Evoked response guided neuromodulation lead placement

Abstract

Systems and methods for modeling a spatial distribution of evoked responses (ERs) of electrical stimulation and using the model to guide neuromodulation are disclosed. An exemplary system includes at least one multi-electrode lead, an electrical stimulator to provide electrical stimulation to a neural target, a sensing circuit to sense an ER to the electrical stimulation, and a controller circuit. In response to electrical stimulation delivered to the neural target via the stimulation electrodes according to stimulation settings, the controller circuit may collect an ER from each of a set of sensing electrodes located at a respective sensing location and selected from electrodes on the at least one lead, generate a model representing a spatial distribution of ER characteristics of the sensed ER, and providing a suggestion to reposition the lead or adjust the stimulation settings based on the comparison of the model to the acceptance criteria.

Description

[0001] Priority requirements

[0002] This application claims priority to U.S. Provisional Application No. 63 / 529,959, filed July 31, 2023, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This document relates generally to medical systems, and more specifically, but not limitingly, to systems, apparatus, and methods for optimizing the placement of neural modulation leads based on evoked responses. Background Technology

[0004] Medical devices may include therapy delivery devices configured to deliver treatment to patients and / or monitors configured to monitor patient conditions via user input and / or one or more sensors. For example, therapy delivery devices for outpatients may include wearable and implantable devices, and may also include, but are not limited to, stimulators (such as electrical, thermal, or mechanical stimulators) and drug delivery devices (such as insulin pumps). Examples of wearable devices include, but are not limited to, transcutaneous electrical neural stimulators (TENS), such as patches that can be attached to glasses, clothing, or configured to adhere to the skin. Implantable stimulators can deliver electrical stimulation to treat a variety of biological conditions, such as pacemakers for treating arrhythmias, defibrillators for treating ventricular fibrillation, cardiac resynchronization therapy devices for heart failure, cochlear stimulators for treating deafness, retinal stimulators for treating blindness, muscle stimulators for generating coordinated limb movements, spinal cord stimulators (SCS) for treating chronic pain, cortical and deep brain stimulators (DBS) for treating motor and psychological disorders, peripheral nerve stimulation (PNS), functional electrical stimulation (FES), and other neurostimulators for treating urinary incontinence, sleep apnea, shoulder subluxation, etc. Neurostimulators (e.g., DBS, SCS, PNS, or TENS) can be configured to treat pain. By way of example, and not limitation, a DBS system can be configured to treat tremor, bradykinesia, and movement disorders, as well as other movement disorders associated with Parkinson's Disease (PD).

[0005] The use of evoked potentials (also known as evoked responses or ERs) to guide neurostimulation therapy has been proposed. For example, evoked resonant neural activity (ERNA) has been proposed as a feedback signal for DBS therapy in the subthalamic nucleus (STN) of Parkinson's disease. ERNA may also be referred to by other names, such as DBS Local Evoked Potential (DLEP), evoked oscillatory neural response (EONR), and other terms. Evoked potentials including ERNAs may be present in other indicative and anatomical structures or locations.

[0006] The aim is to improve lead placement and / or programming of neurostimulators based on ERs such as ERNA, while reducing the time and effort required to identify lead locations and / or programming settings that will produce the desired or target response. Summary of the Invention

[0007] The inventors have recognized that the desired evoked response target is variable. For example, the ERNA target for STN DBS may vary from patient to patient. The inventors also recognize that the ERNA target or other evoked response target can vary based on the anatomical target and lead placement trajectory, and can vary with other factors such as evoked and recorded setup, surgical center, surgeon, surgical or programming technique, preferences, and errors. To accommodate variations in the desired evoked response target, multiple stimulation ER tests can be performed, and a large number of ER records can be collected from multiple locations via corresponding sensing electrodes (also called recording electrodes). This ER can be analyzed to determine whether the measured ER matches the desired or target ER response. Such a decision can be used to guide lead placement, such as pushing, pulling, shifting, or rotating the lead, thereby helping to identify the desired stimulation location, maximizing symptom relief while minimizing side effects. This stimulation effect can be represented by the desired target evoked response. To improve the efficiency of ER lead placement and / or device programming, it is generally desirable to reduce the amount of ER recordings, such as using only fewer sensing electrodes, and to determine whether the reduced ER recordings match the desired or target response. Embodiments of this subject provide systems, apparatus, and methods for fitting ER sensed at a corresponding sensing location to a model representing the spatial distribution of the sensed ER. The model can be a parametric, regression, or nonparametric model. The model can be linear or nonlinear. The fitted model or model parameters or features derived therefrom can be compared with acceptance criteria. Based on the comparison results, suggestions for lead placement or device programming can be provided to the user to make the fitted model or model parameters or features more favorable compared to the acceptance criteria.

[0008] Examples of neural modulation systems (e.g., "Example 1") may include: at least one lead including a plurality of electrodes; an electrical stimulator configured to deliver electrical stimulation to a neural target of a patient; a sensing circuit configured to sense an evoked response (ER) to the electrical stimulation; and a controller circuit operatively connected to the electrical stimulator and the sensing circuit, the controller circuit being configured to: in response to the electrical stimulation delivered to the neural target via the stimulating electrodes on the at least one lead according to a stimulation setting, collect sensed ERs from each of a set of sensing electrodes located at a corresponding sensing location, the sensing electrodes being selected from the plurality of electrodes on the at least one lead; generate ER features based on the sensed ERs; fit the generated ER features to a model to represent the spatial distribution of the generated ER features at the sensing location; and provide a user with a suggestion to reposition the at least one lead or adjust the stimulation settings so that the fitted model is more favorable compared to the acceptance criteria, based at least in part on a comparison of the fitted model with an acceptance criterion.

[0009] In Example 2, the subject matter according to Example 1 may optionally include the ER feature, which may include the signal amplitude of the sensed ER of the electrical stimulation, and wherein the spatial distribution includes the distribution of the signal amplitude of the sensed ER at the sensing location.

[0010] In Example 3, the subject matter according to any one or more of Examples 1-2 may optionally include the at least one lead, which may include a deep brain stimulation (DBS) lead, and wherein the electrical stimulator is configured to deliver DBS to the patient’s brain target based on the ER features or a fitted model of the ER features according to the stimulation settings.

[0011] In Example 4, the subject matter according to any one or more of Examples 1-3 may optionally include the plurality of electrodes, which may include one or more annular electrodes or one or more rows of segmented electrodes disposed at corresponding longitudinal positions along the length of the at least one lead, wherein each row includes segmented electrodes disposed around the circumference of the at least one lead at a specific longitudinal position, wherein the stimulating electrode and a set of selected sensing electrodes are selected from the one or more annular electrodes or the one or more rows of segmented electrodes.

[0012] In Example 5, the subject matter according to Example 4 may optionally include sensed ER, which may include ER sensed from a plurality of longitudinal sensing locations corresponding to a selected sensing electrode along the length of the at least one lead, wherein the fitted model represents the longitudinal distribution of the ER features at the plurality of longitudinal sensing locations.

[0013] In Example 6, the subject matter according to any one or more of Examples 4-5 may optionally include sensed ER, which may include ER sensed from a plurality of circumferential sensing positions corresponding to selected sensing electrodes around the circumference of the at least one lead at a particular longitudinal position, wherein the fitted model represents the directional distribution of the ER features at the plurality of circumferential sensing positions.

[0014] In Example 7, the subject matter described according to any one or more of Examples 1-6 may optionally include the controller circuit, which may be configured to: display on a user interface one or more of the sensed ER, the generated ER features, the model representing the spatial distribution of the generated ER features, or the acceptance criteria.

[0015] In Example 8, the subject matter described in any one or more of Examples 1-7 may optionally include a fitted model, which may include at least one of a parametric model, a regression model, or a nonparametric model.

[0016] In Example 9, the parametric model may be optionally included according to the subject of Example 8, and the parametric model may include a Gaussian distribution model or a periodic or wrapped Gaussian distribution model.

[0017] In Example 10, the subject matter according to any one or more of Examples 1 to 9 may optionally include the controller circuit, which may be configured to: determine model parameters or features of the fitted model; and provide a suggestion for repositioning the at least one lead or adjusting the stimulus setting, at least in part based on a comparison of the determined model parameters or features with target parameters or feature values, wherein the repositioning of the at least one lead or the adjustment of the stimulus setting causes the determined model parameters or features to fall within the range of the target parameters or feature values.

[0018] In Example 11, the subject matter according to Example 10 may optionally include the model parameters or features, which may include one or more parameters of a parametric model.

[0019] In Example 12, the subject matter according to Example 11 may optionally include the acceptance criteria, which may include the ER target location, wherein the controller circuitry is configured to determine the ER distribution center of the generated ER features based at least in part on one or more parameters in the parameter model, and to estimate the distance between the determined distribution center and the ER target location.

[0020] In Example 13, the subject matter according to any one or more of Examples 10-12 may optionally include the model parameters or features, which may include the amplitude, spatial location, or width of a local peak of a model fitted within a range defined by the sensing location, wherein the controller circuitry is configured to provide the suggestion to reposition the at least one lead or adjust the stimulation settings such that the spatial location of the local peak falls within a margin of the target location of the ER peak.

[0021] In Example 14, the subject matter according to any one or more of Examples 10-13 may optionally include the model parameters or features, which may include one or more of the positive peak amplitude or negative peak amplitude of a model fitted within a range defined by the sensing location, wherein the controller circuitry is configured to provide the suggestion to reposition the at least one lead or adjust the stimulation settings based at least in part on a comparison of the positive peak amplitude or negative peak amplitude with a predetermined threshold or value range.

[0022] In Example 15, the subject matter according to any one or more of Examples 10-14 may optionally include the model parameters or features, which may include the ratio of the positive peak amplitude to the negative peak amplitude of the fitted model within a range defined by the sensing location, wherein the controller circuitry is configured to provide the suggestion to reposition the at least one lead or adjust the stimulus settings such that the ratio of the positive peak amplitude to the negative peak amplitude exceeds a predetermined threshold or falls within a predetermined range.

[0023] Example 16 is a method for delivering neural stimulation to a patient's neural target via a neural modulation system, the neural modulation system including an electrical stimulator and at least one lead coupled thereto, the method comprising: delivering electrical stimulation to the neural target via stimulation electrodes on the at least one lead according to stimulation settings; sensing evoked responses (ERs) from each of a set of sensing electrodes electrically connected to sensing circuitry, the sensing electrodes being selected from a plurality of electrodes on the at least one lead and located at corresponding sensing locations; generating ER features based on the sensed ERs using controller circuitry; fitting the generated ER features to a model, the model representing the spatial distribution of the generated ER features at the sensing locations, via the controller circuitry; and providing a user with a suggestion to reposition the at least one lead or adjust the stimulation settings such that the simulated model is more favorable compared to the acceptance criteria, based at least in part on a comparison of the fitted model with an acceptance criterion.

