Neural stimulation device providing sub-perception stimulation

Abstract

Techniques for adjusting sub-perception stimulation applied to a patient by an implantable pulse generator (IPG) are disclosed. The adjustment can be made by using one or more modulation functions associated with a stimulation modulation algorithm that adjusts the total charge provided to the patient by the stimulation as a function of time. The modulation functions and algorithm can adjust the charge by duty cycling the stimulation or by adjusting sub-perception stimulation parameters, and such adjustment can be made in the IPG or an external device. The stimulation modulation algorithm can use one or more models in adjusting the stimulation parameters to keep them at optimal values for sub-perception stimulation while adjusting the charge stimulation as prescribed by the modulation functions.

Description

Technical Field

[0001] This application generally relates to implantable medical devices (IMDs), more specifically to spinal cord stimulators, and to methods for controlling such devices. Background Technology

[0002] Implantable neurostimulatory devices are devices that generate and deliver electrical stimulation to the body's nerves and tissues for the treatment of various biological disorders, such as pacemakers for treating arrhythmias, defibrillators for treating cardiac fibrillation, cochlear stimulators for treating deafness, retinal stimulators for treating blindness, muscle stimulators for generating coordinated limb movements, spinal cord stimulators for treating chronic pain, cortical and deep brain stimulators for treating motor and psychological disorders, and other neurostimulators for treating urinary incontinence, sleep apnea, shoulder subluxation, etc. The following description will generally focus on the use of the invention within spinal cord stimulation (SCS) systems, such as those disclosed in U.S. Patent 6,516,227. However, the invention can find applicability to any implantable neurostimulatory device system.

[0003] SCS systems typically include Figure 1 The implantable pulse generator (IPG) 10 is shown in the diagram. The IPG 10 includes a biocompatible device housing 12 that houses the circuitry and battery 14 required for IPG operation. The IPG 10 is coupled to electrodes 16 via one or more electrode leads 15 forming an electrode array 17. The electrodes 16 are configured to contact the patient's tissue and are supported on a flexible body 18, which also houses an individual lead wire 20 coupled to each electrode 16. The lead wire 20 is also coupled to a proximal contact 22, which can be inserted into a lead connector 24 within a head 23 secured to the IPG 10. This head may include, for example, epoxy resin. Once inserted, the proximal contact 22 connects to a head contact within the lead connector 24, which is then fed through a feedthrough pin to the circuitry within the housing 12, although these details are not shown.

[0004] In the illustrated IPG 10, there are sixteen lead electrodes (E1 to E16) split between two leads 15, with the head 23 containing a 2x1 array of lead connectors 24. However, the number of leads and electrodes in the IPG is application-specific and can therefore vary. The conductive housing 12 may also include electrodes (Ec). In SCS applications, the electrode leads 15 are typically implanted near the dura mater in the patient's spine on either side of the midline of the spinal cord. Proximal electrodes 22 tunnel through the patient's tissue to a distal location such as the hip where the IPG housing 12 is implanted, at which point they are coupled to the lead connectors 24. In other IPG examples designed for direct implantation at sites requiring stimulation, the IPG may be leadless, with 16 electrodes alternatively appearing on the body of the IPG for contact with the patient's tissue. The IPG leads 15 may be integrated with and permanently attached to the housing 12 in other IPG solutions. The purpose of SCS therapy is to provide electrical stimulation from the electrodes 16 to relieve the patient's symptoms, most notably chronic back pain.

[0005] IPG 10 may include an antenna 26a that allows it to communicate bidirectionally with multiple external devices, such as Figure 4 As shown in the image. Figure 1 The antenna 26a depicted is shown as a conductive coil within the housing 12, although a coil antenna 26a could also be present in the head 23. When the antenna 26a is configured as a coil, communication with external devices preferably occurs using near-field magnetic induction. The IPG may also include a radio frequency (RF) antenna 26b. Figure 1 In the diagram, the RF antenna 26b is shown within the head 23, but it may also be within the housing 12. The RF antenna 26b may include patches, slots, or wires and may operate as a single pole or a dual pole. The RF antenna 26b preferably uses far-field electromagnetic waves for communication. The RF antenna 26b can operate according to any number of known RF communication standards, such as Bluetooth, Zigbee, WiFi, and MICS and the like.

[0006] In IPG 10, stimulation is typically provided by pulses, such as... Figure 2 As shown in the diagram. Stimulation parameters typically include: the amplitude of the pulse (A; whether current or voltage); the frequency (F) and pulse width (PW); the electrode 16 (E) activated to provide such stimulation; and the polarity (P) of such active electrode, i.e., whether the active electrode acts as an anode (source current to tissue) or a cathode (sink current from tissue). These stimulation parameters, taken into account together, include the stimulation procedure that the IPG 10 can perform to provide therapeutic stimulation to the patient.

[0007] exist Figure 2 In the example, electrode E5 has been selected as the anode, and thus provides a pulse that draws a positive current of amplitude +A into the tissue. Electrode E4 has been selected as the cathode, and thus provides a pulse that infuses the tissue with a corresponding negative current of amplitude -A. This is an example of bipolar stimulation, where only two lead-based electrodes (one anode and one cathode) are used to provide stimulation to the tissue. However, more than one electrode can act as the anode at a given time, and more than one electrode can act as the cathode at a given time (e.g., tripolar stimulation, quadrupole stimulation, etc.).

[0008] like Figure 2 The pulses shown are biphasic, comprising a first phase 30a followed by a second phase 30b of opposite polarity. The use of biphasic pulses is known to be useful in effective charge recovery. For example, the current path to each electrode of the tissue may include a series-connected DC blocking capacitor, see, for example, U.S. Patent Application Publication 2016 / 0144183, which will be charged during the first phase 30a and discharged (recovered) during the second phase 30b. In the example shown, the first phase 30a and the second phase 30b have the same duration and amplitude (although opposite polarities), which ensures the same amount of charge during both phases. However, the second phase 30b can also be charged in balance with the first phase 30a if the integrals of the amplitude and duration of the two phases are equal in magnitude, as is well known. The width PW of each pulse is defined herein as the duration of the first pulse phase 30a, although the pulse width may also refer to the total duration of the first pulse phase 30a and the second pulse phase 30b. Note that an interphase period (IP) may be provided between the two phases 30a and 30b, during which no stimulation is provided.

[0009] The IPG 10 includes a stimulation circuit 28, which can be programmed to generate stimulation pulses at electrodes as defined by a stimulation program. The stimulation circuit 28 may include, for example, the circuits described in U.S. Patent Application Publications 2018 / 0071513 and 2018 / 0071520, or in USP 8,606,362 and 8,620,436. These documents are incorporated herein by reference.

[0010] Figure 3An external test stimulation environment is shown that can be used before IPG 10 is implanted into a patient. During external test stimulation, stimulation can be attempted on the intended implantation patient without actually implanting IPG 10. In contrast, one or more test leads 15' are implanted in the patient's tissue 32 at a target location 34, such as within the spine as previously described. The proximal ends of one or more test leads 15' exit from the incision 36 and are connected to an external test stimulator (ETS) 40. The ETS 40 generally mimics the operation of IPG 10 and can therefore deliver stimulation pulses to the patient's tissues as described above. See, for example, 9,259,574, which discloses a design for an ETS. The ETS 40 is typically worn externally by the patient for a short period (e.g., two weeks), which allows the patient and their clinician to experiment with different stimulation parameters to try and find stimulation procedures that relieve the patient's symptoms (e.g., pain). If the external test stimulus proves successful, one or more test leads 15' are removed, and a complete IPG10 and one or more leads 15 are implanted as described above; if unsuccessful, only one or more test leads 15' are removed.

[0011] Similar to the IPG 10, the ETS 40 may include one or more antennas that enable bidirectional communication with external devices. Figure 4 Further explanation. Such antennas may include a near-field magnetic induction coil antenna 42a and / or a far-field RF antenna 42b, as previously described. The ETS 40 may also include stimulation circuitry 44 capable of generating stimulation pulses according to a stimulation program; this circuitry may be similar to or include the same stimulation circuitry 28 present in the IPG 10. The ETS 40 may also include a battery (not shown) for operating power.

[0012] Figure 4 Various external devices capable of wirelessly transmitting data with IPG 10 and ETS 40 are shown, including a patient-handheld external controller 45 and a clinician programmer 50. Devices 45 and 50 can both be used to send stimulation programs to IPG 10 or ETS 40—that is, to program their stimulation circuits 28 and 44 to generate the previously described pulses with the desired shape and timing. Devices 45 and 50 can also both be used to adjust one or more stimulation parameters of the stimulation program currently being executed by IPG 10 or ETS 40. Devices 45 and 50 can also receive information from IPG 10 or ETS 40, such as various status information, etc.

[0013] External controller 45 may be as described, for example, in U.S. Patent Application Publication 2015 / 0080982, and may include any dedicated controller configured to work with IPG 10. External controller 45 may also include general-purpose mobile electronic devices such as mobile phones that have been programmed to allow them to function as wireless controllers for IPG 10 or ETS 40 in medical device applications (MDAs), as described in U.S. Patent Application Publication 2015 / 0231402. External controller 45 includes a user interface comprising means for inputting commands (e.g., buttons or icons) and a display 46. The user interface of external controller 45 enables the patient to adjust stimulation parameters, although it may have limited functionality compared to the more powerful clinician programmer 50, as described later.

[0014] The external controller 45 may have one or more antennas capable of communicating with IPG 10 and ETS 40. For example, the external controller 45 may have a near-field magnetic induction coil antenna 47a, which is capable of wireless communication with coil antennas 26a or 42a in IPG 10 or ETS 40. The external controller 45 may also have a far-field RF antenna 47b, which is capable of wireless communication with RF antennas 26b or 42b in IPG 10 or ETS 40.

[0015] The external controller 45 may also have control circuitry 48, such as a microprocessor, microcomputer, FPGA, or other digital logic structure capable of executing instructions from electronic devices. Control circuitry 48 may, for example, receive patient adjustments to stimulation parameters and create stimulation programs that will be wirelessly transmitted to IPG 10 or ETS 40.

[0016] The clinician programmer 50 is further described in U.S. Patent Application Publication 2015 / 0360038, and is only briefly described here. The clinician programmer 50 may include: a computing device 51, such as a desktop computer, laptop computer, notebook computer, tablet computer, mobile smartphone, personal digital assistant (PDA) type mobile computing device, etc. Figure 4 In the diagram, computing device 51 is shown as a portable computer, which includes typical computer user interface devices such as screen 52, mouse, keyboard, speakers, stylus, printer, etc., which are not all shown for convenience. Figure 4 Also shown are auxiliary devices for the clinician programmer 50, which are typically dedicated to its operation as a stimulation controller, such as a communication “stick” 54 and a joystick 58, which can be coupled to a suitable port on the computing device 51, such as, for example, a USB port 59.

[0017] The antenna used in the clinician programmer 50 for communication with the IPG 10 or ETS 40 may depend on the type of antenna included in those devices. If the patient's IPG 10 or ETS 40 includes a coil antenna 26a or 42a, the rod 54 may similarly include a coil antenna 56a for establishing near-field magnetic induction communication at close range. In this example, the rod 54 may be secured close to the patient, for example, by placing it in a strap or sleeve that can be worn by the patient and is near the patient's IPG 10 or ETS 40.

[0018] If IPG 10 or ETS 40 includes RF antenna 26b or 42b, then stick 54, computing device 41, or both may similarly include RF antenna 56b to establish communication with IPG 10 or ETS 40 over greater distances. (In this case, stick 54 may not be necessary). Clinician programmer 50 may also communicate wirelessly or via a wired link provided at an Ethernet or network port with other devices and networks, such as the Internet.

[0019] To program the stimulation procedures or parameters of IPG 10 or ETS 40, the clinician interfaces with a clinician programmer graphical user interface (GUI) 64 provided on a display 52 of computing device 51. As will be understood by those skilled in the art, the GUI 64 can be presented by executing clinician programmer software 66 on computing device 51, which can be stored on the device's non-volatile memory 68. Those skilled in the art will further appreciate that the execution of the clinician programmer software 66 on computing device 51 can be facilitated by control circuitry 70 (such as a microprocessor, microcomputer, FPGA, other digital logic structures capable of executing programs in the computing device, etc.). In addition to executing the clinician programmer software 66 and presenting the GUI 64, such control circuitry 70 can also enable communication via antennas 56a or 56b to transmit selected stimulation parameters to the patient's IPG 10 through the GUI 64.

[0020] exist Figure 5 A portion of GUI 64 is shown as an example. Those skilled in the art will understand that the details of GUI 64 will depend on the position of the clinician programmer software 66 in its execution, and will depend on the GUI choices made by the clinician. Figure 5A GUI 64 is shown at one point, allowing stimulation parameters to be set for a patient and stored as a stimulation program. A program interface 72 is shown on the left, which, as further described in disclosure '038, allows naming, loading, and saving stimulation programs for a patient. A stimulation parameter interface 82 is shown on the right, where specified stimulation parameters (A, D, F, E, P) can be defined for the stimulation program. Values ​​of the stimulation parameters related to waveform shape (A; in this example, current), pulse width (PW), and frequency (F) are shown in the waveform parameter interface 84, which includes buttons that clinicians can use to increase or decrease these values.

[0021] The stimulation parameters associated with electrode 16 (the activated electrode E and its polarity P) can be adjusted in the electrode parameter interface 86. The electrode stimulation parameters are also visible and operable in the lead interface 92, which shows the lead 15 (or 15') approximately in its proper position relative to each other, for example, on the left or right side of the spine. A cursor 94 (or other selection device such as a mouse pointer) can be used to select a specific electrode in the lead interface 92. Buttons in the electrode parameter interface 86 allow the selected electrode (including the housing electrode Ec) to be designated as anode, cathode, or off. The electrode parameter interface 86 also allows the relative intensity of the anodic or cathodic current of the selected electrode to be specified as a percentage X. This is particularly useful if, as described in '038 disclosure, more than one electrode acts as anode or cathode at a given time. Figure 2 The example waveforms shown, as illustrated in lead interface 92, indicate that electrode E5 has been selected as the sole anode for the pull-out current, and this electrode receives X = 100% of the specified anode current +A. Similarly, electrode E4 has been selected as the sole cathode for the sink current, and this electrode receives X = 100% of the cathode current -A.

[0022] As shown, GUI 64 specifies the pulse width PW for only the first pulse phase 30a. Nevertheless, the clinician programmer software 66, which runs GUI 64 and receives input from it, ensures that IPG 10 and ETS 40 are programmed to present the stimulation program as a biphasic pulse when biphasic pulses are used. For example, the clinician programming software 66 can automatically determine the duration and amplitude of both pulse phases 30a and 30b (e.g., each with a duration of PW and opposite polarities +A and -A). The advanced menu 88 can also be used (among other things) to define the relative duration and amplitude of pulse phases 30a and 30b, and allows for other more advanced modifications, such as setting the duty cycle (on / off time) of the stimulation pulses, and the rise time it takes for the stimulus to reach its programmed amplitude (A). The mode menu 90 allows the clinician to select different modes for determining the stimulation parameters. For example, as described in '038 disclosure, mode menu 90 can be used to enable electronic trolling, which includes an automatic programming mode that performs current guidance along the electrode array by moving the cathode in a bipolar manner.

[0023] Although GUI 64 is shown as operating in the clinician programmer 50, the user interface of the external controller 45 can also provide similar functionality. Summary of the Invention

[0024] A system is disclosed that may include: a stimulator device implantable in a patient and including a plurality of electrodes; and an external device programmed using a model, wherein the external device is configured to target the patient and determine, according to the model, a first stimulation parameter defining a first stimulation pulse, wherein the external device is further configured to determine a modulation function to be applied to the first stimulation parameter to form a modulated stimulation pulse, wherein the modulation function modulates the charge delivered to the patient by the modulated stimulation pulse as a function of time, wherein the external device is configured to transmit information to the stimulator device such that the stimulator device provides the modulated stimulation pulse at one or more of the electrodes.

[0025] In one example, the model is derived for the patient based on data obtained from the patient. In one example, the model is configured to determine the first stimulation parameters such that the modulated stimulation pulses provide subsensory stimulation to the patient. In one example, the external device is configured to apply the modulation function to the first stimulation parameters. In one example, the information sent to the stimulator device includes the first stimulation parameters and an on / off schedule or duty cycle, wherein the on / off schedule or duty cycle modulates the charge delivered to the patient by the modulated stimulation pulses as a function of time. In one example, the modulated stimulation pulses at one or more of the electrodes comprise: stimulation pulses formed at one or more electrodes according to the first stimulation parameters modulated as an on / off schedule of the duty cycle. In one example, the information sent to the stimulator device includes modified stimulation parameters, wherein the modulated stimulation parameters determine the modified stimulation pulses according to the modulation function. In one example, the modulated stimulation parameters are determined according to the model. In one example, the information sent to the stimulator device includes modulated stimulation parameters updated as a function of time according to the modulation function. In one example, the stimulator device is configured to apply the modulation function to the first stimulation parameter to provide the modulated stimulation pulse. In one example, the information sent to the stimulator device includes the modulation function and the first stimulation parameter. In one example, the modulation function includes an on / off schedule or duty cycle, wherein the on / off schedule or duty cycle modulates the charge delivered to the patient by the modulated stimulation pulse as a function of time. In one example, the modulated stimulation pulse includes: a stimulation pulse formed at one or more electrodes according to the first stimulation parameter modulated as by the on / off schedule of the duty cycle.

[0026] A method for adjusting stimulation provided by an implantable stimulator device comprising a plurality of electrodes is disclosed. The method may include: determining, for a patient with the stimulator device implanted, a first stimulation parameter defining a first stimulation pulse based on a model; determining a modulation function to be applied to the first stimulation parameter to form a modulated stimulation pulse, wherein the modulation function modulates the charge delivered to the patient by the modulated stimulation pulse as a function of time; and providing the modulated stimulation pulse at one or more of the electrodes of the stimulator device.

[0027] In one example, the method further includes acquiring data from the patient to derive the model. In one example, the model determines the first stimulation parameters such that modulated stimulation pulses provide subsensory stimulation to the patient. In one example, the modulation function is applied to the first stimulation parameters in an external device communicating with the stimulator device. In one example, the method further includes sending the first stimulation parameters and an on / off schedule or duty cycle from the external device to the stimulator device, wherein the on / off schedule or duty cycle modulates the charge delivered to the patient by the modulated stimulation pulses as a function of time. In one example, the modulated stimulation pulses at one or more of the electrodes comprise stimulation pulses formed at one or more electrodes according to the first stimulation parameters modulated by the on / off schedule of the duty cycle. In one example, the method further includes sending modified stimulation parameters from the external device to the stimulator device, wherein the modulated stimulation parameters determine the modified stimulation pulses according to the modulation function. In one example, the modulated stimulation parameters are determined according to the model. In one example, the modulated stimulation parameters sent from the external device are updated according to the modulation function as a function of time. In one example, in the stimulator device, the modulation function is applied to the first stimulation parameter. In one example, the method further includes sending the modulation function and the first stimulation parameter to the stimulator device. In one example, the modulation function includes an on / off schedule or duty cycle, wherein the on / off schedule or duty cycle modulates the charge delivered to the patient by the modulated stimulation pulse as a function of time. In one example, the modulated stimulation pulse includes: a stimulation pulse formed at one or more electrodes according to the first stimulation parameter modulated as by the on / off schedule of the duty cycle.

[0028] Various aspects of the present invention may also be embodied in a computer-readable medium, as further explained below. Attached Figure Description

[0029] Figure 1 An implantable pulse generator (IPG) for use in spinal cord stimulation (SCS) according to the prior art is shown.

[0030] Figure 2 An example of stimulation pulses that can be generated by IPG according to existing technology is shown.

[0031] Figure 3 The use of an external experimental stimulator (ETS) for providing stimulation prior to IPG implantation, based on existing technology, is illustrated.

[0032] Figure 4Various external devices, based on existing technology, are shown that are capable of communicating with IPG and ETS and programming stimuli in IPG and ETS.

[0033] Figure 5 A graphical user interface (GUI) for a clinician programmer external device for setting or adjusting stimulation parameters, based on existing technology, is shown.

[0034] Figure 6 A sweetspot search is shown for determining the effective electrode for a patient using a movable subsensory bipolar point.

[0035] Figures 7A to 7D A dessert search is shown for determining effective electrodes for a patient using a movable ultrasensing bipolar point.

[0036] Figure 8 A stimulation circuit that can be used in IPG or ETS is shown, which is capable of providing multiple independent current controls for independently setting the current at each of the electrodes.

[0037] Figure 9 A flowchart is shown of a study conducted on various patients with back pain, designed to determine optimized subsensory SCS stimulation parameters in the frequency range of 1 kHz to 10 kHz.

[0038] Figures 10A to 10C Various research results are shown as a function of stimulation frequency in the frequency range of 1 kHz to 10 kHz, including: average optimized pulse width ( Figure 10A ), average charge per second and optimized stimulus amplitude ( Figure 10B ), and back pain score ( Figure 10C ).

[0039] Figures 11A to 11C Further analysis of the relationship between the average optimized pulse width and frequencies in the 1kHz to 10kHz frequency range is shown, along with the identification of statistically significant regions for optimization of these parameters.

[0040] Figure 12A Results of patients tested using subsensory therapy at or below 1 kHz are shown, along with the optimized pulse width range determined at the tested frequency and the optimized pulse width v. frequency region for subsensory therapy.

[0041] Figure 12B The relationship between the average optimized pulse width and frequencies of 1 kHz or below is shown in various modeling examples.

[0042] Figure 12C The duty cycle of the optimized pulse width as a function of frequencies of 1 kHz or below is shown.

[0043] Figure 12D The average battery current and battery discharge time are shown at optimized pulse widths as a function of frequencies of 1 kHz or below.

[0044] Figure 13A and Figure 13B The results of an additional test are shown, which validated the previously proposed relationship between frequency and pulse width.

[0045] Figure 14 The fitting module is shown, illustrating how the relationship and region determined with respect to optimized pulse width and frequency (≤10kHz) can be used to set subsensory stimulation parameters for IPG or ETS.

[0046] Figure 15 The algorithm used for supersensory dessert search prior to subsensory therapy is shown, along with possible optimizations for subsensory therapy using a fitting module.

[0047] Figure 16 An alternative algorithm for optimizing subsensory therapy using a fitting module is shown.

[0048] Figure 17A and Figure 17B An analysis of optimized subsensory stimulus parameters is presented, which includes a model of energy (average charge per second) versus frequency.

[0049] Figures 17C-17E Various algorithms are shown, through which... Figure 17A and Figure 17B The model can be used for the selected optimized subsensory stimulus parameters.

[0050] Figure 17F This demonstrates how energy models can be viewed from the perspective of stimulation parameters other than frequency. For example, energy can be modeled in relation to pulse width.

[0051] Figure 18 A model derived from the patient is shown, which illustrates a surface representing optimized subsensory values ​​for frequency and pulse width, and also includes the patient's perception threshold pth as measured at these frequencies and pulse widths.

[0052] Figure 19A and Figure 19B The relationship between the perception threshold pth and pulse width plotted for many patients is shown, and how the results can be curve-fitted.

[0053] Figure 20 A graph showing the relationship between the parameter Z and the pulse width for a patient is shown, where Z includes the patient's optimized amplitude A, expressed as a percentage of the perception threshold pth (i.e., Z = A / pth).

[0054] Figures 21A-21F An algorithm is shown that is used to... Figures 18-20 The modeling information is used to derive the range of optimized sub-sensory stimulation parameters (e.g., F, PW, and A) for patients using sensory threshold measurements performed on them.

[0055] Figure 22 The use of optimized stimulation parameters in an external patient controller is shown, including a user interface that allows the patient to adjust the stimulation within a range.

[0056] Figures 23A-23F The effect of statistical variance in the modeling is illustrated, resulting in the optimized stimulation parameters determined for the patient potentially occupying a certain volume. The user interface of the patient's external controller is also shown, allowing the patient to adjust the stimulation within this volume.

[0057] Figures 24A-24J Various examples of using modulation functions as time functions to regulate the charge supplied to the patient are shown.

[0058] Figure 25 The stimulation mode user interface is shown, from which patients can select different stimulation modes to provide stimulation or allow patients to control stimulation using different subsets of stimulation parameters determined using optimized stimulation parameters.

[0059] Figures 26A-31B Examples of different subsets of stimulation parameters based on the patient's choice of different stimulation modalities are shown. The graph labeled A (e.g.) Figure 26A The graph labeled B shows the frequency and pulse width of the subset, while the graph labeled B (e.g.) Figure 26B The figures show the amplitude and perception threshold of the subset. These figures illustrate that the subsets of stimulus parameters corresponding to different stimulus modes may include parameters that are fully constrained by the determined optimized stimulus parameters (i.e., entirely within them), or parameters that are only partially constrained by the optimized stimulus parameters.

[0060] Figure 32 An automatic mode is shown, in which the IPG and / or external controller are used to determine when a specific stimulation mode should be automatically entered based on the sensed information.

[0061] Figure 33 Another example of a simulation mode user interface is shown, in which stimulation modes are presented for selection on a two-dimensional representation of stimulation parameters, although a three-dimensional representation indicating subset volume can also be used.

[0062] Figure 34 The GUI aspect that allows patients to adjust stimulation is shown, where the adjustment aspect is combined with suggested stimulation areas for the patient.

[0063] Figure 35A and Figure 35C Different methods are shown for automatically adjusting one or more stimulus parameters within a defined range or volume of optimized stimulus parameters.

[0064] Figure 35B It shows what can be used to automatically generate Figure 35A The GUI for adjustment.

[0065] Figure 36 It shows that, in addition to one or more of the determined optimized stimulation parameters, the position or focus of the pole configuration can also be changed.

[0066] Figure 37 A specific example of adjustment within a range or volume of determined optimized stimulation parameters is shown, in which pulse width and frequency are adjusted between different time periods.

[0067] Figure 38A and Figure 38B A specific example of adjustment within a determined range or volume of optimized stimulus parameters is shown, where the stimulus is provided by a stimulus boluse.

[0068] Figure 39 A GUI, preferably presented on an external controller for the patient, is shown, which allows the patient to move to a position within an electrode array where subsensory stimulation is applied.

[0069] Figures 40A-40H The patient was shown using Figure 39 The GUI moves the location of subsensory stimuli to different positions in the electrode array, including marking the location of therapeutic interest and storing new stimulation programs.

[0070] Figure 41A It shows that they can be combined Figure 39 The GUI uses stored datasets, including storing stimulus parameters along with locations marked by patients or new procedures, while Figure 41B The user interface shows a setting that allows patients to select procedures or tags and view their patient rankings.

[0071] Figure 42 The use of a fitting algorithm is illustrated, which selects the optimal stimulation parameters from a range or volume of optimized stimulation parameters determined for the patient using patient fitting information.

[0072] Figures 43A-43C The diagram illustrates receiving patient fitting information, including pain information, mapping information, field information, and patient phenotype information, at the GUI of an external device.

[0073] Figure 44 The results show that fitting information can be determined and used by the fitting algorithm as a function of patient posture.

[0074] Figures 45A-45C The flowchart illustrates how the fitting algorithm can process fitting information and optimized stimulation parameters based on training data to determine the optimal stimulation parameters for the patient.

[0075] Figure 46 Alternative fitting algorithms are shown, in which optimal stimulation parameters are determined using patient-fit information and non-patient-specific models. Detailed Implementation

[0076] While spinal cord stimulation (SCS) can be an effective way to relieve pain, it can also cause sensory disturbances. These disturbances (sometimes referred to as "supra-perception") are sensations that can accompany SCS treatment, such as numbness, tingling, heat, or cold. Typically, the effects of sensory disturbances are mild, or at least do not overly affect the patient. Furthermore, for patients whose chronic pain is now controlled by SCS, sensory disturbances are often a moderate compromise. Some patients even find sensory disturbances comfortable and reassuring.

