adjust a selected patient model and / or patient. For example, a patient may be enabled to select a position (supine, upright, or other) of the patient, and / or control other settings.

[0112] In some implementations, a patient model may be a time-based function presenting a varying body signal in response to a stimulation simulation. The time-based function for each patient model may vary to increase realism for training purposes.

[0113] A patient model as described herein may be a simulation block which processes an input signal of a simulated electrical signal, provides feedback for closed-loop control of the input signal, and generates an output response that represents a simulated patient's reaction to the signal.

[0114] A patient model as described herein may in some implementations include an input interface configured to receive the simulated electrical signal. The input signal may be in various forms, such as a voltage or current waveform, and might represent different types of electrical stimulation (e.g., pulses and / or sinusoidal waves).

[0115] A patient model as described herein may in some implementations include a signal processing unit which processes the simulated electrical signal. The signal processing unit of the patient model may involve various transformations or computations, simulating how a body of a real-world patient might interact with the signal. Such processing may include amplification, filtering, integration, and / or other operations to replicate a patient.

[0116] A patient model as described herein may in some implementations include a feedback generation unit which may generate feedback based on the processed input signal. Such feedback may be used to assess a patient-specific response to the applied simulated signal. Such response may include, for example, pain locations and intensity, paresthesia locations and intensity, verbal feedback from the patient model, and / or other variables as described below.

[0117] A patient model as described herein may in some implementations include a response output unit which may generate a simulated electrical response to the applied simulated electrical signal. Such a simulated electrical response may be described as a body signal and may be capable of being plotted on a graph as described herein in real time.

[0118] In some implementations, a patient model as described herein may be enabled to interface with one or more other models, blocks, and other components. For example, outputs of the patient model may be processed, interpreted, and / or visualized by one or more software components of the computing system 102. For example, the patient model may output data to a data logging block for recording responses or to a visualization block for graphically displaying the behavior or response of the patient model.

[0119] In some implementations, a patient model may include adjustable parameters which may allow a user to modify aspects of the signal processing or feedback mechanisms. This can include altering gain levels, filter characteristics, algorithm parameters, and / or patient biological characteristics.

[0120] In some implementations, a patient model may be a function capable of providing, in response to a simulated electrical signal, an amount of neural excitation given a certain distance. The amount of neural excitation may be a linear model using thresholds or a more complex model. As an example, for a linear model, the following equations may be utilized: curThr(t) = d(t) * 3.5 / 4; curSlope(t) = excSlopeMult / d(t); and Exc(t) = max( (StimAmplitude(t)-curThr(t)),0)*curSlope(t). In these equations, excSlopeMult is a patient- specific constant which controls how fast excitation grows with stimulation. For example, if excSlopeMult is set to 10, then at a d(t) = 4 mm Exc(t) grows by 5 for every mA of current that exceeds threshold (which would equal to 3.5 from the equation above). Given that patients rate paresthesia range of 5 as very strong, this would mean that in this condition (d(t)=4mm) patient can maximally tolerate current which exceeds threshold by 1 mA, which would be achieved at 4.5 mA. By manipulating d(t) and excSlopeMult it is possible to produce scenario where patients have different thresholds and different maximum tolerable currents.

[0121] In some implementations, patient models may be generated based on actual measurements created by a real patient in a testing facility. For example, the response of a patient to various stimulations may be recorded and used to generate a patient model for a simulation as described herein.

[0122] In addition to receiving a selection of a scenario GUI element 309a-c from a user, the computing system 102 may present one or more scenario option GUI elements 312a-b in a scenario column 306 as illustrated in Fig. 3B. Scenario option GUI elements 312a-b may be related to scenario data 114 stored in memory 108 of a computing system as described herein. By selecting a scenario GUI element 309a-c, a user may select a scenario option, further defining the selected scenario to be applied to the patient model.

[0123] A scenario as described herein may be a particular treatment to be applied to a patient model, a particular placement of electrical contacts for application of the simulated electrical signal to the patient model, and / or other potential variations for applying the simulated electrical signal to the patient model.

[0124] Scenarios selectable in the GUI menu 300 may include, for example, one or more of a closed-loop differential target multiplexed DTM scenario; a closed-loop LD boost group scenario; a closed-loop LD cervical scenario; a closed-loop LD, high PTs scenario; a closed-loop LD, nerve rootstim on some contacts scenario; a closed-loop LD, noisy baseline scenario; and / or other scenarios; setting up a closed loop differential targeted multiplexed stimulation, setting up a closed loop low density, closed loop differential targeted multiplexed (DTM) setup, pain flares from activity setup boost group, noisy baseline signal, navigating high perception thresholds, setting up a supine group, cervical spine placement: closed-loop low density stimulation, avoid uncomfortable nerve room stimulation, navigating small evoked compound action potentials (ECAPs), optimizing thresholds, and no clear ECAP on day one post implantation. Such examples should not be considered as limiting in any way.

