This application is related to commonly assigned U.S. Provisional Patent Application Ser. No. 62/451,994, entitled “SUB-PERCEPTION CALIBRATION USING SPACE DOMAIN SCALING”, filed on Jan. 30, 2017, which is incorporated by reference in its entirety.
This document relates generally to medical devices, and more particularly, but not by way of limitation, to systems, devices, and methods to provide a neuromodulation field.
Neural modulation has been proposed as a therapy for a number of conditions. Often, neural modulation and neural stimulation may be used interchangeably to describe excitatory stimulation that causes action potentials as well as inhibitory and other effects. Examples of neuromodulation include Spinal Cord Stimulation (SCS), Deep Brain Stimulation (DBS), Peripheral Nerve Stimulation (PNS), and Functional Electrical Stimulation (FES). SCS, by way of example and not limitation, has been used to treat chronic pain syndromes. Some neural targets may be complex structures with different types of nerve fibers. An example of such a complex structure is the neuronal elements in and around the spinal cord targeted by SCS.
Neuromodulation energy may be delivered to provide sub-perception modulation that is therapeutically effective, but the delivery of the sub-perception modulation energy is not perceivable by the patient. Since the patient does not perceive the delivery of sub-perception energy, it can be difficult to accurately program a target sub-perception field as patient feedback is not available for real-time adjustments to the modulation.
This document discusses, among other things, systems and methods to define electrode parameters for neuromodulation such as sub-perception SCS. The present inventors have recognized, among other things, that real clinical outcomes depend on patient-to-patient differences in electrode-tissue coupling and neural excitability and that the allocation of energy to the electrodes for the target pole can be improved using space domain scaling to compensate for patient-specific displacement between electrodes or between electrode and tissue, and/or using time domain scaling to account for at least one property of a neural target or of a neuromodulation waveform.
An example (e.g. “Example 1”) of a non-transient machine readable medium may contain program instructions for causing a machine to: determine target electrode fractionalization contributions energy allocations for a plurality of electrodes based on at least one target pole to provide a target sub-perception modulation field; and normalize the target sub-perception modulation field, including determining a time domain scaling factor to account for at least one property of a neural target or of a neuromodulation waveform, and applying the time domain scaling factor to the target energy allocations.
In Example 2, the subject matter of Example 1 may optionally be configured such that the determine the time domain scaling factor includes determine the time domain scaling factor based on a type of neural structure.
In Example 3, the subject matter of any one or any combination of Examples 1-2 may optionally be configured such that the determine the time domain scaling factor includes determine the time domain scaling factor based on at least one neural structure property, the at least one neural structure property including a geometrical property or an electrical property.
In Example 4, the subject matter of any one or any combination of Examples 1-3 may optionally be configured such that the determine the time domain scaling factor includes determine the time domain scaling factor based on a pulse width of the neuromodulation waveform.
In Example 5, the subject matter of any one or any combination of Examples 1-4 may optionally be configured such that the determine the time domain scaling factor includes determine the time domain scaling factor based on a frequency of the neuromodulation waveform.
In Example 6, the subject matter of any one or any combination of Examples 1-5 may optionally be configured such that the determine the time domain scaling factor includes determine the time domain scaling factor based on a duty cycle of the neuromodulation waveform.
In Example 7, the subject matter of any one or any combination of Examples 1-6 may optionally be configured such that the determine the time domain scaling factor includes determine the time domain scaling factor based on a shape or pattern of the neuromodulation waveform.
In Example 8, the subject matter of any one or any combination of Examples 1-7 may optionally be configured such that the determine the time domain scaling factor includes determine the time domain scaling factor based on a type of the neuromodulation waveform.
In Example 9, the subject matter of any one or any combination of Examples 1-8 may optionally be configured such that the normalize the target sub-perception modulation field includes determine the time domain scaling factor to account for at least one property of the neural target and for at least one property of the neuromodulation waveform.
In Example 10, the subject matter of any one or any combination of Examples 1-9 may optionally be configured such that the normalize the target sub-perception modulation field includes determine the time domain scaling factor to account for at a first property and a second property selected from the group consisting of: a type of neural structure; at least one neural structure property, the at least one neural structure property including a geometrical property or an electrical property; and a neuromodulation waveform property. The waveform property may include a pulse width of the neuromodulation waveform; a frequency of the neuromodulation waveform; a duty cycle of the neuromodulation waveform; a shape or pattern of the neuromodulation waveform; and a type of the neuromodulation waveform.
In Example 11, the subject matter of Example 10 may optionally be configured such that the determine the time domain scaling factor to account for at the first property and the second property includes determine a first time domain scaling factor to account for the first property, determine a second time domain scaling factor to account for the second property, and multiplying the first time domain scaling factor and the second time domain scaling factor.
In Example 12, the subject matter of any one or any combination of Examples 1-11 may optionally further comprise program instructions for causing the machine to calibrate a plurality of electrode groups in the plurality of electrodes where each of the plurality of electrode groups have an electrode configuration and include an electrode set of at least one electrode from the plurality of electrodes, wherein the calibrating the electrode groups includes, for each of the plurality of electrode groups, delivering modulation energy to a neural target and receiving feedback, and determining a space scaling factor using the feedback to account for actual relative positions between the electrode groups and the neural target, and further applying the space domain scaling factor to the target energy allocations.
In Example 13, the subject matter of any one or any combination of Examples 1-12 may optionally be configured such that the determining the time domain scaling factor includes retrieve the time domain scaling factor from a lookup table or calculate the time domain scaling factor from a modeled analytic relationship.
An example (e.g. “Example 14”) of a method may comprise determining target energy allocations for a plurality of electrodes based on at least one target pole to provide a target sub-perception modulation field, and normalizing the target sub-perception modulation field, including determining a time domain scaling factor to account for at least one property of a neural target or of a neuromodulation waveform, and applying the time domain scaling factor to the target energy allocations.