[0077] Nevertheless, at least for some patients, SCS treatment will ideally provide complete pain relief without sensory abnormalities—often referred to as "sub-perception" or subthreshold treatment, where the patient cannot perceive the pain. Effective sub-perception treatment can provide pain relief without sensory abnormalities by delivering stimulation pulses at higher frequencies. Unfortunately, such higher-frequency stimulation may require more power, which tends to deplete the IPG 10's battery 14. See, for example, U.S. Patent Application Publication 2016 / 0367822. If the IPG's battery 14 is a single-use and non-rechargeable battery, high-frequency stimulation means that the IPG 10 will need to be replaced more frequently. Alternatively, if the IPG battery 14 is rechargeable, the IPG 10 will need to be charged more frequently over longer periods. Either way, this will be inconvenient for the patient.

[0078] In SCS applications, the goal is to determine a stimulation procedure that will be effective for each patient. A crucial part of determining an effective stimulation procedure is identifying the "sweet spot" for stimulation in each patient—that is, choosing which electrode should be activated (E) and what polarity (P) and relative amplitude (X%) to recruit and thus treat the nerve site where the pain originates in the patient. Selecting an electrode adjacent to that nerve site of pain can be difficult to determine, and typically, trial and error is performed to select the optimal combination of electrodes to provide treatment for the patient.

[0079] As described in U.S. Patent Application Publication 2019 / 0366104 (expressly incorporated herein by reference), when using subsensory stimulation, selecting electrodes for a given patient can be even more difficult because the patient does not feel the stimulation, and therefore may have difficulty sensing whether the stimulation is “covering” his pain and thus whether the selected electrode is effective. Furthermore, subsensory stimulation may require a “washin” period before it can become effective. This washin period can take a day or more, and therefore subsensory stimulation may not be immediately effective, making electrode selection even more challenging.

[0080] Figure 6 The technique disclosed in '104 for dessert search is briefly described, namely, how electrodes can be selected for painful nerve sites 298 in a nearby patient when using subsensory stimulation. Figure 6 The technology is particularly useful in trial settings after patients have had an electrode array implanted for the first time (i.e., after they have received their IPG or ETS).

[0081] In the example shown, it is assumed that the pain site 298 is likely within tissue region 299. Such region 299 can then be deduced by a clinician based on the patient's symptoms (e.g., by understanding which electrodes are adjacent to specific vertebrae (not shown), such as within the T9-T10 intervertebral space). In the example shown, region 299 is defined by electrodes E2, E7, E15, and E10, meaning that electrodes outside this region (e.g., E1, E8, E9, E16) are unlikely to affect the patient's symptoms. Therefore, these electrodes may not be necessary. Figure 6 The desserts selected during the search are as described below.

[0082] exist Figure 6 In this design, a subsensory bipolar point 297a is selected, where one electrode (e.g., E2) is chosen as the anode to draw a positive current (+A) into the patient tissue, while the other electrode (e.g., E3) is chosen as the cathode to infuse a negative current (-A) into the tissue. This is similar to previous designs. Figure 2The description explains that biphasic stimulation pulses can be used with effective charge recovery. Because the bipolar 297a provides subsensory stimulation, the amplitude A used during dessert search is down-titrated until the patient no longer experiences paresthesia. This subsensory bipolar 297a is provided to the patient over a period of time (such as several days), which allows the potential effectiveness of the subsensory bipolar to be “washed in” and allows the patient to provide feedback on how well the bipolar 297a helps the patient’s symptoms. Such patient feedback can include pain scale rankings. For example, the patient can use the Numerical Rating Scale (NRS) or the Visual Analogue Scale (VAS) to rank their pain on a scale from 1 to 10, where 1 represents no pain or almost no pain, and 10 represents the most imaginable pain. As discussed in disclosure '104, such pain scale rankings can be input into the patient’s external controller 45.

[0083] After testing the bipolar point 297a at this initial location, different combinations of electrodes (anode E3, cathode E4) are selected, which will move the bipolar point 297a to a different position within the patient's tissue. Again, the amplitude of the current A may need to be titrated to an appropriate sub-sensory level. In the example shown, the bipolar point 297a is moved downwards along one electrode lead and upwards along the other, as shown in path 296 of the combination of electrodes intended to cover the painful area 298. Figure 6 In the example, given the pain site 298 adjacent to electrodes E13 and E14, it can be expected that bipolar points 297a at those electrodes will provide the best relief for the patient, as reflected in the patient's pain score ranking. The specific stimulation parameters selected when forming bipolar points 297a can be selected at the GUI 64 of the clinician programmer 50 or other external device (such as the patient external controller 45) and wirelessly transmitted to the patient's IPG or ETS via a telemetry transmitter for execution.

[0084] Although Figure 6 The dessert search can be effective, but it can also be quite time-consuming when using sub-sensory stimuli. As already noted, sub-sensory stimuli were provided for several days at each bipolar 297 location, and because of the large number of bipolar locations selected, the entire dessert search could take up to a month to complete.

[0085] The inventors have determined through testing with SCS patients that it is beneficial to use extrasensory stimulation during dessert search to select active electrodes for the patient, even if it is expected that subsensory therapy will eventually continue to be used on the patient after dessert search. Using extrasensory stimulation during dessert search significantly accelerates the identification of effective electrodes for the patient compared to using subsensory stimulation (which requires a wash-in period at each set of electrodes being tested). After the electrodes for the patient are identified using extrasensory therapy, the treatment can be titrated to maintain the subsensory level of the same electrodes identified for the patient during dessert search. Since the selected electrodes are known to be recruiting the nerve sites of pain in the patient, applying subsensory therapy to those electrodes is more likely to produce immediate effects, thereby reducing or potentially eliminating the need for wash-in in subsequent subsensory therapy. In summary, effective subsensory therapy can be achieved more quickly for the patient when using extrasensory dessert search. Preferably, the extrasensory dessert search is performed using symmetrical biphasic pulses occurring at low frequencies, such as 40 Hz to 200 Hz in one example.

[0086] According to one aspect of the disclosed technology, a patient will be provided with subsensory therapy. A dessert search for determining electrodes that can be used during subsensory therapy can be performed prior to such subsensory therapy. In some aspects, when subsensory therapy is used on a patient, the dessert search can use a bipolar point 297a (as a subsensory point). Figure 6 As just described. This can be relevant because a subsensory dessert search can match the final subsensory therapy the patient will receive.

[0087] However, the inventors have determined that even if subsensory therapy is ultimately used for patients, the use of extrasensory stimulation (i.e., stimulation with accompanying sensory abnormalities) during dessert search can still be beneficial. This is in Figure 7A As shown, the movable bipolar point 301a provides extrasensory stimulation that can be felt by the patient. In conjunction with... Figure 6 Compared to the subsensory bipolar 297a, providing bipolar 301a as a supersensory stimulus can simply involve increasing its amplitude (e.g., current A), although other stimulation parameters can also be adjustable—such as by providing a longer pulse width.

[0088] The inventors have determined that even if subsensory therapy is eventually used on patients, there are benefits to using extrasensory stimulation during dessert search.

[0089] First, as described above, the use of extrasensory therapy by definition allows the patient to perceive the stimulus, enabling the patient to provide feedback to the clinician, essentially immediately, on whether the sensory abnormality seems to adequately cover their pain site 298. In other words, time is not required to wash in bipolar points 301a at every location as the patient moves along path 296. Therefore, appropriate bipolar points 301a adjacent to the patient's pain site 298 can be established much more quickly, such as within a single clinician visit, rather than over a period of days or weeks. In one example, when searching for a supersensory sweet spot prior to subsensory therapy, the time required to wash in subsensory therapy could be one hour or less, ten minutes or less, or even just a few seconds. This allows the washing in to occur during a single programming session of the patient's IPG or ETS, without requiring the patient to leave the clinician's office. Furthermore, it is noted that subsensory therapy remains effective for a period of time after such treatment has ceased, i.e., during the "wash-out" period following treatment cessation. This wash-out period can be ten minutes or longer, or even an hour or longer. Note that this is beneficial because it means that treatment can be reduced for a period of time (during the washout period), or more precisely, subsensory therapy can be cyclically turned on and off. In short, the IPG does not need to continuously provide subsensory therapy and can be effectively turned off for a period of time, which conserves the IPG's power.

[0090] Second, the use of suprasensory stimulation during the dessert search ensured the identification of electrodes that effectively recruited the pain site 298. As a result, after the dessert search was completed and the final subsensory therapy was titrated for the patient, the washout of this subsensory therapy could not be prolonged because the electrodes required for effective recruitment had been confidently identified.

[0091] Figures 7B to 7D Other supersensory bipolar points 301b to 301d that can be used are shown, and in particular, how a virtual bipolar point can be formed using a virtual pole by activating three or more electrodes in electrode 16 is shown. Virtual poles are further discussed in U.S. Patent Application Publication 2019 / 0175915, the entire contents of which are incorporated herein by reference, and therefore only a brief description of virtual poles is given here. Virtual poles are formed by assistance if the stimulation circuitry 28 or 44 used in the IPG or ETS can independently set current at any of the electrodes—this is sometimes referred to as multiple independent current control (MICC), as discussed below. Figure 8 Let me explain further.

[0092] When using virtual bipolar points, the clinician programmer 50 ( Figure 4 GUI 64 in ) Figure 5) can be used at position 291, which may not necessarily correspond to the position of physical electrode 16. Figure 7B The anode (+) and cathode (-) are defined at location 291. The control circuitry 70 in the clinician programmer 50, which executes the electrode configuration algorithm 110, can calculate from these locations 291 and other tissue modeling information which physical electrodes 16 need to be selected and at what magnitude the virtual anode and cathode are formed at the specified locations 291. As previously described, the magnitude at the selected electrodes can be expressed as a percentage (X%) of the total current magnitude A specified at the GUI 64 of the clinician programmer 50.

[0093] For example, in Figure 7B In this configuration, a virtual anode pole is positioned at location 291 between electrodes E2, E3, and E10. An electrode configuration algorithm 110, operable in the clinician programmer 50, can then calculate, based on this location, the appropriate share (X%) of the total anode current +A that each of these electrodes will receive (during the first pulse phase 30a) to position the virtual anode at that location. Because the virtual anode is closest to electrode E2, electrode E2 can receive the largest share of the specified anode current +A (e.g., 75%*+A). Electrodes E3 and E10, located adjacent to but farther from the virtual anode pole, receive smaller shares of the anode current (e.g., 15%*+A and 10%*+A, respectively). Similarly, it can be seen that, based on the designated location 291 of the virtual cathode poles adjacent to electrodes E4, E11, and E12, these electrodes will receive appropriate shares of the specified cathode current -A (e.g., again during the first pulse phase 30a, 20%*-A, 20%*-A, and 60%*-A, respectively). These polarities will then flip during the second phase 30b of the pulse, as... Figure 7B The waveform is shown in the figure. In any case, using virtual poles in the formation of bipolar 301b allows for field shaping in the tissue and allows for trying many different combinations of electrodes during the sweet spot search. In this respect, it is not strictly necessary to move the (virtual) bipolars sequentially along the path 296 for each electrode, and this path can be random, perhaps guided by feedback from the patient. The electrode configuration algorithm 110 is further described in U.S. Patent Application Publication 2019 / 0175915.

[0094] Figure 7C A useful virtual bipolar point 301c configuration that can be used during dessert search is shown. This virtual bipolar point 301c redefines the target anode and cathode, and its location does not correspond to the location of the physical electrodes. The virtual bipolar point 301c is formed along the leads (basically spanning the length of the four electrodes from E1 to E5). This creates a larger field in the tissue that better recruits the patient's pain site 298. As it moves along path 296, it interacts with... Figure 7A Compared to the smaller bipolar configuration 301a, the bipolar configuration 301c may need to be moved to fewer locations, thus accelerating the detection of the pain site 298. Figure 7D exist Figure 7C The bipolar configuration is extended to create virtual bipolar points 301d using electrodes formed on both leads, for example, from electrodes E1 to E5 and from electrodes E9 to E13. This bipolar point 301d configuration requires movement only along a single path 296 parallel to these leads because its field is large enough to recruit neural tissue adjacent to both leads. This can further accelerate the detection of pain sites.

[0095] In some respects, the supersensory bipolar points 301a to 301d used during dessert search comprise symmetrical biphasic waveforms having the same pulse width PW actively driven (e.g., by stimulation circuits 28 or 44) and pulse phases 30a and 30b of the same amplitude (with reversed polarity during each phase) (e.g., A 30a =A 30b And PW 30a =PW 30b This is beneficial because the second pulse phase 30b provides effective charge recovery, where in this case the charge (Q) provided during the first pulse phase 30a... 30a The charge (Q) equals the second pulse phase 30b. 30b This makes these pulses charge-balanced. The use of biphasic waveforms is also considered beneficial because, as is known, the cathode is largely introduced into the recruitment of neural tissue. When using biphasic pulses, the positions of the (virtual) anode and cathode will flip during the two phases of the pulse. This effectively doubles the neural tissue recruited for the stimulus and thus increases the likelihood that the pain site 298 will be covered by the bipolar point at the correct location.

[0096] The supersensing bipolar points 301a to 301b, however, do not need to include the symmetrical biphase pulses described above. For example, the amplitudes and pulse widths of the two phases 30a and 30b can be different, while keeping the charges (Q) of the two phases balanced (e.g., Q...). 30a =A 30a *PW 30a =A 30b *PW 30b =Q 30b Alternatively, the two phases 30a and 30b can be charge-disequilibrium (e.g., Q). 30a =A 30a *PW 30a >A 30b *PW 30b =Q30b , or Q 30a =A 30a *PW 30a 30b *PW 30b =Q 30b In summary, the pulses at the double poles 301 to 301d can be biphasically symmetrical (and therefore inherently charge balanced), biphasically asymmetrical but still charge balanced, or biphasically asymmetrical and charge unbalanced.

[0097] In a preferred example, the frequency F of the ultrasensory pulses 301a to 301d used during ultrasensory dessert search can be 10 kHz or lower, 1 kHz or lower, 500 Hz or lower, 300 Hz or lower, 200 Hz or lower, 130 Hz or lower, or 100 Hz or lower, or a range defined by two of these frequencies (e.g., 100 to 130 Hz, or 100 to 200 Hz). In a particular example, frequencies of 90 Hz, 40 Hz, or 10 Hz can be used, where the pulses comprise, preferably, biphasic pulses that are symmetrical. However, a single actively driven pulse phase followed by a passively restored phase can also be used. The pulse width PW can also include values ​​in the range of several hundred microseconds, such as 150 microseconds to 400 microseconds. Because the purpose of ultrasensory dessert search is merely to determine electrodes that appropriately cover the patient's pain, frequency and pulse width may not be as important at this stage. Once electrodes have been selected for subsensory stimulation, the frequency and pulse width can be optimized, as discussed further below.

[0098] It should be understood that the supersensory bipolar electrodes 301a to 301d used during dessert search are not necessarily the same electrodes selected when subsequently providing subsensory therapy to the patient. Instead, the optimal location of the bipolar electrode of interest during this search can be used as a basis for modifying the selected electrode. For example, assuming bipolar electrode 301a is used during dessert search (… Figure 7A ​This process involves determining that the bipolar points provide optimal pain relief when located at electrodes E13 and E14. At that point, subsensory therapy can be attempted on the patient using those electrodes E13 and E14. Alternatively, it may be wise to modify the selected electrodes before attempting subsensory therapy to see if the patient's symptoms can be further improved. For example, using dummy poles as already described, the distance between the cathode and anode (focal point) can be varied. Alternatively, a tripolar (anode / cathode / anode) consisting of electrodes E12 / E13 / E14 or E13 / E14 / E15 can be tried. See U.S. Patent Application Publication 2019 / 0175915 (discussion of tripolar poles). Alternatively, electrodes on different leads can be combined with E13 and E14. For example, since electrodes E5 and E6 are typically adjacent to electrodes E13 and E14, adding E5 or E6 as a source of anodic or cathodic current can be useful (recreating dummy poles). All of these types of adjustments should also be understood to include "steering" or adjustment of the "location" of the treatment, even if the center point of the stimulation does not change (because this can occur, for example, when the distance between the cathode and anode or the focal point is changed).

[0099] In one example about Figure 8 Explain multiple independent current controls (MICCs), in Figure 8 The diagram shows a stimulation circuit 28 used in an IPG or ETS to generate a prescribed stimulus at the patient's tissue site. Figure 1 ) or 44 ( Figure 3 The stimulation circuits 28 or 44 can independently control the current or charge at each electrode, and use GUI 64 ( Figure 5 This allows current or charge to be directed to different electrodes, which is useful, for example, when moving the bipolar point 301i along path 296 during a dessert search. Figures 7A to 7D The stimulation circuit 28 or 44 includes one or more current sources 440. i and one or more current sinks 442 i Source 440 i Hesu442 i This can include digital-to-analog converters (DACs), and can be referred to as PDAC 440 based on the positive (pull-in, anode) and negative (sink-in, cathode) currents they emit, respectively. i and NDAC 442 i In the example shown, the NDAC 440 i / PDAC 442 iPairing is dedicated to (hardwired to) a specific electrode node ei 39. Each electrode node ei 39 is preferably connected to electrode Ei 16 via a DC blocking capacitor Ci 38, which acts as a safety measure to prevent DC current injection into the patient in the event of a possible circuit failure, such as in stimulation circuits 28 or 44. PDAC 440 i and DNAC 442 i It may also include a voltage source.

[0100] via GUI 64 to PDAC 440 i and NDAC 442 i Proper control allows either electrode 16 or housing electrode Ec 12 to act as an anode or cathode to generate current through patient tissue. Such control preferably takes the form of digital signals Iip and Iin that set the anode and cathode currents at each electrode Ei. If, for example, it is desired to set electrode E1 as an anode with a current of +3mA, and electrodes E2 and E3 as cathodes each having a current of -1.5mA, then control signal I1p would be set with a 3mA digital equivalent to cause PDAC 4401 to generate +3mA, and control signals I2n and I3n would be set with 1.5mA digital equivalents to cause NDAC 4422 and NDAC 4423 to each generate -1.5mA. Note that the definitions of these control signals can also appear using programmed amplitude A and percentage X% set in GUI 64. For example, A could be set to 3mA, where E1 is specified as an anode with X = 100%, and where E2 and E3 are specified as cathodes with X = 50%. Alternatively, the control signal can be set without a percentage, and the GUI 64 can simply specify that the current will appear at each electrode at any given time.

[0101] In summary, the GUI 64 can be used to independently set the current at each electrode or to direct the current between different electrodes. This is particularly useful in forming virtual bipolar points, which has previously been interpreted as involving the activation of more than two electrodes. The MICC also allows for the formation of finer electric fields in the patient's tissues.

[0102] Other stimulation circuits 28 can also be used to implement MICC. In an example not shown, a switching matrix can be located between one or more PDAC 440s. i Between electrode node ei 39 and one or more NDAC442 iBetween the electrode nodes. The switching matrix allows one or more of the PDACs and one or more of the NDACs to be connected to one or more electrode nodes at a given time. Various examples of stimulation circuits can be found in the following literature: USP6,181,969, 8,606,362, 8,620,436; and U.S. Patent Application Publications 2018 / 0071513, 2018 / 0071520 and 2019 / 0083796.

[0103] Many of the stimulation circuits 28 or 44 (including PDAC 440i and NDAC 442i, the switch matrix (if present), and electrode node ei 39) can be integrated onto one or more application-specific integrated circuits (ASICs), as described in U.S. Patent Application Publications 2012 / 0095529, 2012 / 0092031, and 2012 / 0095519. As illustrated in these documents, the one or more ASICs may also include other circuitry that can be used in the IPG 10, such as telemetry circuitry (for interfacing the shut-off chip with the telemetry antenna of the IPG or ETS), circuitry for generating a constant current output voltage (compliance voltage) VH to power the stimulation circuitry, various measurement circuits, etc.

[0104] While it is preferred to use dessert search (and specifically extrasensory dessert search) to determine the electrodes to be used during subsequent subsensory therapy, it should be noted that this is not strictly necessary. Subsensory therapy may be guided by subsensory dessert search, or it may not be guided by dessert search at all. In short, subsensory therapy as described below does not depend on the use of any dessert search.

[0105] In another aspect of the invention, the inventors have determined, through testing on SCS patients, a statistically significant correlation between pulse width (PW) and frequency (F) in which SCS patients experience a reduction in back pain without sensory abnormalities (subsensory perception). This information can help determine, based on a specific frequency, what pulse width might be optimized for a given SCS patient, and based on a specific pulse width, what frequency might be optimized for a given SCS patient. Advantageously, this information suggests that subsensory SCS stimulation without sensory abnormalities can occur at frequencies of 10 kHz and below. The use of such low frequencies allows for the use of subsensory therapy with much lower power consumption in the patient's IPG or ETS.

[0106] Figures 9 to 11C Results derived from patients tested at frequencies ranging from 1 kHz to 10 kHz are shown. Figure 9This section explains how the data were collected from actual SCS patients and the criteria for patient inclusion in this study. Patients with back pain but not yet receiving SCS treatment were first identified. Key patient inclusion criteria included: persistent lower back pain for more than 90 days; a NRS pain scale score of 5 or higher (NRS explained below); stable opioid therapy for 30 days; and a baseline Oswestry Disability Index score greater than or equal to 20 and less than or equal to 80. Key patient exclusion criteria included: back surgery within the previous 6 months; other confounding medical / psychological conditions; and untreated major psychiatric illness or severe medication-related performance problems.

[0107] Following this initial screening, patients periodically input qualitative indicators of their pain (i.e., pain scores) into a portable electronic diary device, which may include a patient external controller 45, and the external controller 45 may then transmit the data to a clinician programmer 50. Figure 4 This type of pain score can include a Numerical Rating Scale (NRS) score from 1 to 10 and can be entered into an electronic diary three times daily. Figure 10C As shown, the baseline NRS score of patients who were not ultimately excluded from the study and had not yet received subsensory stimulation therapy was approximately 6.75 / 10, with a standard error SE (sigma / SQRT(n)) of 0.25.

[0108] Back Figure 9 The patient then tested the lead 15' ( Figure 3 The test leads 15' are implanted on the left and right sides of the spine and external test stimuli are delivered to the patient as previously described. A clinician programmer 50 is used to deliver the stimulation program to each patient's ETS 40, as previously described. This is done to confirm that the SCS treatment is helpful in relieving the pain of a given patient. If the SCS treatment is not helpful to a given patient, the test leads 15' are removed, and the patient is then excluded from the study.

[0109] Those patients for whom external experimental stimulation was helpful eventually received full implantation of a permanent IPG 10, as previously described. After the cure period, and again using the clinician programmer 50, the “sweet spot” for stimulation was located in each patient, i.e., which electrode should be active (E) and with what polarity (P) and relative amplitude (X%) to recruit and thus treat the nerve site 298 in the patient. The sweet spot search can be performed using previously described… Figures 6 to 7D Any of the described methods may occur, but in the preferred embodiment, due to the benefits previously described, extrasensory stimulation (e.g., such as...) will be included. Figures 7A to 7DHowever, this is not strictly necessary, and subsensory stimuli can also be used during dessert search. Figure 9 In the example, the dessert search occurs at 10 kHz, but again, the frequency used during the dessert search can be varied. A symmetrical biphasic pulse is used during the dessert search, but again, this is not strictly necessary. The selection of electrodes 16 between the T9 and T10 thoracic vertebrae is the starting point for determining which electrodes should be activated. However, electrodes as far as T8 and T11 can also be activated if necessary. Fluorescence imaging of the leads 15 within each patient is used to determine which electrodes are adjacent to the T8, T9, T10, and T1 vertebrae.

[0110] During dessert search, bipolar stimulation using only two electrodes was used on each patient, with only adjacent electrodes used on a single lead 15, similar to... Figure 6 and Figure 7A The content described in [the previous text]. Therefore, a patient's dessert may involve stimulation of adjacent electrodes E4 (as cathode) and E5 (as anode) on the left lead 15, as previously described in [the previous text]. Figure 2 The diagram shows (electrodes can be between T9 and T10), while another patient's treatment might involve stimulation of adjacent electrodes E9 (as anode) and E10 (as cathode) on the right lead 15 (electrodes can be between T10 and T11). It is desirable to use only bipolar stimulation of adjacent electrodes and only between vertebral T8 and T11 to minimize variability in treatment and symptoms between different patients in the study. However, more complex bipolar stimulation, such as that shown... Figures 7B to 7D Those described can also be used during the dessert search. Patients with dessert electrodes in the desired chest location and who experience pain relief of 30% or more per NRS score continue in the study; patients not meeting these criteria are excluded from further study. The study initially started with 39 patients, and by [date missing]... Figure 9 Nineteen patients were excluded from the study so far, leaving a total of 20 remaining patients.

[0111] The remaining 20 patients then underwent a “washout” period, meaning their IPG was not stimulated for a period of time. Specifically, patients’ NRS pain scores were monitored until their pain reached 80% of their initial baseline pain. This was to ensure that the benefits of previous stimulation did not persist into the next analysis period.

[0112] The remaining patients then underwent subconscious SCS treatment using previously identified sweet spot active electrodes at different frequencies ranging from 1 kHz to 10 kHz. However, this is not strictly necessary, as the current at each electrode is also independently controlled, as previously stated, to aid in shaping the electric field within the tissue. Figure 9As shown, patients were tested using stimulation pulses with frequencies of 10 kHz, 7 kHz, 4 kHz, and 1 kHz, respectively. For simplicity, Figure 9 The diagram shows these frequencies being tested in this order for each patient, but in practice, these frequencies are applied to each patient in a random order. Once the test at a given frequency is completed, there is a washout period before the test at another frequency begins.

[0113] At each measured frequency, the amplitude (A) and pulse width (PW) of the stimulus are adjusted and optimized for each patient (first pulse phase 30a); Figure 2 This is done so that each patient experiences potentially good pain relief without sensory abnormalities (subsensory perception). Specifically, using a clinician programmer 50, and keeping the previously identified sweet spot electrodes active (though again this is not strictly necessary), each patient is stimulated at a low amplitude (e.g., 0), which is increased to the maximum point at which the patient can notice a sensory abnormality (sensory threshold). An initial stimulus is then selected for the patient at 50% of this maximum amplitude, i.e., such that the stimulus is subsensory and therefore without sensory abnormalities. However, other percentages of the maximum amplitude (80%, 90%, etc.) can also be selected and can vary with patient activity or position, as further explained below. In one example, the stimulation circuitry 28 or 44 in the IPG or ETS can be configured to receive instructions from the GUI 64 via selectable options (not shown) to reduce the amplitude of the stimulation pulse by a certain amount or percentage, or to reduce it by a certain amount or percentage for presentation, such that the pulse can be made subsensory (if it is not already subsensory). Other stimulation parameters (e.g., pulse width, charge) can also be reduced to achieve the same effect.

[0114] The patient will then leave the clinician's office, and thereafter, all communication with the clinician (or their technician or programmer) will be via their external controller 45. Figure 4 The stimulation (amplitude and pulse width) was adjusted. Simultaneously, the patient would enter their NRS pain score into their electronic diary (e.g., an external controller), again three times daily. Patient-specific adjustments to amplitude and pulse width were typically iterative, but essentially based on patient feedback, attempting to modulate the treatment to reduce their pain while still ensuring the stimulation was sub-perceptual. Testing at each frequency lasted approximately three weeks, with stimulation adjustments possibly every two days or so. At the end of the testing period at a given frequency, optimized amplitude and pulse widths had been determined and recorded for each patient, along with their NRS pain scores for those optimized parameters entered into their electronic diary.