[0125] As illustrated in Fig. 3C, when a user selects a scenario GUI element 309b and a scenario option GUI element 312a, a setup GUI element 315 may be displayed in the user interface 300 enabling the user to confirm the patient and scenario selections and proceed towards performing the testing.

[0126] Example scenarios 400a-d are illustrated in Fig. 4. In some implementations, such scenario illustrations may be displayed in the scenario column 306 of the GUI menu 300. Scenario illustrations may illustrate the position and / or size of virtual contacts used to create the electrical signal simulation. The position of virtual contacts may be shown relative to specific vertebrae and / or other anatomical features in some implementations.

[0127] Measuring ECAPs involves stimulating a group of neurons and then recording a response to the stimulation from a distance away. One or more stimulation contacts or electrodes may be placed at one or more first positions on a patient and one or more recording contacts or electrodes may be placed at one or more second positions on the patient. The stimulation contacts may apply an electrical signal and the recording contacts may record the electrical signal as received after passing through the patient medium. The size and placement of the contacts, such as the distance between stimulation and recording contacts, may have a substantive effect on the resulting signal.

[0128] To simulate the various effects differently placed contacts may have on testing, different scenarios 400a-d may be provided for selection by a user to adjust the simulated placement, size, shape, and / or other characteristics of stimulation contacts and / or recording contacts. The selection of a particular scenario may alter the simulation of the electrical signal to be applied to the patient model.

[0129] The simulated positions of contacts in each scenario may replicate the performance of contacts implanted on or near target anatomical elements such as vertebrae. In some examples, contacts may replicate contacts implanted near the spinal cord and more specifically, in the epidural space between the spinal cord and the vertebrae. In some implementations, users may be enabled toselect between different vertebra for contact placement. Once a scenario is selected, the computing system 102 may generate a simulation of an electrical signal from a device to the target anatomical element (e.g., one or more nerves in the spinal cord, the brain, etc.). In effect, the computing system 102 may simulate a treatment device implanted in or placed on a patient.

[0130] To simulate closed loop SCS, the electrical signal simulation may be applied to the patient model, and a feedback signal, represented by the dotted arrow of Fig. 2, may be received from the patient model. The feedback signal may be used to regulate or adjust the simulated electrical signal generated by the computing system 102. For example, to simulate a patient coughing, arching their back, or changing position, the resulting stimulation created by the patient model may be weaker or stronger, i.e., create larger or smaller changes in the distance. The placement of recording contacts may be used to simulate measured ECAP and the computing system 102 may determine a difference in the ECAP. The difference may be used to adjust the simulated electrical signal to cause the amplitude of the ECAP to remain within a therapeutic range that is comfortable for the patient. As described herein, a user may be enabled to change whether the applied electrical signal simulation 204 uses a closed loop or operates in an open loop configuration.

[0131] In some implementations, scenarios may indicate placements of leads, where each lead may include one or more contacts.

[0132] In some implementations, each scenario may be associated with one or more programs. For example, one scenario may be associated with two programs. Each program may be a different placement of virtual contacts. In this way, by selecting a single scenario a user may be enabled to test multiple programs either simultaneously or separately.

[0133] In some implementations, a user may be enabled to manually edit the size, placement, shape, and / or other characteristics of the virtual contacts used for the testing. For example, the computing system 102 may enable a user to create or edit a scenario and programs as illustrated in Figs. 5A-5B and Fig. 6.

[0134] For example, a user may interact with a user interface as illustrated in Fig. 5 A and 5B and select between different programs. In each program, the user may be enabled to place leads and / or electrodes at different positions relative to the patient model. Such positions may, for example, be relative to specific vertebrae (for example, one of the twelve thoracic vertebrae T-l to T-12). For each contact and / or lead, the user may be enabled to adjust an amplitude, frequency, and / or size. In some implementations, a user may be enabled to select a number of electrodes per lead and to individually place different electrodes of a lead at different positions.

[0135] As illustrated in Fig. 6, a UI may enable a user to edit an amplitude or amperage to be applied to one or more contacts in a particular scenario. Upon selection of a patient model and a scenario by a user, scenario data 116 and patient model data 112 associated with the selected patient model and scenario may be loaded by the computing system 102 from memory 104. Based on the scenario data 116 and the patient model data 112 for the selected patient and scenario, the computing system 102 may be enabled to generate a simulated signal to be applied to the patient model based on the selected scenario.

[0136] In some implementations, the computing system 102 may enable a user to test a signal simulation prior to beginning the test as illustrated in Figs. 7-9. Testing a signal simulation may comprise the computing system 102 applying a simulated electrical signal based on the selected scenario to the selected patient model.