[0024] In Example 17, the subject matter according to Example 16 may optionally include the plurality of electrodes, which may include one or more annular electrodes or one or more rows of segmented electrodes disposed at corresponding longitudinal positions along the length of the at least one lead, wherein each row includes segmented electrodes disposed around the circumference of the at least one lead at a specific longitudinal position, wherein the fitted model represents one or more of the following: (i) a longitudinal distribution of the ER features at a plurality of longitudinal sensing positions corresponding to a sensing electrode selected along the length of the at least one lead, or (ii) an directional distribution of the ER features at a plurality of circumferential sensing positions corresponding to a selected sensing electrode around the circumference at a specific longitudinal position of the at least one lead.

[0025] In Example 18, the subject matter described in any one or more of Examples 16-17 may optionally include a fitted model, which may include at least one of a parametric model, a regression model, or a nonparametric model.

[0026] In Example 19, the subject matter according to any one or more of Examples 16-18 may optionally include determining model parameters or features of a fitted model, wherein the suggestion to reposition the at least one lead or adjust the stimulus setting is based at least in part on a comparison of the determined model parameters or features with target parameters or feature values, wherein the repositioning of the at least one lead or the adjustment of the stimulus setting results in the determined model parameters or features falling within the margin of the target parameters or feature values.

[0027] In Example 20, the subject matter described in Example 19 may optionally include determining, at least in part, the ER distribution center of the generated ER features based on the determined model parameters or features; estimating the distance between the determined distribution center and the ER target location; and providing the estimated distance to the user on a user interface.

[0028] In Example 21, the subject matter according to any one or more of Examples 19-20 may optionally include the model parameters or features, which may include the amplitude, spatial location, or width of local peaks of a model fitted within a range defined by the sensing location, wherein the suggestion to reposition the at least one lead or adjust the stimulus settings is provided such that the spatial location of the local peak falls within a margin of the target location of the ER peak.

[0029] In Example 22, the subject matter according to any one or more of Examples 19-21 may optionally include the model parameters or features, which may include one or more of the positive peak amplitude or negative peak amplitude of a model fitted within a range defined by the sensing location, wherein the suggestion to reposition the at least one lead or adjust the stimulus settings is based at least in part on a comparison of the positive peak amplitude or negative peak amplitude with a predetermined threshold or value range.

[0030] In Example 23, the subject matter according to any one or more of Examples 19-22 may optionally include the model parameters or features, which may include the ratio of the positive peak amplitude to the negative peak amplitude of the fitted model within a range defined by the sensing location, wherein the suggestion to reposition the at least one lead or adjust the stimulus settings is provided such that the ratio of the positive peak amplitude to the negative peak amplitude exceeds a predetermined threshold or falls within a predetermined range.

[0031] This summary is an overview of some of the teachings of this application and is not intended to be an exclusive or exhaustive treatment of the subject matter. Further details regarding the subject matter are found in the detailed description and the appended claims. Other aspects of this disclosure will be apparent to those skilled in the art upon reading and understanding the following detailed description and viewing the accompanying drawings, each of which is not to be considered limiting. The scope of this disclosure is defined by the appended claims and their legal equivalents. Attached Figure Description

[0032] Various embodiments are illustrated by way of example in the accompanying drawings. These embodiments are illustrative and are not intended to be exhaustive or exclusive embodiments of the subject matter.

[0033] Figure 1 An electrical stimulation system that can be used to deliver DBS is shown as an example, not a limitation.

[0034] Figure 2 An implantable pulse generator (IPG) in a DBS system is shown as an example rather than a limitation.

[0035] Figures 3A-3B Leads that can be coupled to the IPG to deliver electrical stimulation, such as DBS, are shown as examples rather than limitations.

[0036] Figure 4 A computing device for programming or controlling the operation of an electrical stimulation system is shown by way of example rather than limitation.

[0037] Figure 5 An example of an electrotherapy delivery system is shown.

[0038] Figure 6 The implementation using an implantable medical device (IMD) is shown as an example, not a limitation. Figure 5 Monitoring systems and / or electrotherapy delivery systems.

[0039] Figure 7 An example of a neuromodulation system is shown, which is configured to provide guided implantation (e.g., lead placement) and device programming based on modeling of the spatial distribution of evoked responses (ER) to electrical stimulation.

[0040] Figure 8 An example of a stimulation sensing electrode configuration is shown, graphically represented by an ER sensing map.

[0041] Figures 9A-9D An example of a stimulation-sensing electrode configuration is shown, which involves a selected subset, rather than all, of the electrodes available on the lead for sensing the ER.

[0042] Figure 10 An example of an ER sensing map is shown, in which ER is sensed from the nearest neighboring electrode relative to the stimulating electrode.

[0043] Figure 11 The comparison between the user-defined ER target location and the calculated ER distribution center is shown as an example rather than a limitation.

[0044] Figure 12A The elements shown are illustrated as examples of elements that can be displayed on a monitor in a user interface that provides simple and clear confirmation of lead wire positions.

[0045] Figure 12B The prediction of lead or electrode location relative to the volume of target tissue based on features derived from the ER distribution model is shown by way of example rather than limitation.

[0046] Figure 13 The display of a user interface for creating a response fit based on the distribution of ER is shown as an example, not a limitation.

[0047] Figure 14 Examples of measuring the distribution of ER or ER features or ER features derived therefrom and matching it with acceptance criteria such as templates are shown by way of example and not limitation.

[0048] Figure 15 The ER distribution for the DBS lead electrode is shown as an example, not a limitation.

[0049] Figure 16The spatial distribution of ER in electrical stimulation and methods for using distribution models or model parameters to guide lead placement and / or device programming are illustrated by way of example rather than limitation.

[0050] Figure 17 The block diagram shows an example machine on which any one or more of the techniques (e.g., methods) discussed in this paper can be executed. Detailed Implementation

[0051] The following detailed description of the subject matter is illustrated with reference to the accompanying drawings, which show, by way of illustration, specific inventions and embodiments in which the subject matter may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the subject matter. Other embodiments may be utilized, and structural, logical, and electrical changes may be made without departing from the scope of the subject matter. References to “a,” “an,” or “various” embodiments in this disclosure do not necessarily refer to the same embodiment, and these references contemplate more than one embodiment. Therefore, the following detailed description should not be considered limiting, and its scope is defined only by the appended claims and the full scope of their legal equivalents.

[0052] There is no single universal "optimal" stimulus target and desired response, as the optimal target can vary depending on the condition or disease, anatomy (anatomical target), lead and trajectory, center (imaging device location, etc.), surgeon (how they implant and position the lead, their preferences for lead placement), or the symptom to be improved (e.g., from tremor to improvement in cognitive skills). Raw responses can be displayed, and the user can determine fitness based on the displayed raw responses. The user can adjust the characteristic target boundaries, acceptance threshold, and maximum number of steps before convergence.

[0053] Evoked responses (ERs) can be used to guide the programming of implantation procedures (e.g., lead placement or electrode positioning) or device settings (e.g., sensing parameters or neurostimulation parameters). ERs can be induced by stimuli that deliver ERs, stimuli that deliver therapy, and stimuli that can both deliver therapy and deliver ERs. Stimuli can be located where (1) an evoked pulse is placed to obtain a desired response in order to maximize ERNA, (2) a response is listened to to obtain a desired response (e.g., maximizing ERNA), (3) a lead needs to be placed (e.g., optimal therapy), and (4) a stimulus needs to be placed on the lead (e.g., maximizing therapy and / or minimizing / counteracting side effects). Responses can be modulated by sensing details, including amplifier settings, the relationship between the stimulus and sensing electrodes, the nature of the stimulus or sensing electrodes (including other factors such as geometry and surface), and signal processing (including processing within analog or digital hardware, firmware, or software) that occurs during and after measurement. The target can be an anatomical target, such as a volume (e.g., STN), a fiber assembly, a subregion (motor STN, or dorsolateral STN), a volume of interest described or associated with a particular patient’s brain (e.g., from atlas or aggregated prior information), or a “point” that can be described by optimizing the location of the stimulus (as in one of (1)-(4) above).

[0054] The various embodiments described in this document model the spatial distribution of the electrical response (ER) to electrical stimulation and use model parameters or features to determine whether the ER measurement matches the desired or target response, guiding lead placement or device programming. Conventional ER-based lead placement and device programming typically require a large number of ER records to produce a reliable characterization or estimate of the spatial distribution of the ER to electrical stimulation. ER is typically sensed from multiple recording electrodes placed at the corresponding sensing locations. For example, the local field potential (LFP) can be sensed by a large array of sensing electrodes in response to electrical stimulation with varying stimulation settings, such as amplitude, frequency, or pulse width. To save time and system resources, it is desirable to use less ER data (such as from a reduced number of sensing electrodes) to determine whether a match with the desired or target response has been found. However, a reduced number of sensing electrodes may not guarantee the capture of ER features of interest, such as peak amplitude at the sensing location. A smaller ER dataset may also affect the accuracy and robustness of the estimation of the spatial distribution of the ER. This document describes systems and methods for fitting reduced ER recordsets to mathematical models and providing recommendations for lead placement or device programming based on a comparison between the fitted model or model parameters or features and acceptance criteria, so that the model or model parameters or features are more favorable compared to the acceptance criteria. This topic can improve the efficiency of ER-based lead placement and device programming using less ER data.

[0055] This disclosure relates to ERNA targets of DBS (such as those used to treat Parkinson's disease) as a non-limiting example of an ER receiving electrical stimulation provided by an electrical stimulator. This subject matter can also be applied to other ERs receiving other electrical stimulation. In some examples, the electrical stimulation may be therapeutic in nature, and in others, diagnostic in nature.

[0056] Figure 1 An electrical stimulation system 100 for DBS delivery is illustrated by way of example, not limitation. The electrical stimulation system 100 typically includes one or more (two illustrated) implantable neural modulation leads 101, a waveform generator such as an implantable pulse generator (IPG) 102, an external remote controller (RC) 103, a clinician programmer (CP) 104, and an external trial modulator (ETM) 105. The IPG 102 may be physically connected to one or more neural modulation leads 101 via one or more percutaneous lead extensions 106, which carry a plurality of electrodes 116. When implanted in a patient, the electrodes form an electrode arrangement. As shown, the neural modulation lead 101 may be a percutaneous lead, with the electrodes arranged in a row along the neural modulation lead or in a circle around the neural modulation lead. Any suitable number of neural modulation leads (including only one) may be provided, as long as the number of electrodes is greater than two (including the IPG shell function as shell electrodes) to allow for lateral current redirection. Alternatively, surgical paddle wires may be used instead of one or more percutaneous leads. IPG 102 includes a pulse generation circuit that delivers electrically modulated energy to the electrodes in the form of pulsed electrical waveforms (i.e., a time sequence of electrical pulses) based on a set of modulation parameters.

[0057] The ETM 105 can also be physically connected to one or more neural modulation leads 101 via a percutaneous lead extension 107 and an external cable 108. The ETM 105 may have a pulse generation circuit similar to that of the IPG 102 to deliver electrically modulated energy to the electrodes according to a set of modulation parameters. The ETM 105 is a non-implantable device that can be used experimentally to test the responsiveness of the modulation to be provided after the neural modulation leads 101 have been implanted and before the IPG 102 has been implanted. The functionality described herein with respect to the IPG 102 can also be performed with respect to the ETM 105.