[0115] In one example, the percentage of the maximum amplitude used to provide subsensory stimulation can be selected based on the patient's level of activity or location. In this regard, the IPG or ETS may include devices for determining the patient's activity or location, such as an accelerometer. If the accelerometer indicates a high level of patient activity or a location where the electrodes will be further away from the spinal cord (e.g., lying down), the amplitude can be increased to a higher percentage to increase the current (e.g., 90% of the maximum amplitude). If the patient is experiencing a lower level of activity or a location where the electrodes will be closer to the spinal cord (e.g., standing), the amplitude can be reduced (e.g., to 50% of the maximum amplitude). Although not shown, the GUI 64 of the external device (…) Figure 5 This could include setting the percentage of maximum amplitude at the point where the sensory abnormality becomes apparent to the patient, thus allowing the patient to adjust the amplitude of the subsensory current.

[0116] Preferably, multiple independent current controls (MICCs) are used to provide or modulate subsensory therapy, as previously discussed. Figure 8 This allows for the independent setting of current at each electrode to facilitate the conduction of current or charge between electrodes, aiding in the formation of virtual bipolar points, and more generally allowing for the shaping of electric fields within the patient's tissues. Specifically, MICC can be used to direct subsensory therapy to different locations within the electrode array and, consequently, the spinal cord. For example, once a set of subsensory stimulation parameters has been selected for the patient, one or more of these parameters can be altered. Such alterations can be guaranteed or governed by the treatment location. A patient's physiology can vary at different spinal locations, and tissues may be more or less conductive at different treatment locations. Therefore, if the subsensory therapy location is directed along the spinal cord to a new location (the change in location may include altering the anode / cathode distance or focal point), at least one of the stimulation parameters, such as amplitude, can be guaranteed to be modulated. As previously noted, subsensory modulation is encouraged and can occur during a programming session because a substantial washout period may be unnecessary.

[0117] Modulation of subsensory therapy may also include varying other stimulation parameters, such as pulse width, frequency, and even the duration of the interphase period (IP). Figure 2 The interphase duration can affect the neural dose, or the rate of charge delivery, allowing for the use of higher subsensory amplitudes with shorter interphase durations. In one example, the interphase duration can vary between 0 and 3 ms. After the washout period, new frequencies can be tested using the same protocol as described.

[0118] The subsensory stimulation pulses used are symmetrical biphasic constant current amplitude pulses, with a first pulse phase 30a and a second pulse phase 30b (having the same duration) (see...). Figure 2However, constant voltage amplitude pulses can also be used. Pulses of different shapes (triangular, sine wave, etc.) can also be used. When providing subclinical therapy, pre-pulses (i.e., providing a small current before providing one or more actively driven pulse phases) can also occur to affect the polarization or depolarization of neural tissue. See, for example, USP 9,008,790, which is incorporated herein by reference.

[0119] Figures 10A to 10C Results of patient testing at 10 kHz, 7 kHz, 4 Hz, and 1 kHz are shown. Data as averages for 20 remaining patients at each frequency are shown in each graph, with error bars reflecting the standard error (SE) between patients.

[0120] by Figure 10B Initially, the optimized amplitude A for the remaining 20 patients is shown at the measured frequency. Interestingly, the optimized amplitude at each frequency is essentially constant, approximately 3 mA. Figure 10B The amount of energy consumed at each frequency is also shown, and more specifically, the average charge per second (MCS) attributable to the pulse (in mC / s) is shown. MCS is determined by employing an optimized pulse width ( Figure 10A (Discussed below) and multiplied by the optimized amplitude (A) and frequency (F) to calculate, the MCS value can include the neural dose. The MCS is related to the current or power that the battery in the IPG 10 must consume to form the optimized pulse. It is worth noting that the MCS decreases significantly at lower frequencies: for example, the MCS at F = 1 kHz is approximately 1 / 3 of its value at higher frequencies (e.g., F = 7 kHz or 10 kHz). This means that optimized SCS therapy to relieve back pain without sensory abnormalities can be achieved at lower frequencies such as F = 1 kHz, where the additional benefit of lower power draw is that the battery of the IPG 10 (or ETS 40) is more taken into account.

[0121] Figure 10A The optimized pulse width as a function of frequency within the tested frequency range of 1 kHz to 10 kHz is shown. As indicated, the relationship follows a statistically significant trend: when modeled using linear regression 98a, PW = -8.22F + 106, where the pulse width is measured in microseconds and the frequency in kilohertz, and the correlation coefficient R0 is shown. 2 The value is 0.974; when modeling using multinomial regression 98b, PW = 0.486F. 2 -13.6F+116, where the pulse width is measured in microseconds and the frequency in kilohertz, and the correlation coefficient is even better, R. 2=0.998. Other fitting methods can be used to establish additional information related to the frequency and pulse width of the stimulus pulses formed to provide pain relief without sensory abnormalities in the frequency range of 1 kHz to 10 kHz.

[0122] Note that the optimized relationship between pulse width and frequency is not simply a matter of the expected relationship between frequency and duty cycle (DC) (i.e., the duration of the pulse divided into "on" states by its period (1 / F). In this respect, note the natural effect of a given frequency on pulse width: it will be expected that higher frequency pulses will have smaller pulse widths. Therefore, a 1kHz waveform with, for example, a pulse width of 100 microseconds can be expected to have the same clinical outcomes as a 10kHz waveform with a frequency of 10 microseconds, because both waveforms have a duty cycle of 10%. Figure 11A The duty cycle for generating a stimulation waveform using an optimized pulse width in the frequency range of 1 kHz to 10 kHz is shown. Here, the duty cycle is determined by considering only the first pulse phase 30a ( Figure 2 The total "on" time is calculated; the duration of the symmetrical second pulse phase is ignored. This duty cycle is not constant in the frequency range of 1kHz to 10kHz: for example, the optimized pulse width at 1kHz (104 microseconds) is not simply 10 times the optimized pulse width at 10kHz (28.5 microseconds). Therefore, the optimized pulse width is not only important for frequency scaling.

[0123] Figure 10C The optimized stimulation parameters (optimized amplitude) are shown for each frequency in the range of 1 kHz to 10 kHz. Figure 7B ) and pulse width ( Figure 7A The mean pain score of patients under the SCS was as follows. As previously noted, patients in this study initially reported a mean pain score of 6.75 before receiving SCS treatment. After SCS implantation and during the study, and using amplitude and pulse width optimized during temporary subsensory therapy, their mean pain scores significantly decreased to a mean pain score of approximately 3 across all measured frequencies.

[0124] Figure 11A An in-depth analysis of the relationship between optimized pulse width and frequency is provided within the frequency range of 1 kHz to 10 kHz. Figure 11AThe graph shows the average optimized pulse width for the 20 patients in the study at each frequency, along with the standard error derived from the variation between them. These were normalized at each frequency by dividing the standard error by the optimized pulse width, and the variation at each frequency ranged between 5.26% and 8.51%. Thus, a variation of 5% (below all calculated values) can be assumed to be statistically significant at all measured frequencies.

[0125] Based on this 5% variation, the maximum average pulse width (PW+5%) and minimum average pulse width (PW+5%) can be calculated for each frequency. For example, the optimized average pulse width PW at 1 kHz is 104 microseconds, and 5% above this value (1.05*104μs) it is 109 microseconds; 5% below this value (0.95*104μs) it is 98.3 microseconds. Similarly, the optimized average pulse width AVG(PW) at 4 kHz is 68.0 microseconds, and 5% above this value (1.05*68.0μs) it is 71.4 microseconds; 5% below this value (0.95*68.0μs) it is 64.6 microseconds. Therefore, a statistically significant reduction in pain without sensory abnormalities occurs within or above the linearly defined region 100a of point 102, which is defined as (1 kHz, 98.3 μs), (1 kHz, 109 μs), (4 kHz, 71.4 μs), and (4 kHz, 64.6 μs). The linearly defined region 100b surrounding point 102 is also defined for frequencies greater than or equal to 4 kHz and less than or equal to 7 kHz: (4 kHz, 71.4 μs), (4 kHz, 64.6 μs), (7 kHz, 44.2 μs), and (7 kHz, 48.8 μs). The linearly defined region 100c surrounding point 102 is also defined for frequencies greater than or equal to 7 kHz and less than or equal to 10 kHz: (7 kHz, 44.2 μs), (7 kHz, 48.8 μs), (10 kHz, 29.9 μs), and (10 kHz, 27.1 μs). Such a region 100 therefore includes information relating to the frequency and pulse width of the stimulation pulses that are formed to provide pain relief without sensory abnormalities in the frequency range of 1 kHz to 10 kHz.

[0126] Figure 11BAn alternative analysis of the resulting relationship between optimized pulse width and frequency is provided. In this example, regions 100a to 100c are defined based on the standard error (SE) calculated at each frequency. Therefore, the point 102 defining each corner of regions 100a to 100c lies only within the range of the SE error bars (PW+SE and PW-SE) at each frequency, although these error bars have different magnitudes at each frequency. Therefore, a statistically significant reduction in pain without sensory abnormalities occurs within or above the linearly defined regions 100a at points (1 kHz, 96.3 μs), (1 kHz, 112 μs), (4 kHz, 73.8 μs), and (4 kHz, 62.2 μs). Linearly defined regions 100b and 100c are similar, and because the point 102 defining them lies within... Figure 11B The diagram at the top illustrates this, so it will not be repeated here.

[0127] Figure 11C Another analysis is provided as a result of the relationship between the optimized pulse width and frequency. In this example, regions 100a to 100c are defined based on the standard deviation (SD) calculated at each frequency, which is greater than the standard error (SE) metric used at that point. Points 102 defining the corners of regions 100a to 100c lie within the range of the SD error bars at each frequency (PW+SD, and PW-SD), although points 102 could also be placed within the error bars, similar to the previous analysis. Figure 11A The content described. In any case, a statistically significant reduction in pain without sensory abnormalities occurs within or above the linearly defined regions 100a at points (1 kHz, 69.6 μs), (1 kHz, 138.4 μs), (4 kHz, 93.9 μs), and (4 kHz, 42.1 μs). Linearly defined regions 100b and 100c are similar, and because the point 102 defining them is within... Figure 11C The diagram at the top illustrates this, so it will not be repeated here.

[0128] More generally, although not shown, the regions in the frequency range of 1 kHz to 10 kHz for achieving subsensory therapeutic effects include linearly defined regions 100a (1 kHz, 50.0 μs), (1 kHz, 200.0 μs), (4 kHz, 110.0 μs), and (4 kHz, 30.0 μs); and / or linearly defined regions 100b (4 kHz, 110.0 μs), (4 kHz, 30.0 μs), (7 kHz, 30.0 μs), and (7 kHz, 60.0 μs); and / or linearly defined regions 100c (7 kHz, 30.0 μs), (7 kHz, 60.0 μs), (10 kHz, 40.0 μs), and (10 kHz, 20.0 μs).

[0129] In summary, one or more statistically significant regions 100 can be defined for the following optimized pulse width and frequency data, taken for patients in the study, to achieve a combination of pulse width and frequency that reduces pain without sensory abnormalities in the frequency range of 1 kHz to 10 kHz, and different statistical measures of error can be used to define one or more regions in this way.

[0130] Figures 12A to 12D Results of testing other patients using subsensory stimulation therapy at frequencies of 1 kHz or below 1 kHz are shown. Patient testing typically occurs during a supersensory sweet spot search (see [link to study]) to select appropriate electrodes (E), polarity (P), and relative amplitude (X%) for each patient. Figures 7A to 7D After this occurs, the subsensory electrodes used again can vary depending on which electrodes were used during the supersensory sweetness search (e.g., using MICC). Although the form of the pulses used during subsensory therapy may vary, symmetrical biphasic bipolar pulses are still used to test patients with subsensory stimulation.

[0131] Figure 12A The relationship between the frequency and pulse width at which patients reported effective subsensory therapy for frequencies of 1 kHz and below 1 kHz is shown. Note the previous ( Figure 9 The same patient selection and testing criteria described can be used when evaluating frequencies at or below 1 kHz, where the frequency is adjusted as appropriate.

[0132] As can be seen, at each measured frequency, the optimized pulse width again falls within a certain range. For example, at 800 Hz, patients report good results when the pulse width falls within the range of 105 to 175 microseconds. The upper end of the pulse width range at each frequency is denoted as PW(High), and the lower end is denoted as PW(Low). PW(Medium) represents the middle (e.g., average) of PW(High) and PW(Low) at each frequency. At each measured frequency, the amplitude (A) of the current supplied is titrated down to a sub-sensory level so that the patient cannot perceive the sensory abnormality. Typically, the current is titrated to 80% of the threshold at which the sensory abnormality can be sensed. Because each patient's anatomy is unique, the sub-sensory amplitude A can vary from patient to patient. The pulse width data depicted includes the pulse width of only the first phase of the stimulation pulse.

[0133] Table 1 below presents the data in tabular form for frequencies of 1 kHz or below. Figure 12A The optimized pulse width and frequency data are provided, where the pulse width is expressed in microseconds:

[0134]

[0135]

[0136] Table 1

[0137] As previously described for frequencies in the range of 1kHz to 10kHz ( Figures 10A to 11C The data can be decomposed into distinct regions 300i, where effective subsensory therapy below 1 kHz is achieved. For example, the effective subsensory therapy region can be linearly defined between various frequencies and high and low pulse widths that define effectiveness. For instance, at 10 Hz, PW(low) = 265 μs and PW(high) = 435 μs. At 50 Hz, PW(low) = 230 μs and PW(high) = 370 μs. Therefore, the region 300a providing good subsensory therapy is defined by linearly defined regions at points (10 Hz, 265 μs), (10 Hz, 435 μs), (50 Hz, 370 μs), and (50 Hz, 230 μs).

[0138] Table 2 defines the linear constraints in Figure 12A Points in each of regions 300a to 300g shown in the figure:

[0139]

[0140]

[0141] Table 2

[0142] The region of subsensory therapeutic effectiveness at frequencies of 1 kHz or below can be defined in other statistically significant ways, such as those previously described for frequencies in the 1 kHz to 10 kHz range. Figures 11A-11CFor example, region 300i can be defined by referencing the pulse width PW(middle) at the midpoint of each range at each frequency. PW(middle) can include, for example, the average optimized pulse width reported by patients at each frequency, rather than the exact midpoint of the effective range reported by those patients. PW(high) and PW(low) can then be determined as the statistical variance of the average PW(middle) at each frequency and can be used to set the upper and lower boundaries of the effective subsensory region. For example, PW(high) can include the average PW(middle) plus a standard deviation or standard error, or a multiple of such a statistical measure; PW(low) can similarly include the average PW(middle) minus a standard deviation or standard error, or a multiple of such a statistical measure. PW(high) and PW(low) can also be determined from the average PW(middle) in other ways. For example, PW(high) can include the average PW(middle) plus a certain percentage, while PW(low) can include PW(middle) minus a certain percentage. In summary, one or more statistically significant regions 300 can be defined for optimized pulse width and frequency data at frequencies of 1 kHz or below 1 kHz to reduce pain without sensory abnormalities.

[0143] In addition Figure 12A The figure shows the mean patient pain score (NRS score) reported by patients at different frequencies of 1 kHz or below using optimized pulse widths. Prior to SCS treatment, the initial patient-reported pain score was 7.92. After SCS implantation, and with subsensory stimulation at optimized pulse widths (with ranges shown at each frequency), the mean patient pain score decreased significantly. At 1 kHz, 200 Hz, and 10 Hz, the mean patient-reported pain scores were 2.38, 2.17, and 3.20, respectively. Therefore, the clinical significance of pain relief is demonstrated when using subsensory therapy with optimized pulse widths at 1 kHz or below.

[0144] exist Figure 12B The analysis was performed from the perspective of the intermediate pulse width PW (middle) at each frequency (F) for frequencies of 1 kHz or below. Figure 12A The optimized pulse width and frequency data are shown in Figure 310a to 310d. As indicated, the relationship between 310a and 310d follows a statistically significant trend, as demonstrated by... Figure 12B The various regression models shown in the figure and summarized in Table 3 below demonstrate the following:

[0145]

[0146] Table 3

[0147] Other fitting methods can be used to establish additional information related to the frequency and pulse width of the stimulus pulses that are formed to provide sub-perceptual pain relief without sensory abnormalities.

[0148] Regression analysis can also be used to define statistically relevant regions, such as 300a to 300g, where subsensory therapy is effective at 1 kHz or below. For example, and although not in Figure 12B As shown, regression can be performed for PW(low)vF to set the lower boundary of the relevant region 300i, and regression can be performed for PW(high)vF to set the upper boundary of the relevant region 300i.

[0149] Notice Figure 12A The optimized pulse width-frequency relationship described in the text is not simply as... Figure 12C The relationship between the expected frequency and the duty cycle (DC) is shown. This is in contrast to the case when testing the frequency range of 1kHz to 10kHz. Figure 11A Similarly, the duty cycle of the optimized pulse width is not constant at and below 1 kHz. Again, the optimized pulse width is only significant for frequency scaling. Nevertheless, most pulse widths observed to be optimized at and below 1 kHz are greater than 100 microseconds. Such pulse widths are even impossible at higher frequencies. For example, at 10 kHz, the phases of two pulses must fit within a 100-microsecond time interval, making a pulse width (PW) longer than 100 microseconds even impossible.

[0150] Figure 12D Further benefits of using subsensory therapy at frequencies of 1 kHz and below are shown, namely reduced power consumption. Two sets of data were plotted. The first set of data includes pulse widths optimized for that patient using a battery in the patient's IPG or ETS. Figure 12AThe average current drawn at each frequency (AVG Ibat) and the current amplitude A required to achieve subsensory stimulation for that patient (again, this amplitude can vary for each patient). At 1 kHz, the average battery current is approximately 1700 microamps. However, as the frequency decreases, the average battery current drops to approximately 200 microamps at 10 Hz. The second data set considers power consumption according to different advantages, namely the number of days an IPG or ETS with a fully charged rechargeable battery can operate before needing recharging (“discharge time”). Based on the average battery current data, it will be expected that when the average battery current is higher, the discharge time is lower at higher frequencies (e.g., approximately 3.9 days at 1 kHz, depending on various charging parameters and settings), and when the average battery current is lower, the discharge time is higher at lower frequencies (e.g., approximately 34 days at 10 Hz, depending on various charging parameters and settings). This is important: using optimized pulse widths not only provides effective subsensory therapy at frequencies of 1 kHz and below, but also significantly reduces power consumption, allowing for less stress on the IPG or ETS and enabling prolonged operation. As noted above, excessive power consumption is a serious problem when subsensory therapy is routinely used at higher frequencies. Note Figure 12D The data can also be analyzed based on average charge per second (MSC), as previously described for data from 1 kHz to 10 kHz. Figure 10B ).

[0151] Figure 13A and Figure 13B Results of an additional test are shown, which validated the frequency-pulse width relationship just presented. Here, data from 25 patients tested using subsensory stimulation at frequencies of 10 kHz and below are presented. Figure 13A Two different plots are shown, illustrating results for frequencies of 10 kHz and below (bottom plot) and frequencies of 1 kHz and below (top plot). The mean value shows the frequency and pulse width values ​​that produce optimized subsensory therapy at that point. The upper and lower limits represent the variance (+STD and –STD) of one standard deviation above and below the mean. Figure 13B The curve fitting results determined using the average value are shown. Data at 1 kHz and below were fitted using an exponential function and then a power function, resulting in the relationship PW = 159e. -0.01F +220e -0.00057F and PW = 761–317F 0.01 Both fit the data well. Data at 10kHz and below were fitted using a power function, yielding PW = -1861 + 2356F. -0.024 The data fits well again. It can also be fitted to other mathematical functions.

[0152] Once determined, information 350 relating to the frequency and pulse width of the optimized subsensory therapy for the absence of sensory abnormalities can be stored in an external device used for programming the IPG 10 or ETS 40, such as the previously described clinician programmer 50 or external controller 45. This is in Figure 14 As shown, the control circuitry 70 or 48 of the clinician programmer or external controller is associated with: region information 100i or relational information 98i for frequencies in the range of 1 kHz to 10 kHz, and region information 300i or relational information 310i for frequencies at or below 1 kHz. This information can be stored in memory within the control circuitry or in memory associated with the control circuitry. Storing this information using an external device is useful in assisting clinicians with subsensory optimization, as further described below. Alternatively, and although not shown, information related to frequency and pulse width can be stored in IPG 10 or ETS 40, thus allowing the IPG or ETS to optimize itself without clinician or patient input.

[0153] Information 350 can be incorporated into the fitting module. For example, the fitting module 350 can operate as a software module within the clinician programmer software 66, and may be implemented in the clinician programmer GUI 64. Figure 6 Options can be selected from the advanced menu 88 or mode menu 90. The fitting module 350 can also be operated within the control circuitry of the IPG 10 or ETS 40.

[0154] The fitting module 350 can be used to optimize pulse width when the frequency is known, or vice versa. For example... Figure 14 As shown at the top, a clinician or patient can input a frequency F into the clinician programmer 50 or an external controller 45. This frequency F is passed to the fitting module 350 to determine the pulse width PW for the patient, which is statistically likely to provide adequate pain relief without sensory abnormalities. The frequency F can, for example, be input to relation 98i or 310i to determine the pulse width PW. Alternatively, the frequency can be compared to a relevant region 100i or 300i into which the frequency falls. Once the correct region 100i or 300i is determined, F can be compared with data within that region to determine the pulse width PW, which may be, for example, the pulse width between the boundary of PW+X and PW–X at a given frequency, as previously described. Other stimulation parameters (such as amplitude A, active electrodes E, their relative percentage X%, and electrode polarity P) can be determined in other ways, such as those described below, to arrive at a complete stimulation procedure (SP) for the patient. Based on data from... Figure 10BThe data shows that an amplitude close to 3.0 mA can be used as a logical starting point because this amplitude is shown to be preferred for patients in the 1kHz to 10kHz range. However, other initial starting amplitudes can also be selected, and the amplitude for subsensory therapy can depend on the frequency. Figure 14 The bottom of the diagram illustrates the use of the fitting module 350 in the opposite manner (i.e., selecting a frequency for a given pulse width). Note that in subsequent algorithms, and even in algorithms used outside of any other algorithm, in one example, the system may allow the user to correlate frequency and pulse width so that when either frequency or pulse width changes, the other is automatically adjusted to correspond to the optimized setting. In one embodiment, correlating frequency and pulse width in this way may include optional features (e.g., in GUI 64) that can be used when subsensory programming is desired, and correlating frequency and pulse width may be unselectable or unselectable for use with other stimulation patterns.

[0155] Figure 15 Algorithm 355, which can be used to provide subsensory therapy to SCS patients at frequencies of 10 kHz or lower, is shown, and some of the steps already discussed above are summarized. Steps 320 to 328 describe the subsensory sweet spot search. The user (e.g., a clinician) selects electrodes, for example, using the GUI of a clinician programmer, to create a bipolar point (320) for the patient. This bipolar point is preferably a symmetrical biphasic bipolar point and may include virtual bipolar points, as previously described.

[0156] The bipolar point, along with other analog parameters, is transmitted via a telemetry transmitter to the IPG or ETS for execution (321). Other stimulation parameters can also be selected in the clinician programmer using a GUI. By default, the frequency F can be equal to 90 Hz and the pulse width (PW) can be equal to 200 microseconds, although this is not strictly required and these values ​​can be modified. At this point, if the bipolar point provided by the IPG or ETS is not extrasensory, i.e., if the patient does not experience sensory abnormalities, the amplitude A or other stimulation parameters can be adjusted to make it so (322). The effectiveness of the bipolar point is then measured by the patient (324) to determine the extent to which the bipolar point covers the patient's pain site. The NRS or other scoring systems can be used to determine effectiveness.

[0157] If the bipolar point is invalid, or if it is still necessary to search for it, a new bipolar point (326) can be tried. That is, the new electrode can preferably be selected in such a way that the bipolar point is moved to a new location along path 296, as previously discussed. Figures 7A to 7DAs described above. The new bipolar point can then be transmitted again to the IPG or ETS via a telemetry transmitter (321) and, if necessary, adjusted to present bipolar hypersensory perception (322). If the bipolar point is effective, or if the search has been completed and the most effective bipolar point has been located, the bipolar point can be optionally modified prior to subsensory therapy (328). Such modifications, as described above, can involve selecting other electrodes adjacent to the selected bipolar point to modify the field shape in the tissue to potentially better cover the patient's pain. Thus, the modification in step 328 can change the bipolar point used during the search to a virtual bipolar point, or a tripolar point, etc.

[0158] Other stimulation parameters can also be modified at this point. For example, the frequency and pulse width can be modified. In one example, a working pulse width that provides good, comfortable coverage of sensory abnormalities (>80%) can be selected. This can be achieved by using, for example, a frequency of 200 Hz and starting with, for example, a pulse width of 120 microseconds. The pulse width can be increased at this frequency until good sensory abnormality coverage is felt. An amplitude in, for example, the range of 4 mA to 9 mA can be used.

[0159] At this point, the electrodes (E) selected for the stimulus, their polarity (P), and the fraction of current they will receive (X%) (and the possible working pulse width) are known and will be used to provide subsensory therapy. To ensure the provision of subsensory therapy, the amplitude A of the stimulus is titrated down to a subsensory, non-abnormal level (330) and transmitted to the IPG or ETS via a telemetry transmitter. As mentioned above, the amplitude A can be set below an amplitude threshold (e.g., 80% of the threshold), where the patient can just begin to experience a sensory abnormality.

[0160] At this point, optimizing the frequency and pulse width of the subsensory therapy provided to the patient (332) can be useful. While the frequency (F) and pulse width (PW) used during the sweet spot search can be used for subsensory therapy, it is also beneficial to adjust these parameters to optimized values ​​based on region 100i or relation 98i established at frequencies in the range of 1 kHz to 10 kHz, or region 300i or relation 310i established at frequencies of 1 kHz or below 1 kHz. Such optimization can be achieved using... Figure 14 The fitting module 350 is used, and it can occur in different ways, and some optimization methods 332a to 332c are shown in Figure 15Option 332a, for example, allows software in a clinician programmer or IPG or ETS to automatically select a frequency (≤10kHz) and pulse width using region or relational data that correlates frequency with pulse width. Option 332a can use a previously determined working pulse width (328) and select the frequency using a region or relation. In contrast, option 332b allows the user (clinician) (using the GUI of the clinician program) to specify a frequency (≤10kHz) or pulse width. The software can then again use a region or relation to select appropriate values ​​for other parameters (pulse width or frequency (≤10kHz)). Similarly, this option can use a previously determined working pulse width to select an appropriate frequency. Option 332c allows the user to input a frequency (≤10kHz) and pulse width PW, but in a manner constrained by a region or relation. Similarly, this option allows the user to input a working pulse width and frequency suitable for that working frequency, depending on the region or relation. The GUI 64 of the clinician programmer can, in this example, not accept inputs for F and PW that do not fall within the region or are not along the relationship, because such values ​​do not provide optimized subsensory therapy.

[0161] Frequency or pulse width optimization can occur in other ways to more efficiently search the desired portion of the parameter space. For example, gradient descent, binary search, simplex methods, genetic algorithms, etc., can be used for the search. Machine learning algorithms that have already been trained using data from patients can also be considered.

[0162] Preferably, when optimizing the frequency (≤10kHz) and pulse width at step 332, these parameters are selected in a manner that reduces power consumption. In this regard, it is preferable to select the lowest frequency, as this will reduce the average charge per second (MCS), reduce the average current drawn from the battery in the IPG or ETS, and thus increase the discharge time, as previously discussed. Figure 10B and Figure 12D As discussed, reducing the pulse width (if possible) will also reduce battery draw and increase discharge time.