In Example 15, the subject matter of Example 14 may optionally be configured such that the determining the time domain scaling factor includes determining the time domain scaling factor based on a type of neural structure.
In Example 16, the subject matter of any one or any combination of Examples 14-15 may optionally be configured such that the determining the time domain scaling factor includes determining the time domain scaling factor based on at least one neural structure property, the at least one neural structure property including a geometrical property or an electrical property.
In Example 17, the subject matter of any one or any combination of Examples 14-16 may optionally be configured such that the determining the time domain scaling factor includes determining the time domain scaling factor based on a pulse width of the neuromodulation waveform.
In Example 18, the subject matter of any one or any combination of Examples 14-17 may optionally be configured such that the determining the time domain scaling factor includes determining the time domain scaling factor based on a frequency of the neuromodulation waveform.
In Example 19, the subject matter of any one or any combination of Examples 14-18 may optionally be configured such that the determining the time domain scaling factor includes determining the time domain scaling factor based on a duty cycle of the neuromodulation waveform.
In Example 20, the subject matter of any one or any combination of Examples 14-19 may optionally be configured such that the determining the time domain scaling factor includes determining the time domain scaling factor based on a shape or pattern of the neuromodulation waveform.
In Example 21, the subject matter of any one or any combination of Examples 14-20 may optionally be configured such that the determining the time domain scaling factor includes determining the time domain scaling factor based on a type of the neuromodulation waveform.
In Example 22, the subject matter of any one or any combination of Examples 14-21 may optionally be configured such that the normalizing the target sub-perception modulation field includes determining the time domain scaling factor to account for at least one property of the neural target and for at least one property of the neuromodulation waveform.
In Example 23, the subject matter of any one or any combination of Examples 14-22 may optionally be configured such that the normalizing the target sub-perception modulation field includes determining the time domain scaling factor to account for at a first property and a second property selected from the group consisting of: a type of neural structure; at least one neural structure property, the at least one neural structure property including a geometrical property or an electrical property; and a neuromodulation waveform property. The neuromodulation waveform property may include a pulse width of the neuromodulation waveform; a frequency of the neuromodulation waveform; a duty cycle of the neuromodulation waveform; a shape or pattern of the neuromodulation waveform; and a type of the neuromodulation waveform.
In Example 24, the subject matter of Example 23 may optionally be configured such that the determining the time domain scaling factor to account for at the first property and the second property includes determining a first time domain scaling factor to account for the first property, determining a second time domain scaling factor to account for the second property, and multiplying the first time domain scaling factor and the second time domain scaling factor.
In Example 25, the subject matter of any one or any combination of Examples 14-24 may optionally be configured such that the method may further comprise calibrating a plurality of electrode groups in the plurality of electrodes where each of the plurality of electrode groups have an electrode configuration and include an electrode set of at least one electrode from the plurality of electrodes, wherein the calibrating the electrode groups includes, for each of the plurality of electrode groups, delivering modulation energy to a neural target and receiving feedback, and determining a space scaling factor using the feedback to account for actual relative positions between the electrode groups and the neural target, and further applying the space domain scaling factor to the target energy allocations.
In Example 26, the subject matter of any one or any combination of Examples 14-25 may optionally be configured such that the determining the time domain scaling factor includes retrieving the time domain scaling factor from a lookup table.
In Example 27, the subject matter of any one or any combination of Examples 14-26 may optionally be configured such that the determining the time domain scaling factor includes calculating the time domain scaling factor from a modeled analytic relationship between a threshold of the neural target and the at least one property of the neural target or of the neuromodulation waveform.
An example (e.g. “Example 28”) of a system to program a neuromodulator to deliver neuromodulation to a neural target using a plurality of electrodes may comprise a programming control circuit configured to determine target energy allocations for the plurality of electrodes based on at least one target pole to provide a target sub-perception modulation field, and normalize the target sub-perception modulation field, including determine a time domain scaling factor to account for at least one property of a neural target or of a neuromodulation waveform, and apply the time domain scaling factor to the target energy allocations.
In Example 29, the subject matter of Example 28 may optionally be configured such that system may further comprise an external device that includes the programming control circuit and a user interface, wherein the external device is configured to program parameter sets into an implantable modulation device.
In Example 30, the subject matter of any one or any combination of Examples 14-26 may optionally be configured such that the programming control circuit is configured to determine the time domain scaling factor to account for at least one property of the neural target and to account for at least one property of the neuromodulation waveform.
This summary is intended to provide an overview of subject matter of the present patent application. It is not intended to provide an exclusive or exhaustive explanation of the disclosure. The detailed description is included to provide further information about the present patent application. Other aspects of the disclosure will be apparent to persons skilled in the art upon reading and understanding the following detailed description and viewing the drawings that form a part thereof, each of which are not to be taken in a limiting sense.
In the drawings, which are not necessarily drawn to scale, like numerals may describe similar components in different views. Like numerals having different letter suffixes may represent different instances of similar components. The drawings illustrate generally, by way of example, but not by way of limitation, various embodiments discussed in the present document.
Target poles have been used to define electrode parameters for spinal cord stimulation (SCS) such as sub-perception SCS. The present inventors have recognized, among other things, that the allocation of energy to the electrodes for the target pole can be improved using space domain scaling to compensate for patient-specific displacement between electrodes or between electrode and tissue, and/or using time domain scaling to account for at least one property of a neural target or of a neuromodulation waveform.
Various embodiments described herein involve spinal cord modulation. A brief description of the physiology of the spinal cord is provided herein to assist the reader.