[0058] RC 103 can be used to telemetry control ETM 105 via bidirectional RF communication link 109. RC 103 can also be used to telemetry control IPG 102 via bidirectional RF communication link 110. This control allows IPG 102 to be turned on or off and programmed with different sets of modulation parameters. IPG 102 can also be operated to modify the programmed modulation parameters, thereby actively controlling the characteristics of the electrically modulated energy output by IPG 102. Clinicians can use CP 104 to program modulation parameters into IPG 102 and ETM 105 in the operating room and during follow-up.

[0059] CP 104 can communicate indirectly with IPG 102 or ETM 105 via IR communication link 111 or another link through RC 103. CP 104 can also communicate directly with IPG 102 or ETM 105 via RF communication link or other links (not shown). The detailed modulation parameters provided by CP 104 can also be used to program RC 103, allowing subsequent modification of the modulation parameters through operation of RC 103 in stand-alone mode (i.e., without the assistance of CP 104). Various devices can be used as CP 104. Such devices can include portable devices such as laptops, minicomputers, personal digital assistants (PDAs), tablets, mobile phones, or remote controllers (RCs) with extended capabilities. Therefore, programming methods can be performed by executing software instructions contained in CP 104. Alternatively, firmware or hardware can be used to perform such programming methods. In any case, CP 104 can actively control the characteristics of the electrical modulation generated by IPG 102 to allow the determination of desired parameters based on patient feedback or other feedback, and subsequently program IPG 102 with the desired modulation parameters. To allow the user to perform these functions, CP 104 may include user input devices (e.g., a mouse and keyboard) and a programming display screen housed within a housing. Other directional programming devices, such as a trackball, touchpad, joystick, touchscreen, or directional keys included as part of the keys associated with the keyboard, may also be used in addition to or in place of a mouse. External devices (e.g., CPs) can be programmed to provide one or more displays that allow clinicians to select or input patient profile information (e.g., name, date of birth, patient identifier, physician, diagnosis, and address), input procedural information (e.g., programming / follow-up, implantation of experimental systems, implantation of IPGs, implantation of IPGs and one or more leads, replacement of IPGs, replacement of IPGs and leads, replacement or modification of leads, transplantation, etc.), generate patient pain maps, define lead configuration and orientation, initiate and control electrically modulated energy output from neuromodulation leads, and select and program IPGs with modulation parameters (including electrode selection) in surgical and clinical settings. One or more external devices (e.g., CPs and / or RCs) can be configured to communicate with one or more other devices (including one or more local devices and / or one or more remote devices). For example, wired and / or wireless communication can be used for communication between devices or other than devices.

[0060] The external charger 112 can be a portable device used to percutaneously charge the IPG 102 via a wireless link, such as inductive link 113. Once the IPG 102 has been programmed and its power has been charged by the external charger or otherwise replenished, the IPG 102 can operate as programmed in the absence of RC 103 or CP 104.

[0061] Figure 2 IPG 202 in a DBS system is illustrated by way of example, not limitation. IPG 202 is as follows: Figure 1 An example of an IPG 102 in the illustrated electrical stimulation system 100 may include a biocompatible device housing 214 housing circuitry and a battery 215 for powering the operation of the IPG 102, although the IPG 102 may also be battery-free and may be wirelessly powered by an external power source. The IPG 102 may be coupled to one or more leads, such as lead 201 shown herein. Each lead 201 may include multiple electrodes 216 for delivering electrical stimulation energy, recording electrical signals, or both. In some examples, the leads 201 may be rotatable, such that the electrodes 216 can be aligned with target neurons after the neurons have been located, such as based on the recorded signals. The electrodes 216 may include one or more ring electrodes, and / or one or more rows of segmented electrodes (or any other combination of electrodes), examples of which will be referenced below. Figure 3A and Figure 3B Let's have a discussion.

[0062] The lead 201 can be implanted near or inside the desired site of stimulation in the body. In an example of DBS operation, access to the desired location in the brain can be achieved by drilling holes in the patient's skull or cranial bone with a skull drill (often called a burr) and coagulating and cutting the dura mater or brain covering. The lead can then be inserted into the skull and brain tissue with the aid of a probe (not shown). For example, a stereotactic frame and a micro-motor system can be used to guide the lead to the target location within the brain. In some examples, the micro-motor system can be fully or partially automated. The micro-motor system can be configured to perform actions such as inserting, advancing, rotating, or retracting the lead.

[0063] The wires 217 within the leads can be coupled to electrodes 216 and proximal contacts 218 of a lead connector 219, which is secured in a connector 220 on the IPG 202. This connector may include, for example, epoxy resin. Alternatively, the proximal contacts 218 can be connected to a lead extension (not shown), which is then inserted into the lead connector 219. Once inserted, the proximal contacts 218 connect to connector contacts 221 within the lead connector 219, which are then coupled via a housing feedthrough 223 and feedthrough pins 222 to stimulation circuitry 224 within the housing 214. The type and number of leads and electrodes in the IPG may vary depending on the application.

[0064] IPG 202 may include an antenna 225 that allows it to communicate bidirectionally with multiple external devices. Antenna 225 may be a conductive coil within housing 214, although the coil of antenna 225 may also be present in connector 220. When antenna 225 is configured as a coil, communication with external devices can be achieved using near-field magnetic induction. IPG 202 may also include a radio frequency (RF) antenna. This RF antenna may include a patch, slot, or wire and may operate as a monopole or dipole antenna, preferably using far-field electromagnetic waves for communication, and may operate according to any number of known RF communication standards such as Bluetooth, Zigbee, WiFi, Medical Implant Communication System (MICS), and the like.

[0065] In DBS applications, such as those used to treat tremors in Parkinson's disease, the IPG 202 is typically implanted below the patient's clavicle (cervical bone). A lead 201 (possibly extending via a lead extension, not shown) is tunneled through the neck and scalp and below it, with electrodes 216 implanted through holes drilled into the skull and positioned, for example, in the subthalamic nucleus (STN) in each hemisphere. The IPG 202 can also be implanted under the scalp closer to the electrode implantation site. In other solutions, the lead 201 or its extension can be integrally formed with and permanently attached to the IPG 202.

[0066] Stimulation in the IPG 202 is typically provided by pulses, each pulse may include one or more phases. For example, a unipolar stimulation current may be delivered between a lead-based electrode (e.g., one of electrodes 216) and a housing electrode. A bipolar stimulation current may be delivered between two lead-based electrodes (e.g., two of electrodes 216). Stimulation parameters typically include current amplitude (or voltage amplitude V); frequency; pulse width of the pulse or its respective phase; the electrode selected to provide stimulation; and the polarity of such selected electrodes, i.e., whether they act as anodes to source current into the tissue or as cathodes to sink current into the tissue. Each of the electrodes may be used (active electrode) or not used (off). When an electrode is used, it may be used as an anode or cathode and carry an anodic or cathode current. In some cases, the electrode may be an anode for a period of time and a cathode for a period of time. These, and possibly other stimulation parameters employed, collectively constitute a stimulation program that the stimulation circuitry 224 in the IPG 202 may execute to provide therapeutic stimulation to the patient.

[0067] In some examples, the measuring device, coupled to the muscle or other tissue stimulated by the target neuron, or the unit responding to the patient or clinician, can be coupled to the IPG 202 or a micro-drive motor system. The measuring device, user, or clinician can instruct the target muscle or other tissue to respond to stimulation or one or more recording electrodes to further identify the target neuron and facilitate the localization of one or more stimulating electrodes. For example, if the target neuron is pointed at a muscle experiencing a tremor, the measuring device can be used to observe the muscle and indicate, for example, changes in tremor frequency or amplitude in response to neuronal stimulation. Alternatively, the patient or clinician can observe the muscle and provide feedback.

[0068] Figures 3A-3B Leads that can be coupled to the IPG to deliver electrical stimulation, such as DBS, are shown as examples rather than limitations. Figure 3A A lead 301A is shown having an electrode 316A arranged at least partially around the circumference of the lead 301A. The electrode 316A can be positioned along the distal portion of the lead. As shown herein, the electrode 316A is a ring electrode spanning 360 degrees around the circumference of the lead 301A. The ring electrode allows current to be projected uniformly from the electrode's location in every direction and typically cannot direct the stimulation current only from a specific angular location or a limited angular range around the lead. A lead consisting only of a ring electrode can be referred to as a non-directional lead.

[0069] Figure 3B A lead 301B is shown having electrodes 316B including ring electrodes (such as E1 at the proximal end and E8 at the distal end). Furthermore, the lead 301 also includes multiple segmented electrodes (also called split-ring electrodes). For example, an assembly of segmented electrodes E2, E3, and E4 is positioned circumferentially in a longitudinal location, each electrode spanning less than 360 degrees around the lead axis. In the example, each of electrodes E2, E3, and E4 spans 90 degrees, with each electrode spaced 30 degrees apart from the others. Another assembly of segmented electrodes E5, E6, and E7 is positioned circumferentially in a different longitudinal location than the segmented electrodes E2, E3, and E4. The segmented electrodes (such as E2-E7) can direct stimulation current to a selected angular range around the lead.

[0070] Segmented electrodes typically provide better current redirection than ring electrodes because the target structure in DBS or other stimuli is typically not symmetrical about the axis of the distal electrode array. Instead, the target can be located on one side of a plane passing through the axis of the lead. By using a radially segmented electrode array, current redirection can be performed not only along the length of the lead but also around the circumference of the lead. This provides precise three-dimensional targeting and delivery of current stimulation to the neural target tissue while potentially avoiding stimulation of other tissues. In some examples, segmented electrodes can be used in conjunction with ring electrodes. A lead that includes at least one or more segmented electrodes can be referred to as a directional lead. In one example, all electrodes on the directional lead can be segmented electrodes. In another example, there can be a different number of segmented electrodes at different longitudinal locations.

[0071] Segmented electrodes can be divided into several rows of segmented electrodes, with each set of electrodes positioned around a circumference at a specific longitudinal location on the directional lead. The directional lead can have any number of segmented electrodes in a given set of segmented electrodes. As an example, and not a limitation, a given set can include any number between 2 and 16 segmented electrodes. In the example, all rows of segmented electrodes can contain the same number of segmented electrodes. In another example, a set of segmented electrodes can include a different number of electrodes than at least other sets of segmented electrodes.

[0072] The size and shape of the segmented electrodes can vary. In some examples, the segmented electrodes all have the same size, shape, diameter, width, or area, or any combination thereof. In some examples, the size and shape of the segmented electrodes in each circumferential group (or even all segmented electrodes arranged on the lead) can be identical. Several rows of segmented electrodes can be positioned at irregular or regular intervals along the length of lead 201.

[0073] Figure 4 A computing device 426 for programming or controlling the operation of an electrical stimulation system 400 is shown by way of example and not limitation. The computing device 426 may include a processor 427, a memory 428, a display 429, and an input device 430. Optionally, the computing device 426 may be connected to the electrical stimulation system 400 (such as...) Figure 1 The system 100 is separate from and communicatively coupled to the electrical stimulation system 400. Alternatively, the computing device 426 can be integrated with the electrical stimulation system 100, such as... Figure 1 A portion of IPG102, RC 103, CP 104, or ETM 105 shown. The computing device can be used to perform one or more processes for sensing one or more parameters.