[0163] At this point, all relevant stimulation parameters (E, P, X, I, PW, and F (≤10 kHz)) are determined and can be sent from the clinician programmer to the IPG or ETS for execution (334) to provide subsensory stimulation therapy to the patient. Adjustment (332) of the pulse width and frequency (≤10 kHz) can potentially optimize these stimulation parameters to provide sensory abnormalities. Therefore, if necessary, the amplitude of the current A can be further titrated down to the subsensory level (336). If necessary, the prescribed subsensory therapy can be allowed to be washed in for a period of time (338), although this may not be necessary as previously stated, since the ultrasensory sweet spot search (320-328) has already selected electrodes for cases where the patient's pain site has been well recruited.

[0164] If subsensory therapy is ineffective, or if modulation can be used, the algorithm can return to step 332 to select a new frequency (≤10kHz) and / or pulse width based on the previously defined region or relationship.

[0165] It should be pointed out that, Figure 15 Not all steps of the algorithm in the algorithm need to be executed in actual implementation. For example, if the effective electrodes (i.e., E, P, X) are known, the algorithm can start with subsensory optimization using information related to frequency and pulse width.

[0166] Figure 16 Another approach is shown, in which the fitting module 350 ( Figure 14 This can be used to identify optimized subsensory stimuli for patients at frequencies of 10 kHz or less. Figure 16 In this process, the fitting module 350 is again incorporated into or used by algorithm 150, which can again be executed as part of the software on the control circuitry of an external device or within the IPG 10. In algorithm 105, the fitting module 350 is used to select the initial pulse width at a given specific frequency. However, algorithm 105 is more comprehensive because it tests and optimizes the amplitude and also optimizes the pulse width at different frequencies. As further explained below, algorithm 105 may also optionally help select the optimized stimulation parameters that will result in the minimum power requirement of most interest to the IPG's battery 14. Regarding algorithm 105 in... Figure 16 Some of the steps shown are optional, and other steps may be added. It is assumed that a dessert search for the patient has already occurred via Algorithm 105, and that the electrodes (E, P, X) have been selected and preferably will remain constant throughout the operation of the algorithm. However, this is not strictly required, as these electrode parameters can also be modified, as described above.

[0167] Algorithm 105 begins by selecting an initial frequency (e.g., F1) within a range of interest (e.g., ≤10kHz). Algorithm 105 then transmits this frequency to fitting module 350, which selects an initial pulse width PW1 using a previously determined relationship and / or region. For simplicity, in Figure 16 The fitting module 350 is shown as a simple lookup table of pulse width and frequency, which may include another form of information relating to the frequency and pulse width of the stimulus pulses formed to provide pain relief without sensory abnormalities. The selection of pulse width using the fitting module 350 can be more refined, as previously described.

[0168] After selecting the pulse width for a given frequency, the stimulation amplitude A (120) is optimized. Here, multiple amplitudes are selected and applied to the patient. In this example, the selected amplitude preferably uses the optimized amplitude A determined at each frequency (see, for example...). Figure 10B This is determined by the patient's experience over a period of time (e.g., every two days) at amplitudes ranging from A=A2, below (A1), to above (A3). The optimal amplitude among these is chosen by the patient. At this point, further adjustments to the amplitude can be made to experiment and refine the amplitude optimized for the patient. For example, if A2 is preferred, amplitudes slightly higher (A2+Δ) and slightly lower (A2-Δ) can be tried over a period of time. If a lower value of A1 is preferred, even lower amplitudes (A1-Δ) can be tried. If a higher value of A3 is preferred, even higher amplitudes (A3+Δ) can be tried. Finally, iterative testing of such amplitudes leads to an effective amplitude that does not cause sensory abnormalities for the patient.

[0169] Next, the pulse width can be optimized for the patient (130). Similar to amplitude, this can be achieved by slightly decreasing or increasing the previously selected pulse width (350). For example, at a frequency of F1 and an initial pulse width of PW1, the pulse width can be decreased (PW1-Δ) and increased (PW1+Δ) to see if such a patient-managed setting is preferred. Further iterative adjustments to the amplitude and pulse width can occur at this point, although this is not shown.

[0170] In summary, at a given frequency, an initial pulse width (350) (and preferably also an initial amplitude (120)) is selected for the patient because it is expected that these values ​​will provide effective pain relief without sensory abnormalities. Nevertheless, since each patient is different, the amplitude (120) and pulse width (130) are also adjusted according to the initial values ​​for each patient.

[0171] Subsequently, optimized stimulation parameters determined for the patient at the tested frequency are stored in the software (135). Optionally, the average charge per second (MCS) indicating the neuronal drug delivery received by the patient, or other information indicating power draw (e.g., average Ibat, discharge time) is also calculated and stored. If other frequencies within the range of interest (e.g., F2) are still not tested, they are tested as described above.

[0172] Once one or more frequencies have been tested, patient-specific stimulation parameters (140) can be selected using the optimized stimulation parameters (135) previously stored for each frequency. Since the stimulation parameters are applicable to the patient at each frequency, the selected stimulation parameters can include those causing the lowest power draw (e.g., lowest) MSC. This is desirable because these stimulation parameters will be most readily available for the IPG's cells. It can be expected that the stimulation parameters with the lowest MCS determined by algorithm 105 will include those taken at the lowest frequency. However, each patient is different, and therefore this may not be the case. Once the stimulation parameters have been selected, further amplitude optimization (150) can be performed, where the aim of selecting the minimum amplitude is to provide subsensory pain relief without sensory abnormalities.

[0173] As previously mentioned, an interesting aspect of the trends and modeling of the disclosed optimized subsensory stimulation parameters is the recognition that lower frequency stimuli can provide good results. As previously mentioned, using lower frequencies leads to stimulation parameters requiring lower power draw from the IPG. This has been evaluated in several different ways previously. For example, in Figure 10B The explanation given for frequencies ranging from 1 kHz to 10 kHz states that optimized subsensory stimulation parameters (including pulse width and amplitude) lead to a decrease in the average charge per second (MCS) value with frequency. Figure 12D Similarly, for frequencies of 1 kHz and below, we see that the total energy consumed to generate optimized subsensory stimuli decreases with frequency. Figure 12D In this context, energy extraction is represented by an assessment of the IPG's battery current (Ibat) and by the discharge time (i.e., the amount of time an IPG with a rechargeable battery can operate before needing to be recharged). Figure 12D The results show that a lower frequency leads to a lower battery current and a longer discharge time, which reflects a lower MCS value and lower energy used to form optimized stimulation parameters.

[0174] Figure 17A and Figure 17B The analysis of neural dosing was further conducted by reconsidering the use of average charge per second (MCS) data as a function of the frequency of optimized stimulation parameters obtained from the patient population. Figure 17AThe relationship between MSC and frequency logarithm is shown for frequencies from 10 Hz to 10 kHz; Figure 17B More precisely, MSC data are focused on the lower frequency range of 10 Hz to 1 kHz. As previously mentioned, the MCS can be calculated by multiplying the optimal frequency, pulse width, and amplitude for each test patient. Because the optimized subsensory stimulation parameters differ for each patient, the error bars and... Figure 17A and Figure 17B The MCS data in the data is correlated, which represents + / - one standard deviation (STD). From Figure 17A and Figure 17B It can be seen that the MCS data generated by the optimized subsensing parameters follows a predictable trend and increases non-linearly with frequency, as discussed further below.

[0175] exist Figure 17B In this study, MSC data at frequencies of 1 kHz and below were analyzed with particular interest, as previously mentioned, because such frequencies provide good subsensory therapy results, but with significantly lower energy. That is, although not shown, data up to 10 kHz can also be analyzed and used in various algorithms as shown below, but not shown. Figure 17B The data are shown in Table 4 below, which also shows the optimized stimulation parameters for the average values ​​of the test patients:

[0176]

[0177] Table 4

[0178] Although the MCS and frequency data can be curve-fitted to other functions, the data fits well when modeled as a polynomial (relation 380), resulting in MSC = -0.0002F. 2 +0.55F+9.64(R 2 =0.9986), where MSC is calculated in microcoulombs per second. A statistically significant region 381i can also be established around this relationship 380, as described above, which can be defined by various error measures. Figure 17B In this context, these regions 381i are bounded by standard deviation error bars. For example, at 10 Hz, MCS-STD = 6 μC / s and MCS+STD = 12 μC / s. At 50 Hz, MCS-STD = 27 μC / s and MCS+STD = 55 μC / s. Therefore, the region 381a providing good subsensory therapy is defined by linearly bounded regions at points (10 Hz, 6 μC / s), (10 Hz, 12 μC / s), (50 Hz, 55 μC / s), and (50 Hz, 27 μC / s). Table 4 defines the linearly bounded regions. Figure 17B Points in each of regions 381a to 381f shown in the figure:

[0179]

[0180]

[0181] Table 5

[0182] Of particular interest are the results at frequencies equal to or below 400 Hz (e.g., regions 381a to 381d). Such frequencies produce exceptionally low energy values ​​(i.e., low MSC values), and these frequencies were previously unknown to inventors in the field of subsensory therapy. As mentioned elsewhere, conventional subsensory therapy is thought to focus on higher stimulation frequencies.

[0183] Figure 17A and 17B The diagram shows that the deviation in the MCS increases with increasing frequency. This is assumed to occur because the MCS becomes more sensitive as the frequency increases to step sizes previously used for adjusting amplitude (0.1 mA) and pulse width (10 μs). This suggests that using smaller step sizes is prudent when using higher frequencies. At this point, and although not shown in the figure, an algorithm can be employed to adjust the step sizes for amplitude and pulse width as a function of frequency, with larger step sizes used for lower frequencies and smaller step sizes for higher frequencies. Note that the ability to adjust the step size can depend on the clinician's programming software used to adjust the stimulation parameters, or on the DAC circuitry used for a given patient.

[0184] The data in Table 4 represent the expansion of amplitude and pulse width data across patient populations, further illustrating specific optimized subsensory parameters. These optimized stimulation parameters can be expressed in terms of the volume of frequency, amplitude, and pulse width. For example, at 10 Hz, A-STD = 1.73 mA, A+STD = 3.39 mA, PW-STD = 326 μs, and PW+STD = 374 μs. At 50 Hz, A-STD = 1.65 mA, A+STD = 3.43 mA, PW-STD = 283 μs, and PW+STD = 333 μs. Therefore, the volume providing good subsensory therapy is defined by the linearly bounded volumes of the following eight points: (10Hz, 1.73mA, 326μs), (10Hz, 1.73mA, 374μs), (10Hz, 3.39mA, 326μs), (10Hz, 3.39mA, 374μs), (50Hz, 1.65mA, 283μs), (50Hz, 1.65mA, 333μs), (50Hz, 3.43mA, 283μs), (50Hz, 3.43mA, 333μs). Table 6 below lists these optimized stimulation parameter volumes:

[0185]

[0186]

[0187] Table 6

[0188] The fact that the MCS of optimized stimulation parameters follows a predictable trend across patients suggests that such trend data can be used to select optimized stimulation parameters for a given patient. Figures 17C-17E Different methods or algorithms that could lead to this are illustrated. As mentioned elsewhere, the algorithms shown can be implemented as software in an external device used to control the patient's IPG or ETS. These algorithms can also be implemented within the IPG or ETS itself, which would allow for at least semi-automatic selection of optimized subsensory stimulation parameters for the patient.

[0189] Algorithms for selecting optimized subthreshold stimulation parameters can be implemented using MSC data alone, or alternatively, other previously established modeling data can be considered, such as the relationship between frequency and pulse width (see example...). Figure 10A and Figure 12A ). Figure 17C Algorithm 379 uses this frequency and pulse width data in step 382 to select frequencies and pulse widths consistent with the various relationships described previously (98i, 310i) or regions of statistical significance (100i, 300i). Figure 17C The left figure reproduces an example of the frequency-pulse width relationship 310 and region 300c. Figure 17C In the example shown, a frequency of 300 Hz and a time of 200 microseconds are selected at step 382, ​​which falls within region 300c (see [link]). Figure 12A Alternative sites, using relation 310 (see...) Figure 12B You can also select the frequency and pulse width.

[0190] Next, in step 383, the MCS value or range is determined using the selected frequency and either relation 380 or region 381i. For example, using relation 380, an MSC value of 157 μC / s can be calculated when F = 300 Hz. Alternatively, defining an error bar for region 381d (where F = 300 Hz decreases) allows for the determination of an MSC range at that frequency, such as between approximately 96 and 273 μC / s. Figure 17C As shown in the right figure.

[0191] Based on the determined MCS value (or range), the amplitude value (or range) can be determined, as shown in step 384. This amplitude will consist of the MSC value (or range) divided by the product of the selected frequency (300 Hz) and pulse width (200 μs), which in the example shown yields a single amplitude value of 2.61 mA, or an amplitude range of 1.6 mA to 4.55 mA. In short, algorithm 379 produces optimized subsensory stimulation parameters, including frequency (e.g., 300 Hz), pulse width (e.g., 200 μs), and amplitude (e.g., 2.61 mA). Because the underlying data can reflect some extensions (e.g., regions 300, 381), algorithm 379 can identify multiple potential candidate sets of stimulation parameters for trial on a given patient. Multiple different sets of stimulation can also be applied to the patient in a time-multiplexed manner. This will be referred to later. Figure 34 A to Figure 36 To provide a more detailed description.

[0192] The trend of MSC and frequency suggests that when selecting optimized subsensing parameters, it may not be strictly necessary to select the pulse width based on the frequency (or vice versa), and Figure 17D Another example of Algorithm 385 is shown, which can be used to select optimized subsensory stimulus parameters. In this example, the previously discussed frequency and pulse width trends (e.g., regions 100i, 300i, or relations 98i and 310i) are not used. Instead, only the MSC and frequency trend (using region 381i or relation 380) are used, with the aim of selecting frequencies, pulse widths, and amplitudes consistent with such trend data. Algorithm 385 can operate to select a single set of stimulus parameters (i.e., a single frequency, pulse width, and amplitude), although... Figure 17D Three such parameter sets are shown. Parameter set "A" includes F = 150 Hz, PW = 100 μs, and A = 4 mA, which results in an MCS value of 60 μC / s. The stimulus parameter set "A" is as follows... Figure 17D As shown on the curve, and note that it lies within region 381c, which is statistically significant, although it is not a point on relation 380. Parameter set "B" comprises F = 500 Hz, PW = 100 μs, and A = 4.7 mA, resulting in an MCS value of 234 μC / s. This point lies within region 381e, although it is also assumed that this includes a point on relation 380—that is, relation 380 calculates an MSC of 234 when F = 500 Hz. Parameter set "C" comprises F = 800 Hz, PW = 87.5 μs, and A = 5 mA, resulting in an MCS value of 350 μC / s. This stimulus parameter set "C" lies within region 381f, although it is not a point on relation 380.

[0193] Figure 17DThe resulting waveforms for these different sets of stimulus parameters are also shown. In this example, it is assumed that active charge recovery is used, and that the active charge recovery phase (the second phase of each pulse) is symmetrical to the first phase (but opposite in polarity). However, this is not strictly necessary, and the charge recovery phase can include asymmetric active or passive phases, as previously described, and subsequently as follows. Figure 17E The example is shown. Note that these stimulus parameter sets may include frequencies and pulse widths consistent with the previously explained pulse width and frequency trends (100i, 300i, 98i, 310i), or they may not. Similar to Algorithm 379, Algorithm 385 may select a single stimulus parameter set for a given patient, or multiple such parameter sets may be tried or applied in a time-multiplexed manner.

[0194] Because MSC and frequency data ultimately reflect the energy of the stimulus, other algorithms can determine the waveform used by the patient that is not strictly defined by the pulse, or at least not strictly defined by a constant frequency, pulse width, or amplitude. Figure 17E Algorithm 387 selects waveforms that conform to MSC and frequency trend data (region 381 or relation 380), where the waveform has an irregular shape. For example, the pulses in the waveform selected by algorithm 387 may not have a constant amplitude, or may be subdivided into multiple pulses.

[0195] For example, the pulses in stimulus parameter set "D" have the same pulse width (100 μs) and frequency (150 Hz) as the pulses in set "A". Figure 17C However, the pulse amplitude is not constant, but rather increases from 0 to a maximum amplitude (Amax) of 8 mA above the pulse width. Therefore, the region below the pulse in set "D" (Area1) is equal to the region below the pulse in set "A". Since MSC can also be defined as the pulse region multiplied by the frequency, the MSC of the pulses in sets "A" and "D" is the same (60 μC / s), which again aligns with the mentioned trend of MSC versus frequency (380, 381).

[0196] In another example, the pulses in stimulus parameter set "E" have the same maximum amplitude (4.7 mA) and frequency (500 Hz) as the pulses in set "B". Figure 17C However, the pulse width doubles (reaching 200 μs), and the amplitude rises and falls during the pulse width. Therefore, the region below the pulse in set "E" (Area 2) is equal to the region below the pulse in set "B", and both sets again have the same MSC value (234 μC / s), which is consistent with the MSC trend data again.

[0197] The stimulus parameter set "F" shows that each pulse can be formed into a set of pulses by algorithm 387, sometimes referred to as a "burst" of pulses. In this example, this pulse is compared with the pulses in set "C" ( Figure 17C Similar to set "C", and with the same pulse width (87.5 μs) and frequency (800 Hz). However, set "F" creates multiple pulses (two, but possibly more) for each pulse shown in set "C", and their amplitude is half (2.5 mA). The sum of the areas under the two pulses in set "F" (Area 3) is equal to the area under a single pulse in set "C", and therefore the pulses in sets "C" and "F" again have the same MCS value (350 μC / s), which again aligns with the MSC trend data. In short, Algorithm 387 allows for a more general definition of waveforms consistent with MSC trend data.

[0198] Thus far, MSC trend data (e.g., 380, 381) have been shown as varying with frequency. However, useful MSC trend data in the disclosed algorithm can also be defined relative to other stimulus parameters. For example, as... Figure 17F As shown in the diagram and as illustrated in Table 4 above, the MSC values ​​for predicting good subsensory therapy also vary with pulse width in a predictable manner, as shown in relation 380'. This relation 380' can also be modeled (e.g., curve fitting) and correlated with error bars to determine region 381', which can then be used in the algorithm just described to help select optimized subsensory stimulation parameters. Figure 17F The table (and again taken from Table 4) also shows the variation of MSC values ​​with amplitude. Note that the amplitude remains fairly constant (e.g., approximately 2.6 mA) as MSC changes. While this trend may not be very useful in selecting stimulation parameters, it is noted that it is consistent with previously reported optimized subsensory stimulation amplitudes (see, for example...). Figure 10B ).

[0199] The results of further investigation Figures 18-23DThe aim, as illustrated, is to provide optimized subsensory modeling that takes into account the perception threshold (pth) as well as frequency (F) and pulse width (PW). The perception threshold is often an important factor to consider when modeling subsensory stimuli and when using such modeling information to determine optimized subsensory stimulation parameters for each patient. The perception threshold pth comprises the minimum amplitude (e.g., in mA) at which a patient can perceive the effects of sensory abnormalities, below which subsensory stimulation occurs. In fact, different patients will have different perception thresholds. This variation is largely due to the fact that electrode arrays in some patients may be closer to the spinal nerve fibers than in others. Therefore, such patients will experience perception at a lower amplitude; that is, for these patients, pth will be lower. If the electrode arrays in other patients are farther from the spinal nerve fibers, the perception threshold pth will be higher. The improved modeling takes into account this understanding of pth because, in addition to optimizing frequency and pulse width, the inclusion of this parameter can also be used to suggest an optimized amplitude A for subsensory stimulation of the patient.

[0200] With this in mind, acquiring data from patients involves not only determining the frequencies and pulse widths they found optimized, as previously described, but also determining the perception thresholds at those frequencies and pulse widths. The resulting model 390, as... Figure 18 As shown in the figure. This model 390 was determined based on tests conducted on a patient sample (N=25), in which... Figure 18 The average value determined by three-dimensional regression fitting is shown, which produces model 390 as a surface in the frequency-pulse width-sensing threshold space. Figure 18 The data presented were acquired at frequencies of 1 kHz and below. Data at these frequencies are of particular interest because, as already mentioned, lower frequencies take into account energy usage in the IPG or ETS, and therefore, it is especially important to demonstrate the utility of subsensory stimuli within this frequency range. For example, from... Figure 18 The equations show that, by assuming that the frequency varies with the pulse width (a(PW)b) and the sensing threshold pth (c(pth)d) according to the power function, the data obtained from the patient are modeled with a good fit. While these functions provide a suitable fit, other types of mathematical equations can also be used for fitting. Model 390, as a surface fit, yields the following result: F(PW, pth) = 4.94 x 10⁻⁶. 8 (PW)-2.749+1.358(pth) 2 Note that the frequency, pulse width, and sensing threshold are not simply correlated proportionally or inversely in a model 390, but rather correlated by a nonlinear function instead.

[0201] Figure 19AFurther observations noted from the tested patients are shown, and another modeling aspect is provided, which, together with model 390, can be used to determine the optimized subthreshold stimulation parameters for the patients. Figure 19A This illustrates how the perception threshold pth of the tested patients varies with pulse width, where each patient... Figure 19A The graph is represented by different lines. Analysis of each line shows that the relationship between pth and PW can be well modeled using a power function, i.e., pth(PW) = i(PW)j + k, although other mathematical functions can also be used for fitting. Figure 19A The data were acquired for each patient at nominal frequencies (such as 200 to 500 Hz), and further analysis confirmed that the results did not vary significantly with frequency (at least at 150 Hz and higher, using biphasic pulses with active charging). Figure 19A The pulse widths were limited to a range of approximately 100 to 400 microseconds. Limiting the analysis to these pulse widths is reasonable because previous tests (e.g., Figure 12A This indicates that pulse widths within this range have a unique subsensory therapeutic effect at frequencies of 1 kHz and lower. Figure 19B Another example of pth versus pulse width for different patients is shown, along with another equation that can be used to model the data. Specifically, the Weiss-Lapicque, or intensity-duration equation, is used in this example, relating the amplitude and pulse width required to reach the threshold. The equation takes the form pth = (1 / a)(1+b / PW), and when averaging the data across different patients, the constants a = 0.60 and b = 317 show a good fit, where these values ​​represent the mean constant parameters extracted from the population data.

[0202] Figure 20 Further observations noted by the tested patients are presented, providing yet another aspect of the modeling. In fact, Figure 20 This illustrates how the patient's optimized subsensory amplitude A varies based on the patient's sensory threshold pth and pulse width. Figure 20 In the graph, the vertical axis plots the parameter Z, which is related to the patient's perceptual threshold pth and its optimized amplitude A (which will be lower than pth in sub-sensory therapy). Specifically, Z is the optimized amplitude expressed as a percentage of pth, i.e., Z = A / pth. Figure 20As shown, Z varies with pulse width. At shorter pulse widths (e.g., 150 microseconds), Z is relatively low, meaning that the patient's optimized amplitude A is noticed to be significantly below their perception threshold (e.g., A = 40% of pth). At longer pulse widths (e.g., 350 microseconds), Z is higher, meaning that the patient's optimized amplitude A is noticed to be closer to their perception threshold (e.g., A = 70% of pth). As noted from testing various patients, Z and PW generally have a linear relationship over the tested pulse width, and therefore a linear regression was used to determine the relationship between them, yielding Z = 0.0017(PW) + 0.1524 (395). Similarly, Figure 20 The tests were limited to a general range of 100 to 400 microseconds, which was noted to be useful for sub-sensory therapy below 1 kHz. It can be expected that testing over a wider range of pulse widths (e.g., less than 100 microseconds or greater than 400 microseconds) will show some variation from the indicated linear relationship. For example, for pulse widths greater than 400 microseconds, Z may flatten to a value less than 1, and for pulse widths less than 100 microseconds, Z may flatten to a value greater than 0. Because Z varies with pulse width as the curve fits, and because Z also varies with the optimized amplitude A and the sensing threshold pth (Z = A / pth), therefore... Figure 20 The modeling allows the optimized amplitude A to be modeled as a function of the sensing threshold pth and the pulse width PW, i.e., A = pth[0.0017(PW) + 0.1524] (396). The inventors observed that the optimized amplitude A is generally invariant to changes in frequency and pulse width. However, the sensing threshold varies with the pulse width. Therefore, Z varies with the pulse width, while the optimized amplitude A may not.

[0203] Recognizing and modeling these observations, the inventors have developed an algorithm 400 that can be used to provide personalized subsensory therapy for a specific patient. This algorithm 400 can be largely implemented on a clinician programmer 50 and results in the determination of a range of optimized subsensory parameters (e.g., F, PW, and A) for the patient. Preferably, as a final step of the algorithm 400, the range or volume of the optimized subsensory parameters is sent to an external controller 45 for the patient, allowing the patient to adjust their subsensory therapy within this range or volume.

[0204] from Figure 21AThe algorithm 400 shown at the beginning begins in step 402 by determining the sweet spots in the electrode array to which treatment should be applied for a given patient, i.e., by identifying which electrodes should be activated and what polarity and percentage (X%) they should have. The results of the sweet spot search may be known for a given patient, and therefore step 402 should be understood as optional. Step 402 and subsequent steps can be completed using a clinician programmer 50.

[0205] At step 404, a new patient is tested by providing a condition pulse, and in algorithm 400, this test involves measuring the patient's perception threshold pth at various pulse widths using the sweet spot electrode already identified at step 402 during the testing procedure. (As previously discussed...) Figure 19A and Figure 19B The tests discussed can occur at nominal frequencies (such as in the range of 200 to 500 Hz) with varying pulse widths. Determining the pth at each given pulse width involves applying a pulse width and gradually increasing the amplitude A until the patient reports feeling the stimulus (paresthesia), thus producing a pth expressed in amplitude (e.g., milliamperes). Alternatively, determining the pth at each given pulse width can involve decreasing the amplitude A until the patient reports no longer feeling the stimulus (subthreshold). Figure 21A The test 404 for a specific patient is shown in graph and table form. Here, it is assumed that the patient in question has a sensory abnormality threshold pth of 10.2 mA at a pulse width of 120 microseconds. At a pulse width of 350 microseconds, the pth is 5.9 mA, and other values ​​are in between.

[0206] Next, in step 406, algorithm 400 in clinician programmer 50 models the pthv.PW data points measured in step 404 and curve-fits them to a mathematical function. This mathematical function may have been noted earlier to model pth and PW well in other patients, such as the power function pth(PW) = i(PW). j +k or Weiss-Lapicque equations, as previously discussed... Figure 19A and Figure 19B The above discussion is incomplete. However, any other mathematical function can be used to curve fit the current patient data measurements, such as polynomial and exponential functions. In the data shown, the power function models the data well, yielding pth(PW) = 116.5 x PW. -0.509 (For simplicity, the constant "k" has been ignored). The measured data in Table 404 and the determined curve fitting relationship pth(PW) 406 for the patient can be stored in the memory of the clinician programmer 50 for use in subsequent steps.

[0207] Next, and refer to Figure 21B Algorithm 400 then continues to compare the pth(PW) relationship determined in step 406 with model 390. This will refer to... Figure 21B The table shown below illustrates this. In this table, fill in the blanks as shown in the image. Figure 21A The values ​​of pth and PW are determined in (406). As can be seen, discrete pulse width values ​​of interest (100 microseconds, 150 microseconds, etc.) can be used (which may differ from the exact pulse width used during the patient test in step 404). Although in Figure 21B The table shows only six rows of PWv.pth values, but this could be a longer vector of values ​​where pth is determined in discrete PW steps (such as 10-microsecond steps).

[0208] In step 408, the pthv.PW values ​​(from function 406) are compared with the 3D model 390 to determine the frequencies F optimized at these various pthv.PW pairings. In other words, the pth and PW values ​​are provided as variables to... Figure 18 The surface fitting equation (F(PW, pth)) 390 in the equation is used to determine the optimal frequencies, which are also shown as the frequencies filled into the surface. Figure 21B In the chart. At that point, Figure 21B The table in the table represents vector 410, which relates to pulse width and frequency optimized for the patient, and also includes the patient's perception threshold at these pulse width and frequency values. In other words, vector 410 represents the patient-optimized values ​​within model 390. Note that vector 410 for the patient can be represented as a curve along the three-dimensional model 390, such as... Figure 21B As shown in the image.