[0137] As illustrated in Fig. 7, a capture signal UI may be displayed. The capture signal UI may include an indication of the selected patient model (e.g., Margo Strata in the illustrated example). The computing system 102 may apply, as an example, apply a pulse wave of a simulated electrical signal to the patient model and may record a body signal (e.g., measured in Volts or micro-Volts) output by the patient model and may display a graph of the recorded body signal over time.

[0138] The computing system 102 may also be enabled to determine whether the resulting signal output by the patient model is of a usable signal size. In some implementations, a usable signal size may be determined based on a maximum or average body signal size output by a patient model in response to a test pulse of a simulated electrical signal. The selection of the signal size may be in accordance with the methods implemented in the SCS system that the simulation is trying to simulate.

[0139] In addition to signal size, the computing system 102 may also be enabled to preview a quality of signal as illustrated in Fig. 7. A user may be enabled to adjust amplitudes or amperages of one or more programs to be used during the simulation and to preview the resulting body signal (in terms of voltage) in real time before beginning the simulation.

[0140] A body signal as described herein may be a simulated electrical signal measurable in terms of voltage and / or amperage output in response to an applied simulated electrical signal. A body signal may replicate a body signal as may be output by a human patient undergoing ECAP treatment. Each patient model may output a different body signal in response to a simulated electrical signal, even when the same scenario is selected. Such differences between different patient models may replicate the differences expected when the same ECAP treatment is applied to different human patients.

[0141] As illustrated in Fig. 8, a UI may be displayed which may enable a user, when reviewing a body signal prior to beginning a simulation, to view the body signal at different time points, to compare body signal and input program signals, preview signal quality, adjust program amperages, and to determine whether the resulting body signal is of a usable signature size. In some implementations, the computing system 102 may be enabled to provide notifications such as alerts as to whether any potential issues with the body signal and / or programs may exist. In some implementations, the preview of the body signal may be used by the computing system 102 to calculate initial thresholds.

[0142] As illustrated in Fig. 9, the computing system 102 may in some implementations provide a review signal UI which may enable to analyze signal quality of a body signal before configuring thresholds for the simulation. A slider interface may enable a user to select a particular time range of the body signal, to view maximum and minimum values within a selected time range, and / or view other factors which may be relevant to the simulation.

[0143] If, during the review process, any issues or alerts appear with the sample body signal, the user may be enabled to adjust settings such as virtual contact placement, program amperages, threshold values, and / or other adjustable configuration settings. Once the user is satisfied with the quality of the sample body signal, the simulation may commence with a simulator display UI as illustrated in Figs. 10-16.

[0144] A simulator display UI may include a number of different views. For example, a simulator display UI may include a configure thresholds view as illustrated in Fig. 10 and Fig. 11, which may include body signal and program graphs as illustrated Fig. 12 and a threshold and / or target amplitude adjustment view as illustrated in Fig. 13, a patient feedback view as illustrated in Fig. 14, which may include a visualization of simulated paresthesia and pain locations and intensities, examples of which are illustrated in Fig. 15, as well as an assessment view as illustrated in Fig. 16 which may provide feedback on the simulation. Each of these views are described in greater detail below.

[0145] A configure thresholds view as illustrated in Fig. 10 and Fig. 11 may include information such as a name of the patient model being used, a GUI switch to enable and disable the simulation of the electrical signal being applied to the patient model, a switch to switch between closed loop (e.g., active feedback) and open loop (e.g., sense only), graphs illustrating applied electrical signal amplitudes (e.g., in terms of amplitude or amperage), and graphs illustrating body signal size (e.g., in terms of micro-voltage). The illustrated body signal may be output by a patient model. The body signal may be based upon an input simulated electrical signal. The input simulated electrical signalmay be a closed loop (in which the input simulated electrical signal is based at least in part on a feedback signal created by the patient model) or an open loop signal.

[0146] By interacting with a configure thresholds view as illustrated in Figs. 10, 11, and 13, a user may be able to adjust a reaction threshold and / or a recovery threshold, as well as an amplitude of one or more programs, reaction settings, recovery settings, and / or other variable factors.

[0147] For example, the user may be enabled to begin a stream, initiating the generation of the electrical signal simulation and the application of the signal simulation to the patient model.

[0148] The signal simulation may be generated by a device model which may implement a control algorithm in the event that the signal is of a closed loop type.

[0149] The device model may be configured to output a simulated stimulation signal, e.g., StimulationAmplitude(t). In some implementations, separate stimulation signals may be generated for multiple virtual contacts which may have different perception and ECAP, for example, ping and governed stimulation. A device model may implement a mapping between an ECAP(t) signal and a StimulationAmplitude(t) for ping and governed based on device settings. Example settings which may be used to configure outputs of the device model include, but are not limited to, programmed Pl current, programmed P2 current, reaction and recovery thresholds, attack, and release times, averaging of waveforms, and artifact cancellation methods.

[0150] For example, during the simulation, the computing system 102 may receive additional user inputs, such as the adjustment of thresholds, patient position, treatment program settings, aggressor settings, and / or other settings as described below.