[0074] Computing device 426 (also known as a programming device) can be a computer, tablet, mobile device, or any other suitable device for processing information. Computing device 426 can be user-local or may include computer-non-local components, including one or both of processor 427 and memory 428 (or portions thereof). For example, a user can operate a terminal connected to a non-local processor or memory. The functionality associated with computing device 426 can be distributed among two or more devices, such as two or more storage devices performing storage functions, two or more processors performing processing functions, two or more displays performing display functions, and / or two or more input devices performing input functions. In some examples, computing device 406 may include a watch, wristband, smartphone, etc. Such a computing device can be integrated with other components of an electrical stimulation system (such as...). Figure 1 The computing device 426 (shown as CP 104, RC 103, ETM 105, or IPG 102) communicates wirelessly. The computing device 426 can be used to collect patient information (such as general activity levels) or present queries or tests to the patient to identify or rate pain, depression, stimulation effects or side effects, cognitive abilities, etc. In some examples, the computing device 426 may prompt the patient to perform cognitive ability tests periodically (e.g., daily) to monitor, for example, Alzheimer's disease. In some examples, the computing device 426 may detect or otherwise receive the patient's clinical response to electrical stimulation such as DBS as input and determine or update stimulation parameters based on the patient's clinical response using a closed-loop algorithm. Examples of patient clinical responses may include physiological signals (e.g., heart rate) or motor parameters (e.g., tremor, stiffness, bradykinesia). The computing device 426 may communicate with the CP 104, RC 103, ETM 105, or IPG 102 and direct changes in stimulation parameters to one or more of these devices. In some examples, the computing device 426 may be a wearable device used by the patient only during programming sessions. Alternatively, the computing device 426 can be worn continuously and the stimulation parameters adjusted periodically. In the example, the closed-loop algorithm for determining or updating the stimulation parameters can be implemented in a mobile device (such as a smartphone) connected to the IPG or evaluation device (e.g., a wristband or watch). These devices can also record information and send it to the clinician.

[0075] Processor 427 may include one or more processors, which may be user-local, user-external, or other components of computing device 426. Stimulation settings (e.g., parameter sets) include electrode configurations and values ​​for one or more stimulation parameters. Electrode configurations may include information about electrodes (ring electrodes and / or segmented electrodes) selected to be active (on) or inactive (off) for delivering stimulation, the polarity of the selected electrodes, electrode positions (e.g., longitudinal position of a ring electrode along the length of a non-directional lead, or longitudinal and angular position of a segmented electrode at the longitudinal position of a directional lead on the circumference), stimulation modes (such as monopolar pacing or bipolar pacing), etc. Stimulation parameters may include, for example, current amplitude values, fractionalization of current between electrodes, stimulation frequency, stimulation pulse width, etc.

[0076] Processor 427 can identify or modify stimulation settings through an optimization process until search criteria are met, such as until an optimal, desired, or acceptable clinical response in the patient is achieved. Electrical stimulation programmed with the settings can be delivered to the patient, clinical effects (including therapeutic effects and / or side effects, or motor symptoms such as bradykinesia, tremor, or rigidity) can be detected, and the clinical response can be assessed based on the detected clinical effects. When actual electrical stimulation is performed, these settings can be referred to as tested settings, and the clinical response can be referred to as tested clinical response. In contrast, for settings where electrical stimulation is not delivered to the patient, clinical effects can be predicted using a computational model, at least based on clinical effects detected from tested settings, and the predicted clinical effects can be used to estimate the clinical response. When electrical stimulation is not delivered, this setting can be referred to as predicted or estimated settings, and the clinical response can be referred to as predicted or estimated clinical response.

[0077] In various examples, some of the functionality of processor 427 may be implemented as part of a microprocessor circuit. The microprocessor circuit may be a special-purpose processor, such as a digital signal processor, an application-specific integrated circuit (ASIC), a microprocessor, or other type of processor for processing information. Alternatively, the microprocessor circuit may be a processor that receives and executes a set of instructions that perform the functions, methods, or techniques described herein.

[0078] Memory 428 may store instructions executable by processor 427 to perform various functions, including, for example, determining a narrowed or restricted electrode configuration and parameter search space (also referred to as a “restricted search space”), creating or modifying one or more stimulation settings within the restricted search space, etc. Memory 428 may store the search space, stimulation settings (including “tested” stimulation settings and “predicted” or “estimated” stimulation settings), clinical effects (e.g., therapeutic effects and / or side effects), and clinical responses to the settings.

[0079] Memory 428 may be a computer-readable storage medium, including, for example, non-volatile, non-transitory, removable, and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Examples of computer-readable storage media include RAM, ROM, EEPROM, flash memory or other storage technologies, CD-ROM, digital versatile disc (“DVD”) or other optical storage, magnetic tape cassettes, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible by a computing device.

[0080] Communication methods provide another type of computer-readable medium; namely, a communication medium. A communication medium typically contains computer-readable instructions, data structures, program modules, or additional data in modulated data signals (such as carrier waves, data signals, or other transmission mechanisms), and includes any information delivery medium. The terms "modulated data signal" and "carrier signal" include signals that set or alter one or more of their characteristics in a manner that encodes information, instructions, data, etc., in the signal. As examples, communication media include wired media (such as twisted-pair cables, coaxial cables, optical fibers, waveguides, and other wired media) and wireless media (such as acoustic, RF, infrared, Bluetooth, near-field communication, and other wireless media).

[0081] Display 429 can be any suitable display or presentation device, such as a monitor, screen, display, etc., and may include a printer. Display 429 may be part of a user interface configured to display information about stimulus settings (e.g., electrode configuration and stimulus parameter values ​​and ranges) and user control elements for programming stimulus settings into the IPG. Computing device 426 may include one or more other outputs, such as one or more speakers and one or more tactile outputs (e.g., vibration motors).

[0082] Input device 430 can be, for example, a keyboard, mouse, touchscreen, trackball, joystick, voice recognition system, or any combination thereof. Another input device 430 can be a camera that allows a clinician to observe the patient. Yet another input device 430 can be a microphone through which the patient or clinician can provide responses or queries.

[0083] The electrical stimulation system 400 may include, for example Figure 1 Any of the components shown. The electrical stimulation system 400 may communicate with the computing device 426 via a wired or wireless connection, or additionally or alternatively, a user may provide information between the electrical stimulation system 400 and the computing device 426 using a computer-readable medium or some other mechanism.

[0084] Figure 5 An example of an electrotherapy delivery system is illustrated by way of example. The illustrated system 531 includes an electrotherapy device 532 configured to deliver electrotherapy to electrodes 533 according to a programmed set of parameters 534 for the therapy to treat a disease. System 531 may include a programming system 535, which may be used as at least part of a processing system that may include one or more processors 536 and a user interface 537. The programming system 535 can be used to program and / or evaluate one or more sets of parameters for the delivery of the therapy. The illustrated system 531 may be a DBS system.

[0085] In some embodiments, the system 531 shown may include an SCS system for treating pain and / or a system for monitoring pain. As an example, the therapeutic goal for conventional SCS programming may be to maximize stimulation (i.e., recruitment) of dorsal column (DC) fibers that extend in the white matter along the longitudinal axis of the spinal cord and minimize stimulation of other fibers (e.g., dorsal root fibers) that extend perpendicular to the longitudinal axis of the spinal cord.

[0086] Therapies can be delivered based on a set of parameters. This set of parameters can be programmed into a device to deliver a specific therapy using specific values ​​for multiple therapy parameters. For example, therapy parameters controlling a therapy may include pulse amplitude, pulse frequency, pulse width, and electrode configuration (e.g., selected electrodes, polarity, and fractionalization). The parameter set includes specific values ​​for the therapy parameters. The number of available electrodes, combined with the ability to generate various complex electrical waveforms (e.g., pulses), provides clinicians or patients with a vast selection of modulation parameter sets. For example, if a neural modulation system to be programmed has 16 electrodes, millions of modulation parameter sets are available for programming into the neural modulation system. To facilitate this selection, clinicians typically program the modulation parameter set via a computer programming system to allow the determination of optimal parameters based on patient feedback or other means, and subsequently program the desired modulation parameter set.

[0087] Figure 6 The use of implantable medical devices (IMDs) is illustrated as an example, not a limitation. Figure 5 The system 631 shown includes a monitoring system and / or an electrotherapy delivery system. The external system 638 may include at least one programming device. The external system 638 shown may include a clinician programmer 604 (similar to...). Figure 1 CP 104 (in which), is configured for use by clinicians to communicate with and program the neural modulator; and remote control device 603 (similar to Figure 1The remote control device 603 (RC 103) is configured for patient use to communicate with and program the neural modulator. For example, the remote control device 603 may allow the patient to turn the therapy on and off, change or select programs, and / or may allow the patient to adjust one or more patient-programmable parameters among a plurality of modulation parameters. Figure 6 IMD 639 is shown, although the monitor and / or therapeutic device can be an external device such as a wearable device. External system 638 can include a network of computers, including one or more computers located remotely from IMD 639 capable of communicating with programmer 604 and / or remote control device 603 via one or more communication networks. One or more remotely located computers and IMD 639 can be configured to communicate with each other via another external device, such as programmer 604 or remote control device 603. Remote control device 603 and / or programmer 604 can allow users (e.g., patients and / or clinicians or representatives) to answer questions as part of a data collection process. External system 638 can include personal devices such as telephones or tablets 640, wearable devices such as watches 641, sensors, or therapeutic application devices. Watches can include one or more sensors, such as one or more sensors for detecting activity, movement, and / or posture. One or more other wearable sensors can be configured to detect patient activity, movement, and / or posture. External system 638 can include, but is not limited to, telephones and / or tablets. Notifications can be sent to patients, doctors, device representatives, or other users via external systems and through remote portals provided by remote systems (e.g., web-based portals).

[0088] Figure 7 An example of a neuromodulation system 700 is shown, configured to provide guided implantation (e.g., lead placement) and device programming based on modeling of the spatial distribution of evoked responses (ER) to electrical stimulation. System 700 includes sensing circuitry 710, controller circuitry 720, storage device 730, electrical stimulator 740, and user interface 750. Parts of system 700 may be implemented in IPG 102 or CP 104.

[0089] Sensing circuitry 710 can be operatively connected to one or more leads and associated electrodes (such as loop electrodes or segmented electrodes on non-directional leads 301A or directional leads 301B). Loop electrodes and / or segmented electrodes can also be electrically coupled to electrical stimulator 740. Loop electrodes and / or segmented electrodes can be configured as sensing electrodes for sensing ER, or as stimulating electrodes for delivering electrical stimulation pulses. Sensing circuitry 710 can sense ER from one or more sensing electrodes on leads placed at a target tissue (e.g., STN) in patient 701 in response to electrical stimulation pulses delivered from stimulating electrodes at a stimulation site (e.g., a brain target).