[0209] Next, and as Figure 21C As shown in step 412, vector 410 can optionally be used to form another vector 413, which contains values ​​of interest or, more practically, values ​​that can be supported by the IPG or ETS. For example, note that vector 410 for a patient includes frequencies at higher values ​​(e.g., 1719 Hz) or at odd values ​​(such as 627 and 197 Hz). Using frequencies at higher values ​​may not be desirable because such frequencies would involve excessive power extraction, even if effective for the patient. See, for example... Figure 12DFurthermore, the IPG or ETS in question may only provide pulses with discrete intervals (e.g., in 10 Hz increments). Therefore, in vector 413, the frequency of interest or supported frequency (e.g., 1000 Hz, 400 Hz, 200 Hz, 100 Hz, etc.) is selected, and then the corresponding values ​​of PW and pth are interpolated using vector 410. Although not shown, it may be useful to formulate vector 410 as equation F(PW, pth) to make vector 413 easier to fill. Nevertheless, vector 413 includes essentially the same information as vector 410, albeit at the desired frequency. It is recognized that the IPG or ETS may only support certain pulse widths (e.g., in 10 microsecond increments). Therefore, although this is not shown in the figures, the pulse width in vector 413 can be adjusted (e.g., rounded) to the closest supported value.

[0210] Next, and refer to Figure 21D In step 414, algorithm 400 determines the optimized amplitude of the pulse width and pth value in vector 413 (or vector 410 if vector 413 is not used). This is achieved by using... Figure 20 The earlier determined amplitude function 396, namely A(pth, PW), is used to generate this amplitude. Using this function, an optimized amplitude A can be determined for each pth, PW pair in the table.

[0211] At this point, in step 416, the optimized subthreshold stimulation parameters F, PW, A420 are determined as a patient-specific model. The optimized stimulation parameters 420 may not need to include the perceptual threshold pth: although pth is useful for determining the patient's optimized subthreshold amplitude A (step 414), it may no longer be a parameter of interest because it is not a parameter generated by the IPG or ETS. However, in other examples discussed later, including pth in the optimized parameters 420 may be useful because this allows the patient to modulate their stimulation to a supra-sensory level if needed. At this point, the optimized stimulation parameters 420 can then be sent to the IPG or ETS for execution, or, as shown in step 422, they can be sent to the patient's external controller 45, as described below.

[0212] Figure 21E and Figure 21F The optimized parameters 420 are depicted graphically. Although the optimized parameters 420 in this example include three-dimensional ranges or coordinate lines (F, PW, and A), for ease of illustration, they are depicted in two two-dimensional plots: Figure 21E The relationship between frequency and pulse width is shown, and Figure 21F The relationship between frequency and amplitude is shown. It should also be noted that... Figure 21FThe sensory abnormality threshold pth is shown, and additionally, on the X-axis, it is shown in relation to the threshold pth from the sensory abnormality threshold pth. Figure 21E The pulse widths corresponding to various frequencies. Note that the shape of the data in these graphs can vary from patient to patient (e.g., based on...). Figure 21A The pth measurement results can also vary depending on the underlying modeling used (e.g., Figures 18-20 Therefore, the various shapes of the trends shown should not be interpreted as restrictive.

[0213] The optimized stimulus parameters 420 determined by algorithm 400 include a range or vector of values, including those based on modeling ( Figures 18-20 ) and patient testing ( Figure 21A The frequency / pulse width / amplitude coordinates in step 404) will result in subthreshold stimulation optimized for this patient. Although for simplicity, in Figure 22 The optimized parameters 420 are shown in tabular form, but it should be understood that these optimized parameters (O) can be curve-fitted using equations that include frequency, pulse, and amplitude (i.e., O = f(F, PW, A)). Since each of these coordinates is optimized, it may be reasonable to allow patients to use them with their IPG or ETS, and as a result, the optimized parameters 420 can be sent from the clinician programmer 50 to the patient's external controller 45. Figure 4 This allows patients to choose among them. In this regard, the optimized parameters 420, whether in tabular or equation form, can be loaded into the control circuit 48 of the external controller 45.

[0214] Once loaded, the patient can access menus in the external controller 45 to adjust the treatment provided by the IPG or ETS to match these optimized parameters 420. For example, Figure 22A graphical user interface (GUI) for an external controller 45, as shown on its screen 46, is illustrated. The GUI includes means that allow the patient to simultaneously adjust the stimulation within a range of determined optimized stimulation parameters 420. In one example, the GUI includes a slider with a cursor 430. The patient can select the cursor 430, and in this example, moves the cursor left or right to adjust the frequency of the stimulation pulses in their IPG or ETS. Moving it to the left decreases the frequency to the minimum value (e.g., 50 Hz) included in the optimized parameters 420. Moving the cursor 430 to the right increases the frequency to the maximum value (e.g., 1000 Hz) included in the optimized parameters. As the cursor 430 moves and the stimulation frequency changes, the pulse width and amplitude are simultaneously adjusted, as reflected in the optimized parameters 420. For example, at F = 50 Hz, the amplitude is automatically set to A = 4.2 mA, and the pulse width is set to 413 microseconds. At F = 1000 Hz, the amplitude is set to A = 3.7 mA, and the pulse width is set to 132 microseconds. In practice, cursor 430 allows patients to browse optimized parameters 420 to find their preferred F / PW / A settings, or simply select stimulation parameters that are still effective but require drawing lower power from the IPG or ETS (e.g., at a lower frequency). Note that frequency, pulse width, and amplitude may not be adjusted proportionally or inversely to each other, but will follow a non-linear relationship based on the underlying modeling.

[0215] In another example, allowing patients to adjust the stimulus without knowing the parameters—that is, without displaying the parameters—could be useful, as this would be too technical for them to understand. In this regard, the slider could be labeled with more general parameters, such as... The patient can adjust this parameter, such as between 0% and 100%. The three-dimensional simulation parameters A, PW, and F can be mapped to this one-dimensional parameter. (For example, as shown, 4.2mA, 413μs, and 50Hz can be equal to 0%). Generally, patients can set the parameters... This can be understood as an increasing percentage of "intensity" or "neural dose." In reality, depending on the optimized stimulation parameter 420, it is mapped to... This approach is likely correct. In another modification, not shown, the average charge per second (MSC) model (e.g., 380, 381) can be used to select the optimized stimulation parameters, as previously discussed. Figure 17A-17F The GUI can then allow users to browse within the MSC model to select optimized stimulation parameters.

[0216] It should be understood that while the GUI of the external controller 45 does allow patients some flexibility to modify their IPG or ETS stimulation parameters, it is also simple and advantageously allows patients to adjust all three stimulation parameters simultaneously using a single user interface element, while ensuring that the resulting stimulation parameters will provide optimized subthreshold stimulation.

[0217] Other stimulus modulation controls can also be provided by an external controller 45. For example, such as Figure 22 As shown, another slider allows the patient to adjust the duty cycle to control how far the pulse will run (100%) or be completely off (0%). An intermediate duty cycle (e.g., 50%) would mean the pulse will run for a period of time (from seconds to minutes) and then be off for the same duration. Since "duty cycle" can be a technical concept that patients may not intuitively understand, note that it can be labeled in a more visually appealing way. Therefore, and as shown, duty cycle adjustments can be labeled differently. For example, since a lower duty cycle results in lower power draw, the duty cycle slider could be labeled as "Power Saving" function, "Total Energy" function, "Total Neural Charge Dose" function, or something similar, which might be easier for the patient to understand. The duty cycle can also include functionality locked to the patient's external controller 45 and is only accessible to the clinician after, for example, entering the appropriate password or other credentials. Note that the duty cycle can be adjusted smoothly or in preset logical increments (such as 0%, 10%, 20%, etc.). For simplicity, duty cycle adjustment is not shown in the subsequent user interface examples, but it can be used in such examples.

[0218] Figures 23A-23D This addresses the practicality issue that might lead to imperfect modeling of the determined optimization parameter 420. For example, model 390 (which treats frequencies as functions of PW and pth (F(PW, pth)); Figure 18 Modeling involves averaging across various patients and can have some statistical variance. This is in... Figure 23A The diagram illustrates this simply by showing surfaces 390+ and 390-, which are higher and lower than the average reflected in surface model 390. Surfaces 390+ and 390- can represent some degree of statistical variance or error measure, such as adding or subtracting a sigma, and can in general include error bars beyond which model 390 would no longer be reliable. These error bars 390+ and 390- (which may not be constant across the entire surface 390) can also be determined from an understanding of the statistical variance of various constants assumed during modeling. For example, different confidence measures can be used to determine the values ​​a, b, c, and d in model 390. Figure 18 As shown, constants a, b, and c vary within a 95% confidence interval. For example, the range of constant "a" can be from 5.53 x 10⁻⁶.7 Up to 9.32x10 8 As shown in the figure. (Here, it is assumed that the constant d is only 2 and does not change). Similarly, the value used to model the relationship between pth and pulse width ( Figure 19A and Figure 19B The confidence measures may differ, and the values ​​m and n used to model the relationship between optional magnitude, pth, and PW may also differ. Figure 20 As time progresses and more patient data is collected, the confidence level of these models can be expected to increase. In this regard, note that the algorithm 400 can be easily updated periodically using new modeling information by loading it into the clinician programmer 50.

[0219] Statistical variance implies that the optimized stimulus parameters may not include discrete values, but instead may fall within a volume. This is in... Figure 23A The vector 410 determined for the patient is shown in the figure (see Figure 410). Figure 21B Given the statistical variance, vector 410 may include a rigid line within volume 410'. In other words, there may not be a one-to-one correspondence between PW, pth, and F, such as... Figure 21B As in the case of vector 410. Alternatively, for any given variable (such as pulse width), the pth determined for the patient (using the pth(PW) model in step 406) can vary within a range of statistically significant maximum and minimum values, such as... Figure 23B As shown in the image. Model 390 ( Figure 18 The statistical changes in ) may also mean that the maximum and minimum frequencies can be determined for each maximum and minimum pth in step 408. Since this is done via trickle by algorithm 400, the optimized stimulation parameters 420 may not have a one-to-one correspondence between frequency, pulse width, and amplitude. Instead, and as Figure 23B As shown, for any frequency, there may exist a range of statistically significant maximum and minimum optimized pulse widths, and similarly, a range of optimized amplitudes A. Then, effectively, the optimized stimulation parameter 420' can be defined in a frequency-pulse-width-amplitude space rather than in a coordinate volume that is statistically significant on a coordinate line, although 420' can also include the optimized stimulation parameter 420. The sensory abnormality threshold pth may also vary within a range, and as mentioned earlier, it may be useful to include it in the optimized stimulation parameter 420' because pth may help allow the patient to change stimulation from subsensory to hypersensory, as discussed in some later examples.

[0220] Figure 23C and Figure 23DThe optimized parameters 420' are depicted graphically, showing the statistical correlation ranges of the pulse width and amplitude suitable for the patient at each frequency. Although the optimized parameters 420' in this example include three-dimensional coordinate volumes (F, PW, and A), for ease of illustration, they are depicted in two two-dimensional plots, similar to those previously shown. Figure 21E and Figure 21F The following situations occurred: Figure 23C The relationship between frequency and pulse width is shown, and Figure 23D The relationship between frequency and amplitude is shown. Also note that... Figure 23D The sensory abnormality threshold pth is shown, which, like pulse width and amplitude, can vary statistically within a certain range. Optimized stimulation parameters 420 for each of the parameters are also shown (determined without statistical variance, see [reference]). Figure 21E and Figure 21F And as expected, it falls within the wider volume of the parameter specified by 420'.

[0221] Given a defined volume of optimized parameters 420', it might be useful to allow the patient to navigate different settings within that volume using their external controller 45. This is in Figure 23E One example is shown. Here, instead of displaying a single linear slider, the GUI of the external controller 45 displays a three-dimensional volume 420' representing the optimized parameters, where different axes represent changes the patient can make in frequency, pulse width, and amplitude. As previously described, the GUI of the external controller 45 allows the patient a degree of flexibility to modify their IPG or ETS stimulation parameters and allows the patient to adjust all three stimulation parameters simultaneously through a single adjustment action and using a single user interface element.

[0222] It is possible to allow patients to browse different GUIs of the determined optimized parameter volume of 420', and Figure 23F Another example is shown. In Figure 23FThe diagram shows two sliders. A first linear slider, controlled by cursor 430a, allows the patient to adjust the frequency based on the frequency reflected in the optimized volume 420'. A second two-dimensional slider, controlled by cursor 430b, allows the patient to adjust the pulse width and amplitude at that frequency. Preferably, the range of pulse width and amplitude is constrained by the optimized parameters 420' and the frequency already selected using cursor 430a. For example, if the user selects a frequency F = 400 Hz, the external controller 45 can consult the optimized parameters 420' to automatically determine an optimized range of pulse width (e.g., 175 to 210 microseconds) and amplitude (3.7 to 4.1 mA) for the patient to use at that frequency. When the patient changes the frequency using cursor 430a, the allowed range of pulse width and amplitude selected using cursor 430b is automatically changed to ensure that subthreshold stimulation remains within the volume 420' determined to be statistically useful to the patient. In another modification, not shown, an average charge per second (MSC) model (e.g., 380, 381) can be used to select the optimized stimulation parameters, as previously discussed regarding... Figure 17A-17F The GUI can then allow users to browse within the MSC model to select optimized stimulation parameters.

[0223] Figures 24A-24J Other ways in which the optimized subsensory parameters determined in various ways in this disclosure can be applied to a patient are shown. In these examples, the optimized subsensory stimulation parameters once determined for the patient are not continuously applied, but are subject to a modulation function M(t) that will change the amount of charge generated by the treatment over time. This can be particularly useful for reducing the amount of charge received by the patient over time. In this regard, it is assumed that the subsensory stimulation may be therapeutic, which means that less stimulation (less charge) is needed over time. As explained below, modulating the charge provided by the treatment may involve duty cycling of the optimized subsensory stimulation, and / or may involve adjusting one or more parameters of the stimulation. Although shown to be particularly useful for modulating the charge provided by subsensory stimulation, the disclosed modulation techniques can be applied to any type of stimulation, including extrasensory stimulation.

[0224] Figure 24A A first example of a modulation function M(t) is shown, which can be used to adjust the average charge per second (MCS) delivered over time in subsensory stimulation therapy. The example modulation function includes exponential decay (M = e^(-t / t)). (-t / τ) This can be multiplied by the nominal MSC0 determined by treatment, thus causing MSCs to decrease over time, i.e., MSC(t) = M(t) * MCS0 = MSC0 * e (-t / τ)Modeling the modulation function M as exponential decay is just one example. Further experience treating patients may show that other modulation functions (linear decrease, etc.) will provide good clinical outcomes. Furthermore, and although not shown, the modulation function preferably does not ultimately reduce MSCs to zero: that is, MSCs can approach an asymptote or minimum to ensure that the patient will always receive a sufficient amount of charge over time. Moreover, the modulation function does not need to include an actual mathematical function, but can simply include a data structure (e.g., a table) that indicates how the MSCs of subsensory therapy will decrease over time (a 90% decrease at time t1; an 80% decrease at time t2, etc.). The modulation function M(t) can also cause the charge delivered by the treatment to increase over time, or to increase and decrease the charge over time (e.g., in a cyclic manner), although these details are not shown.

[0225] The nominal MSC value MSC0 used in the modulation function can be determined based on normally determined (e.g., based on a patient population) or optimized subsensory stimulation parameters determined for a given patient (as discussed in other parts of this disclosure). For example, Figure 24A The data from Table 4, summarized above, illustrates the optimized subsensory stimulation parameters for frequency (F), pulse width (PW), and amplitude (A). Based on these parameters, the nominal average charge per second (MSC0) useful to the patient at time t = 0 can be determined (e.g., MSC0 = F * PW * A). A modulation function M(t) can be applied to adjust the charge (MSC(t)) that these stimulation parameters will provide over time. In the simplified example depicted, the modulation function M(t) reduces the MCS by 55% at time = 100 days and by 40% at time = 40 days. Figure 24A The table at the bottom illustrates the effect of the modulation function M on the MCS(t) of the stimulation therapy. For example, at a frequency of 1000 Hz, the MCS of the treatment delivery starts at 326 μC / s, but decreases to 179 at t = 100 days and further to 130 at t = 200 days. Figure 24A In the example, it is assumed that all optimized stimulus parameters are subject to the same modulation function M(t). However, the modulation function may differ for different optimized sets of stimulus parameters. For example, a stimulus at 1000 Hz may use the first stimulus function, while a stimulus at 600 Hz may use the second modulation function, and so on.

[0226] Figure 24B This illustrates a first approach where a modulation function can be applied to optimize the stimulation parameters F, PW, and A. In this example, the modulation function is applied as a duty cycle to turn the stimulus on and off at appropriate time intervals (t). on and t off This affects the reduction of charge as a function of time. Figure 24BThe first row of pulses shows the optimized stimulation pulse, and the following rows show the effect of applying a modulation value of 1 at time t=0. As shown, the pulse remains unchanged.

[0227] The next line shows the effect of applying a modulation value of 0.55 at time t = 100 days, which results in only 55% of the time (at t)... on During the period, a pulse is emitted. During the remaining 45% of the time, no pulse is emitted (during t). off (During the period). As shown, these on and off periods can be staggered and applied periodically. In one example, t on It might be 55 minutes, and t off It is 45 minutes, but this is just one example affecting the 55% duty cycle. Note that during the on-phase, stimulation is delivered with the initially determined pulses, where F, PW, and A remain constant with their other optimized values. As mentioned earlier, note that subsensory therapy continues to be effective for some time after this treatment stops, and is effective during the "wash-out" period following treatment cessation. In this respect, stimulation during the on-phase is expected to provide symptom relief during the subsequent off-phase, especially if the duration of the off-phase is t. off If it's equivalent to the washing period. Figure 24B The last line shows the effect of applying a modulation value of 0.4 at time t = 200 days, which results in pulses being emitted only 40% of the time. Therefore, when compared to the previous line, t on Shorter, and t off Longer. In another example, as M decreases, t on It can remain constant and t off It can be increased, or t off It can remain constant and t on This can be reduced. Note that the modulation function M(t) therefore scales (reduces) over time the charge delivered to the patient.

[0228] Figure 24C and Figure 24D Different examples are shown of how a modulation function M(t) can be applied to an optimized subsensory stimulus 420' in the disclosed system (e.g., derived in any manner provided in this disclosure). In any example, one or more modulation functions M(t) are stored in memory 452, and a stimulus modulation algorithm 450 is provided that uses one or more modulation functions to modulate the charge provided by the therapy over time, thereby adjusting the stimulus.

[0229] Figure 24CAn implementation of the stimulus modulation algorithm 450 in an external device such as an external controller 45 or a clinician programmer 50 is illustrated, and more specifically, as firmware programmed into the control circuitry 48 or 70 of those devices. The stimulus modulation algorithm 450 receives optimized stimulus parameters 420' and processes them using one or more modulation functions M from within or associated with the memory 452 of the control circuitry 48 or 70. A timer circuit 451 assists the stimulus modulation algorithm 450 by providing a time basis that informs the algorithm 450 when it may need to change the stimulus according to the modulation function M(t) (e.g., from 1 to 0.55, or to 0.4, etc.). The stimulus modulation algorithm 450 can then use the modulation function to determine the on and off times of the stimulus, and in particular, can provide an on / off schedule and optimized stimulus parameters to the IPG 10 or ETS 40. Alternatively, the external device 45 or 50 may send stimulus on and off instructions to the IPG 10 or ETS 40 at appropriate times, rather than in advance according to a predetermined schedule. Receiving such stimulation scheduling or stimulation on and off commands will allow the IPG 10 or ETS 40 to know when to activate the stimulation circuit 28 in the IPG 10 or ETS 40 to generate pulses, thereby providing the appropriate amount of charge to the patient over time.

[0230] Figure 24D An implementation of the stimulation modulation algorithm 450 in the IPG 10 or ETS 40 is shown. Here, the control circuit 600 of the IPG 10 or ETS 40 is programmed using the stimulation modulation algorithm 450 and one or more necessary modulation functions (452), which may have been previously programmed using an external device. The IPG 10 or ETS 40 can then receive optimized stimulation parameters 420' from the external device and send the stimulation parameters along with any on / off commands to the stimulation circuit 28. Therefore, in Figure 24D In the example, the external device does not need to prepare stimulus scheduling for IPG 10 or ETS 40 because such scheduling is determined by the stimulus modulation algorithm 450 in IPG 10 or ETS 40 using one or more modulation functions 452.

[0231] Figure 24E and Figure 24FAnother example is shown where a modulation function M(t) can be applied to modify the charge delivered by optimized subsensory stimulation parameters. In this example, the stimulation parameters themselves (e.g., one or more of F, PW, and A) change over time to achieve the charge adjustment specified by the modulation function. In this example, it is assumed that the optimized stimulation parameters initially determined for the patient are those obtained from Table 4 at F = 600 Hz, PW = 153 μs, and A = 2.66 mA, which would produce a nominal average charge per second value MCS0 of 249 μC / s. The modulation function M(t) applied to this stimulation, shown in the upper right corner, will, as before, reduce the charge delivered to the patient to 55% at time t = 100 days; to 40% at time t = 200 days; and so on.

[0232] In this example, the stimulus is not duty-controlled (e.g., Figure 24B The charge reduction is achieved by the modulation function M(t). Instead, one or more stimulation parameters are changed. Thus, and again assuming a nominal MSC0 = 249 μC / s, the MSC specified by the modulation function at time t = 100 should be 137 μC / s (i.e., 137 = 0.55 * 249); at time t = 200 it should be 100 μC / s (i.e., 100 = 0.40 * 249); and so on. Then one or more stimulation parameters are adjusted to achieve the specified MSC value at the appropriate time. Thus, at time t = 100 days, the frequency is adjusted to 280 Hz, the PW is adjusted to 190 μs, and the amplitude A is adjusted to 2.58, which produces the desired MSC value of 137 (i.e., 137 μC / s = 280 Hz * 190 μs * 2.58 mA). At time t = 100 days, the frequency is adjusted to 180Hz, the PW is adjusted to 215μs, and the amplitude A is adjusted to 2.58, at which point it produces the expected MSC value of 100 (i.e., 100μC / s = 180Hz * 215μs * 2.58mA); and so on. Figure 24E The effect of applying the modulation function M(t) is shown, as well as the effect when the stimulus parameters vary in this way according to the modulation function M(t). Note that the stimulus pulses are not duty-controlled on and off, but rather run freely while achieving the appropriate amount of charge according to the modulation function. That said, these two techniques—adjustment of the stimulus parameters and duty control—can be used together to adjust the charge of the stimulus as specified by the modulation function, although this is not shown for simplicity.

[0233] exist Figure 24E and Figure 24F In this process, one or more stimulation parameters are adjusted in any desired manner to achieve the desired charge modulation as set by the modulation function M(t) at the appropriate time. However, and as... Figure 24GAs shown, the stimulation parameters can also be adjusted according to other models that correlate the stimulation parameters with the total average charge per second (such as Relation 380) and / or correlate the stimulation parameters with each other (such as Relation 310d). Figure 24G A graph showing MSC versus frequency is presented, which previously pertained to... Figure 17B A description is provided, and it shows the optimized subthreshold stimulation parameters for the MSC in Table 4. As discussed earlier, the relationship between MCS and frequency can be modeled as Relation 380. It has also been discussed previously that optimized subsensory stimulation parameters such as frequency and pulse width can be correlated with each other, as reflected in Relation 310d, as previously discussed regarding the frequency range discussed. Figure 12B What is described and drawn.

[0234] These models 380 and 310d can be used to determine how to adjust the substimulation parameters according to the modulation function M(t). For example, again assuming that the optimized stimulation parameters initially determined for the patient are those obtained from Table 4 at F = 600 Hz, PW = 153 μs, and A = 2.66 mA, this would produce a nominal average charge per second value MCS0 of 249 μC / s. Figure 24G As shown in the graph, the charge value at that frequency can be normalized to 1. When it is desired to change the stimulus according to the modulation, relation 380 can be used to determine the new frequency of the stimulus, and then relation 310d is used to determine the new pulse width. For example, at M = 0.55 (time = 100), using relation 380, it can be seen that a frequency of 265 Hz can be selected. Starting from this frequency value and using relation 310d, a pulse width value of 199 μs can be selected. Since the MSC at t = 100 should be equal to 55% of MSC0 (i.e., MSC = 137 = MSC0 * 0.55), the amplitude A can be determined, which provides the MSC value at the selected location (i.e., A = 265 Hz * 199 μs / 137 μC / s = 2.59 mA). Later, at M = 0.40 (time = 200), it can be seen that a frequency of 170 Hz (relation 380) and a pulse width of 222 μs (relation 310d) can be selected, where the selected amplitude (A = 2.65 mA) reaches the expected MSC value (100 = 249 * 0.4). In fact, the modulation function M(t) is applied to select the value at relation 310d ( Figure 12B The stimulation parameters (specifically F and PW) are adjusted, and the amplitude is adjusted as needed to achieve the appropriate amount of charge at that time. This is beneficial because, as mentioned earlier, relation 310d provides information related to frequency and pulse width, which has been recorded to provide optimized subsensory perception for the patient. Therefore, when the modulation function M(t) operates, the adjusted stimulation parameters are still those indicated by the earlier modeling for optimization. This is consistent with... Figure 24EIn contrast, the stimulus parameters are adjusted over time according to the modulation function M(t) to achieve the desired charge, but not necessarily in a way that produces optimized parameters validated by other models.

[0235] Figure 24H and Figure 24I Different methods are shown, in which different methods can be used depending on... Figure 24E and Figure 24G The modulation function described in the text is used to determine the optimal stimulus parameters. Figure 24H An implementation of the stimulation modulation algorithm 450' operating in an external device such as an external controller 45 or a clinician programmer 50 is shown, which is similar to previous implementations. Figure 24C The content described. Figure 24I The implementation of stimulus modulation algorithm 450' in IPG 10 or ETS 40 is shown, similar to the previous description. Figure 24D The content described. In any case, the stimulus modulation algorithm 450' determines how to adjust the initially provided stimulus parameters (F, PW, A) according to the modulation function (452) to achieve an appropriate charge level over time. As previously mentioned... Figure 24G As noted, one or more models 453 (such as relations 380 and 310d) can be stored along with the control circuitry and used to determine stimulus parameter adjustments as explained above. One or more models 453 may also be unnecessary or unused when the stimulus modulation algorithm adjusts the stimulus parameters, as, for example, regarding... Figure 24E As shown.

[0236] Figure 24J The initially selected modulation function can vary based on objective or subjective measurement results. In this respect, the modulation function may need adjustment from time to time. For example, if the modulation function reduces the charge delivered to the patient too quickly, the patient may not receive enough charge to resolve their symptoms. If the patient's symptoms are improving, the patient can use the GUI of their external controller 45 to rate or rank their symptoms (pain), as shown in input 454. Based on the subjective measurement of the patient's assessment, input 454 can be received by the stimulus modulation algorithm 450 or 450', which can evaluate the measurement results and, according to its programming, determine whether the modulation function needs adjustment.

[0237] In this example, upon receiving measurement result 454, algorithm 450 or 450' decides to increase the modulation function to provide more charge to the patient. This increase in modulation is represented by a new modulation function M'(t), which may include an adjustment to the selected original modulation function (M(t)), or it may include another modulation function selected by algorithm 450 or 450' from memory 452. The adjusted modulation function M'(t) can also be stored back into memory 452 by algorithm 450 or 450', and the algorithm can also store the history of modulation it used to treat the patient in memory 452. The patient or clinician may be interested in the historical modulation functions used so that they can be read from memory 452 and reviewed in the future to understand how algorithm 450 or 450' adjusted the patient's charge and prevent overtreatment.