[0151] As a simulation progresses, the computing system may provide additional information in the form of live feedback visualizations and assessments of the performance of the user in conducting the test.

[0152] As illustrated in Fig. 13, a UI may enable a user to adjust reaction and / or recovery thresholds during a simulation. In some implementations, a user may be enabled to adjust each threshold independently, in terms of micro voltage, such as from 0 microvolts to 100 microvolts on a 1 microvolt step for example. In some implementations, a maximum recovery threshold may be capped based on a current reaction threshold setting and / or a minimum reaction threshold may be capped based on a current recovery threshold setting. For example, as the reaction threshold increases, the recovery threshold can be increased, and as the recovery threshold decreases, the recovery threshold can be decreased.

[0153] In some implementations, a UI, as illustrated in Fig. 13, may enable a user to adjust programs applied by the device model to the patient model in real time during a simulation. Forexample, a device model may output streams at one or more different program levels. A user may be enabled to adjust an amplitude, such as in terms of amperage, for each program independently. As an example, a user may be enabled to adjust a milliamp value for a first program and a second program from zero to 10 milliamps on 0.1 milliamp intervals.

[0154] Users may also in some implementations be enabled to adjust reaction speeds and / or recovery speeds between fast, medium, and slow. Upon such setting changes being made, the simulation may be updated in real time by the computing system 102 to reflect the changes made by the user.

[0155] The patient model, being applied with the simulated electrical signal, may output a body signal. The body signal output by the patient model may be represented in terms of size. Size of a body signal may be measured in voltage, such as in terms of micro volts. As illustrated in Figs. 10- 12, the body signal may be plotted in a graph with body signal size on a vertical axis and time on a horizontal axis. In some implementations, the amplitude of the simulated electrical signal applied to the body model may be plotted on another axis alongside the body signal graph. For example, as illustrated in Fig. 12, the size of a body signal may be plotted in a first graph along with recovery and reaction thresholds. An amplitude of any programs may be plotted in a second graph. The graphs may update in real time during the simulation. Upon stopping the simulation, the user can zoom in and out and see values at different times.

[0156] A patient feedback view of the simulator display UI may include, as illustrated in Fig. 14, an identifier of the patient model and the scenario (patient model of Dave Dankswamp and scenario of cathode: Left+Top, Closed-loop LD, Noisy in the illustrated example), a description of the patient model and the scenario, and real time feedback relating to the response of the patient model to the simulated treatment.

[0157] When the electrical signal simulation is applied to the patient model, the computing system 102 may provide responses from the patient model to simulate the reaction of a real patient responding to a real electrical signal. For example, when a simulated electrical signal is applied to a patient model in accordance with a scenario, the patient model may be configured to output a patient response. A patient response may be in terms of pain, indication of a tingling sensation, paresthesia, and preference.

[0158] A pain response output by a patient model may indicate one or more locations associated with pain and a pain level for each location. Because different patients in real life exhibit different pain tolerances, each patient model may be configured to output different levels and locations of pain in response to input simulated signals.

[0159] Similarly, a paresthesia response output by a patient model may indicate one or more locations associated with paresthesia and a paresthesia level for each location. Because different patients in real life experience paresthesia differently, each patient model may be configured to output different levels and locations of paresthesia in response to input simulated signals.

[0160] An implementation of a paresthesia response output by a patient block may be calculated by the patient block using a formula, such as Paresthesia(t) = Paresthesia(t-dt)*(l- alpha!nt)+Exc*alphaInt*centralGain, where dt is a time step (such as 20 msec), alphalnt is an integration time constant, and central Gain is to translate excitation to Paresthesia strength (e.g. 0 to 5). This model indicates that patients will feel stronger sensation if excitation is applied over time (e.g., over 200 msec). The alphalnt simulates the integration time constant. For example, for a time constant of 200 msec, with dt equal to 20 msec, a good choice for alphalnt equals to 0.1 (e.g. roughly 10 samples are needed for paresthesia to reach asymptotic value). In some implementations, the patient model may be capable of outputting an indication as to whether the simulated patient is experiencing a sensation of tingling.

[0161] Each of pain, indication of a tingling sensation, and paresthesia can be shown graphically by an area of the body where patient model outputs an indication of a location of each of the pain and / or paresthesia, and by face expression, as illustrated in Figs. 14 and 15. In some implementations, a patient model may use a function such as ParesthesiaMapping(Paresthesia(t)), which may be a patient variable that generates an area of paresthesia based on paresthesia strength. For example, for each value of paresthesia, a different area of the body might be selected, with higher paresthesia value corresponding to bigger area of the body (e.g., progressing from small part of the leg for paresthesia of 0 to most of the leg and lower trunk for paresthesia of 5).