[0090] The ER can be sensed according to the stimulation sensing electrode configuration 712. Figure 8 An example of a stimulation sensing electrode configuration 712 is shown, graphically represented by an ER sensing map 800 comprising a two-dimensional (2D) array, wherein the sensing electrodes are indexed on a horizontal axis and the stimulation electrodes are indexed on a vertical axis. Both the sensing and stimulation electrodes are selected from electrodes on a portion of the DBS lead. By way of example and not limitation, the electrodes include ring electrodes 804A (electrode “T1”) and 804B (electrode “T4”) and two rows of segmented electrodes 805A and 805B, both arranged in rows along the DBS lead. Each of the two rows of segmented electrodes 805A and 805B includes three segmented electrodes (T2a, T2b, and T2c in 805A, and T3a, T3b, and T3c in 805B) arranged circumferentially around the DBS lead. Other numbers of segmented electrodes may be included in one or more rows along the lead. In some examples, the sensing or stimulation electrodes may be selected from electrodes not on the DBS lead. For example, at least some of the sensing or stimulation electrodes may be selected from skin patch electrodes. Other terms and methods used to describe evoking and recording electrodes, including those involving multiple evoking electrodes with proportional or fractional currents, or multiple independent current controls (MICCs), can be used to generate precise controls to refine the size and shape of the stimulation field, designed for a customized therapy for an individual patient.

[0091] As shown in ER sensing diagram 800, the diagonal elements in the 2D array represent "diagonal" sensing configurations, where the same electrodes are used to deliver electrical stimulation pulses and to record the ER for that electrical stimulation in the same stimulation session. Such electrodes are also referred to as "diagonal" sensing electrodes, and the ER sensed from them is called "diagonal" ER 810. The off-diagonal elements in the 2D array represent "off-diagonal" sensing configurations, where different electrodes are used to deliver electrical stimulation pulses and to record the ER for that electrical stimulation in the same stimulation session. These sensing electrodes are also referred to as "off-diagonal" sensing electrodes, and the ER sensed from them is called "off-diagonal" ER 820. ER can be recorded in multiple stimulation ER recording sessions. For example, when an electrical stimulation pulse is delivered from electrode T1, "diagonal" ER can be recorded from electrode T1, and "off-diagonal" ER can be recorded from one or more of the remaining electrodes (T2a-Tac, Ta3a-T3c, and T4). Stimulation ER recording sessions can be repeated when stimulation is delivered from other electrodes. For example, in response to a stimulus delivered at electrode T2b, an extra-diagonal ER 822 is recorded from sensing electrode T4. The ER recorded according to the ER sensing map 800 can then be analyzed to determine the spatial distribution of the ER, which can then be used to guide lead placement and device programming, as will be discussed further below.

[0092] like Figure 8 As depicted, recording ER from a complete ER sensing map (i.e., including both "on-diagonal" and "off-diagonal" configurations) can be time-consuming and consume significant system resources and storage space. Furthermore, while "on-diagonal" ERs at the stimulation site can provide useful information about ER distribution for identifying matches with the desired or target response, they are prone to stimulation artifacts strong enough to affect the ER component of interest. Incorporating "on-diagonal" ERs into ER analyses (such as spatial distribution estimation) can introduce errors and degrade the quality of ER-based lead placement and / or device programming. To avoid this effect, in the example, sensing circuitry 710 can be configured to sense ERs only from "off-diagonal" electrodes, not from "on-diagonal" electrodes, and only "off-diagonal" ERs are used to determine the spatial distribution of ERs and to guide lead placement and device programming.

[0093] As an alternative to using an "outside-diagonal" sensing configuration that employs all available electrodes except the stimulation electrode used for ER sensing, Figures 9A-9D and Figure 10 An example of a stimulation-sensing electrode configuration is shown, which pertains to a selected subset, not all, of the available electrodes for ER sensing. The selected subset may include off-diagonal electrodes having a specific proximity or geometric relationship to the stimulation electrode used to deliver electrical stimulation pulses. Such an ER sensing configuration is also referred to as a “nearest neighbor” configuration. In the example, the nearest neighbor configuration includes two or more sensing electrodes directly adjacent to the stimulation electrode on the lead. This is intended as an example and not a limitation. Figure 9A The ER is shown in response to electrical stimulation delivered at the stimulating electrode T1, sensed only from the three nearest neighboring electrodes T2a, T2b and T2c of a row of segmented electrodes 805A. Figure 9B The ER is shown in response to electrical stimulation delivered at the stimulating electrode T2a, sensed only from the four nearest neighboring electrodes, including T2b and T2c of a row of segmented electrodes 805A, T3a of a row of segmented electrodes 805B, and the ring electrode T1. Figure 9C The ER is shown in response to electrical stimulation delivered at the stimulating electrode T2b, sensed only from the four nearest neighboring electrodes, including T2a and T2c of a row of segmented electrodes 805A, T3b of a row of segmented electrodes 805B, and the ring electrode T1. Figure 9DThe diagram illustrates the ER sensed only from the four nearest neighbor electrodes comprising a row of segmented electrodes 805A (T2a and T2b), a row of segmented electrodes 805B (T3c), and a ring electrode (T1) in response to electrical stimulation delivered at the stimulating electrode T2c. The aforementioned stimulation ER recording session continues with stimulation at electrodes T3a, T3b, T3c, and T4, and recording is performed at the corresponding three or four nearest neighbor “off-diagonal” electrodes. Figure 10 An ER sensing diagram 1000 is shown, wherein, relative to, Figures 9A-9D The “nearest neighbor” electrode sensing ER 1020 for each stimulation electrode is depicted in the diagram. Similar to ER sensing diagram 800, the “diagonal” ER 1010 can be excluded from the ERs used to estimate the spatial distribution of the ER and to guide lead placement and device programming.

[0094] Controller circuitry 720 may include a circuitry that includes one or more other circuits or sub-circuits (such as signal processor 722 and therapy controller 728). Signal processor 722 may also include filter circuitry 724 and signal analyzer circuitry 725. The circuits or sub-circuits may individually or in combination perform the functions, methods, or techniques described herein. In the example, the hardware of the circuitry may be invariably designed to perform a specific operation (e.g., hardwired). In the example, the hardware of the circuitry may include variable-connected physical components (e.g., execution units, transistors, simple circuits, etc.) including computer-readable media that are physically modified (e.g., invariably aggregated magnetic ground, electric ground, movable placement, etc.) to encode instructions for a specific operation. When the physical components are connected, the underlying electrical characteristics of the hardware configuration, for example, change from an insulator to a conductor, and vice versa. Instructions enable embedded hardware (e.g., execution units or loading mechanisms) to create components of the circuitry in the hardware via variable connections to perform portions of a specific operation during operation. Thus, when the device is operating, the computer-readable medium can communicatively couple to other components of the circuitry components. In the example, any physical component can be used in more than one component of more than one circuit set. For example, in operation, an execution unit can be used at one point in time in a first circuit of a first circuit set and reused by a second circuit in the first circuit set, or reused at a different time by a third circuit in a second circuit set.

[0095] In various examples, a portion of the functionality of the controller circuit 720 may be implemented as part of a microprocessor circuit. The microprocessor circuit may be a special-purpose processor, such as a digital signal processor, an application-specific integrated circuit (ASIC), a microprocessor, or other type of processor for processing information including bodily activity information. Alternatively, the microprocessor circuit may be a general-purpose processor that can receive and execute a set of instructions that performs the methods or techniques described herein.

[0096] Filter circuit 724 may include filters or a filter library to filter the recorded ER signal. Signal analyzer circuit 725 may extract signal features from the filtered ER signal. Examples of ER features may include signal amplitude, magnitude, peak value, value range, signal curve length, or signal power or RMS value of the ER signal within a time window, such as epoch-averaged ER. The signal amplitude range or value range (also known as peak-to-peak (P2P) value) may be measured as the difference between the dominant peak value in the sensed evoked response or the maximum or minimum value of the epoch-averaged evoked response within the time window (also known as the "maximum P2P" amplitude). Alternatively, the P2P value may be measured as the difference between a negative peak (trough) and the immediately following positive peak value (also known as the "N1-P2 P2P" amplitude). The signal curve length may be measured as the cumulative difference in signal values ​​of the sensed evoked response (or epoch-averaged evoked response) within a continuous unit of time (e.g., a continuous data sampling interval) within the time window. Signal power can be measured as the area under the curve (AUC) of the sensed evoked response (or epoch-average evoked response) within a time window. In some examples, the signal analyzer circuit 725 can generate sensing electrodes (such as...) Figure 10 The spatial distribution of extracted signal features at the sensing locations of the selected subset of sensing electrodes shown.

[0097] Signal analyzer circuitry 725 can fit ER features to one or more ER distribution models 726, which represent the spatial distribution of ER features on sensing electrodes selected based on ER sensing map 800 (i.e., an "outside-diagonal" configuration including only the out-of-diagonal electrodes) or ER sensing map 1000 (i.e., an "nearest neighbor" configuration including a subset of electrodes within a specific proximity distance of the stimulating electrodes). One or more ER distribution models 726 may include parametric models such as Gaussian distribution (also known as normal distribution) models, periodic or wrapped Gaussian distribution models, exponential distribution models, Poisson distribution models, Weibull distributions, etc. One or more ER distribution models 726 may include regression models such as linear regression or logistic regression models, etc. One or more ER distribution models 726 may include nonparametric models such as decision trees, K-nearest neighbor models, support vector machines (e.g., with Gaussian kernels) or artificial neural networks, and various other machine learning models.

[0098] Signal analyzer circuit 725 can determine one or more model features or parameters 727 from one or more fitted distribution models 726. In an example where ER features are fitted to a Gaussian model, model features or parameters 727 may include one or more of the mean or standard deviation of the ER features. In another example, model features or parameters 727 may include morphological or statistical features of one or more fitted distribution models 726, such as the magnitude, spatial location, or width of the peaks of the fitted model within a range defined by multiple sensing locations. In yet another example, model features or parameters 727 may include one or more of the positive peak amplitude (or local maximum) or negative peak amplitude (or local minimum) of the fitted model within a range defined by multiple sensing locations. In yet another example, model features or parameters 727 may include composite features, such as the ratio of the positive peak amplitude to the negative peak amplitude of the fitted model within a range defined by multiple sensing locations.

[0099] Signal analyzer circuitry 725 can compare one or more ER distribution models 726 or model features or parameters 727 with one or more acceptance criteria 732 to determine if a match with the expected or target response can be found. In some examples, acceptance criteria are set, modulated, examined, and accepted by a clinical user, including before or during the procedure. In some examples, signal analyzer circuitry 725 can accumulate sensed ER from multiple stimulation ER recording sessions during which stimulation pulses are delivered via specific stimulation electrodes with varying stimulation parameter settings (e.g., stimulation amplitude, frequency, or pulse width), determine one or more ER distribution models 726 or model features or parameters 727 from the accumulated ER, and compare one or more ER distribution models 726 or model features and parameters 727 with acceptance criteria 732. Acceptance criteria 732 can be provided by the user, such as via user interface 750. Alternatively, acceptance criteria 732 can be predetermined and stored in a storage device 730 accessible to signal analyzer circuitry 725. In the example, acceptance criterion 732 is the acceptance limits (e.g., upper and lower limits, location limits, attribute limits such as the presence, absence, or value of a feature) of the user-provided model feature or parameter 727. In another example, acceptance criteria include a target ER distribution template that represents a patient-specific ER distribution or a population-based ER distribution. In the example, a target ER template can be selected to alleviate symptoms or for other goals, such as lead placement for disease-improving therapies or combination therapies (e.g., lead for injecting drugs or light), and to avoid side effects. In some examples, acceptance criteria may include one of multiple candidate ER templates indexed by region, clinical institution, group, participant information, implanter information, or symptom relief target, and the target ER template may be selected from multiple candidate ER templates based at least in part on one or more of the following: identification of the institution, group, or participant information where the patient was implanted or received electrical stimulation treatment, identification of the implanter of the lead, or indications of sensed symptom relief in the patient. See below. Figure 13-15 Examples of ER signal features, ER templates, and spatial distribution of ER signal features are discussed.