[0238] Other objective measurements can be used as inputs 454 to algorithm 450 or 450' to adjust the modulation function or to select a new modulation function. For example, and as described in U.S. Patent Application Publication 2019 / 0209844, the IPG can detect neural responses to stimuli, such as evoked compound action potentials (ECAPs). Various characteristics of the sensed ECAPs can provide an indication of the effectiveness of the stimulus and can therefore be used, in whole or in part, to adjust the modulation function. Other subjective and objective measurements of the effectiveness of the stimulus therapy can be used as inputs 454 to algorithm 450 or 450', some of which are discussed elsewhere in this disclosure.

[0239] Figure 25 Another example is shown where a user can program their IPG 10 (or ETS) settings using exported optimized stimulation parameters. A subsequent example for completeness uses the determined volume 420' of the optimized stimulation parameters, but a vector or range 420 of the optimized stimulation parameters could also be used. The optimized stimulation parameters can also be selected using an average charge per second (MSC) model (e.g., 380, 381), as previously discussed. Figure 17A-17F As stated above.

[0240] Figure 25A user interface on the screen 46 of the patient's external controller 45 is shown, allowing the patient to select from a variety of stimulation modes. Such stimulation modes can include various methods, wherein the IPG can be programmed to correspond to optimized stimulation parameters 420' determined for the patient, such as: an economy mode 500, which provides stimulation parameters with low power draw; a sleep mode 502, which optimizes stimulation parameters for the patient while asleep; a sensory mode 504, which allows the patient to feel the stimulation (extrasensory); a comfort mode 506, for general daily use; an exercise mode 508, which provides stimulation parameters suitable for the patient while exercising; and an intensity mode 510, which can be used, for example, if the patient is experiencing pain and will benefit from a more intense stimulation. This stimulation mode can also indicate the patient's posture or activity. For example, sleep mode 502 provides stimulation optimized for sleep (e.g., when the patient is lying down and not moving significantly), while exercise mode 508 provides stimulation optimized for exercise (e.g., when the patient is standing and moving significantly). Although not shown, stimulation modes may also be included, which provide stimulation optimized for different patient postures (such as supine, prone, standing, sitting, etc.) or for different conditions (such as cold or inclement weather). Although illustrated in the context of an external controller for the patient, it is understood in other examples that another external device, such as a clinician programmer 50, which can be used to select stimulation modes, can also be used to program the patient's IPG.

[0241] Patients can select from these stimulation modes, and such selection can program the IPG 10 to provide a subset of stimulation parameters useful to that mode, controlled by optimized stimulation parameters 420'. For some stimulation modes, the subset of stimulation parameters may be fully constrained (entirely within) the volume of the optimized stimulation parameters 420' determined for the patient, and thus will provide the patient with optimized subsensory stimulation therapy. As explained further below, subsets for other modes may be only partially constrained by the optimized stimulation parameters. However, in all cases, the subset is determined using the optimized stimulation parameters (420 or 420'). Preferably, the subset is determined for the patient at the clinician programmer 50 and sent to and stored in the patient's external controller 45. Alternatively, the determined optimized stimulation parameters may be sent to the external controller 45, leaving it to the external controller 45 to determine the subset from the optimized stimulation parameters.

[0242] The number of stimulation modes available for the patient to select on the external controller 45 can be limited or programmed by the clinician. This is likely to be reliable, as some stimulation modes may be irrelevant to some patients. In this regard, the clinician can program the patient's external controller 45 to specify the available stimulation modes, such as by entering the appropriate clinician's password. Alternatively, the clinician can program the external controller 45 using a clinician programmer 50.

[0243] An example of the stimulus parameter subset 425x is in Figures 26A-31B As shown in the image. Figure 26A and Figure 26B The diagram shows a subset 425a of stimulus parameter coordinates used when the economic mode 500 is selected, which includes a subset of the optimized stimulus parameters 420' with low power draw. Like the optimized stimulus parameters 420', subset 425a can include the three-dimensional volumes of the F, PW, and A parameters, and similarly (compared to...) Figure 23C and Figure 23D The subset 425a is represented using two two-dimensional graphs, where Figure 26A The relationship between frequency and pulse width is shown, and Figure 26B The relationship between frequency and amplitude is shown.

[0244] To influence low-power absorption, the frequencies within subset 425a are low, such as limited to a frequency range of 10 to 100 Hz, even if optimized stimulation parameters 420' may have already been determined over a wider range, such as 10 to 1000 Hz. Furthermore, while optimized pulse widths within this frequency range can vary more significantly within optimized stimulation parameters 420', subset 425a can be constrained to the smaller of these pulse widths, such as the lower half of such pulse widths, as... Figure 26A As shown in the diagram. Similarly, using a lower pulse width will result in lower power draw. Furthermore, as... Figure 26B As shown, subset 425a can be constrained to a lower amplitude within the optimized stimulation parameters 420' for the relevant frequency range, again resulting in lower power draw. In short, subset 425a can include a smaller volume of stimulation parameters within the entire volume of the optimized stimulation parameters 420' that provide sufficient subthreshold stimulation to the patient, while providing lower power draw from the IPG cell 14. Not all of these are related to the selected stimulation mode ( Figure 25 The corresponding subset 425x contains stimulus parameters that must be fully contained within the determined optimized stimulus parameters 420', as shown in some subsequent examples.

[0245] When the economy mode 500 is selected, the external controller 45 can simply send a single low-power optimized parameter (F, PW, A) within subset 425a to the IPG for execution. However, and more preferably, the user interface will include means that allow the patient to adjust the stimulation parameters to those within subset 425a. In this regard, the user interface may include a slider interface 550 and a parameter interface 560. The slider interface 550 may be as described above (see...). Figure 22 The slider interface 550 may include a cursor to allow the patient to slide through parameters in subset 425a. In the example shown, the pulse width, which is set to a specific value (e.g., 325 μs), may not be adjusted, but the frequency and amplitude can be varied. This is just one example, and in other examples, all three—frequency, pulse, and amplitude—can be changed via the slider, or other parameters can be kept constant. Note that a more sophisticated user interface can be used to allow the patient to navigate subset 425a. For example, although not shown, user interface elements with higher three-dimensional quality, such as those previously described... Figure 23E and Figure 23F The volumes discussed herein can be used for navigation of subset 425a. The parameter interface 560 also allows the patient to navigate within subset 425a and is simply displayed with optional buttons to increase or decrease the parameters within the identified subset 425a. The parameter interface 560 may also include a field displaying the current values ​​of frequency, pulse width, and amplitude. Initially, these values ​​can be filled with parameters approximately at the center of the identified subset 425a, allowing the patient to adjust the stimulation near that center.

[0246] Figure 27A and Figure 27B The selection of sleep mode 502 is illustrated, along with a subset 425b of optimized stimulation parameters 420' generated when this selection is made. In this example, subset 425b is determined using optimized stimulation parameters 420' in a manner that makes subset 425b only partially constrained by optimized stimulation parameters 420'. Subset 425b may include low to medium frequencies (e.g., 40 to 200 Hz) within optimized stimulation parameters 420', and may include medium pulse widths allowed by 420' within this frequency range, such as those specified by 420'. Figure 27A As shown.

[0247] Because the intensity of stimulation may not need to be as high during sleep, the amplitude within subset 425b may fall outside the amplitude suggested by the optimized parameter 420', such as... Figure 27BAs shown in the diagram. For example, although optimized parameters 420' might suggest, for example, that the amplitude based on earlier modeling would fall within the range of 3.6 to 4.0 mA for the frequency and pulse width range of interest, in this example, the amplitude within subset 425b is set to even lower values. Specifically, as shown in slider interface 550, the amplitude can be set between 1.5 mA and 4.0 mA. To know where the lower boundary of the amplitude should be set, modeling information can include an additional model 422, which can be determined separately based on patient testing and optimized stimulation parameters 420'. In the case of sleep, due to the expected change in the position of the electrode leads within the patient's spine when the patient lies down, it is permissible to use amplitudes lower than those suggested by optimized parameters 420'. Furthermore, patients may be less bothered by pain while sleeping, and therefore lower amplitudes can still be reasonably effective. That said, subset 425b can also include values ​​(including amplitude) that are entirely within and constrained by optimized stimulation parameters 420', similar to Figure 26A and Figure 26B The values ​​shown are for subset 425a.

[0248] Figure 28A and Figure 28B The selection of sensory mode 504 and the resulting subset 425c available to a given patient during this mode are illustrated. The purpose of this mode is to allow the patient to perceive the stimulation provided by their IPG according to their judgment. In other words, the stimulation provided to the patient in this mode is supra-sensory. The optimized stimulation parameters 420' preferably define a volume of stimulation parameters in which sub-sensory stimulation is optimized for the patient. However, as described earlier, the sensory threshold pth is measured and modeled as part of the determination of the optimized sub-threshold stimulation parameters 420'. In this way, the sensory threshold pth, as determined earlier, is useful for selecting the amplitude that the patient will perceive during this mode, i.e., amplitudes (especially pulse width) higher than pth for other stimulation parameters. Thus, sensory mode 504 is an example in which it is beneficial to include pth values ​​(or pth ranges) within the optimized stimulation parameters 420'.

[0249] like Figure 28A As shown, patients are generally more likely to perceive stimuli at lower frequencies, and therefore, the choice of sensory mode can constrain stimuli in subset 425c to lower frequencies (e.g., 40 to 100 Hz). Pulse width control may not be a major issue, and therefore, for this frequency range, the pulse width may have a moderate range allowed by 420', similarly... Figure 28A As shown.

[0250] However, because patients in this mode want to feel stimulation, the amplitude within the subset 425c is set to a higher value, such as... Figure 28BAs shown in [the diagram]. Specifically, the amplitudes of the relevant frequencies and pulse widths are not only set above the upper limit of amplitudes determined for the optimized stimulation parameters 420'; they are also set at or above the perception threshold pth. As mentioned earlier, the perception threshold pth, and the more particularly important range of pth determined for the patient (taking into account statistical variation), can be included in the optimized stimulation parameters 420' (see [the diagram]). Figure 23D In order to produce a useful effect in this mode, based on earlier measurements and modeling, subset 425c is defined as setting the amplitude within a value or range that should provide extrasensory stimulation. If the range pth is limited according to the statistical variance, the permissible range of the amplitude of sensory mode 504 can be set to an upper limit value beyond that range, such as... Figure 28B As shown in the diagram. Therefore, while the optimized (subsensory) amplitude (per 420') for the frequency range of interest may range from approximately 3.7 to 4.5 mA, the amplitude within subset 425c is set to approximately 5.8 to 7.2 mA, exceeding the upper limit of the pth range, to ensure that the stimulation produced for the patient in question is suprasensory. In this example, note that subset 425c was determined using optimized stimulation parameters 420', but is only partially constrained by these optimized parameters. The frequency and pulse width are constrained; the amplitude is not, because the amplitude in this subset 425c is set beyond 420', and more specifically beyond pth.

[0251] Figure 29A and Figure 29B The selection of comfort mode 506 is shown, along with a resulting subset 425d of stimulation parameters for that mode. In this mode, the stimulation parameters are set via subset 425d to nominal values ​​within optimized stimulation parameters 420': moderate frequencies (such as 200 to 400 Hz) and moderate pulse widths for these frequencies (such as 175 to 300 μs as shown in slider interface 550). Figure 29A As shown in the image. Figure 29B As shown, the amplitude within subset 425d can also be a moderate amplitude within the optimized stimulation parameters 420' for the frequency and pulse width in question. In this example, the stimulation parameters in subset 425d are fully constrained by the optimized stimulation parameters 420', although, as mentioned earlier, this is not necessary for every subset.

[0252] Figure 30A and Figure 30B The selection of exercise mode 508 and the subset of stimulation parameters 425e associated with this mode are shown. In this mode, mid-to-high frequencies (e.g., 300-600 Hz) can be guaranteed, but for these frequencies, the pulse width is higher than those specified by the optimized stimulation parameters 420', such as... Figure 30AAs shown in the diagram. This is because the position of the electrode leads within the patient's body may change more significantly as the patient moves, and therefore, delivering a higher charge injection into the patient's body can be useful; a higher pulse width can achieve this. (As shown in the diagram...) Figure 30B As shown, the amplitudes used can span a moderate range of frequencies and pulse widths involved, but higher amplitudes (not shown) exceeding 420' can also be used to provide additional charge injection. Subset 425e illustrates an example where the frequency and amplitude are constrained by the optimized stimulation parameters 420', while the pulse width is unconstrained; therefore, subset 425e is only partially constrained by the optimized stimulation parameters 420'. In other examples, subset 425e can be fully constrained within the earlier determined optimized stimulation parameters 420'.

[0253] Figure 31A and Figure 31B The selection of a strong stimulus mode 510 is shown. In this mode, the stimulus is more aggressive, and a subset of stimulus parameters 425f can occur at higher frequencies (e.g., 500 to 1000 Hz). However, the pulse width and amplitude at these frequencies may be moderate for the frequencies involved, as shown below. Figure 31A and Figure 31B As shown in the example, the subset of stimulation parameters in subset 425f can be fully constrained by the optimized stimulation parameters 420' (included therein). As in the previous example, the patient can use interface 550 or 560 or other interface elements not shown to adjust the stimulation in subset 425x corresponding to the patient's stimulation mode selection. Figure 25 Less preferably, the selection of the stimulation mode may cause the external controller 45 to send a single set of stimulation parameters (F, PW, A) determined using optimized stimulation parameters 420' (or 420).

[0254] Note that the stimulation parameters in subsets 425x may overlap; some F, PW, and A values ​​in one subset (e.g., 425a) may also exist in another subset (e.g., 425b). In other words, while this may be the case, it is not strictly necessary for the stimulation parameters in a given subset to be unique for that subset or the stimulation pattern represented by that subset. Furthermore, the boundaries of the individual subsets 425x may be adjustable. For example, although not shown, the external controller 45 may have options to change the boundaries of the individual subsets. Using such options, a patient or clinician may, for example, change one or more of the stimulation parameters (e.g., frequencies) in the subsets (e.g., by increasing the frequency within subset 425a from 10 Hz to 100 Hz to 10 Hz to 150 Hz). The modulation of subsets 425x may also be influenced in response to certain feedback, such as the patient's pain level that may be input to the external device 45, or the detection of patient activity or posture. More complex modulations can be locked to the patient and accessible only to the clinician, for example, by providing this accessibility through a password entered into the external controller 45. Behind this password protection, the subset 425x can be adjustable, and / or can be configured with other stimulation modes (e.g., beyond). Figure 25 Those shown are only accessible to clinicians. As previously mentioned, clinicians can also use the clinician programmer 50 to perform such clinician adjustments.

[0255] The subset 425x can also be updated automatically from time to time. This can be advantageous because as data is collected from more patients, the underlying modeling leading to the optimized stimulation parameters 420' may change or become better informed. It can also be learned later that different stimulation parameters can better produce the desired effect of the stimulation pattern, and therefore it is possible to ensure which parameters are included in the tuning subset. Different stimulation patterns provided for different reasons or to produce different effects may also become apparent later, and therefore such new patterns and their corresponding subsets can later be programmed into the external controller 45, and... Figure 25 The stimulation patterns are presented to the patient in the user interface. Updates to subsets and / or stimulation patterns can occur wirelessly by connecting an external controller 45 to a clinician's programmer or to a network such as the Internet. It should be understood that the disclosed stimulation patterns and subsets of stimulation parameters 425x corresponding to such patterns are merely exemplary, and different patterns or subsets may be used.

[0256] Refer again Figure 25The stimulation mode user interface may include option 512 to allow a patient or clinician to define a custom mode of stimulation. This custom mode 512 may allow the user to select frequency, pulse width, and amplitude, or define a subset at least partially defined by optimized stimulation parameters 420'. The selection of this option can provide a user interface that allows the patient to navigate within the optimized stimulation parameters 420', such as previously defined... Figure 23E and Figure 23F Those shown in the image. If the patient finds stimulation parameters through this option that appear to be effective as simulation mode operation, the user interface can allow the stimulation mode to be stored for future use. For example, and refer to... Figure 23E The patient may have already found the optimal stimulation parameters within 420', which is beneficial when the patient walks. Such parameters can then be saved by the patient and appropriately labeled, such as... Figure 23D The user interface element at position 580 is shown. This newly saved stimulation pattern can then be presented to the patient as an optional stimulation pattern. Figure 25 The logic in the external controller 45 can further define a subset 425 of stimulation parameters (e.g., 425g) that the patient can navigate through when the user-defined stimulation mode is selected later. The subset 425g may include, for example, stimulation parameters that limit the parameters selected by the patient (e.g., + / - 10% of the patient's selected frequency, pulse width, and amplitude), but are still wholly or partially constrained by the optional stimulation parameters 420'.

[0257] like Figure 25 As shown, the stimulation mode user interface may also include option 514, which automatically selects and adjusts the stimulation mode for the patient based on various factors that the IPG 10 can detect. Figure 32 The selection of the automatic mode 514 is shown in more detail below. Preferably, the selection of the automatic mode 514 allows the patient to select 570 which of the stimulation modes he / she wants to detect, and which will be automatically used by his / her IPG 10. In the depicted example, the user has selected sleep mode 502, comfort mode 506, and exercise mode 508. The IPG 10 will attempt to automatically detect when these stimulation modes should be input, and in this respect, the IPG 10 may include a stimulation mode detection algorithm 610. As shown, this algorithm can be programmed into the control circuitry 600 of the IPG 10. The control circuitry may include a microprocessor, microcomputer, FPGA, other digital logic structures, etc., capable of executing instructions from electronic devices. Alternatively, the algorithm 610 in the IPG 10 may attempt to detect and adjust stimulation for all stimulation modes (e.g., 500-510) supported by the system without requiring the user to select 570 the stimulation mode of interest.

[0258] Algorithm 610 can receive various inputs related to the detection of stimulus patterns, and thus receive a subset 425x that should be used for the patient at any given time. For example, algorithm 610 can receive inputs from various sensors, such as accelerometer 630, that indicate the patient's posture and / or activity level. Algorithm 610 can also receive inputs from various other sensors 620. In one example, sensor electrodes 620 may include electrodes Ex of IPG 10, which can sense various signals related to stimulus pattern determination. For example, and as discussed in USP 9,446,243, signals sensed at the electrodes can be used to determine the (complex) impedance between the various pairs of electrodes, which can be correlated in algorithm 610 with various impedance characteristics indicating the patient's posture or activity. Signals sensed at the electrodes may include those generated by stimulation, such as evoked compound action potentials (ECAPs). As disclosed in US Patent Application Publication 2019 / 0209844, examination of various characteristics of the detected ECAPs can be used to determine the patient's posture or activity. Signals sensed at the electrodes may also include stimulation artifacts generated by stimulation, as disclosed in U.S. Patent Application Publication 2020 / 0251899, which can also indicate the patient's posture or activity. Signals sensed at the electrodes can also be used to determine the patient's heart rate, as disclosed in U.S. Patent Application Publication 2019 / 0290900, which can also be related to the patient's posture or activity.

[0259] Algorithm 610 can receive additional information related to determining the stimulation mode. For example, clock 640 can provide time information to algorithm 610. This can be related to determining or confirming whether the patient is participating in activities that occur during certain times of the day. For example, it can be anticipated that the patient might sleep at night or exercise in the morning or afternoon. Although not shown, the user interface can allow programming of the time range of the anticipated activity, such as whether the patient prefers to exercise in the morning or afternoon. Algorithm 610 can also receive input from battery 14, such as the current state of the battery voltage Vbat, which can be provided by any number of voltage sensors, such as analog-to-digital converters (ADCs; not shown). This can be useful, for example, in determining when to automatically switch to economy mode 500 or other power-based stimulation modes (i.e., if Vbat is low).

[0260] In any case, the stimulus pattern detection algorithm 610 can wirelessly receive indications of the selected automatic mode 514 and any selected modes 570 of interest to the patient. The algorithm 610 can then use its various inputs to determine when those modes should be input, and thus enable the use of a subset 425x corresponding to the detected stimulus patterns at appropriate times. For example, in Figure 32In the example, algorithm 610 can use accelerometer 630, sensor 620, and clock 640 to determine whether a person is at rest, lying supine or prone, and / or whether their heart rate is slow at night, and thus determine that the person is currently sleeping. Algorithm 610 can then automatically activate sleep mode 502 and activate the subset 425b corresponding to that mode. Figures 27A-27B The use of stimulation parameters within the (). Furthermore, IPG 10 can send a notification of the current stimulation mode determination back to external controller 45, which can be displayed at 572. This is useful for allowing the patient to review whether algorithm 610 has correctly determined the stimulation mode. Additionally, notifying external controller 45 of the currently determined mode allows external controller 45 to use an appropriate subset 425x of the mode to allow the patient to adjust the stimulation. That is, external controller 45 can use the determined mode (sleep) to adjust ( Figures 27A-27B The constraint is the corresponding subset (425b) of the pattern.

[0261] If Algorithm 610 uses one or more of its inputs to determine that a person is rapidly changing position, standing upright, and / or has a high heart rate, it can determine that the person is currently exercising, which is the stimulus mode of interest selected by the patient. Algorithm 610 can then automatically activate exercise mode 508 and activate the subset 425e corresponding to that mode. Figures 27A-27B The use of stimulation parameters within the IPG 10. Again, the IPG 10 can send a notification of the current stimulation mode determination back to the external controller 45 to adjust (…). Figures 30A-30B The constraint is the corresponding subset (425e) of this mode. If algorithm 610 cannot determine whether the patient is sleeping or exercising, it can default to the selection of comfort mode 506 and provide stimulation, notification, and constraint adjustment accordingly (subset 425d). Figures 29A-29B ).

[0262] External controller 45 may also be useful in determining the relevant stimulus mode to be used during automatic mode selection. In this respect, although not in… Figure 32As shown, however, the external controller 45 may include sensors, such as an accelerometer, for determining patient activity or posture. The external controller 45 may also include a clock and may wirelessly receive information about the battery voltage of the IPG 10 and information about signals detected at the electrodes of the IPG from the sensor 620. Therefore, the external controller 45 may also include a stimulation pattern detection algorithm 610' in response to such input. This algorithm 610' may replace algorithm 610 in the IPG 10, or may supplement the information determined from algorithm 610 to improve stimulation pattern determination. In short, and facilitated by bidirectional wireless communication between the external controller 45 and the IPG 10, the stimulation pattern detection algorithm can be efficiently split between the external controller and the IPG 10 in any desired manner.

[0263] In addition, the external controller 45 can receive relevant information from various other sensors to determine which stimulation mode should be input. For example, the external controller 45 can receive information from an external device 612 worn by the patient (such as a smartwatch or smartphone). Such a smart device 612 includes sensors indicating motion (e.g., an accelerometer) and may also include biosensors (heart rate, blood pressure), which can help understand different patient states and therefore determine which stimulation mode should be used. More generally, other sensors 614 can also provide relevant information to the external controller 45. Such other sensors 614 may include other implantable devices that detect various biological states of the IPG patient (glucose, hearing rate, etc.). Such other sensors 614 can provide additional information. For example, since cold or inclement weather has been shown to affect stimulation therapy for IPG patients, sensor 614 may include a weather sensor that provides weather information to the external controller 45. Note that sensor 614 may not need to communicate directly with the external controller 45. Information from such sensors 614 can be sent via a network (e.g., the Internet) and provided to the external controller 45 via various gateway devices (routers, WiFi, Bluetooth antennas, etc.).

[0264] Figure 33Another example of a user interface on the patient's external controller 45 is shown, which allows the patient to select from different stimulation modes. In this example, the different stimulation modes (consistent with the optimized stimulation parameters 420' determined for the patient) are displayed in a two-dimensional representation. In the example shown, the two-dimensional representation includes a graph of pulse width (Y-axis) versus frequency (X-axis), but any two stimulation parameters (amplitude and frequency or pulse width and amplitude) can also be used. However, note that if the goal is to provide a simple user interface for the patient that is not hindered by technical information that the patient may not understand, these X and Y axes may not be labeled, and specific pulse width or frequency values ​​may not be labeled.

[0265] The different stimulation modes discussed earlier are marked in this two-dimensional representation, where the boundaries indicate the range of a subset 425x for each stimulation mode. Using this representation, the patient can position the cursor 430 to select a specific stimulation mode, and in doing so, select the frequency and pulse width and their corresponding subset 425x. Because subsets 425x can overlap, more than one stimulation mode and more than one subset 425x can be selected at a specific frequency and pulse width, allowing the patient to browse more than one subset of stimulation parameters. Because amplitude is not represented in the two-dimensional representation, the amplitude can be automatically adjusted to an appropriate value when a stimulation mode / subset 425x or a specific frequency / pulse width is selected. Alternatively, separate sliders can be included to allow the patient to further adjust the amplitude for each stimulation mode based on subset 425x. As explained above, the amplitude can be fully constrained within the optimized stimulation parameters 420' by the selected mode / subset, or it can be allowed to range beyond 420' (e.g., Figure 27B , Figure 28B In more complex examples, the representation could include a three-dimensional space (F, PW, A) in which the patient can move the cursor 430, similar to... Figure 23E The three-dimensional subset 425x shown here has the displayed stimulation pattern.

[0266] Figure 34 Another aspect of the GUI is shown, which allows patients to adjust stimulation based on a model developed for them. In these examples, a suggested stimulation area 650 is shown, overlaid on user interface elements, which otherwise allow the user to adjust the stimulation. Figure 34 The example in shows the... Figure 22 and Figure 33The graphical user interface shown is a modification, but it can also be applied to other user interface examples. In these examples, the suggested stimulation region 650 provides a visual indicator for the patient, in which the patient may wish to select (e.g., using cursor 430) a stimulation setting consistent with the optimized stimulation parameters 420 or 420' or subset 425x. These regions 650 can be determined in different ways. They can be mathematically determined using the optimized stimulation parameters 420 or 420' or subset 425x, such as by determining the center or "centroid" of these regions. They can also be determined by focusing specifically on providing the patient with stimulation parameters having appropriate amplitude, intensity, or total charge. This can be particularly useful if the patient's previous choices have deviated from such ideal values. Regions 650 can also be determined during the fitting procedure by identifying the area or volume within the patient's preferred optimized stimulation parameters 420 or 420' or subset 425x.

[0267] Furthermore, region 650 can be determined for the patient over time based on previously selected stimulus parameters. Therefore, region 650 can be associated with the settings most frequently used by the patient. In an improved example, the patient can also provide feedback related to the location of region 650. For example, external device 45 can include option 652 to allow the patient to provide indications of their symptoms (e.g., pain) using the illustrated rating scale. Over time, the external controller can track and correlate the pain level entered at 652 with the selected stimulus parameters and plot or update region 650 to the appropriate location covering the stimulus modulation where the patient has experienced optimal symptom relief. Again, a weighted mathematical analysis of the stimulus parameters relative to their pain levels, or a centroid method, can be used.

[0268] It should be noted that the use of the disclosed technique should not necessarily be limited to the specific frequencies tested. Other data suggest that the disclosed technique is applicable to providing pain relief without sensory abnormalities at frequencies as low as 2 Hz.

[0269] In summary, modeling and patient fitting allow for the determination of optimized, and preferably subsensory, stimulus parameters (in the form of range 420, volume 420', or subset 425) for a given patient. However, once such optimized stimulus parameters are found, it may be expected that these parameters will change over time when the stimulus is applied to the patient. This is because providing the same, unchanging stimulus to neural tissue—even ideally—can lead to tissue habituation, making the stimulus less effective than before.