SCS has been used to alleviate pain. A therapeutic goal for conventional SCS programming has been to maximize stimulation (i.e., recruitment) of the DC fibers that run in the white matter along the longitudinal axis of the spinal cord and minimal stimulation of other fibers that run perpendicular to the longitudinal axis of the spinal cord (dorsal root fibers, predominantly), as illustrated in
Activation of large sensory DC nerve fibers also typically creates the paresthesia sensation that often accompanies conventional SCS therapy. Although alternative or artifactual sensations, such as paresthesia, are usually tolerated relative to the sensation of pain, patients sometimes report these sensations to be uncomfortable, and therefore, they can be considered an adverse side-effect to neuromodulation therapy in some cases. Some embodiments deliver sub-perception therapy that is therapeutically effective to treat pain, for example, but the patient does not sense the delivery of the modulation field (e.g. paresthesia). Sub-perception therapy may include higher frequency modulation (e.g. about 1000 Hz or above) of the spinal cord that effectively blocks the transmission of pain signals in the afferent fibers in the DC. Some embodiments may implement this higher frequency modulation may include 1200 Hz or above, and some embodiments may implement this higher frequency modulation may include 1500 Hz or above. Some embodiments herein selectively modulate DH tissue, such as the presynaptic terminals of pain inhibitory neurons in the spinal cord, over DC tissue. Some embodiments selectively stimulate DR tissue and/or dorsal root ganglion over DC tissue to provide sub-perception therapy. As will be described in further detail below, some embodiments described herein target axons from inhibitory interneurons that propagate in anterior-posterior direction aligned with an electric field. Certain myelinated presynaptic terminals of inhibitory neurons oriented in the anterior-posterior (AP) direction, i.e. in parallel with electric field, may polarize more than their unmyelinated, differently oriented counterparts. Polarization may produce both subthreshold and suprathreshold effects that result in positive clinical effects, and sub-threshold progressive effects may also explain clinical observations of wash-in and wash-out effects. The terminal appears to may be the point of the greatest polarization. The unmyelinated dendrites to not polarize as much.
Such selective modulation is not delivered at these higher frequencies. For example, the selective modulation may be delivered at frequencies less than 1,200 Hz. The selective modulation may be delivered at frequencies less than 1,000 Hz in some embodiments. In some embodiments, the selective modulation may be delivered at frequencies less than 500 Hz. In some embodiments, the selective modulation may be delivered at frequencies less than 350 Hz. In some embodiments, the selective modulation may be delivered at frequencies less than 130 Hz. The selective modulation may be delivered at low frequencies (e.g. as low as 2 Hz). The selective modulation may be delivered even without pulses (e.g. 0 Hz) to modulate some neural tissue. By way of example and not limitation, the selective modulation may be delivered within a frequency range selected from the following frequency ranges: 2 Hz to 1,200 Hz; 2 Hz to 1,000 Hz, 2 Hz to 500 Hz; 2 Hz to 350 Hz; or 2 Hz to 130 Hz. Systems may be developed to raise the lower end of any these ranges from 2 Hz to other frequencies such as, by way of example and not limitation, 10 Hz, 20 Hz, 50 Hz or 100 Hz. By way of example and not limitation, it is further noted that the selective modulation may be delivered with a duty cycle, in which stimulation (e.g. a train of pulses) is delivered during a Stimulation ON portion of the duty cycle, and is not delivered during a Stimulation OFF portion of the duty cycle. By way of example and not limitation, the duty cycle may be about 10%±5%, 20%±5%, 30%±5%, 40%±5%, 50%±5% or 60%±5%. For example, a burst of pulses for 10 ms during a Stimulation ON portion followed by 15 ms without pulses corresponds to a 40% duty cycle. The selected modulation may be delivered with fixed widths. Although the target field can be applied to any pulse width that the device is capable of delivering, longer pulses widths are believed to be more effective.
The neuromodulation system may be configured to modulate spinal target tissue or other neural tissue. The configuration of electrodes used to deliver electrical pulses to the targeted tissue constitutes an electrode configuration, with the electrodes capable of being selectively programmed to act as anodes (positive), cathodes (negative), or left off (zero). In other words, an electrode configuration represents the polarity being positive, negative, or zero. An electrical waveform may be controlled or varied for delivery using electrode configuration(s). The electrical waveforms may be analog or digital signals. In some embodiments, the electrical waveform includes pulses. The pulses may be delivered in a regular, repeating pattern, or may be delivered using complex patterns of pulses that appear to be irregular. Other parameters that may be controlled or varied include the amplitude, pulse width, and rate (or frequency) of the electrical pulses. Each electrode configuration, along with the electrical pulse parameters, can be referred to as a “modulation parameter set.” Each set of modulation parameters, including allocated energy to the electrodes which may be provided as a fractionalized current distribution to the electrodes (as percentage cathodic current, percentage anodic current, or off), may be stored and combined into a modulation program that can then be used to modulate multiple regions within the patient.
The number of electrodes available combined with the ability to generate a variety of complex electrical waveforms (e.g. pulses), presents a huge selection of modulation parameter sets to the clinician or patient. For example, if the neuromodulation system to be programmed has sixteen electrodes, millions of modulation parameter sets may be available for programming into the neuromodulation system. Furthermore, for example SCS systems may have thirty-two electrodes which exponentially increases the number of modulation parameters sets available for programming. To facilitate such selection, the clinician generally programs the modulation parameters sets through a computerized programming system to allow the optimum modulation parameters to be determined based on patient feedback or other means and to subsequently program the desired modulation parameter sets.
In various embodiments, circuits of neuromodulation, including its various embodiments discussed in this document, may be implemented using a combination of hardware, software and firmware. For example, the circuit may be implemented using an application-specific circuit constructed to perform one or more particular functions or a general-purpose circuit programmed to perform such function(s). Such a general-purpose circuit includes, but is not limited to, a microprocessor or a portion thereof, a microcontroller or portions thereof, and a programmable logic circuit or a portion thereof.
The neuromodulation lead(s) of the lead system 517 may be placed adjacent, i.e., resting near, or upon the dura, adjacent to the spinal cord area to be stimulated. For example, the neuromodulation lead(s) may be implanted along a longitudinal axis of the spinal cord of the patient. Due to the lack of space near the location where the neuromodulation lead(s) exit the spinal column, the implantable modulation device 512 may be implanted in a surgically-made pocket either in the abdomen or above the buttocks, or may be implanted in other locations of the patient's body. The lead extension(s) may be used to facilitate the implantation of the implantable modulation device 512 away from the exit point of the neuromodulation lead(s).