[0100] The therapy controller 728 can generate control signals for the electrical stimulator 740 to adjust the neuromodulation therapy based on selected signal characteristics. The electrical stimulator 740 can be configured to deliver electrical stimulation according to stimulation settings. The electrical stimulation can be delivered using a monopolar (far field) or bipolar (near field) configuration. Examples of therapy setups may include electrode selection and configuration, stimulation parameter values ​​(including, for example, amplitude, pulse width, frequency, pulse waveform, active or passive charging mode, on-time, off-time, therapy duration, and fractionalization). In the example, therapy controller 728 may be implemented as a proportional-integral (PI) controller, a proportional-integral-derivative (PID) controller, or other suitable controller that uses a comparison of the sensed ER (or its characteristics or distribution) with acceptance criterion 752 as feedback for adjusting the stimulation setup. The data type and the recording used to generate the data type may vary depending on the acceptance criterion and the type of operation employed. For example, the ER data used to drive decisions about electrode selection and configuration may differ from the data and evoked / recorded configuration used for comparison with the acceptance criterion and as control signals for amplitude adjustment. One ER measurement may be used to inform lead positioning (e.g., by sweeping a non-therapeutic sampling pulse across the space of the lead electrode), and another ER measurement may be used to determine or adjust stimulation parameters (e.g., by sweeping a therapeutic sampling pulse across the amplitude).

[0101] The electrical stimulator 740 may be an implantable module, such as one integrated within the IPG 10. Alternatively, the electrical stimulator 740 may be an external stimulation device, such as one integrated with the ETS 40. In some examples, the user may have the option to send notifications for therapy reminders (e.g., sent with the patient to the RC 45 or a smartphone), or to automatically initiate or adjust neuromodulation therapy according to the adjusted therapy settings. If automatic therapy initiation is selected, the electrical stimulator 740 can deliver stimulation according to the adjusted therapy settings.

[0102] In some examples, the therapy controller 728 may generate suggestions to the user to reposition the lead or adjust device settings (e.g., programmable parameters of the electrical stimulator 740). During implantation, lead repositioning or device setting adjustments may align the sensed ER with or be more favorable than a acceptance criterion (e.g., an ER template). In some examples, the therapy controller 728 may determine or modify the treatment stimulation settings based on the sensed ER or its characteristics or the distribution of those characteristics. The electrical stimulator 740 may deliver treatment stimulation (e.g., DBS) based on the determined or modified treatment stimulation settings.

[0103] In some embodiments, the display may provide the user with suggestions for adjusting stimulation parameters to make the self-generated response more favorable than the acceptance criteria (e.g., ER templates). This suggestion may be displayed on a user interface 750. The user interface 750 may be a portable (e.g., handheld) device, such as an RC 45, or a smartphone (with an executable software application), which the patient can operate at home without additional clinic visits or consultation with a device specialist. In another example, the user interface 750 may be a programmer device, such as a CP 50. In addition to suggestions for lead replacement, the user interface 750 may also display other information, by way of example and not limitation, including one or more of the following: sensed ER (e.g., including before and / or after filtering), ER characteristics, distribution of ER characteristics, acceptance criteria (e.g., one or more ER templates), or a comparison between sensed ER and acceptance criteria.

[0104] In some examples, the user interface 750 allows physicians to remotely view therapy settings and treatment history, consult with patients to obtain information including pain relief and SCS-related side effects or symptoms, perform remote programming of the electrical stimulator 740, or provide patients with additional treatment options. The user interface 750 can allow users (e.g., patients, physicians managing patients, or device specialists) to view, program, or modify device settings. For example, a user can use one or more user interface (UI) control elements to provide or adjust values ​​for one or more device parameters, or select from a plurality of predefined stimulation programs for future use. Each stimulation program may include a set of stimulation parameters with corresponding predetermined values. In some examples, the user interface 750 may include a display to show textual or graphical information provided by the user via an input unit, as well as device settings including, for example, feature selection, sensing configuration, signal preprocessing settings, therapy settings, and optionally any intermediate calculations. In examples, according to various embodiments discussed in this document, the user interface 750 may present the user with “optimal” or improved therapy settings, such as those determined based on closed-loop or adaptive feedback control of electrical stimulation based on the characteristics of the selected evoked response signals. In some examples, users can use the user interface 750 to provide feedback on neuromodulation therapy, including, for example, the occurrence or persistence of side effects or symptoms associated with nerve stimulation, or the severity of the symptoms or side effects.

[0105] Figure 11 The display 1100 on the user interface is shown by way of example, not limitation, showing a comparison between a user-defined ER target location 1125 and a calculated ER distribution center 1127, which is overlaid on a portion of the electrode configuration on the lead 1120 (including electrodes T1, T2a-T2c, T3a-T3c, and T4), as shown. Figure 8Similarly, as shown in the diagram. The user-defined ER target position 1125 is an example of the acceptance standard 732 and can represent the target ER distribution center. The calculated ER distribution center 1127 can be determined based on the sensed ER distribution, such as that determined by the signal analyzer circuit 725. The sensed ER distribution can be estimated along the longitudinal direction and around the rotation direction, as referenced. Figure 13-15 Further discussion. The distribution of the sensed ER can be depicted as a two-dimensional (2D) hotspot view 1126, which provides indicators of hotspots in the ER distribution (e.g., shown as a heatmap as a color map or grayscale image). The calculated ER distribution center 2127 can represent the peak amplitude of the ER distribution at the peak location, such as the peak of a Gaussian distribution. Based on a comparison of the user-defined ER target location 1125 and the calculated ER distribution center 1127, a suggestion 2128 can be provided to the user to adjust the lead placement, thereby modifying the hotspot view 1126 so that the calculated ER distribution center 1127 corresponds more closely to the user-defined ER target location 1125 (e.g., the calculated ER distribution center 1127 falls within a predetermined proximity distance of the user-defined ER target location 1125). The user-defined ER target location 1125 can be presented on the representation of the lead electrode. In the example shown, the suggestion 1128 is to advance the lead 1120 so that the calculated ER distribution center 1127 is closer to the ER target location. In some examples, the user interface 750 can determine the distance between the calculated ER distribution center 1127 and the user-defined ER target location 1125, and provide said distance to the user. During wire implantation, the calculated ER distribution center 1127 can be updated substantially in real time as the wire position is adjusted. A comparison (including the distance between them) between the user-defined ER target location 1125 and the updated calculated ER distribution center 1127 can be displayed to the user to guide wire implantation.

[0106] Figure 12AThe elements that can be presented on a display in a user interface that provides simple and clear confirmation of lead placement are illustrated by way of example. The target can be specific to anatomy, surgical center, and surgeon. ER targets can be updated, for example, through analysis or machine learning, and patient outcomes can be combined with patient bioelectrical records. The expected hotspot of ER can be represented using an ERNA ellipse 1212, where ERNA is the maximum value (i.e., the peak amplitude of ER). Users can provide some boundaries along lead 1201 to identify what constitutes an acceptable response. These boundaries can be based on prior knowledge, such as, but not limited to, patient data from a group of patients at the center. The illustrated interface can include indicators (e.g., arrows) for suggesting movement based on the relative position of the measured response to the expected response. In the first example 1210, the measured ERNA matches the expected ERNA. An acceptable response is considered achieved, and no adjustment of lead placement is recommended. In examples 1220 and 1230, the measured ERNA does not match the expected ERNA. Movement suggestions can include moving the lead up / down, moving the lead longitudinally, translating the lead body, or rotating the lead. The response is considered acceptable when the sensed ER hotspot is located in the same or nearly the same position as the ER target (within the limits). The response is also considered acceptable when the distribution of the sensed ER is determined to be similar to the distribution of the target. Comparisons of distributions can be based on various statistical measures of the distribution or on the user's visual appearance.

[0107] Figure 12B The example, not a limitation, illustrates the prediction of lead or electrode location relative to target tissue volume based on features derived from an ER distribution model. In the example shown, model features include the ratio of positive peak amplitude to negative peak amplitude (also known as the "maximum / minimum ratio"). The ER distribution model can be generated using ER records from a row of segmented electrodes 1202A, 1202B, and 1202C surrounding the circumference of the lead. In the example, the ER distribution model is a Gaussian distribution model or a periodic or wrapped Gaussian distribution of ER features (e.g., signal amplitude), as will be referred to below. Figure 13The discussion continues. In the first example 1240, the maximum / minimum ratio is close to 1, indicating that the ER characteristic values ​​are approximately the same across segmented electrodes 1202A-1202C. In this case, an acceptable response is considered to have been achieved. The lead is considered correctly positioned, and no adjustment of the lead position is required. In another example 1250, the maximum / minimum ratio is close to 2, indicating that the ER characteristic values ​​differ across segments 1202A-1202C. This could indicate that segments 1202A-1202C are pointing in different directions, and the lead is located within the volume of the target tissue, which produces this ER distribution on the electrode (represented by the ERNA ellipse 1212). In the third example 1260, the maximum / minimum ratio is close to 3. The larger the maximum / minimum ratio, the greater the ER difference between segments 1202A-1202C. This could mean that all segmented electrodes are in undesirable lead positions outside the ERNA ellipse 1212 or the desired hotspot of ER. In some examples, other ER characteristics (different from the maximum / minimum ratio), such as smaller ER distribution peak amplitudes (e.g., below the amplitude threshold), can be used to confirm the inference of segmented electrode locations relative to ERNA ellipse 1212. Recommendations for lead repositioning can be provided to the user.

[0108] Figure 13 The display of a user interface for creating a response fit based on the distribution of ER is shown by way of example, not limitation. The figure shows a longitudinal view of a portion of lead 1391, which includes several rows of segmented electrodes 1392 and ring electrodes 1393. The segmented electrodes 1392 are shown as three electrodes per row (see electrodes a, b, and c in radial figure 1394). This subject matter is not limited to a specific example of a lead with this particular electrode arrangement. Figure 13 The diagram also shows a portion of lead 1391, depicted as 1395, which includes two rows of segmented electrodes (each row comprising three segmented electrodes) arranged along lead 1391 between two annular electrodes. This configuration is similar to... Figure 8 As shown in the illustration. Overlaid on drawing 1395 are a two-dimensional (2D) hotspot view that provides an indicator of the distribution of ER or ER features (e.g., a color thermogram), an oriented drawing 1396 of the distribution of ER features around the angular positions of the segmented electrodes on the circumference of the lead, and a longitudinal drawing 1397 of the distribution of ER features along the longitudinal position of the lead. In the example shown, the ER features include ER amplitudes, which can be calculated as either a “maximum P2P” amplitude or an “N1-P2 P2P” amplitude. As mentioned above regarding... Figure 7Other ER features may be used. As an example, directional plot 1396 shows a wrapped Gaussian distribution of the ER features at angular positions of the segmented electrodes on the circumference of the lead. Longitudinal plot 1397 shows a Gaussian distribution of the ER features at longitudinal positions of the electrodes along the lead. One or more model parameters or features may be derived from each of the ER distributions. In one example, the model parameters may include the positive peak amplitude 1396A or the negative peak amplitude 1396B of the wrapped Gaussian distribution model in directional plot 1396. In another example, the model parameters may include composite parameters, such as the ratio of the positive peak amplitude 1396A to the negative peak amplitude 1396B (also referred to as the “maximum / minimum ratio”), as similar to those described above with reference to FIG12. In yet another example, the model parameters or features may include the amplitude and position of the peak 1397A and the peak width 1397B of the Gaussian distribution in longitudinal plot 1397, representing the mean (and its spatial position) and standard deviation of the ER feature, respectively.