[0270] Therefore, once the optimized stimulation parameters are determined, it can be useful to automatically change the stimulation applied by IPG 10 or ETS 40 over time within these parameters. This is in Figure 35AThe first example in the example is shown. In this example, a subset 425 (specifically 425e, see [reference]) of the volume of the optimized stimulus parameter 420' is assumed. Figure 29A and Figure 29B This has been identified for patient use. However, although not shown, average charge per second (MSC) models (e.g., 380, 381) can also be used to select optimized stimulation parameters, as previously discussed. Figure 17A-17F As stated above.

[0271] To prevent habituation, the simulation applied to the patient varies over time within subset 425, as represented by adjustment 700. Adjustment 700 can change any simulation parameters within subset 425, including frequency, pulse width, and amplitude, and any one or more of these parameters can be changed at any given time. Figure 35A In the example shown at the top, the frequency and pulse width change within a subset 425 at different times (t1, t2, tec.), while the amplitude remains constant. Figure 35A In the example shown at the bottom, the frequency and amplitude change within subset 425 at different times, while the pulse width remains constant.

[0272] Figure 35B An example of how regulation 700 can be formulated and how a program for an IPG including instructions can be generated is shown. This is illustrated in a graphical user interface (GUI) 710, which is described in more detail in PCT (International) Patent Application Serial No. PCT / US2020 / 049054, filed September 2, 2020, and is assumed to be familiar to the reader. GUI 710 can operate on a clinician programmer 50 or an external controller 45 and allows the user to specify pulses in a manner that achieves desired variations in regulation 700. GUI 710 includes multiple blocks 711 in which the user can specify a time-sequential pulse sequence. The first block (1) specifies the pulse to be formed at t1, at electrodes (E1 and E2) as specified by the guiding procedure A, and its frequency (200 Hz), pulse width (225 μs), and amplitude (4 mA) are within the volume of subset 25. The frequency, pulse width, and amplitude can be specified by pulse procedure I, as explained in further detail in application '054. During time period t1 (and all other time periods in this example), ten of these pulses will be formed, although the number of pulses can vary and can be set in GUI170. The second block (2) specifies ten pulses to be formed at t2, at the same electrode, and their frequency (200Hz), pulse width (325μs), and amplitude (4mA) are as specified by the pulse procedure J. Therefore, only the pulse width changes from time period t1 to t2. Figure 35B The pulse width and frequency in the example undergo other changes to affect Figure 35AThe adjustment 700 is shown at the top. In this example, the pulse width and frequency are adjusted in a serpentine manner between different time periods, but this is just an example, and the adjustment 700 can be performed in different ways or even randomly within the optimized stimulation parameters. The amplitude determined within the optimized stimulation parameters can also be changed, as will be discussed later. Figure 35C The explanation given.

[0273] Even if certain stimulation parameters are changed via adjustment 700, they remain within the previously determined optimized stimulation parameters, and specifically within subset 425. Note that adjustment 700, however, does not need to occur within subset 425. More generally, adjustment to prevent habituation may occur within the optimized stimulation parameters 420 or 420' previously determined for the patient.

[0274] GUI 710 may include option 712 to allow a user to import previously determined optimized stimulation parameters (patient model) into GUI 170, which may reside in clinician programmer 50 or external controller 45. Once imported, another option 714 can be used to automatically form adjustment 700 within those optimized stimulation parameters. Selecting option 714 allows GUI 710 to automatically populate block 711 to produce the desired adjustment 700 by changing one or more stimulation parameters as needed. Although not shown, option 714 allows a user to select which of one or more stimulation parameters (e.g., frequency, amplitude, pulse width) should vary within the optimized stimulation parameters, and may further allow a user to determine the order or pattern within the optimized stimulation parameters where the stimulation parameters can be changed. Option 714 may also allow a user to select to randomly change the stimulation parameters during adjustment 700. If necessary, the user can adjust these stimulation parameters after automatically creating independent blocks 711 to optimally influence a specific adjustment 700.

[0275] Figure 35C Further examples are shown of how adjustments 700 within the optimized stimulation parameters can be influenced. As shown, the pulse width, amplitude, and frequency of the stimulation pulse can be adjusted between maximum and minimum values ​​(e.g., PW(min), PW(max)) within the optimized stimulation parameters. Note that these maximum and minimum values ​​may not be constant but may be influenced by other stimulation parameter values. For example, and as... Figure 35A As shown, when the frequency is 200 Hz, PW(max) and PW(min) can include 325 and 225 μs, but when the frequency is 400 Hz, they can include 225 and 150 μs. Figure 35CThe bottom shows an example where adjustment 700 varies the amplitude, pulse width, and frequency within a volume of optimized stimulation parameters 420' or within a subset 425 of such parameters. Again, this is simply shown as a cube where the parameters have maximum and minimum values, but the resulting volume may actually have a more random shape. In another modification, not shown, different optimized stimulation parameters can be selected using an average charge per second (MSC) model (e.g., 380, 381), as previously discussed... Figure 17A-17F As mentioned above, and applied at different times.

[0276] Besides helping to prevent tissue habituation, tuning to 700 is expected to be beneficial because tuning the stimulus over time within a range or volume of optimized stimulation parameters increases the likelihood that, at least occasionally, optimized stimulation parameters (or combinations thereof) for the patient will be provided within that range during tuning to 700. This can be important because the lead may move within the patient, such as with activity, which could cause the optimized stimulation parameters to change from time to time. Therefore, tuning the stimulation parameters helps ensure that the best parameters within that range or volume will be applied at least for some time during tuning to 700. Furthermore, when tuning the stimulation parameters, it may not be necessary to spend time fine-tuning the stimulus to determine a single, unchanging set of optimized stimulation parameters for the patient.

[0277] Figure 36 Other adjustments 700 that can be used to influence the electric field formed in patient tissue are shown, and can also be used to prevent tissue habituation. A specific pole configuration 730 selected for a patient is shown. In the depicted example, the pole configuration 730 includes a virtual bipolar pole with a virtual anode pole (+) and a virtual cathode pole (-). Virtual poles are further discussed by review in U.S. Patent Application Publication 2019 / 0175915, and previously referenced. Figure 7B This has been discussed. As discussed earlier, the positions of the anode and cathode poles do not necessarily correspond to the positions of the physical electrodes 16 in the electrode array. Also as discussed earlier, the pole configuration 730 can have a different number of poles and can include three poles or other configurations, although for simplicity... Figure 36 The double poles are depicted in the text.

[0278] Figure 36 The top shows different ways in which the pole configuration 730 can be moved in the electrode array 17. Figure 36The upper left corner shows how the bipolar poles 730 can be moved to different xy positions within the electrode array 17 while still maintaining their relative positions to each other. The upper right corner shows how the focal length (i.e., the distance d between the poles) varies. These two means of adjusting the pole positions can be achieved by changing the activation of the electrodes 16 in the array, such as by providing a specific polarity and current percentage to the selected electrodes, as discussed earlier.

[0279] Figure 36 The bottom of the diagram illustrates how the adjustment of the pole position can be combined with adjustments consistent with previously determined optimized stimulation parameters. The left figure shows how one of the stimulation parameters (in this case, the pulse width) can be varied while simultaneously changing the xy position of the pole configuration 730. This adjustment 700 can vary the pulse width over time between maximum and minimum values ​​(PW(max) and PW(min)), as determined for optimized stimulation parameters 420' or 425. This adjustment can also vary the xy position of the pole configuration 730 over time. Preferably, the (x, y) position of the pole configuration 730 is previously determined (using a sweet spot search, as explained above), but the maximum and minimum values ​​are varied from that position during adjustment 700. For example, the position (x, y)(min) could include a position where both x and y are less than 1 mm, and the position (x, y)(max) could include a position where both x and y are greater than 1 mm. In other words, if the optimal position (x, y) is located at (5 mm, 6 mm) in the electrode array, then (x, y)(min) will include the position located at (4 mm, 5 mm), and (x, y)(max) will include the position located at (6 mm, 7 mm). Therefore, adjustment 700 can move the position of the pole configuration 730 anywhere within the two-dimensional region defined by these maximum and minimum positions. The pulse width also varies during adjustment 700, and other stimulation parameters (frequency, amplitude) can also be changed, again between the maximum and minimum values ​​determined using the optimized stimulation parameters 420' or 425. The stimulation parameters and poles can be adjusted over time according to the pattern shown, or randomly adjusted between the maximum and minimum values.

[0280] The middle figure illustrates how one of the stimulation parameters (again, the pulse width) can be changed while simultaneously altering the focal length d of the pole configuration 730. Preferably, the focal length d is previously determined (e.g., during dessert search), but its maximum and minimum values ​​are varied from that distance during adjustment 700. For example, if d equals 10 mm, then d(max) might be 12 mm, and d(min) might be 8 mm. Thus, adjustment 700 can move the focus of the pole configuration 730 anywhere within these maximum and minimum distances. The pulse width (and / or at least one other parameter, such as amplitude or frequency) also varies consistently during adjustment 700 with the previously determined optimized stimulation parameters 420' or 425. Similarly, the stimulation parameters and focal length can be adjusted over time according to a pattern, or randomly between maximum and minimum values.

[0281] The right figure illustrates that during adjustment 700, both the xy position of the pole configuration 730 and the focal length d of the pole configuration can vary with at least one other stimulation parameter (e.g., pulse width). In all these examples, adjustment 700, including slight adjustments to the position of the poles in the pole configuration 730, is expected to be useful in preventing tissue habituation.

[0282] Adjustment 700 can prioritize the adjustment of certain parameters over others, and this prioritization can be based on the patient's state or symptoms. For example, if a patient is noted to be particularly sensitive to the location of the stimulus, it may be desirable to prioritize adjustments 700 to consider changes in the location of the poles, or by altering the xy position of the pole configuration 730 in the electrode array and / or by changing the focal length d. In contrast, if a patient is particularly sensitive to the amount of stimulus (e.g., the received neural dose), it may be desirable to prioritize adjustments 700 to consider changes in one or more of the stimulus parameters (pulse width, frequency, amplitude). Subjective or objective measurements, such as by receiving patient feedback, or by performing measurements indicating the effectiveness of the stimulus (e.g., by measuring the ECAP as described above), can be used to determine the patient sensitivity useful in the prioritized adjustment 700.

[0283] Figure 37Another example of the adjustment 700 of the stimulation parameters within the optimized stimulation parameters 420' is shown. In this example, the pulse width and frequency are adjusted between different time periods. Unlike the previous example, the stimulation duration at each time period is longer, approximately several hours. Furthermore, the pole configuration is changed at different time periods to achieve different beneficial effects. For example, at time periods t1 and t2, a bipolar 740 that forms a relatively small field in the tissue is used. This can be useful because, as described previously, such a subsensory bipolar 740 can provide rapid relief and short-duration washes, especially at the lower frequencies present during these time periods. However, smaller bipolars such as 740 can be sensitive: it only produces a small field in the tissue, and therefore, if the leads in the electrode array 17 migrate within the tissue, the bipolar 740 may migrate away from the patient's pain site and become less effective. Therefore, in subsequent time periods (e.g., t3–t5), the pole configuration is changed to a larger bipolar 745, which provides a larger field in the tissue. A larger field makes lead migration less likely to result in effective stimulation away from the patient's pain site, and thus makes it easier to recruit such pain sites. The higher frequency used in conjunction with a larger bipolar 745 can additionally make it easier to recruit the patient's tissue and is less susceptible to lead migration. Therefore, during modulation 700, the bipolar size is increased to facilitate a larger field in the tissue, and the pulse frequency is increased until a constant (but still optimized) stimulation is provided at time t5.

[0284] like Figure 38A and Figure 38B As shown, when stimulation is delivered by bolus, adjustment 700 within the previously determined optimized stimulation parameters can also be used. Providing stimulation bolus is described in more detail in U.S. Patent Application Publication 2020 / 0147400, the entire text of which is incorporated herein by reference. A bolus includes stimulation delivered within a set time unit, such as ten minutes, thirty minutes, one hour, two hours, or any other effective duration, wherein there is no stimulation between bolus administrations. Some patients have been observed to respond well to “bolus pattern” treatment. Patients may initiate a stimulation bolus (shown as lightning) when they sense impending pain (shown as...). Figure 38A (The capsules inside). Figure 38A The diagram shows three days during which nine stimulating boluses were administered. As discussed in disclosure '400, bolus administration can also be automated. Providing a simulation during the bolus may be beneficial because some patients experience prolonged pain relief lasting for hours or longer after receiving bolus stimulation, i.e., during the intervals between boluses in which no stimulation occurs during their period. Furthermore, providing stimulating boluses conserves energy in the IPG because the simulation is not continuous and also helps prevent tissue overstimulation and habituation.

[0285] like Figure 38B As shown, the stimulation parameters used during each stimulation bolus can be adjusted. This adjustment 700 can be similar to... Figure 37 The adjustments described herein can occur on shorter timescales. For example, the stimulation bolus shown is 100 minutes long and consists of five distinct time intervals t1-t5, each lasting 20 minutes. As previously mentioned, one or more stimulation parameters (e.g., pulse width and frequency) are adjusted during different time intervals within the earlier determined optimized stimulation parameters 420'. Preferably, the initially used analog parameters (e.g., during t1) are designed to produce rapid symptom relief. As previously mentioned, the location, size, or focal length of the pole configuration can also be changed during the different time intervals including adjustment 700.

[0286] As previously mentioned, an important aspect of providing effective therapeutic relief for SCS patients when delivering subsensory stimulation is ensuring that the treatment is well targeted to the neuropathic pain sites within the patient's tissues. For example, Figure 6 and Figures 7A-7D Methods for identifying the “sweet spot” in an electrode array, at which stimulation should be applied to target the painful nerve site, are discussed. As previously mentioned, identifying the “sweet spot” involves selecting which electrodes should be active and with what polarity and relative amplitude to recruit and thus treat the painful nerve site.

[0287] While the sweet spot used to provide subsensory stimulation can be determined as previously described, the location of the sweet spot in the electrode array may need to be changed or updated from time to time. As previously mentioned, the leads in electrode array 17 may migrate within the patient's tissues over time, which may cause the sweet spot established in the electrode array to move away from the patient's pain site and become less effective. Alternatively, the patient's condition or activities may cause the leads to move relative to the spinal cord, again requiring adjustment of the sweet spot. Anti-inflammatory responses in the tissue may also cause the initially determined sweet spot to become less effective over time. Finally, it may simply be that the initially determined sweet spot was not well determined and therefore could not accurately target the painful nerve site in the tissue.

[0288] For whatever reason, it may be necessary to adjust the position of the stimulus in the electrode array. Typically, moving the stimulus requires the patient to report back to the clinician, which is both time-consuming and inefficient. Instead, it would be beneficial to allow patients some ability to adjust their subsensory stimuli to new positions in the electrode array without clinician intervention.

[0289] To solve this problem, Figures 39-41BA method is described in which a patient can adjust the position of subsensory stimuli in an electrode array using their external controller 45. Figure 38 shows a graphical user interface (GUI) 900 that can be presented on the patient's external controller. Figures 39-41B The focus is on the GUI aspect displayed on the monitor 45 of the external controller, but it should be understood that the GUI 900 may include other input and output aspects of the external controller 45, including its input buttons and switches, etc.

[0290] exist Figure 39 The image shows two different electrode arrays 17 that can be implanted into a patient. The left side shows an array comprising two percutaneous electrode leads, while the right side shows an array formed on paddle-shaped leads. These different types of SCS stimulation leads are described in more detail in U.S. Patent Application Publication 2020 / 0155019. These electrode arrays can be shown as part of GUI 900, although this is not absolutely necessary.

[0291] The image also shows (left) a current subsensory pole configuration used to deliver subsensory stimulation to a patient. In this example, the pole configuration includes a bipolar pole with an anode (A) and a cathode (C). An indication of the location or position of this bipolar pole is also shown, in this example, including a stimulation center point (CPS) between the anode and cathode. The location of the pole configuration can be specified in other ways, such as by the location of the anode or cathode, by determining the location of the maximum electric field strength in the patient's tissue, etc. However, the CPS can generally effectively represent the location of the bipolar pole and is represented as a single point in xy space. Other pole configurations can also be used to deliver subsensory stimulation (tripolar, etc.), and in such other configurations, the CPS will be defined differently. For example, when using a tripolar pole, the CPS may correspond to the location of the center pole. Figure 39 In this context, CPS is distinguished by a movable cursor 922, as explained further below.

[0292] This pole configuration is established as part of a previously determined stimulation procedure P1, as shown at element 902. The stimulation procedure P1 will include pulse parameters necessary for forming subsensory pulses at the poles in the pole configuration, including pulse amplitude, pulse width, and frequency. Preferably, these pulse parameters will include optimized subsensory stimulation parameters 420' previously described and determined in various ways. The stimulation procedure will also include electrode parameters necessary for forming poles (anodes and cathodes) at the correct locations in the electrode array. As previously described, the relevant electrode parameters may include which electrodes in the array are active, the polarity of those active electrodes, and the percentage of total current to be received by each active electrode. Also as previously described, once the electrode parameters of the poles are known, the electrode configuration algorithm 110 can be used to determine the location of the poles, and conversely, once the location of the poles is known, the electrode configuration algorithm 110 can determine the electrode parameters. Electrodes may be virtual and located in positions in the electrode array that do not necessarily correspond to the locations of physical electrodes. In short, using the electrode configuration algorithm 110, the control circuitry in the external controller 45 can determine the location of the poles, and thus the location of the CPS.

[0293] Note that the electrode placement algorithm 110 must know the relative positions of the electrodes within the electrode array. This is straightforward when using paddle-shaped leads 908, as... Figure 39 As shown on the right, the electrodes are fixed in place on the blade and do not move relative to each other. This becomes more complex when multiple percutaneous leads 906 are used to form an electrode array, as the leads may move relative to each other. In this case, the control circuitry in the external controller 45 can run an alternative algorithm to determine the relative positions of the electrodes, such as those disclosed in USP 8,233,992 and 6,993,384, the entire contents of which are incorporated herein by reference.

[0294] As shown at element 916, the location of the CPS, and therefore the general location of the pole configuration, can be represented as (x, y) coordinates. These (x, y) coordinates can be determined relative to any useful origin O. For simplicity, the origin O is... Figure 39 The location of the origin O is shown as the center of a specific electrode. However, the origin O can also be at any other location in the array, or it can include the current CPS itself, in which case the CPS would be located at (0, 0). Figure 39 In this configuration, the CPS is located relative to the origin O at a position (2.0 mm, 6.0 mm). The origin O can also be established as a specific point in the patient's tissue, rather than a specific point in the electrode array. In particular, the origin O can include physiological coordinates, as further described below.

[0295] The goal of GUI 900 is to allow patients to move the pole configuration (e.g., CPS) a short distance in the x and y directions around the electrode array to see if a better treatment outcome can be achieved. In the example shown, the CPS can be moved in these directions using arrow buttons 904. However, it is desirable that patients do not move the CPS too far from the initial position determined by the clinician. (If the CPS requires significant movement, such as due to significant lead migration, this may indicate that the patient should return to the clinician for further evaluation). In short, it is desirable to limit how far the patient can move the CPS, and option 914 allows setting such a limit. As explained further below, Figure 39 Option 914 shown allows the user to move the CPS within an 8×8mm area 910. This area 910 can be drawn on the GUI 900, although this is not strictly required. Constraint 914 is preferably set by the clinician, and although not shown, selection of constraint 914 at the patient external controller 45 can be locked to the patient after a clinician password. Alternatively, constraint 914 can be set at the clinician programmer 50, which can send the necessary constraint telemetry to the external controller 45.

[0296] In the example shown, constraint 914 (by the clinician) is used to define an area 910 in which the CPS can move. In the example shown, constraint 914 sets minimum and maximum x and y positions for area 910. For example, it might be desirable to allow the patient to move the CPS + / - 4 mm from its initial position, and thus within an 8 x 8 mm area 910. Preferably, constraint 914 restricts area 910 to be smaller than the entire electrode array. Then, assuming the CPS is at position (2, 6) (916), x(min) is set to -2, x(max) is set to 6, y(min) is set to 2, and y(max) is set to 10 mm. Again, these minimum and maximum values ​​are referenced to the origin O. If the origin O includes the current position of the CPS, then x(min) and y(min) will be -4, while x(max) and y(max) will be 4. Note that area 910 does not need to be defined as a perfect square; for example, it can be rectangular. Furthermore, area 910 does not need to be defined with the current CPS at its center, although it is within... Figure 39 This is illustrated in this manner. Preferably, the step size by which the CPS can be moved within region 910 is set at option 918, which is... Figure 39 The value is shown as 0.2mm. Therefore, whenever the patient selects one of the arrow buttons 904, the CPS will move 0.2mm in the relevant direction within area 910.

[0297] Note that in Figure 39On the right side, the CPS can be conveniently labeled using procedure P1 (option 902). Also as shown, it may not be necessary to show the patient the specific poles (anode, cathode) of the pole configuration that constitutes the procedure. This is preferred because the patient may only need to know the general location of the stimulus and may not know (and may not need to know) the details related to the stimulus provided by procedure P1. In fact, option 916 may more generally refer to the location of the stimulus than the CPS (which could be unnecessarily technical for the patient). Furthermore, the GUI 900 does not need to display the electrode array (leads 906 or 908), although this is shown in the example for convenience. In short, for simplicity, the GUI 900 may simply indicate the procedure, its location, and the area 910 in which the stimulus can be moved to the patient in graphical and / or textual form.

[0298] Figures 40A-40H The diagram illustrates a patient using the GUI 900 to move subsensory stimuli to their locations, as well as marked locations and the possibility of storing them as new programs. From Figure 40A Initially, the patient moves the CPS using arrow button 904. Specifically, as shown in option 916 and at the new position of cursor 922, the patient moves the CPS down and to the right in increments of 0.2 mm (918) to new positions (4.2 mm, 4.4 mm). Moving the CPS moves the positions of the anode and cathode poles by the same distance, although, as mentioned above, in a practical implementation, these poles may not be displayed in GUI 900 (and are not shown in subsequent figures). As explained above, moving the pole positions will result in electrode configuration algorithm 110 ( Figure 7B The new electrode parameters are determined to place the poles at the desired location. When moving the CPS using the GUI 900, the external controller 45 can send the stimulation parameters (including the adjusted electrode parameters) to the IPG 10 for immediate execution.

[0299] Assuming the sub-sensory procedure P1 is determined as previously described, the perception threshold pth should be known and stored in the external controller 45 along with the procedure. By review, the perception threshold pth comprises the lowest amplitude (e.g., in mA) at which the patient first perceives the stimulus. Figure 40A In this context, we assume the pth of P1 is 5 mA, as shown in option 920. When moving the position of the CPS, it is preferable to set the amplitude to a default value, which is a fixed percentage of the pth, such as 80% (i.e., amplitude A will be set to 4 mA). This ensures that the stimulation remains at a perceptible amplitude providing sub-sensory therapy as the CPS moves, although the patient can adjust the amplitude later, as referenced below. Figure 40B Discussed. When moving the CPS, note that the patient is no longer strictly aligned with P1 (centered on different locations (916, ...). Figure 39 The stimulus is provided. Therefore, option 902 can be hidden. Note that the location of the original procedure P1 can still be displayed in area 910. This can be useful as it allows the patient to return to the procedure by selecting that location using cursor 922.

[0300] Once the CPS has been moved, and as in Figure 40B As shown, the patient can adjust some stimulation parameters (amplitude, pulse width, or frequency). While all these parameters are freely adjustable by the patient, it is assumed that the optimized subsensory stimulation parameters 420' were determined earlier to be optimal for the patient, and therefore it may not be recommended that the patient adjust all of these stimulation parameters. Instead, the patient may only be able to adjust the amplitude of the stimulation, as shown at option 920. Amplitude adjustment can be guaranteed when the CPS is moved and different electrodes are used for stimulation, because these electrodes may be at different distances from the recruited spinal cord nerve tissue, for example. Electrodes closer to the nerve tissue may require a lower amplitude, while electrodes farther away from the nerve tissue may require a higher amplitude.

[0301] In option 920, the amplitude can be adjusted using a slider, although the amplitude can be adjusted in other ways using GUI 900. Because the stimulus is preferably sub-perceptual, the amplitude adjustment using the slider can be limited to a percentage of the perception threshold (pth), i.e., from 0 to 100%. Figure 40B In the process of moving the CPS to its new position, the user can adjust the amplitude from the default 80%*pth, and as shown, the user has reduced the amplitude to 60%*pth, or 3mA. Note that in... Figure 40B In practice, the exact value of pth at this new location may not be (yet) known. However, pth can be assumed, such as based on its last known value (e.g., P1 is 5 mA), thus allowing the patient to select an amplitude between 0 and 5 mA (0 to 100%). Even if pth is different in reality at this new location, it may not be significantly different, and therefore it will be expected that for at least some (if not all) amplitudes selected by the patient using option 920, the stimulus will still be sub-perceptual. As explained further below, GUI 900 can also allow the patient to eventually measure and determine the exact value of pth at this new location.

[0302] After experimenting with stimulation at this location, and possibly after a modulation amplitude (920), the patient can wait for a period of time to see if a good therapeutic outcome has been achieved. As previously explained, targeted subsensory therapy is beneficial for its ability to quickly penetrate and become effective.

[0303] If a satisfactory treatment outcome is achieved, the patient may wish to mark the location, such as in Figure 40C As shown in the diagram. Here, the patient has selected a marking option, one of several control options 912. As explained later, marking the location will store its stimulation parameters within the external controller 45, allowing the patient to return to these settings later if needed. Note that the GUI 900 can indicate the location of the marking within area 910, such as by using... Figure 40C The small square shown. GUI 900 can also use dots of different colors to represent the location of the marker. Furthermore, GUI 900 can additionally provide the location of the marker with a text label, such as M1. Although in Figure 40C It is not yet displayed, but the marked position can also be saved to the program, as explained further below.

[0304] Although not shown, note that locations previously marked like M1 may be subject to further optimization beyond what is shown. For example, stimulation parameters such as frequency and pulse width could be further optimized at the marked location. Alternatively, the focal point or distance between the poles (anode and cathode) could be changed. This could be particularly beneficial in mitigating potential side effects at newly marked locations. Furthermore, the angle of the poles around the CPS could be altered.

[0305] GUI 900 may also include options that allow patients to rate or rank the effectiveness of subsensory stimulation therapy (such as by selecting a number of stars from five, or a score from 1 to 10). The ability to rank specific marked locations (or stored procedures) can appear in different ways, but... Figure 40C This is shown as one of the control options. See below for more details. Figure 41B The discussion reviews the ranking marker positions or procedures.

[0306] After completing this position (M1), the patient can continue the experiment by moving the CPS to a new position. For example, in Figure 40D In this case, the patient has moved the CPS to a new location within region 910 and has remarked that location as potentially interesting (M2). As described above, the patient may have modulated the sub-perceptual amplitude of the stimulus (920) before marking it at this new location (e.g., to 70%*pth or 3.5mA, as shown). As previously described, the patient may also have performed other stimulus modulations and ranked this new location (812). Figure 40E The image shows that the patient has moved the CPS to a third new location, which has been similarly marked (M3).

[0307] At this point, the patient can use cursor 922 to select between various marked locations (M1-M3) or from any pre-established program (e.g., P1) displayed in area 910. This allows the patient to further experiment with subsensory stimulation by switching back and forth between these different locations to determine which provides the best therapeutic outcome. If the patient finds a marked location particularly beneficial or important, the patient can save that marked location as a program, such as in... Figure 40F As shown in the diagram. Here, suppose the patient wants to save the marked location M1 as a new program, perhaps because it has good (perhaps optimal) treatment results. Therefore, the patient moves the cursor 922 to M1 and selects the save option (912). This allows the patient to name the program using option 902, and suppose the patient names the program P2. Of course, a more intuitive program name can also be entered.