The ETM 629 may also be physically connected via the percutaneous lead extensions 632 and external cable 633 to the neuromodulation leads 625. The ETM 629 may have similar waveform generation circuitry as the waveform generator 626 to deliver electrical modulation energy to the electrodes accordance with a set of modulation parameters. The ETM 629 is a non-implantable device that is used on a trial basis after the neuromodulation leads 625 have been implanted and prior to implantation of the waveform generator 626, to test the responsiveness of the modulation that is to be provided. Functions described herein with respect to the waveform generator 626 can likewise be performed with respect to the ETM 629.
The RC 627 may be used to telemetrically control the ETM 629 via a bi-directional RF communications link 634. The RC 627 may be used to telemetrically control the waveform generator 626 via a bi-directional RF communications link 635. Such control allows the waveform generator 626 to be turned on or off and to be programmed with different modulation parameter sets. The waveform generator 626 may also be operated to modify the programmed modulation parameters to actively control the characteristics of the electrical modulation energy output by the waveform generator 626. A clinician may use the CP 628 to program modulation parameters into the waveform generator 626 and ETM 629 in the operating room and in follow-up sessions.
The CP 628 may indirectly communicate with the waveform generator 626 or ETM 629, through the RC 627, via an IR communications link 636 or other link. The CP 628 may directly communicate with the waveform generator 626 or ETM 629 via an RF communications link or other link (not shown). The clinician detailed modulation parameters provided by the CP 628 may also be used to program the RC 627, so that the modulation parameters can be subsequently modified by operation of the RC 627 in a stand-alone mode (i.e., without the assistance of the CP 628). Various devices may function as the CP 628. Such devices may include portable devices such as a lap-top personal computer, mini-computer, personal digital assistant (PDA), tablets, phones, or a remote control (RC) with expanded functionality. Thus, the programming methodologies can be performed by executing software instructions contained within the CP 628. Alternatively, such programming methodologies can be performed using firmware or hardware. In any event, the CP 628 may actively control the characteristics of the electrical modulation generated by the waveform generator 626 to allow the desired parameters to be determined based on patient feedback or other feedback and for subsequently programming the waveform generator 626 with the desired modulation parameters. To allow the user to perform these functions, the CP 628 may include a user input device (e.g., a mouse and a keyboard), and a programming display screen housed in a case. In addition to, or in lieu of, the mouse, other directional programming devices may be used, such as a trackball, touchpad, joystick, touch screens or directional keys included as part of the keys associated with the keyboard. An external device (e.g. CP) may be programmed to provide display screen(s) that allow the clinician to, among other functions, to select or enter patient profile information (e.g., name, birth date, patient identification, physician, diagnosis, and address), enter procedure information (e.g., programming/follow-up, implant trial system, implant waveform generator, implant waveform generator and lead(s), replace waveform generator, replace waveform generator and leads, replace or revise leads, explant, etc.), generate a pain map of the patient, define the configuration and orientation of the leads, initiate and control the electrical modulation energy output by the neuromodulation leads, and select and program the IPG with modulation parameters in both a surgical setting and a clinical setting.
An external charger 637 may be a portable device used to transcutaneously charge the waveform generator via a wireless link such as an inductive link 638. Once the waveform generator has been programmed, and its power source has been charged by the external charger or otherwise replenished, the waveform generator may function as programmed without the RC or CP being present.
Electrical modulation occurs between or among a plurality of activated electrodes, one of which may be the case of the waveform generator. The system may be capable of transmitting modulation energy to the tissue in a monopolar or multipolar (e.g., bipolar, tripolar, or more than three poles) fashion. Monopolar modulation occurs when a selected one of the lead electrodes is activated along with the case of the waveform generator, so that modulation energy is transmitted between the selected electrode and case. Any of the electrodes E1-E16 and the case electrode may be assigned to up to k possible groups or timing “channels.” In one embodiment, k may equal four. The timing channel identifies which electrodes are selected to synchronously source or sink current to create an electric field in the tissue to be stimulated. Amplitudes and polarities of electrodes on a channel may vary. In particular, the electrodes can be selected to be positive (anode, sourcing current), negative (cathode, sinking current), or off (no current) polarity in any of the k timing channels. The waveform generator may be operated in a mode to deliver electrical modulation energy that is therapeutically effective and causes the patient to perceive delivery of the energy (e.g. therapeutically effective to relieve pain with perceived paresthesia), and may be operated in a sub-perception mode to deliver electrical modulation energy that is therapeutically effective and does not cause the patient to perceive delivery of the energy (e.g. therapeutically effective to relieve pain without perceived paresthesia).
The waveform generator may be configured to individually control the magnitude of electrical current flowing through each of the electrodes. For example, a current generator may be configured to selectively generate individual current-regulated amplitudes from independent current sources for each electrode. In some embodiments, the pulse generator may have voltage regulated outputs. While individually programmable electrode amplitudes are desirable to achieve fine control, a single output source switched across electrodes may also be used, although with less fine control in programming. Neuromodulators may be designed with mixed current and voltage regulated devices.
The energy may be allocated to electrodes to provide a desired modulation field. Some non-limiting examples of modulation fields are provided below.
The SCS system may be configured to deliver different electrical fields to achieve a temporal summation of modulation in the DH elements. For embodiments that use a pulse generator, the electrical fields can be generated respectively on a pulse-by-pulse basis. For example, a first electrical field can be generated by the electrodes (using a first current fractionalization) during a first electrical pulse of the pulsed waveform, a second different electrical field can be generated by the electrodes (using a second different current fractionalization) during a second electrical pulse of the pulsed waveform, a third different electrical field can be generated by the electrodes (using a third different current fractionalization) during a third electrical pulse of the pulsed waveform, a fourth different electrical field can be generated by the electrodes (using a fourth different current fractionalized) during a fourth electrical pulse of the pulsed waveform, and so forth. These electrical fields may be rotated or cycled through multiple times under a timing scheme, where each field is implemented using a timing channel. The electrical fields may be generated at a continuous pulse rate, or may be bursted on and off. Furthermore, the interpulse interval (i.e., the time between adjacent pulses), pulse amplitude, and pulse duration during the electrical field cycles may be uniform or may vary within the electrical field cycle.