[0162] In some implementations, the patient model may also be enabled to output text representing vocal responses from a patient, such as "I am not feeling any tingling," as illustrated in Fig. 14.Other example patient responses may include, "I am not feeling any tingling," "I am feeling that weird tingling sensation," "I am feeling tingling in my legs and low back," "I am feeling a grabbing sensation in the ribs on my left side," "The tingling is getting stronger, I do not like it," "The tingling in my legs and low back is getting stronger," "I really do not like that," "The tingling is too strong," "Ouch," "Crikey," and "Stop It," for examples.

[0163] In some implementations, pain and / or paresthesia of a patient may be scored with words and / or numbers. For example, each of pain and / or paresthesia may be scored on a scale from zero to five and / or by words such as none, subtle, moderate, intense, very intense, and / or very intense plus.

[0164] The output text representing vocal responses from a patient and / or the pain and / or paresthesia scores may be triggered based on certain thresholds being reached or certain triggers being met. For example, different threshold amounts may trigger a non-response, a subtle response, a moderate response, an intense response, a very intense response, and / or a very intense plus response.

[0165] During a simulation, a user may be enabled to interact with the patient model such as by activating one or more aggressors, changing a posture of the virtual patient, applying one or more medications (or drugs or alcohol) to the patient model, simulating the performance of any other action which may affect a patient in a real-life scenario, and / or causing the occurrence of any event or circumstance which may affect a patient in a real-life scenario. Aggressors may include, for example, a body arch or a cough. When a patient in real life coughs during an electrical stimulation treatment or arches their back, the body signal output from their body may respond with a spike or drop. To replicate such a response, patient models may be configured to elicit a similar response when a user clicks a back arch or cough aggressor. Similarly, when a patient in real life changes their position, such as from upright to supine or vice versa, during a simulation, the body signal output from their body may respond with a spike or drop or may on average elevate or lessen. To replicate such a response, patient models may be configured to elicit a similar response when a user requests a change in posture of the patient model.

[0166] In some implementations, a user may be enabled to cause a simulated medication to be applied to the patient model. Because certain medications may affect the morphology and / or magnitude of the body signal from a patient, a system as described herein may allow a user to simulate the patient taking a drug or medicine or alcohol. Simulating a drug, medicine, or alcohol may be accomplished by providing a GUI element selectable by a user enabling the user to select from a list of options. After prompting the application of a particular medication, drug, or alcohol, the patient model may be modified to simulate a patient having taken the selected medication, drug, or alcohol. After modifying the patient model, the body signal output by the patient model may update to simulate a body signal which would be output by a patient having taken the selected medication, drug, or alcohol. Drugs and medications as described herein may include one or more of isoflurane, general anesthesia, alcohol, nicotine, gabapentin, opioids, or any other substance.

[0167] A patient feedback view of the simulator display UI may include a visual of a front and a back of the patient model as illustrated in Fig. 14. The visual of a front and a back of the patient model may display a location and in some implementations an intensity of pain and / or paresthesia.Additional example illustrations of visuals of fronts and backs of patient models are illustrated in Fig. 15.

[0168] As should be appreciated, illustrations of visuals of a front and a back of a patient may include areas of different colors in some implementations. Paresthesia may be represented by one color, e.g. blue, and pain may be represented by another color, e.g., red. Intensity of each of paresthesia and pain may be represented by brightness of each color or by a glowing illustration. Intensity may also be indicated by the progressively larger area of the body covered by sensation.

[0169] In some implementations, an assessment view as illustrated in Fig. 16 may provide feedback on the simulation in real time during the simulation and / or following an end of the simulation. In various aspects discussed herein, such an assessment may include feedback such as whether a patient needs a supine group set up, whether a closed loop is turned off or on, a recommendation as to whether a closed loop should be turned off or on, an indication as to whether an amount of paresthesia for a patient is too high, too low, or within a desirable zone, an indication as to whether an amount of pain for a patient is too high or within a desirable zone, and / or other indications.

[0170] Such an assessment may be displayed as part of a configure thresholds UI as illustrated in Fig. 11 or as a separate UI. In some implementations, the assessment may include recommendations for either improving a current simulation or guidance on improving future simulations. For example, an assessment may describe whether a patient would prefer greater or lesser levels of paresthesia in a particular posture, whether the user should increase or decrease an amplitude of a particular program, whether the user has managed aggressors adequately or insufficiently, etc. In some implementations, the assessment may recommend actions. Examples may include instructing the user to try reducing a reaction threshold if the reaction threshold is above a particular value or if the aggressor management is not adequate, to try decreasing a program amplitude, to search for a configuration that has a larger ECAP, to increase a program current level or reaction thresholds. The assessment may include instructing a user as to whether the patient model may need an upright group or a supine group. The assessment may include an indication of a therapeutic current level for the patient model, such as 3.5 mA.