[0109] exist Figure 13 As elsewhere in this application, different patterns are shown in the figures to represent different color regions. Fewer or more colors may be used. The color heatmap may include color gradients. The hotspot view may be overlaid on an electrode template representing the lead electrodes. Distribution depictions 1396 and 1397 may include best-fit lines for plotted values ​​of the sensed ER. Various embodiments may enable the user to set boundaries around a desired target for the distribution, such as boundary 1398 on a longitudinal view of the lead. Arrow 1399 or some other indicator may provide a method to identify the maximum distribution along the longitudinal lead and along the segmented electrodes. The user may select the accept lead location and / or stimulation settings based on the image displayed on the display (e.g., based on arrow 1399 within boundary 1398).

[0110] Various embodiments provide a user interface with a data view that allows a user to set an ER target, sense the ER, and provide an indicator (e.g., a highlight) indicating whether a given sensed response matches the ER target set by the user. The sensed response target may include multiple sensed response values ​​from multiple electrodes that can form a distribution of sensed response values.

[0111] Figure 14An example, not a limitation, is shown of an embodiment for measuring the distribution of ER or ER features, or ER features derived therefrom, and matching it with acceptance criteria (such as a template). Each square in the view of the ERNA measurements and extracted features represents an electrode (or an electrode segment of a segmented electrode with a directional lead). The figure shows two rows of segmented electrodes, not a limitation. Less processed ERNA measurements are shown in Figure 1401. ER measurements can be taken at different locations / electrode vertical positions, or at directional positions (a, b, c) at fixed vertical positions (e.g., T3a, T3b, T3c). During a stimulated ER recording session, further multiple measurements can be taken from one or more electrodes in each set, during which stimulation pulses are delivered via specific stimulation electrodes with varying stimulation parameter settings (e.g., stimulation amplitude, frequency, or pulse width).

[0112] One or more features can be extracted from the raw ERNA measurement data and presented in the extracted feature view 1402. For example, features such as amplitude, magnitude, first peak, width, RMS value, and similar features can be extracted from the raw ER signal. Figure 13 As depicted in Figures 1396 and 1397, the distribution of extracted features can be generated by segmented electrodes at locations such as longitudinal positions along the lead and / or angular positions around the circumference of the lead. The extracted features can be used to create an optimal dot plot or hotspot fit 1403. Hotspot fitting can be color-coded similarly to heat mapping, where more features result in a “hotter” color. Hotspots can correspond to peaks in the distribution of evoked responses (e.g., ERNA). The measured hotspots can be displayed to the user, along with indicators (e.g., highlighting) indicating whether the measured response matches the ER target for the user or center. The display to the user can also include fixed or variable offsets of the hotspots. For example, the measured ER data (feature distribution) or hotspots can be overlaid with a visual frame of the desired hotspot (i.e., the area covered by the institution / template). A match indicates that the desired or correct location is being stimulated. If there is no match, the ER target provides guidance for moving electrodes or adjusting the stimulation setup until the measured ER (sensed distribution) matches the ER target (target distribution). The desired hotspot or target ER distribution can depend on the symptom to be treated (e.g., cognitive or motor function). For example, ER targets for tremor can be located near the STN, while ER targets for bradykinesia can be located higher on the lead. ER targets can be selected from a database of available targets (e.g., a library) to provide an acceptable distribution in different directions. Some methods of eliciting and recording ER may not be conducive to the visualization of “hot spots” in two-dimensional (2D) plots. This does not preclude the use of the techniques described herein.

[0113] Figure 15The ER distribution of the DBS lead electrodes is illustrated by way of example, not limitation. The lead may include two ring electrodes 1504 and two segmented electrodes 1505, each segmented electrode comprising three electrodes. The sensed ER at each electrode is plotted in the vertical and directional directions. Best-fit curves can be created for both the vertical distribution 1506 and the directional distribution 1507. In this example, regression analysis can be used to determine the best-fit curve. The ER target may include at least a portion of the expected best-fit curve in the vertical and / or directional distributions. That is, for example, the best-fit curve of the sensed ER, representing the distribution of sensed ER (or ER characteristics), can be compared with the best-fit curve of the ER target, representing the distribution of the target ER.

[0114] Figure 16 Method 1600 for modeling the spatial distribution of evoked responses (ER) to electrical stimulation and using a distribution model or model parameters to guide lead placement and / or device programming is illustrated by way of example, not limitation. Method 1600 can be performed using a medical system such as a neuromodulation system 700. In the example, method 1600 can be implemented in a programmer device (such as an RC 45 or CP 50) that communicates with an electrical stimulator (such as an IPG 10 or an electrical stimulator 740). Method 1600 can be used to deliver ER-based deep brain stimulation (DBS) at a brain target. Method 1600 can alternatively be used to deliver ER-based neuromodulation therapy at other neural targets, such as spinal cord stimulation (SCS) at a spinal nerve target.

[0115] At 1610, electrical stimulation can be delivered to the neural target via a stimulation electrode selected from a plurality of electrodes on at least one lead, depending on the stimulation setup. At 1620, an evoked response (ER) can be sensed from each of a set of sensing electrodes electrically connected to a sensing circuit (such as sensing circuit 710). The set of sensing electrodes can be selected from a plurality of electrodes on at least one lead and located at corresponding sensing positions. The ER can be sensed according to the stimulation sensing electrode configuration, such as... Figure 8 The ER sensing diagram 800 is shown. Stimulus sensing electrode configurations can include "diagonal" sensing configurations, where the same electrodes are used to deliver electrical stimulation pulses and to record the ER of that electrical stimulation during the same stimulation session. The ER sensed therefrom is referred to as the "diagonal" ER. Stimulus sensing electrode configurations can also include "off-diagonal" sensing configurations, where different electrodes are used to deliver electrical stimulation pulses and to record the ER of that electrical stimulation during the same stimulation session. The ER sensed therefrom is referred to as the "off-diagonal" ER. (See above regarding...) Figure 7While diagonal ERs at the stimulation site can provide useful information about ER distribution for identifying matches with desired or target responses, diagonal ERs are prone to stimulation artifacts and channel saturation, potentially introducing errors or failing to provide usable data, and degrading the quality of ER-based lead placement and / or device programming. In one example, during the determination of the spatial distribution of ERs and the guidance of lead placement and device programming, the stimulation sensing electrode configuration may include only off-diagonal ERs, excluding diagonal ERs. Alternatively, the stimulation sensing electrode configuration may be a "nearest neighbor" configuration, which includes a selected subset, rather than all, of the available electrodes for ER sensing. The selected subset may be those off-diagonal electrodes within a specific proximity distance of the stimulation electrodes used to deliver electrical stimulation pulses, an example of which is shown in Figure 9- Figure 10 The selective ER from a subset of sensing electrodes, defined by the stimulus sensing electrode configuration, can improve efficiency without affecting the accuracy of identifying the ER that matches the desired or target response to different desired evoked responses.

[0116] At 1630, ER features can be extracted from the sensed ER. Examples of signal features may include the signal amplitude, magnitude, peak value, value range, signal curve length, or signal power or RMS value of the ER signal within a time window, such as epoch-averaged ER. The signal amplitude range or value range (also known as peak-to-peak (P2P) value) can be measured as the difference between the maximum or minimum values ​​of the dominant peaks in the sensed evoked response or epoch-averaged evoked response within a time window (also known as the "maximum P2P" amplitude), or as the difference between a negative peak (trough) and the immediately following positive peak (also known as the "N1-P2 P2P" amplitude). In some examples, the spatial distribution of the extracted signal features of the sensed ER at the sensing location of the sensing electrode can be generated, such as... Figure 10 As shown, a comparison can be made between the spatial distribution of ER signal features and the target distribution of ER features to determine if a match can be found.

[0117] At 1640, ER features can be fitted to a model that represents the spatial distribution of ER features generated at the sensing location (such as the location of the sensing electrode selected according to ER sensing map 800 (i.e., a "diagonal out" configuration including only the diagonal out electrodes) or ER sensing map 1000 (i.e., a "nearest neighbor" configuration including a subset of electrodes within a specific proximity distance of the stimulating electrode)). The ER distribution model can be a linear or nonlinear model. Examples of ER distribution models can include parametric models (e.g., Gaussian distribution models, periodic or wrapped Gaussian distribution models, exponential distribution models, Poisson distribution models, Weibull distribution, etc.), regression models (e.g., linear regression or logistic regression models, etc.), or nonparametric models (e.g., decision trees, K-nearest neighbor models, support vector machines (e.g., with Gaussian kernels) or artificial neural networks, and various other machine learning models).

[0118] One or more model features or parameters can be generated from the fitted distribution model. As an example and not a limitation, model features or parameters may include: one or more of the mean or standard deviation of the ER features in the Gaussian distribution model; morphological or statistical features (such as the magnitude, spatial location, or width of the peak of the fitted distribution model within a range defined by multiple sensing locations; the magnitude of the positive peak (or local maximum) or the magnitude of the negative peak (or local minimum) of the fitted distribution model within a range defined by multiple sensing locations); or composite features (such as the ratio of the magnitude of the positive peak to the magnitude of the negative peak of the fitted distribution model within a range defined by multiple sensing locations).

[0119] At point 1650, the ER distribution model, or model features or parameters derived therefrom, can be compared with acceptance criteria to determine if a match with the desired or target response can be found. The comparison results can be displayed on the user interface. Based on this comparison, suggestions for repositioning at least one lead (such as pushing, pulling, shifting, or rotating the lead to achieve the desired target response) can be provided to the user. The comparison results can be used additionally or alternatively to guide adjustments to the stimulus setup. During the repositioning of at least one lead and / or adjustment of the stimulus setup, the ER can be sensed, ER features and / or distribution can be determined, and the comparison with the acceptance criteria can be updated substantially in real time and displayed to the user. The user can continue to reposition at least one lead and / or adjust the stimulus setup until the sensed ER is more favorable compared to the acceptance criteria. The sensed ER or features, or the feature distribution derived therefrom, can be used as feedback for modifying the therapeutic stimulus setup.

[0120] Figure 17 A block diagram of an example machine 1700 on which any one or more of the techniques (e.g., methods) discussed herein can be generally shown. The portions of this description can be applied to the computational framework of various parts of a neural modulation device or an external programmer device.