An embodiment may modify the fractionalized current delivered to each electrode to minimize the electrical field gradient in the longitudinal direction, so as to minimize activation of the DC elements. Minimizing activation of the DC elements can include a model-based calculation, where the model includes the information from the calibration. A discrete activating function can be calculated by the formula: AF(n)=Ga/(π×d×1)×[Ve(n−1)−2 Ve(n)+Ve(n+1)], wherein Ga is the axonal intermodal conductance, d is the axonal diameter, 1 is the length of the node of Ranvier, Ve(n) is the strength of the electric field at the node for which the activating function is determined, Ve(n−1) is the strength of the electric field at the node preceding the node for which the activating function is determined, and Ve(n+1) is the strength of the electric field at the node following the node for which the activating function is determined. Using this formula, the discrete activating function is calculated from the conductance normalized to the surface area of the node of Ranvier.
Modulation thresholds vary from patient to patient and from electrode to electrode within a patient. An electrode/tissue coupling calibration of the physical electrodes may be performed to account for these different modulation thresholds and provide a more accurate fractionalization of the current between electrodes. For example, perception threshold may be used to normalize the physical electrodes. The RC or the CP may be configured to prompt the patient to actuate a control element, once paresthesia is perceived by the patient. In response to this user input, the RC or the CP may be configured to respond to this user input by storing the modulation signal strength of the electrical pulse train delivered when the control element is actuated. Other sensed parameter or patient-perceived modulation values (e.g. constant paresthesia, or maximum tolerable paresthesia) may be used to provide the electrode/tissue coupling calibration of the physical electrodes. These sensed parameter or patient-perceived modulation values may be used to estimate the current fractionalization by minimizing the sum of the square of the discrete activating function divided by the determined value (e.g. perception threshold) at each physical electrode on an electrical modulation lead as is described in more detail below. Squaring the discrete activating function, or any driving force from the electrical field, eliminates the differences in depolarizing and hyperpolarizing fields. The current fractionalization that results in a minimize sum minimizes the field gradient in the longitudinal direction.
Various embodiments of the present subject matter may use “target multipoles.” For example, target multipoles may be used to provide a linear field that may maximize the electric field in a region while minimizing the activation of dorsal columns. These target multipoles may be referred to as “ideal” or “virtual” multipoles. Each target pole of a target multipole may correspond to one physical electrode, but may also correspond to a space that does not correspond to one electrode, and may be emulated using electrode fractionalization. By way of examples, U.S. Pat. Nos. 8,412,345 and 8,909,350 describe target multipoles. U.S. Pat. Nos. 8,412,345 and 8,909,350 are hereby incorporated by reference in their entirety. Target multipoles are briefly described herein.
A stimulation target in the form of a target poles (e.g., a target multipole such as a target bipole or target tripole or a target multipole with more than three target poles) may be defined and the stimulation parameters, including the allocated energy values (e.g. fractionalized current values) on each of the electrodes, may be computationally determined in a manner that emulates these target poles. Current steering may be implemented by moving the target poles about the leads, such that the appropriate allocated energy values (e.g. fractionalized current values) for the electrodes are computed for each of the various positions of the target pole.
With reference to
The contacts for stimulation may be determined automatically or manually 1760 from the lead configuration and contact status. A selected field model may be used to estimate the field induced by unit current from the contact 1761. The field is calibrated using the threshold 1762. For example, the unit current field may be weighted. Constituent sources are formed based on the selected contacts 1763, and a transfer matrix 1764 is constructed to use to compute the minimal mean square solution 1766 using contributions from the constituent sources and using a specified target field 1765. The solution can be used to compute the current fractionalization on each contact 1767.
With reference to
Although target current source poles are one way to represent a “target electrical field”, other representations of target fields may be used. The locations of the target current source poles may be determined in a manner that places the resulting electrical field over an identified region of the patient to be stimulated. The spatial observation points may be spaced in a manner that would, at the least, cover the entire tissue region to be stimulated and/or a tissue region that should not be stimulated. The locations of the target current source poles may be defined by the user, and may be displayed to the user along with the electrode locations, which as briefly discussed above, may be determined based on electrical measurements taken at the electrodes. Referring to
Once the constituent sources are selected, the CP may determine the relative strengths of the constituent current sources that, when combined, result in estimated electrical field potential values at the spatial observation points that best matches the desired field potential values at the spatial observation points. In particular, the CP may model the constituent current sources (e.g., using analytical and/or numerical models) and estimate the field potential values per unit current (V/mA) generated by each of the constituent current sources at the spatial observation points, and may generate an m×n transfer matrix (shown in
The optimization function may be a least-squares (over-determined) function expressed as: |φ−Aĵ|2, where φ is an m-element vector of the desired electrical field parameter values (e.g. desired field potential values), A is the transfer matrix, and ĵ is an n-element vector of the strengths of the constituent current sources. The constituent current source strengths ĵ may be solved such that the optimization function |φ−Aĵ|2 is minimized. The square difference is minimized if φ=Aĵ. One approach for solving this problem may be to invert the transfer matrix A and pre-multiply, such that A−1=φA−1Aĵ, which yields the solution ĵ=A−1φ. Once the strengths of the constituent current sources are determined, the CP converts these strengths to energy allocations (e.g. current distributions) on the electrodes in the form of a polarity and percentage.