[0171] Fig. 17 depicts a method 1700 that may be used, for example, to generate patient models, scenarios, and signal simulations as described herein. Such a method 1700 may be utilized by a computing system 102 in conjunction with or prior to a method 1800 as illustrated in Fig. 18 of a user interacting with the computing system 102 (or another device) to test the simulation and receive an assessment.

[0172] The methods 1700 and 1800 (and / or one or more steps thereof) may be carried out or otherwise performed, for example, by at least one processor. The at least one processor may be the same as or similar to the processor 104 or the processor(s) of the computing system 102 described above. The at least one processor 104 may be part of the computing system 102 or part of a user device 118 in communication with the computing system 102. A processor other than any processor described herein may also be used to execute the methods 1700 and 1800. The at least one processor may perform the methods 1700 and 1800 by executing elements stored in a memory (such as a memory 106 in the computing system 102 as described above or a control unit). The elements stored in the memory and executed by the processor may cause the processor to execute one or more steps of a function as shown in methods 1700 and 1800. One or more portions of methods 1700 and 1800 may be performed by the processor executing any of the contents of memory, such as by accessing patient model data 112, simulation data 114, scenario data 116, and / or any associated operations as described herein.

[0173] A method 1700 of generating patient models, scenarios, and signal simulations may be implemented using a computing system 102. The method 1700 may begin at 1702 with the computing system 102 generating one or more patient models. Generating a patient model as described herein may comprise creating or initializing a block of a control system capable of accepting one or more input signals, which may be simulated electrical signals as described herein. Each patient model may also be configured to output one or more signals, which may be simulated patient responses and / or body signals, replicating the behavior of a real- world patient. A patient model may comprise one or more simulation algorithms and data analysis tools. A patient model may also be enabled to accept as inputs one or more settings and / or user-configurable features, such as positions and aggressors.

[0174] A patient model may be created based on theoretical frameworks, empirical data, or a combination of both, to accurately reflect the characteristics and behavior of the target system. In some implementations, a patient model may be created in whole or in part based on data generated by one or more machine learning algorithms, neural networks, and the like.

[0175] A patient model may be created to replicate a real-world patient or a model patient representing a set of patients sharing particular characteristics. For example, a patient model may be created by a computing system in such a way as to replicate responses of a particular patient or a group of patients to particular input signals, such as electrical signals replicating SCS procedures.

[0176] The output signals from a patient model may be used to provide insights into the behavior and response of the patient model to input electrical signals. Such outputs may be in the form ofbody signal data, visual responses, or other forms which may represent a patient's response to similar input signals.

[0177] At 1704, the computing system 102 may generate a scenario. Generating a scenario as described herein may include determining a distance between virtual contacts, determining locations of virtual contacts relevant to anatomical structures such as vertebra, determining a size of virtual contacts, and / or other factors. For example, as illustrated in Figs. 5A and 5B, and as described above, a scenario may include one or more programs. Each program may be associated with one or more contacts of various sizes and positions. In some implementations, the generation of the scenario may be assisted or controlled by a user, such as by interacting with a user interface such as that illustrated in Figs. 5A and 5B.

[0178] At 1706, the computing system 102 may generate a signal simulation. Generating a signal simulation may comprise creating a device model by the computing system 102 which may be crafted to replicate the behavior of an actual system capable of measuring ECAPS. The device model may be enabled to output one or more electrical signals and, in some implementations, to receive one or more feedback signals. A feedback signal may represent the response of a patient model to the output simulated electrical signals generated by the device model. Such a feedback signal may be used to enable the device model to implement a closed loop system. The device model may process the feedback signal according to predefined rules or algorithms, which could include adjusting the amplitude, frequency, phase, or other characteristics of the output signal based on the feedback received.

[0179] The output of the device model may be a simulated electrical signal, which can vary from simple waveforms (like pulse trains) to complex, time-varying signals found in real-world SCS systems. The output signals may be based at least in part on the scenario(s) generated above at 1704 and as described elsewhere herein. The signal output by the device model may also be in some implementations controlled at least in part by user input. For example, a user may be enabled to adjust amplitudes of the output signal during a simulation.

[0180] A method for simulating a spinal cord stimulation (SCS) procedure may be as illustrated in Fig. 18. Such a method 1800 may include, at 1802, a computing system 102 receiving a patient model selection. For example, a user may interact with a UI 300 as illustrated in Fig. 3A to select from among a group of patient models. Each patient model may be configured to replicate a patient with different types of physical attributes.

[0181] At 1804, the computing system 102 may receiving a selection of a scenario. For example, a user may interact with a UI 300 as illustrated in Fig. 3B to select from among a group of scenarios.Each scenario may be configured to replicate a different arrangement of contacts for performing the SCS procedure. In some implementations, the scenario indicates positions of a plurality of electrodes in relation to a spinal column.

[0182] At 1806, the computing system 102 may initiate one or more simulated electrical signals. In some implementations, the electrical signal may be generated by a pulse generation simulator. The simulated electrical signal may be applied to the patient model based at least in part on the scenario. Applying the simulated electrical signal to the patient model may comprise simulating a reaction of physiological qualities associated with the patient model to the simulated electrical signal.