[0121] In alternative embodiments, machine 1700 may operate as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, machine 1700 may operate as a server machine, a client machine, or both in a server-client network environment. In the example, machine 1700 may act as a peer-to-peer (P2P) (or other distributed) network environment. Machine 1700 may be a personal computer (PC), tablet PC, set-top box (STB), personal digital assistant (PDA), mobile phone, network appliance, network router, switch, or bridge, or any machine capable of executing instructions (sequentially or otherwise) specifying the actions to be taken by the machine. Furthermore, although only a single machine is shown, the term "machine" should also be considered to include any collection of machines, such as cloud computing, Software as a Service (SaaS), and other computer cluster configurations, that individually or jointly execute a set (or more) of instructions to perform any one or more methods discussed herein.

[0122] As described herein, examples may include logic or multiple components or mechanisms, or those that can be operated by them. A circuit set is a collection of circuits implemented in a tangible entity including hardware (e.g., simple circuits, gates, logic, etc.). Circuit set members can be flexible and potentially variable in hardware over time. A circuit set includes members that can perform a specified operation individually or in combination during operation. In the examples, the hardware of the circuit set may be designed invariably to perform a specific operation (e.g., hardwired). In the examples, the hardware of the circuit set may include physically connected components (e.g., execution units, transistors, simple circuits, etc.) including computer-readable media that are physically modified (e.g., invariantly aggregated magnetic ground, electric ground, movable placement, etc.) to encode instructions for a specific operation. When connecting physical components, the underlying electrical characteristics of the hardware configuration, for example, change from an insulator to a conductor, and vice versa. Instructions enable embedded hardware (e.g., execution units or loading mechanisms) to create members of the circuit set in the hardware via variable connections to perform a specific operation during operation. Thus, when the device is running, the computer-readable medium is communicatively coupled to other components of the circuit set members. In the example, any physical component can be used in more than one member of more than one set of circuits. For example, in operation, an execution unit can be used at one point in time in a first circuit of a first set of circuits and reused by a second circuit in the first set of circuits, or reused at a different point in time by a third circuit in the second set of circuits.

[0123] Machine (e.g., computer system) 1700 may include a hardware processor 1702 (e.g., a central processing unit (CPU), graphics processing unit (GPU), hardware processor core, algorithm-specific ASIC, or any combination thereof), main memory 1704, and static memory 1706, some or all of which may communicate with each other via an interconnect link (e.g., bus) 1708. Machine 1700 may also include a display unit 1710 (e.g., a raster display, vector display, holographic display, etc.), an alphanumeric input device 1712 (e.g., a keyboard), and a user interface (UI) navigation device 1714 (e.g., a mouse). In this example, display unit 1710, input device 1712, and UI navigation device 1714 may be a touchscreen display. Machine 1700 may additionally include a storage device (e.g., a drive unit) 1716; a signal generation device 1718 (e.g., a speaker); a network interface device 1720; and one or more sensors 1721, such as a global positioning system (GPS) sensor, a compass, an accelerometer, or other sensors. Machine 1700 may include output controller 1728, such as serial (e.g., Universal Serial Bus (USB)), parallel or other wired or wireless (e.g., infrared (IR), near field communication (NFC) etc.) connections, to communicate or control one or more peripheral devices (e.g., printers, card readers, etc.).

[0124] Storage device 1716 may include machine-readable medium 1722 on which one or more sets of data structures or instruction sets 1724 (e.g., software) are stored, embodying or being used by any one or more of the technologies or functions described herein. Instructions 1724 may also reside wholly or at least partially within main memory 1704, static memory 1706, or hardware processor 1702 during execution by machine 1700. In this example, one or any combination of hardware processor 1702, main memory 1704, static memory 1706, or storage device 1716 may constitute a machine-readable medium.

[0125] Although machine-readable medium 1722 is shown as a single medium, the term "machine-readable medium" can include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) configured to store one or more instructions 1724.

[0126] The term "machine-readable medium" can include any medium capable of storing, encoding, or carrying instructions for execution by machine 1700 and causing machine 1700 to perform any one or more of the techniques of this disclosure, or capable of storing, encoding, or carrying data structures used by or associated with such instructions. Examples of non-limiting machine-readable media can include solid-state memory as well as optical and magnetic media. In examples, mass-capacity machine-readable media includes machine-readable media having multiple particles (which have invariant (e.g., rest) masses). Therefore, mass-capacity machine-readable media are non-transient propagating signals. Specific examples of mass-capacity machine-readable media can include: non-volatile memory, such as semiconductor memory devices (e.g., electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EPSOM)) and flash memory devices; magnetic disks, such as internal hard disks and removable hard disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

[0127] Instruction 1724 can also be sent or received on communication network 1726 via a transmission medium using a network interface device 1720 that utilizes any of a variety of transport protocols, such as Frame Relay, Internet Protocol (IP), Transmission Control Protocol (TCP), User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), etc. Example communication networks may include local area networks (LANs), wide area networks (WANs), packet data networks (e.g., the Internet), mobile phone networks (e.g., cellular networks), conventional telephone (POTS) networks, and wireless data networks (e.g., the IEEE 802.11 standard series known as WiFi®, the IEEE 802.16 standard series known as WiMax®), the IEEE 802.15.4 standard series, peer-to-peer (P2P) networks, etc. In the example, network interface device 1720 may include one or more physical jacks (e.g., Ethernet, coaxial, or telephone jacks) or one or more antennas for connection to communication network 1726. In the example, network interface device 1720 may include multiple antennas to perform wireless communication using at least one of single-input multiple-output (SIMO), multiple-input multiple-output (MIMO), or multiple-input single-output (MISO) technologies. The term "transmission medium" should be considered to include any intangible medium capable of storing, encoding, or carrying instructions for execution by machine 1700, and includes digital or analog communication signals or other intangible media to facilitate communication of such software.

[0128] Various embodiments are illustrated in the accompanying drawings. One or more features from one or more of these examples can be combined to form other examples.

[0129] The method examples described herein may be implemented, at least in part, by a machine or computer. Some examples may include a computer-readable or machine-readable medium encoded with instructions operable to configure an electronic device or system to perform the methods described in the examples above. Implementations of such methods may include code, such as microcode, assembly language code, or high-level language code. Such code may include computer-readable instructions for performing various methods. The code may form part of a computer program product. Furthermore, the code may be tangibly stored on one or more volatile or non-volatile computer-readable media during execution or at other times.

[0130] The above detailed description is intended to be illustrative and not restrictive. Therefore, the scope of this disclosure should be determined by reference to the appended claims and the full scope of their equivalents.

Claims

1. A neuromodulation system, comprising: at least one lead including a plurality of electrodes; an electrical stimulator configured to provide electrical stimulation to a neural target of a patient; sensing circuitry configured to sense evoked responses (ERs) to the electrical stimulation; and controller circuitry operably connected to the electrical stimulator and the sensing circuitry, the controller circuitry configured to: collect, from each of a set of sensing electrodes located at respective sensing locations, sensed ERs in response to the electrical stimulation delivered to the neural target via a stimulating electrode on the at least one lead according to stimulation settings, the sensing electrodes selected from the plurality of electrodes on the at least one lead; generate ER features from the sensed ERs; fit the generated ER features to a model to represent a spatial distribution of the generated ER features across the sensing locations; and based at least in part on a comparison of the fitted model to an acceptance criterion, provide a suggestion to a user to reposition the at least one lead or adjust the stimulation settings so that the fitted model is more favorable compared to the acceptance criterion.

2. The neuromodulation system of claim 1, wherein, the ER features include signal amplitudes of the sensed ERs to the electrical stimulation, and wherein the spatial distribution includes a distribution of signal amplitudes of the sensed ERs across the sensing locations.

3. The neuromodulation system of any of claims 1-2, wherein the at least one lead includes a deep brain stimulation (DBS) lead, and wherein the electrical stimulator is configured to provide DBS to a brain target of the patient based on the ER features or the fitted model of the ER features according to stimulation settings.

4. The neuromodulation system of any of claims 1-3, wherein the plurality of electrodes includes one or more ring electrodes or one or more rows of segmented electrodes disposed at respective longitudinal locations along a length of the at least one lead, wherein each row includes segmented electrodes disposed around a circumference of the at least one lead at a particular longitudinal location, wherein the stimulating electrode and the set of selected sensing electrodes are each selected from the one or more ring electrodes or the one or more rows of segmented electrodes.

5. The neuromodulation system of claim 4, wherein the sensed ERs include ERs sensed from a plurality of longitudinal sensing locations corresponding to the selected sensing electrodes along the length of the at least one lead, wherein the fitted model represents a longitudinal distribution of the ER features across the plurality of longitudinal sensing locations.

6. The neuromodulation system of claim 4, wherein the sensed ERs include ERs sensed from a plurality of circumferential sensing locations corresponding to the selected sensing electrodes around the circumference of the at least one lead at the particular longitudinal location, wherein the fitted model represents an orientation distribution of the ER features across the plurality of circumferential sensing locations.

7. The neuromodulation system of any of claims 1-6, wherein the controller circuitry is configured to display one or more of the sensed ERs, the generated ER features, the model representing the spatial distribution of the generated ER features, or the acceptance criterion on a user interface.

8. The neuromodulation system of any of claims 1-7, wherein the fitted model comprises at least one of a parametric model, a regression model, or a non-parametric model.

9. The neuromodulation system of claim 8, wherein, the parametric model comprises a Gaussian distribution model or a periodic or wrapped Gaussian distribution model.

10. The neuromodulation system of any of claims 1-9, wherein the controller circuit is configured to: determine a model parameter or feature of the fitted model; and provide a recommendation to reposition the at least one lead or adjust the stimulation settings based at least in part on a comparison of the determined model parameter or feature to a target parameter or feature value such that the determined model parameter or feature falls within a margin of the target parameter or feature value.

11. The neuromodulation system of claim 10, wherein the model parameter or feature comprises one or more parameters of a parametric model.

12. The neuromodulation system of claim 11, wherein the acceptance criteria comprises an ER target location, wherein the controller circuit is configured to determine an ER distribution center of the generated ER feature based at least in part on the one or more parameters of the parametric model, and estimate a distance between the determined distribution center and the ER target location.

13. The neuromodulation system of any of claims 10-12, wherein the model parameter or feature comprises an amplitude, spatial location, or width of a local peak of the fitted model within a range defined by the sensing location, wherein the controller circuit is configured to provide a recommendation to reposition the at least one lead or adjust the stimulation settings such that the spatial location of the local peak falls within a margin of a target location of an ER peak.

14. The neuromodulation system of any of claims 10-13, wherein, the model parameter or feature comprises one or more of a positive peak amplitude or a negative peak amplitude of the fitted model within a range defined by the sensing location, wherein the controller circuit is configured to provide a recommendation to reposition the at least one lead or adjust the stimulation settings based at least in part on a comparison of the positive peak amplitude or negative peak amplitude to a predetermined threshold or range of values.

15. The neuromodulation system of any of claims 10-14, wherein, the model parameter or feature comprises a ratio of a positive peak amplitude to a negative peak amplitude of the fitted model within a range defined by the sensing location, wherein the controller circuit is configured to provide a recommendation to reposition the at least one lead or adjust the stimulation settings such that the ratio of the positive peak amplitude to the negative peak amplitude exceeds a predetermined threshold or falls within a predetermined range of values.

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