The remainder of this document discusses various embodiments that relate to optimizing electrode configurations to be used with sub-perception SCS. The sub-perception SCS may include, but is not limited to, burst SCS, high rate SCS, long pulse width, and/or high density forms of SCS. The burst SCS may include 2 to 7 pulses wherein each burst of pulses may have a pulse frequency, also referred to as an intraburst pulse frequency, within a range between 250 Hz to 500 Hz. The burst-to-burst frequency (also referred to as an interburst frequency, may be within a range of 20 Hz to 60 Hz. High rate SCS may include SCS with a frequency equal to or greater than 1 kHz. Long pulse width SCS may include pulse widths of 90 microseconds or more for the active phase, rather than the total length of the waveform, of a pulse. High density forms of SCS may include SCS using high duty cycle or high “charge per phase” waveforms. A waveform may be defined as “high density” if charge delivered per unit time is above a threshold. By way of example and not limitation, waveforms may be considered to be “high density” if over 20% of the duty cycle consists of an active stimulation phase. A high density classification is based more on a duty cycle and/or a charge per time measurement rather than a pulse rate. For example, both a 500 Hz and a 1 kHz waveform may be defined as “high density” if the proportion of the duty cycle occupied by the active phase exceeds a threshold (e.g. 20% to 25%). Collectible patient feedback data may be used to provide a feedback metric used to optimize the energy allocations to electrodes (e.g. electrode fractionalizations) and total amplitudes to best match a user-specified target pole. Examples of such collectible feedback data may include, but are not limited to patient-provided paresthesia data (e.g. amplitude and sensation) such as may be used to identify paresthesia threshold, evoked action potentials (ECAPs) such as ECAPs representing activity in the dorsal roots, dorsal horn, and/or dorsal column, and Local Field Potentials (LFPs).
As discussed above, target poles with inverse modeling have been used to define electrode parameters for SCS. Additionally, U.S. Pat. No. 8,412,345, entitled “System and Method for Mapping Arbitrary Electric Fields to Pre-Existing Lead Electrodes” which is incorporated herein by reference in its entirety, discusses the use of target poles, which have also been referred to as “ideal” poles. Also, as discussed above and in U.S. Pat. Pub. No. 20160082251, entitled “Neuromodulation Specific to Objective Function of Modulation Field for Targeted Tissue” which is incorporated herein by reference in its entirety, target poles can be specified to provide field features to target neural elements.
However, real clinical outcomes depend on patient-to-patient differences in electrode-tissue coupling and neural excitability. For sub-perception modulation for which the patient does not perceive paresthesia or another indication of the delivered energy, patient feedback is not available for real-time adjustments to the modulation.
For example, electrode-tissue coupling may be affected by the cerebral spinal fluid (CSF) space, which may vary by patient and by the position along the spinal column. The patient-specific and position-specific differences in electrode-tissue coupling affect the field geometry. Various embodiments of the present subject matter provide space domain scaling to account for displacement (e.g. a distance in a given direction) between electrodes or between electrode and tissue. Also, different neural tissue exhibit different excitability to the same field. For example, synaptic terminals and axons have different excitability to modulation fields. Various embodiments of the present subject matter provide time domain scaling for targeted neural tissue. Various embodiments of the present subject matter may provide both space domain scaling and time domain scaling for targeted neural tissue.
At 2275, electrode groups in the electrode array(s) are calibrated. The electrode groups may be one electrode for monopolar stimulation, two electrodes for bipolar stimulation, three electrodes for tripolar stimulation, or multiple electrodes for multipolar stimulation. The ability to test specific electrode groups may be more accurate in determining the subjective and/or object responses to modulation fields that use more than one target poles. Each of the electrode groups to be tested, which may include one or more electrodes, may be turned on sequentially and/or cycled individually up to a stimulus setting (amplitude, pulse width, relevant stimulation frequency) that is sufficient to produce sensation for subject feedback or a biomarker for objective feedback. The cycling may be automatic or guided by a physician or other user. For example, a physician or other user may limit electrodes over which cycling will happen.
At 2276, subjective or objective feedback may be received to provide a patient-specific metric for the patient's response to the stimulation. Examples of such metrics may include but are not limited to tolerability or perception thresholds, and spatial distribution of the perception. An example of subjective feedback may include patient reports of paresthesia. Examples of objective feedback may include, but are not limited to, sensed evoked compound action potentials (ECAPs) over dorsal horn, local field potentials (LFPs), and dorsal root potential (indicating primary afferent depolarization) recorded by another electrode, biopotential templates, signal amplitudes, signal-to-noise ratio of electroneurograms (ENGs), and latency between stimulation artifact and evoked action potentials. One or more patient-specific metrics may be recorded for use in normalizing the target sub-perception modulation field. By way of example and not limitation, some metrics may include the lowest value, the highest value, the mean value or another aggregate value representing a quantitative output metric that may be user-defined or designed into the system. In an example illustrated in
At 2277, the target sub-perception modulation field is normalized for the patient. The target pole(s) referenced in the calibration is calculated with the assumption that the patient' spinal cord is uniform and flat. Values for an electrode's contribution to the sub-perception modulation field may be scaled in proportion to the magnitude of the output metric that was recorded for that electrode. This adjustment may be further adjusted according to a user-specified or built-in neural target, the pulse width of stimulation, the frequency of stimulation, the waveform shape, or another stimulation-relevant factor (time domain scaling). In the example illustrated in
As discussed above, inverse modeling method matches potentials/fields/activation functions produced by an electrode configuration to potentials/fields/activating functions specified by an idealized target pole. The observation points are defined. Prior systems assumed a constant electrode tissue coupling efficiency by assuming a constant distance between the electrode contacts and the observation points. For example, the spinal cord and electrode arrays were uniform and straight.
However, in reality, the electrode may not be fully aligned, the field may not be fully aligned, and/or the spinal cord is not perfectly straight.