[0183] In some implementations, simultaneously with applying the simulated electrical signal to the patient model, the computing system 102 may display a visualization of a response of the patient model to the simulated signal. For example, the computing system 102 may display one or more of an audible reaction, a location of pain, a location of paresthesia, such as illustrated in Fig. 14. The computing system 102 may also display a visualization of a body signal output by the patient model in response to the simulated electrical signal as illustrated in Figs. 10-12.

[0184] At 1808, the computing system may receive input from a user during the application of the simulated electrical signal to the patient model. The input from the user may comprise one or more of a change in an amperage and a voltage of the simulated electrical signal. The input from the user may comprise an adjustment to the patient model. In some implementations, such an adjustment to the patient model may include one or more of an aggressor and a position of the patient. The user input may cause changes to the patient model, to the scenario, and / or to the simulation of the electrical signal. Changes to the patient model may include causing one or more aggressors to occur and / or a change in posture to occur. Changes to the simulation of the electrical and / or the scenario may include changes to amplitudes of one or more programs being applied and / or changes in reaction and / or recovery thresholds.

[0185] At 1810, in response to the input from the user, the computing system 102 may update the simulation. For example, the output of the patient model may change in response to the user input. The user input may cause changes to the patient model, to the scenario, and / or to the simulation of the electrical signal. As a result of such changes, the output body signal and / or patient response may change.

[0186] At 1812, the computing system 102 may generate an assessment. Generating an assessment may include calculating and / or determining scores and statistics relating to the simulation. An assessment may indicate one or more of a closed loop status, a paresthesia levelscore, an aggressor management score, and an average current level score. An assessment may be generated and displayed during a simulation and updated in real-time and / or after a simulation. As illustrated in Fig. 16, an assessment may be a visualization of various statistical information which may assist a user in analyzing their performance during the simulation.

[0187] It will be appreciated that the steps of methods 1700 and 1800 may be repeated continuously separately or simultaneously. The present disclosure encompasses embodiments of the methods 1700 and 1800 that comprise more or fewer steps than those described above, and / or one or more steps that are different than the steps described above.

[0188] As noted above, the present disclosure encompasses methods with fewer than all of the steps identified in Figs. 17 and 18 (and the corresponding descriptions of the methods 1700 and 1800), as well as methods that include additional steps beyond those identified in Figs. 17 and 18 (and the corresponding description of the methods 1700 and 1800). The present disclosure also encompasses methods that comprise one or more steps from one method described herein, and one or more steps from another method described herein.

[0189] The foregoing is not intended to limit the disclosure to the form or forms disclosed herein. In the foregoing Detailed Description, for example, various features of the disclosure are grouped together in one or more aspects, embodiments, and / or configurations for the purpose of streamlining the disclosure. The features of the aspects, embodiments, and / or configurations of the disclosure may be combined in alternate aspects, embodiments, and / or configurations other than those discussed above. This method of disclosure is not to be interpreted as reflecting an intention that the claims require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects he in less than all features of a single foregoing disclosed aspect, embodiment, and / or configuration. Thus, the following claims are hereby incorporated into this Detailed Description, with each claim standing on its own as a separate preferred embodiment of the disclosure.

[0190] Moreover, though the foregoing has included description of one or more aspects, embodiments, and / or configurations and certain variations and modifications, other variations, combinations, and modifications are within the scope of the disclosure, e.g., as may be within the skill and knowledge of those in the art, after understanding the present disclosure. It is intended to obtain rights which include alternative aspects, embodiments, and / or configurations to the extent permitted, including alternate, interchangeable and / or equivalent structures, functions, ranges or steps to those claimed, whether or not such alternate, interchangeable and / or equivalent structures,functions, ranges or steps are disclosed herein, and without intending to publicly dedicate any patentable subject matter.

[0191] Aspects of this disclosure may be further described by reference to the following examples:

[0192] Example 1. A method for simulating a spinal cord stimulation procedure, the method comprising: generating a simulated electrical signal; applying the simulated electrical signal to a patient model based upon a selected scenario; and displaying a response of the patient model to the simulated electrical signal.

[0193] Example 2. The method of example 1, wherein the patient model is selected based on a user input.

[0194] Example 3. The method of example 1, wherein the scenario is selected based on an interaction with a graphical user interface (GUI).

[0195] Example 4. The method of example 1, wherein the simulated electrical signal is generated by a pulse generation simulator.

[0196] Example 5. The method of example 1, wherein the selected scenario indicates one or more positions of one or more electrodes in relation to the patient’s spinal cord.

[0197] Example 6. The method of claim 5, wherein the one or more positions affect a distance variable in the patient model.