Various embodiments may adjust the spatial observation points used to calculate the contributions of active electrodes to a target pole or to target poles such as target bipoles, target tripoles or other target multipoles. The adjustments to the spatial observation points 2579 may be based on characteristics of the target field and/or environment. For example, the adjustments to the spatial observation points 2579 may be based on the coupling strength between the neural tissue in the spinal cord 2578 and the electrode 2580 or patient-reported differences in paresthesia thresholds across electrode contacts.
A linear recalibration process or non-linear recalibration process 2785 may be used to normalize to the greater sum of anodic or cathodic currents. For example, the recalibration process 2785 may be a proportional recalibration process. The baseline for the recalibration process may be a common total amplitude for the ideal currents on the electrodes (e.g. 1.125 mA+0.375 mA=1.5 mA). The calibration may then be the product of the ideal current contribution and the feedback metric for each electrode or electrode group divided by the common total amplitude (1.5 mA). Therefore, in the illustrated the energy contribution of the first electrode is 1.125×1.5/1.5=1.125, the energy contribution of the second electrode is 0.375×3.0/1.5=0.75, the energy contribution of the third electrode is −0.375×3.0/1.5=−0.75, and the energy contribution of the fourth electrode is 0.375×3.0/1.5=0.75.
It is noted that the total anodic current will equal the total cathodic current. Extra cathodic current or extra anodic current may be allocated to the case electrode of the waveform generator. In the illustrated recalibration example 2785, the anodic currents add up to 1.875 mA (1.125+0.75=1.875) and the cathodic currents add up to 1.5 mA (−0.75 mA−0.75 mA=−1.5 mA). As the total anodic currents (1.125 mA+0.75 mA=1.875 mA) is larger than the total cathodic currents (0.75 mA+0.75 mA=1.5 mA), the allocated energy may be normalized to the 1.875 mA value of the total anodic currents. The normalization includes the ideal target energy allocations multiplied by the feedback metric divided by the normalized value. Thus, the contribution of the electrodes may be calculated as follows: 1.125/1.875=60%; 0.75/1.875=40%; −0.75/1.875=−40%; and −0.75/1.875=−40%. The remaining 20% of the anodic current may go onto the case of the implanted pulse generator.
In an observation point movement process to recalibrate, as generally illustrated at 2786, values for the potential (De or electric field E at the spatial observation point are not recalculated. Rather, the distance of the spatial observation points may be moved based on the space domain scaling factors determined from the subject and/or objective feedback. Thus, rather than scaling raw amplitude, the space domain scaling factors 2784 may be used to scale the spatial observation point distances. The baseline for the observation point movement recalibration process may be a common total amplitude for the ideal currents on the electrodes (e.g. 1.125 mA+0.375 mA=1.5 mA). Therefore, the distance between the observation points and the first electrode does not change (1.5/1.5=1), but the distance of the second and third electrodes is doubled (3/1.5=2) and the distance of the fourth electrode is ⅔ (1/1.5=⅔). In some embodiments, the calibration can globally change weighting for the inverse matrix.
Furthermore, in some embodiments, the spatial observation points may be repositioned based on electrode geometry, rather than or in addition to spinal geometry.
Calibrations may also vary according to the neural element(s) being targeted, especially if multiple field shapes and target poles meant to target distinct neural populations are placed. Various embodiments may scale the allocated energy (e.g. electrode fractionalizations/amplitudes) to provide the sub-perception field according to the electrode-to-electrode relative thresholds and a specific, user-defined neural target.
The threshold current required to stimulate excitable tissue has a relationship to a pulse duration, where shorter pulse widths require larger current amplitudes to stimulate the excitable tissue, and longer pulse widths require smaller current amplitudes to stimulate the excitable tissue. This relationship between pulse widths and stimulation threshold (amplitude) may be plotted as a strength duration curve. However, strength duration concepts are not limited to pulsed stimulation, as these concepts may also be applied to different waveform shapes. Strength-duration relationships may also differ by waveform shape, and separate strength-duration equations may be fit/saved for each waveform type (e.g. passive recharge vs. biphasic active recharge vs. sinusoidal). Strength-duration curves from different waveforms may be displayed at the same time and compared, as shown, for user reference. A user may be presented with strength-duration curve corresponding to neural element being targeted that may adapt after user changes settings. For example, when a neural element and a waveform are selected at same time, then multipliers from neural element effect and waveform effect may be stacked by multiplying the neural element factor with the waveform factor, and applying the resulting product to the original target pole configuration. Strength-duration relationships may also differ by waveform shape, and separate strength-duration equations may be fit/saved for each waveform type (e.g. passive recharge vs. biphasic active recharge vs. sinusoidal). Visually, strength duration curves for different neural element targets may differ. Strength duration curve may be displayed as current/voltage vs. pulse width or normalized threshold vs. pulse width.
In a non-exclusive embodiment, strength-duration relationships (Weiss, Lapicque, or otherwise) derived from offline simulations and/or on-board biophysical simulators may be saved on the device as a lookup table. That is, simulation apriori may be used to derive a scaling factor. The stored lookup table may be used to scale individual thresholds at different electrodes to maintain target pole effects across different neural elements. The lookup table may be specific to a specific combination of a waveform and target. In some embodiments, a built-in graphical user interface (GUI) function may be designed to enable a user to specify different neural targets at distinct electrodes and electrode groups.
In some embodiments, an algorithm may automatically specify different targets depending on the waveform being delivered. By way of example and not limitation, a 50 Hz signal may default to dorsal columns, and a high rate stimulation may default to terminals. Various embodiments of the system may display scaling factors to the user and/or strength-duration curves at a given frequency.
For example the pulse width for the first lead (Target 1) is illustrated 200 μs and the pulse width for the second lead (Target 2) is illustrated as 90 μs and a frequency of 1.2 kHz. A different multiplication may be applied for the first lead depending on the nerve size of terminals. In the illustrated non-limiting example, a nerve size of 5.7 μm may have a factor of 1, a nerve size of 2.5 may have a factor of 2, and a nerve size of 1.3 μm may have a factor of 4. Similarly, a different multiplication may be applied for the second lead depending on the neural target, such as but not limited to terminal, dorsal column (DC) axon, dorsal root (DR) axon, or nerve cell, and waveform. Examples of waveform may include, but are not limited to pulse, rectangular, sinusoidal, or custom.
Space domain factors may be used to first to calculate electrode amplitudes/configurations based on electrode and spinal geometry as previously described, and then time domain targeting may be used to scale the total current delivered through an electrode or group of electrodes corresponding to specific waveform neural target according to values of the applicable strength duration curve.
Various embodiments of the present subject matter allow for both space domain and time domain scaling. Some embodiments enable a user-selected weighting for spatial versus temporal fractionalization adjustments. The weighing may be limited to selection of preprogrammed weight or may be at the discretion of the user. For example, a user interface may include a feature such as a slider or dial used to define transitions between spatial and temporal scaling.
In
The transition between spatial and temporal could be linear (as illustrated by the 50% and 100% temporal scaling in
The illustrated programming control circuitry 3308 includes circuitry 3312 configured to program ideal target pole(s) and/or target modulation fields. Some embodiments may further include circuitry 3313 configured for use in selecting the electrode groups (e.g. monopolar, bipolar, tripolar or other multipolar arrangement) to be used in the calibration process. Calibration routine circuitry 3314, and scaling circuitry 3315 may be incorporated only into the external device(s), only into the modulation device, or distributed between or among the external device(s) and the modulation device. The calibration routine circuitry controls the calibration of the different electrode groups to measure feedback and provide the feedback metric used to scale the allocated energy used to provide the ideal target pole. For example, a calibration routine may be configured to test different combinations if electrodes in a bipole arrangement to provide a feedback metric for each bipole arrangement. The scaling circuitry may be configured to perform functions described above to determine and apply the space domain scaling factor and/or the time domain scaling factor to the allocated energy.
It is noted that a circuit or circuitry may be implemented as part of a microprocessor circuit, which may be a dedicated processor such as a digital signal processor, application specific integrated circuit (ASIC), microprocessor, or other type of processor for processing information including physical activity information. The microprocessor circuit may be a general purpose processor that may receive and execute a set of instructions of performing the functions, methods, or techniques described herein. The circuit or circuitry may be implemented as one or more other circuits or sub-circuits that may, alone or in combination, perform the functions, methods or techniques described herein. In an example, hardware of the circuit set may be immutably designed to carry out a specific operation (e.g., hardwired). In an example, the hardware of the circuit set may include variably connected physical components (e.g., execution units, transistors, simple circuits, etc.) including a computer readable medium physically modified to encode instructions of the specific operation. Instructions may enable embedded hardware (e.g., the execution units or a loading mechanism) to create members of the circuit set in hardware via variable connections to carry out portions of the specific operation when in operation. Accordingly, the computer readable medium is communicatively coupled to the other components of the circuit set member when the device is operating. In an example, any of the physical components may be used in more than one member of more than one circuit set. For example, under operation, execution units may be used in a first circuit of a first circuit set at one point in time and reused by a second circuit in the first circuit set, or by a third circuit in a second circuit set at a different time.
The terms “tangible” and “non-transitory,” as used herein, are intended to describe a machine-readable storage medium such as a computer-readable storage medium (or “memory”) excluding propagating electromagnetic signals, but are not intended to otherwise limit the type of physical computer-readable storage device that is encompassed by the phrase computer-readable medium or memory. By way of example and not limitation, a machine may include a modulation device or a programming device such as a remote control or clinician programmer. For instance, the terms “non-transitory computer readable medium” or “tangible memory” are intended to encompass types of storage devices that do not necessarily store information permanently, including for example, random access memory (RAM).
The term “machine readable medium” may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) configured to store instructions, and includes any medium that is capable of storing, encoding, or carrying instructions for execution by the machine and that cause the machine to perform any one or more of the techniques of the present disclosure, or that is capable of storing, encoding or carrying data structures used by or associated with such instructions. Non-limiting machine readable medium examples may include solid-state memories, and optical and magnetic media. In an example, a machine readable medium include: nonvolatile memory, such as semiconductor memory devices (e.g., Electrically Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM)) and flash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. Program instructions and data stored on a tangible computer-accessible storage medium in non-transitory form may further be transmitted by transmission media or signals such as electrical, electromagnetic, or digital signals, which may be conveyed via a communication medium such as a network and/or a wireless link.
Method examples described herein can be machine or computer-implemented at least in part. Some examples may include a computer-readable medium or machine-readable medium encoded with instructions operable to configure an electronic device or system to perform methods as described in the above examples. An implementation of such methods can include code, such as microcode, assembly language code, a higher-level language code, or the like. Such code can include computer readable instructions for performing various methods. The code can form portions of computer program products. Further, the code can be tangibly stored on one or more volatile or non-volatile computer-readable media during execution or at other times.
Various embodiments are illustrated in the figures above. One or more features from one or more of these embodiments may be combined to form other embodiments.
The above detailed description is intended to be illustrative, and not restrictive. The scope of the disclosure should, therefore, be determined with references to the appended claims, along with the full scope of equivalents to which such claims are entitled.
The above detailed description is intended to be illustrative, and not restrictive. The scope of the disclosure should, therefore, be determined with references to the appended claims, along with the full scope of equivalents to which such claims are entitled.
This application is a continuation of U.S. application Ser. No. 17/081,752, filed Oct. 27, 2020, which is a continuation of U.S. application Ser. No. 15/882,549, filed Jan. 29, 2018, which claims the benefit of priority under 35 U.S.C. § 119(e) of U.S. Provisional Patent Application Ser. No. 62/451,999, filed on Jan. 30, 2017, each of which is herein incorporated by reference in its entirety.
Number | Date | Country | |
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62451999 | Jan 2017 | US |
Number | Date | Country | |
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Parent | 17081752 | Oct 2020 | US |
Child | 18775815 | US | |
Parent | 15882549 | Jan 2018 | US |
Child | 17081752 | US |