[0198] Example 7. The method of example 1, wherein the applying of the simulated electrical signal to the patient model comprises simulating a reaction of physiological qualities associated with the patient model to the simulated electrical signal.

[0199] Example 8. The method of example 1, wherein the displaying of the response of the patient model comprises displaying one or more of a patient’s reaction, a location of pain, or a location of paresthesia.

[0200] Example 9. The method of example 1, wherein the displaying the response of the patient model comprises generating and displaying an assessment.

[0201] Example 10. The method of example 1, the assessment indicates one or more of: a closed loop status, an open loop status, a paresthesia level score, an aggressor management score, or an average current level score.

[0202] Example 11. The method of example 1, the assessment indicating a closed-loop status, wherein the closed-loop status indicates whether a closed loop feature was enabled or disabled during the application of the simulated electrical signal to the patient model.

[0203] Example 12. The method of example 1, the assessment indicating a paresthesia level score, wherein the paresthesia level score indicates whether a level of paresthesia was within an acceptablezone based on the patient model during the application of the simulated electrical signal to the patient model.

[0204] Example 13. The method of, the assessment indicating an aggressor management score and an average current level score, wherein the aggressor management score indicates whether aggressors occurring during the application of the simulated electrical signal to the patient model were managed by a user and the average current level score indicates whether an average amount of current of the simulated electrical signal applied to the patient model was within an acceptable zone based on the patient model during the application of the simulated electrical signal to the patient model.

[0205] Example 14. A system for simulating a spinal cord stimulation procedure, the system comprising: a processor; and memory storing instructions which, when executed by the processor, cause the system to: generate a simulated electrical signal; apply the simulated electrical signal to a patient model based on a selected scenario; and display a response of the patient model to the simulated electrical signal.

[0206] Example 15. A user device for training a user to program a spinal cord stimulation therapy, the user device comprising: a processor; and memory storing instructions which, when executed by the processor, cause the user device to: generate a simulated electrical signal; apply the simulated electrical signal to a patient model based on a selected scenario; and display a response of the patient model to the simulated electrical signal.

Claims

CLAIMSWhat is claimed is:

1. A method for simulating a spinal cord stimulation procedure, the method comprising: generating a simulated electrical signal; applying the simulated electrical signal to a patient model based upon a selected scenario; and displaying a response of the patient model to the simulated electrical signal.

2. The method of claim 1, wherein the patient model is selected based on a user input.

3. The method of claims 1 or 2, wherein the scenario is selected based on an interaction with a graphical user interface (GUI).

4. The method of any of the preceding claims 1-3, wherein the simulated electrical signal is generated by a pulse generation simulator.

5. The method of any of the preceding claims 1-4, wherein the selected scenario indicates one or more positions of one or more electrodes in relation to the patient’s spinal cord.

6. The method of claim 5, wherein the one or more positions affect a distance variable in the patient model.

7. The method of any of the preceding claims 1-6, wherein the applying of the simulated electrical signal to the patient model comprises simulating a reaction of physiological qualities associated with the patient model to the simulated electrical signal.

8. The method of any of the preceding claims 1-7, wherein the displaying of the response of the patient model comprises displaying one or more of a patient’s reaction, a location of pain, or a location of paresthesia.

9. The method of any of the preceding claims 1-8, wherein the displaying the response of the patient model comprises generating and displaying an assessment.

10. The method of any of the preceding claims 1-9, the assessment indicates one or more of: a closed loop status, an open loop status, a paresthesia level score, an aggressor management score, or an average current level score.

11. The method of any of the preceding claims 1-10, the assessment indicating a closed- loop status, wherein the closed-loop status indicates whether a closed loop feature was enabled or disabled during the application of the simulated electrical signal to the patient model.

12. The method of any of the preceding claims 1-11, the assessment indicating a paresthesia level score, wherein the paresthesia level score indicates whether a level of paresthesia was within an acceptable zone based on the patient model during the application of the simulated electrical signal to the patient model.

13. The method of any of the preceding claims 1-12, the assessment indicating an aggressor management score and an average current level score, wherein the aggressor management score indicates whether aggressors occurring during the application of the simulated electrical signal to the patient model were managed by a user and the average current level score indicates whether an average amount of current of the simulated electrical signal applied to the patient model was within an acceptable zone based on the patient model during the application of the simulated electrical signal to the patient model.

14. A system for simulating a spinal cord stimulation procedure, the system comprising: a processor; and memory storing instructions which, when executed by the processor, cause the system to: generate a simulated electrical signal; apply the simulated electrical signal to a patient model based on a selected scenario; and display a response of the patient model to the simulated electrical signal.

15. A user device for training a user to program a spinal cord stimulation therapy, the user device comprising: a processor; andmemory storing instructions which, when executed by the processor, cause the user device to: generate a simulated electrical signal; apply the simulated electrical signal to a patient model based on a selected scenario; and display a response of the patient model to the simulated electrical signal.

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