[0308] After deciding to save the marked locations as part of a new program, it may be beneficial to establish an accurate perception threshold (pth) for that program, such as in... Figure 40G As shown in the diagram. As previously stated, the pth based on the predetermined pth of the initial procedure P1 has only been assumed at this new location (5mA), but the actual pth may be different. Therefore, after selecting the save option (912), and after or along with the naming of the new procedure, a pop-up window 924 can be presented on the GUI 900 to instruct the patient to determine the pth for the new procedure. When the pop-up window 924 is presented, the GUI 900 can set the amplitude of the stimulus to zero, or to a low value that would be sub-perceptible to the patient. As indicated by the pop-up window 924, the patient can gradually increase the amplitude using, for example, the + button in the pop-up window. The patient continues to select this button to increase the amplitude until the patient just begins to feel the stimulus. At this point, the user can select the “Record pth” button as instructed, which will store the pth in association with the newly created procedure at this new location. Figure 40G In this context, it is assumed that this measured Pth value is lower than the previously used value, specifically 4.0 mA, as shown at 920. After determining the new Pth value, the GUI 900 can initially adjust the amplitude to 80% of the previously described default Pth value (3.2 mA). However, if needed, the patient can subsequently change the amplitude using option 920, although as previously stated, the amplitude adjustment will be constrained to sub-sensory values ​​(from 0% to 100% of Pth, or 0 to 4 mA). Although not shown, the GUI 900 may also include options similar to pop-up window 924 to allow the Pth to be redefined and stored for any procedure, even those created earlier (such as P1).

[0309] The usable Pth or percentage of Pth can be determined in different ways at the marked location or procedure. For example, and as described in U.S. Patent Application Publication 2019 / 0209844, the IPG can detect neural responses to stimuli, such as evoked compound action potentials (ECAPs). Various characteristics of the sensed ECAPs can provide an indication of the effectiveness of the stimulus and can therefore be used, in whole or in part, to set a given percentage of Pth. Pth or its percentage can also be set based on sensed patient activity or posture, as previously described; in one example, it can be determined by an accelerometer in the IPG. Other phenotypic information can also be used to set Pth or its percentage, and such phenotypic information will be discussed later.

[0310] After creating a new program P2 at the new location, for convenience, the previously marked location can be removed from area 910, such as in... Figure 40H As shown in the diagram. However, the locations of these previously marked locations can also be stored in the external controller 45, as shown below regarding... Figure 41A Further explanation: If needed, patients can select the "History" option in area 912 to recall these previously marked locations and display them in area 910 or other programs of interest. As a safety precaution, the "On / Off" option allows stimulation to be turned on or off.

[0311] Figure 41A A dataset 926, stored in an external controller 45, is shown, which can be combined with the operation and use of the GUI 900. If necessary, the dataset 926 can also be sent to the clinician programmer 50. Note that both the program (Px) and the marker location (Mx) can be represented and stored so that when these locations are selected, the associated stimulation parameters can be sent from the external controller 45 to the IPG 10 to execute and provide stimulation. The data stored for the program and the marker location may be similar. For example, each stores pulse parameters such as amplitude, frequency, and pulse width, although in a preferred example, the pulse width and frequency (which may have been set earlier by the clinician) may not change. Furthermore, at least the program Px is stored along with a perception threshold pth, which may have been determined earlier by the clinician or the patient (924). Figure 40G Because each procedure is associated with a perceptual threshold pth, dataset 926 can also include stimulus amplitudes smaller than that value, and thus these amplitudes will provide sub-perceptual stimulation. In one example, the amplitudes in dataset 926 could include a fixed percentage of pth (e.g., 80%) as a default value, as already mentioned. Thus, when the patient later selects these procedures using GUI 900, external controller 45 will provide the IPG with an appropriate sub-perceptual amplitude (80% * pth).

[0312] The marked positions Mx may not be associated with specific pth values, but may simply be stored in the amplitude values ​​to be executed when these positions are selected, although, as previously mentioned, these amplitude values ​​should be subaware in nature, as they depend on previously known pth values, such as those established for program P1. Although not shown, pth values ​​can be determined for and associated with marked positions, or these marked positions can inherit pth from previously established programs (such as P1). When marking positions (912, Figure 40C When ), a pop-up window 924 can also be presented to the user. Figure 40G This allows for the determination of pth for the marked location. However, when the location is merely marked, this step may not be necessary compared to formalizing and storing it procedurally by the patient.

[0313] Further shown in dataset 926 are the CPS locations (x, y) and the corresponding locations of the poles involved in the pole configuration to be produced, in this case, a single anode and cathode. The CPS locations are useful for allowing the GUI 900 to render appropriate symbols (squares, dots) within region 910. Furthermore, the CPS locations can be used to determine the locations of the poles, which will include a fixed distance from the CPS location, even when the CPS is moved. Based on the pole locations, the electrode parameters (active electrode, polarity, relative percentage) can be determined using the previously described electrode configuration algorithm 110. Finally, dataset 926 may include any ranking information previously provided by the patient for each marked location or program input (912).

[0314] Figure 41B An aspect of the GUI 900 of the external controller 45 is shown, which can be used to select the location or procedure of a marker. In this example, the locations or procedures of various markers are displayed along with patient ranking information. When a patient selects to use this aspect of the GUI 900, data from dataset 926 ( Figure 41A Pull out in ) as in Figure 41BThis type of information is shown in the diagram, and further information from dataset 926 can be displayed. This aspect of the GUI is particularly useful because it allows the patient to select a marked location or procedure after reviewing the ranking information previously entered by the patient. Although not shown, to make the selection of a marked location or procedure easier, GUI 900 can sort the marked locations and procedures by their ranking, with higher-ranked entries near the top. This effectively allows the creation of a "preference" list in GUI 900, although this is not shown. An option can be included to allow the patient to explicitly select a marked location or procedure as a preference. Entries can also include descriptors entered by the patient and stored in dataset 926. For example, the patient could enter P1 for walking, P2 for sleeping, etc. Note that input to GUI 900 can be voice-controlled.

[0315] Please note that, if in Figures 39-41B The GUI 900 described herein can also be used to modulate subsensory stimulation. Furthermore, the GUI 900 can be used and presented on the clinician programmer 50, although its preferred use is on the external controller 45 to allow the patient additional flexibility in adjusting the location for applying subsensory therapy.

[0316] Figures 42-45C A fitting algorithm 740 is shown, which can be used to determine the best 750 among optimized stimulation parameters 420 or 420' for use by a specific patient. In this example, a preferred range or volume of optimized stimulation parameters 420 or 420' is determined for the patient, which preferably provides subsensory stimulation for the patient, as previously described. Although not shown, an average charge per second (MSC) model (e.g., 380, 381) can be used to select optimized stimulation parameters, as previously discussed. Figure 17A-17F As stated above.

[0317] The fitting algorithm 740 then uses the fitting information 760 to determine one or more optimal stimulation parameters for patient use. The optimal stimulation parameters 750 may include a single set of stimulation parameters—for example, a single frequency, pulse width, and amplitude value 750a—or a subset 750b of a parameter set similar to the previously described subset 425. In short, by using the additional information included in the fitting information 760, the fitting algorithm 740 can determine one or more stimulation parameters within the optimized stimulation parameters 420 or 420' that are most logical for the patient, and can accordingly set subsensory stimulation in the patient's IPG.

[0318] Fitting information 760 is preferably acquired during a fitting procedure after implantation, which typically occurs in a clinical setting. Therefore, the fitting algorithm 740 is preferably implemented as clinician programmer software 66 executable on the clinician programmer 50. Figure 4 It is part of the ). However, the fitting algorithm 740 can also be used with any device or system capable of communicating with the patient's IPG, including the patient's external controller 45. Figure 4 Aspects of the fitting algorithm 740 may be presented as part of a clinician programmer GUI. The fitting algorithm 740 may also include instructions in a computer-readable medium, as described elsewhere. The fitting algorithm 740 may be executed in conjunction with other logically occurring operations during the fitting procedure and fitting information 760 may be received. For example, when fitting information 760 is received, tests occurring during algorithm 400—such as different pulse widths (404, 420') used during the determination of optimized stimulus parameters 420 or 420'—may be considered. Figure 21A The sensory abnormality threshold pth can be measured at the same time and during the same procedure.

[0319] Fitting information 760 may include various data indicative of the patient, his / her symptoms, and the stimuli provided during the fitting procedure. For example, fitting information 760 may include pain information 770 characterizing the patient's pain in the absence of stimulation. Fitting information 760 may also include mapping information 780 indicative of the effectiveness of the stimuli used during the fitting procedure. Fitting information 760 may also include spatial field information 790, indicating the stimuli used during the fitting procedure and the electric field they generate in the patient's tissues. Fitting information 760 may also include phenotypic information 800, such as the patient's age, sex, and other patient-specific details.

[0320] The fitting algorithm 740 also receives or includes training data 810. Essentially, the training data 810 is used to correlate the fitting information 760 with the optimal outcome, as will be described in further detail below. For example, the training data 810 may suggest that the fitting information 760 for a particular patient guarantees the use of a lower frequency of treatment (e.g., 10-400 Hz), and the fitting algorithm 740 will therefore select a lower frequency stimulus when choosing the optimal optimized stimulus parameter 750 from the optimized stimulus parameters 420 or 420' for that patient. Alternatively, the training data 810 may suggest that the fitting information 760 for a particular patient guarantees the use of a higher frequency of treatment (e.g., 400-1000 Hz), and the fitting algorithm 740 will therefore select a higher frequency stimulus when choosing the optimal optimized stimulus parameter 750 for that patient. The training data 810 can be acquired over time and can be derived from the treatment of previous patients, and in this respect, the training data 810 will improve over time as more patients are treated and data is received from more patients. In this regard, the information including training data 810 can be received at the fitting algorithm 740 from a source other than an external device, such as a server that can receive data from different patients to evolve or update the training data 810 over time. Training data 810 may also include or contain historical data taken from the current patient. In one example, training data 810 can be obtained using machine learning techniques and may include weights or coefficients to be applied to the various segments of fitting information 760, as explained further below.

[0321] Figures 43A-43C A GUI of an external system (e.g., a clinician programmer) is shown, which can be used to receive various segments of fitting information 760 during the fitting procedure, wherein Figure 43A The reception of pain information 770 is shown. Figure 43B The reception of mapping information 780 is shown, and Figure 43C The reception of spatial field information 790 and patient phenotype information 800 is illustrated. The fitting algorithm 740 is not required to receive all segments of the fitting information shown in these figures, and the algorithm 740 may receive additional information segments (not shown) that may be related to the optimal stimulus parameters 750 for predicting the best outcome. In short, Figures 43A-43C Only examples of potentially relevant fitting information 760 are provided. Furthermore, while it is sensible to categorize the shown relevant fitting information 760 into pain information 770, mapping information 780, spatial field information 790, and patient phenotype information 800, the fitting information 760 could be further subdivided into more or fewer categories. Alternatively, the fitting information 760 could not be subdivided into categories at all, but could instead include one, several, or all of the information fragments within those categories.

[0322] First refer to Figure 43A The GUI receives pain information 770, which, as previously mentioned, includes information fragments characterizing the patient's pain in the absence of stimulation. In a preferred example, pain information 770 is provided for individual body regions Xx. In this regard, the GUI may include graphics or images 771 showing different body regions where pain may occur. For example, in Figure 43A In Figure 771, body region X1 represents the upper part of the lower back, while body region X2 represents the lower part of the lower back. Both regions X1 and X2 appear on the right side of the body. Body region X3 represents the right gluteus maximus, and region X4 represents the upper part of the right thigh. Other body regions are not marked in Figure 771 and may also appear on the left side of the body.

[0323] For each body region Xx, multiple different pain measurements are recorded and can be input into the GUI by the patient or clinician. For example, and considering body region X1, the presence of pain in the region can be recorded (e.g., no (0), yes (1)), the intensity of pain in the region (e.g., 3 out of 10), and how the patient perceives the pain in the region (e.g., burning (1), numbness (2), sharpness (3), etc.). The type of pain can also be categorized; for example, 1 might represent neuropathic pain that can be well treated by the SCS, while 0 might represent pain originating from other mechanisms (bruising, arthritis, etc.) that the SCS may not treat well. Such pain information 770 can be input into the GUI for each body region as shown, thereby generating a pain matrix P, which can also be viewed as multiple pain vectors, each containing information about the patient's pain in different body regions Xx.

[0324] Figure 43B This illustrates receiving mapping information 780 at the GUI, as previously mentioned, which indicates the effectiveness of the stimuli used during the fitting procedure. (Details of the stimuli provided during the fitting procedure are available regarding...) Figure 43CThe spatial field information 790 is discussed further. The mapping information 780 can again be specified by body region Xx, and the GUI can again provide a graph or image 771 showing the different body regions where the effects of stimulation can be felt. For example, and considering body region X1, it can be recorded whether stimulation is felt in that region (e.g., no (0), yes (1)), the intensity of stimulation perceived in that region (e.g., 7 out of 10), and the degree to which the patient feels the stimulation “masks” their pain (e.g., 60%). In addition, the mapping information 760 can include a pain intensity level that is similar to the pain intensity previously provided in the pain information 770, but affected by the stimulation; if the stimulation treatment is effective, the pain intensity can be expected to improve (or at least not worsen) in the mapping information 780 when compared to the pain intensity received during the pain information 770 when the stimulation was not present. The mapping information 780 can also include a representation of the stimulation sensations perceived by the patient. For example, the patient may report stimulation sensations like a persistent stinging (1), vibration (2), massage (3), light pressure, pulsation, diffuse field, etc.

[0325] Other mapping information 760 can quantify, for example, the intensity of the stimulus as perceived by the patient. For instance, a sensory abnormality threshold can be determined. As previously discussed, this threshold (which is also useful during algorithm 400) can include, for example, the minimum amplitude of the stimulus that the patient can perceive. Similarly, mapping information 780 can also include an discomfort threshold, which can include, for example, the maximum amplitude of the stimulus that the patient can tolerate. Other objective measures, such as various ECAP features recorded in response to stimuli, can also be included within mapping information 780. Mapping information 780 can produce a mapping matrix M, which can also be viewed as multiple mapping vectors, each containing information characterizing the effectiveness of the stimulus in different body regions Xx.

[0326] Figure 43C The diagram illustrates receiving spatial field information 790 and patient phenotypic information 800 at the GUI. The spatial field information 790 includes information indicating the stimulus used during the fitting procedure, such as the shape, size, and location of the electric field created by this stimulus in the patient's tissues, and may also include information indicating the physiological location of the applied stimulus, as discussed further below. In this regard, note that different types of stimuli can be tried for the patient during the fitting procedure, for example, using… Figure 5 The GUI aspects shown.

[0327] The spatial field information 790 may include the type of pulse used during fitting. For example, the GUI may receive indications such as: using a monophasic pulse followed by passive charge recovery (0), a biphasic pulse for active charge recovery (1), a biphasic pulse with additional passive charge recovery (2), etc. Such pulse types have been previously described, and other pulse types may also be used and received at the GUI. The GUI may also receive information about the pole configuration used to provide stimulation, including the number and polarity of poles in the configuration, such as whether bipolar poles are used (0; for example...). Figure 6 ), triple poles (1; see U.S. Patent Application Publication 2019 / 0175915), extended double poles (2; for example, Figure 7D The spatial field information 790 may also include information about the dimensions of the pole configuration and the electric field it generates in the tissue. For example, the focal length between the poles and / or the area defined by the poles (or an estimated area of ​​the electric field generated in the tissue) may be received.

[0328] Fragments of spatial field information 790 can be associated with physiological coordinates, which the fitting algorithm 740 can determine using other techniques. Physiological coordinates describe the physiological positions between patients in a common manner and reference common physiological structures. For example, in SCS applications, coordinates (0, 0, 0) might correspond to the center of the T10 vertebra, while (20, 0, 0) corresponds to the center of the T9 vertebra, and (-20, 0, 0) corresponds to the center of the T11 vertebra. In this respect, physiological coordinates may not necessarily specify actual dimensions; for example, the actual distance between the T10 and T9 vertebrae in a larger patient might be greater than the same distance in a smaller patient. Nevertheless, physiological coordinates generally describe general anatomical positions. In SCS applications, the position of the electrode array 17, and therefore the physiological coordinates of the electrodes 16, relative to known physical structures, is generally known, such as through the use of fluorescence microscopy imaging techniques that show the position of the patient array 17 / electrodes 16 relative to such structures. Although not shown (e.g. in...) Figure 5 (In the image), but such physiological structures (e.g., different known vertebrae) can be superimposed on the image of the electrode array. Depending on how they are calculated, physiological coordinates can be two-dimensional (x, y), but can also be three-dimensional (x, y, z), and as... Figure 43C As shown in the image.

[0329] Knowing the location of anatomical structures within the patient's body, the physiological coordinates of electrode 16 relative to these structures, and the active electrodes forming a pole configuration in the array, fitting algorithm 740 can determine the physiological coordinates of various spatial field parameters. For example, knowing the current at each anode and cathode pole allows fitting algorithm 740 to determine the physiological coordinates corresponding to the locations of those poles, which, as previously mentioned, may not correspond to the physical location of electrode 16. Note that, as described earlier, knowing the locations of these poles also allows for the calculation of focal length and field area.

[0330] The physiological coordinates of the anode and cathode poles also allow for the determination of further physiological coordinates that generally indicate the physiological location of the electric field generated within the patient's body. For example, stimulation will result in various voltages V in the patient's tissues, which can be estimated in three dimensions, especially if the tissue resistance is known or measurable. This, in turn, allows the three-dimensional electric field E in the tissue to be determined as its first spatial derivative, E = dV / dx, and its second spatial derivative, d... 2 V / dx 2 Physiological coordinates indicating the location of any of these derivatives are useful for fitting algorithm 740. As described in U.S. Patent Application Publication 2020 / 0147390, while fibers in the dorsal column travel parallel to the long axis x of the spinal cord (i.e., in the head-to-tail direction), fibers in the dorsal horn can be oriented in many directions, including perpendicular to the long axis of the spinal cord. Dorsal horn fibers and dorsal column fibers respond differently to electrical stimulation. The stimulation intensity (i.e., depolarization or hyperpolarization) of the dorsal column fibers is determined by a so-called “activation function” d along the longitudinal axis (x) of the spine. 2 V / dx 2 This is because dorsal column fibers propagating through the stimulating electrode are more likely to be activated along the axons. This is partly because the large myelinated axons in the dorsal column fibers are primarily arranged longitudinally along the spine. On the other hand, the likelihood of generating action potentials in dorsal horn fibers and neurons is better described by dV / dx (also known as the electric field, E), because dorsal horn fibers and neurons generally confined directly below the electrode are more likely to respond at the dendrites and terminals. Therefore, the dorsal horn “activation function” is not proportional to the second derivative, but rather to the first derivative of the voltage along the fiber axis.

[0331] The physiological coordinates of these activation functions may include spatial field information 790 calculated and used by the fitting algorithm 740. Specifically, and as... Figure 43C The maximum values ​​of these activation functions (maxdV / dt, maxd) are shown in the figure. 2 V / dx 2The activation volume can be determined at physiological coordinates, such as the maximum voltage (maximum V) in the tissue. It can also be determined at physiological coordinates indicating the volume of the recruited neural tissue. See, for example, USP 8,606,360 and 9,792,412 (discussing the calculation of the activation volume). The physiological coordinates of the activation volume can include the center point of the volume, such as the centroid, or any other coordinates that tend to indicate the physiological location of the activation volume in the patient.

[0332] Providing physiological coordinate information for various relevant field parameters may be important for fitting algorithm 740. As just described, such physiological coordinates generally indicate the physiological location of the stimulus in a given patient, and therefore generally indicate the physiological neural location of the patient's pain (see, for example, 298). Figure 7A Knowing this physiological location of the patient being fitted can be meaningful because it allows the fitting algorithm 740 to determine the optimal 750 among the optimized stimulation parameters 420 or 420'. For example, the training data 810 can reflect specific field parameters (e.g., maximum d). 2 V / dx 2 When the field parameter 750 is located at or near a specific physiological coordinate (e.g., x7, y7, z7, corresponding to a specific neural structure), a higher frequency optimal stimulation parameter 750 can be guaranteed. In contrast, the location of this parameter at different physiological coordinates (x11, y11, z11, corresponding to different neural structures) may suggest using a lower frequency optimal stimulation parameter 750. This may be reflected in the training data 810. That is, the training data 810 will reflect from past patient history that patients with field parameters close to (x7, y7, z7) respond better when using higher frequency stimulation, while patients with field parameters close to (x11, y11, z11) respond better when using lower frequency stimulation.

[0333] Patient phenotypic information 800 includes information about the patient, such as their sex, age, type, or indication of the patient's disease, the duration of their disease, and the duration since the patient received their implant. Information about the patient's posture and / or activity (referred to as posture) in which their symptoms are particularly problematic (e.g., when sitting (1), when standing (2), etc.) may also be included in patient phenotypic information 800. Although Figure 43C Not shown, but the GUI may include an option to allow input of all problematic poses, as there may be more than one. Together, phenotypic information 800 can generate a vector Y.

[0334] As previously discussed, different patient postures or activities (hereinafter referred to as postures) can also affect the best stimulus for a given patient, and thus select the optimal stimulus parameter 750 from 420 or 420'. In this regard, Figure 44 The diagram illustrates that fitting information 760 can be received based on posture. For example, fitting information 760 can be received when the patient is sitting (e.g., pain matrix P1, mapping matrix M1, spatial field vector F1), standing (P2, M2, F2), supine (P3, M3, F3), etc., because the fitting information 760 may differ for each of these postures. For example, a patient may experience pain in different body areas or may perceive pain differently in different postures, resulting in pain matrices Px with different information. Similarly, the effectiveness of stimuli may differ in different postures, resulting in mapping matrices Mx with different information. Furthermore, the stimuli used may differ when in different postures, as reflected by different spatial field vectors Fx. (In contrast, information within the patient phenotypic vector Y is agnostic to patient posture, as...) Figure 44 (As shown).

[0335] Such fitting information 760—for example, the pain matrix P, the mapping matrix M, the spatial field vector F and / or the phenotypic vector P, or independent fragments of information within each—is useful for the fitting algorithm 740 to receive and consider, because such information can suggest the optimal stimulation parameters for a given patient, and especially the optimal stimulation parameters 420 or 420' already determined for the patient. Experience will teach which fragments of the fitting information 760 will include the best predictor of the optimal stimulation parameters 750, and such experience can be reflected in the training data 810 used to predict the optimal stimulation parameters 750. Figure 42 )middle.

[0336] For example, the percentage of pain coverage – mapping matrix M( Figure 43B The fitted information within the data should correlate well with the frequency or neural dose of the optimal optimized stimulus parameter 750. If the stimulus covers the patient's pain well (high percentage), meaning the stimulus recruits the patient's pain well, then a stimulus at a lower neural dose or frequency might be appropriate, and thus the fitting algorithm 740 can select one or more (e.g., a subset) of the optimal optimized stimulus parameters 750 that have a lower frequency within the optimal stimulus parameter 420 or 420' determined for the patient. In contrast, if the stimulus does not cover the patient's pain well (low percentage), then a stimulus at a higher neural dose or frequency within the optimal stimulus parameter 420 or 420' can be selected as the optimal optimized stimulus parameter 750 for the patient. In this way, when the optimal optimized stimulus parameter 750 is determined, the training data 810 can attribute high correlation to (e.g., specifying high weights) the percentage of pain coverage, or more generally to the mapping matrix M.

[0337] Figure 45A The flowchart illustrates how the fitting algorithm 740 uses the fitting information 760 to determine the optimal stimulation parameters 750 for the patient. It should be noted that... Figure 45A Only a simple example is provided of how the fitting algorithm 740 can be executed and how the training data 810 can be applied to the fitting information 760. As mentioned earlier, although those skilled in the art will understand that the training data 810 can be obtained by using machine learning techniques or other statistical techniques that are inherently complex.

[0338] exist Figure 45A In this example, training data 810 is applied to fitting information 760 in the form of weights wx, which essentially assign a degree of relevance to each segment of the fitted information. The weights are shown as being applied to the pain matrix P, mapping matrix M, spatial field vector F, and phenotypic vector Y. In the example shown, the weights are applied to each of the matrices or vectors, and in this respect, it may be useful to process each matrix or vector such that each is represented by a single number. Although not shown, it should be understood that weights can be applied to each of the independent segments of information comprising various matrices or vectors, and therefore the fitted information 760 need not include matrices or vectors of information. Furthermore, the fitting algorithm 740 need not strictly consider all pain information (P), mapping information (M), spatial field information (F), and patient phenotypic information (Y), because in practical implementations, these categories, or some of the information therein, may not prove statistically relevant to the selection of the optimal stimulus parameters 750.

[0339] Preferably, applying the training data 810 to the fitting information 760 results in the determination of the fitted variable J. Although not shown, the fitted variable J may have an associated variance or error, which may be generated by the statistical manner in which the training data 810 is operated. In this respect, the fitted variable J may comprise a single variable or a range of variables. The fitting algorithm 740 may use the fitted variable J to select one or more optimal 750s of the optimized stimulation parameters 420 or 420'. In one example, the fitted variable J may be related to neural dose. For example, a high value of J may correspond to a high value of frequency because the optimized stimulation parameters 420 or 420' tend to include a higher neural dose at higher frequencies. Figure 45BIn the bottom plot, a relatively high value of J leads to the selection of a single point of optimal stimulation parameters 750a, such as a pulse with a frequency of 600 Hz, a pulse width of approximately 150 microseconds, and an amplitude of approximately 4 mA. Alternatively, the fitted variable J (which may include a range of values ​​or may be associated with the error term) may lead to the selection of optimal stimulation parameters 750, including a subset of parameters 750b, such as frequencies (e.g., 400 to 800 Hz) and pulse widths and amplitudes associated with those frequencies within 420 or 420'. In contrast, Figure 45A The top shows how a lower J value leads to the selection of the optimal stimulation parameter 750 from the optimized stimulation parameters 420 or 420' which have a lower frequency and therefore a lower neural dose.

[0340] When the optimal stimulus parameter 750 is selected from 420 or 420', the fitting algorithm 740 can handle the fitted variable J more qualitatively. In this respect, and as... Figure 45C As shown, the fitting algorithm 740 can classify the fitted variable J into categories rather than determining J as an absolute value. For example, J can be classified as "1," indicating that a stimulation parameter with a lower neural dose should be selected from the optimized stimulation parameters 420 or 420'. Such a lower dose parameter, just explained, can include parameters at lower frequencies, and thus the fitting algorithm 740 can select a subset 750x of stimulation parameters for the patient, including optimized stimulation parameters 420 or 420' at lower frequencies (e.g., 100-200 Hz) and whose pulse width and amplitude are consistent with those frequencies within 420 or 420'. Similarly, J can be classified as "2" or "3," indicating the use of a moderate or higher neural dose, which can lead to the selection of an appropriate optimal subset 750y (e.g., parameters within 420 or 420' with a moderate frequency range of 200-400 Hz) or 750z (e.g., parameters within 420 or 420' with a higher frequency range of 400-1000 Hz). If necessary, the system (e.g., the patient's IPG or an associated external programming device) can restrict adjustments to these determined subsets 750x-z, similar to what was explained earlier. As previously mentioned, in contrast to subsets of parameters, a single set of parameters can also be selected via fitting algorithm 740.

[0341] Alternatively, with regard to the fitting information 760 being determined as a function of the patient's posture x, such as earlier Figure 44As described above, the fitting algorithm 740 can determine a fitting variable Jx corresponding to each patient posture x (e.g., J1 sitting, J2 standing, etc.), where posture-specific fitting information (or at least information not specific to any posture, such as patient phenotypic information 800) is used to determine each fitting variable. For example, J1 = w1*P1 + w2*M1 + w3*F1 + w4*Y, while J2 = w5*P2 + w6*M2 + w7*F2 + w4*Y, and so on. Each of these posture-specific fitting variables Jx can be used to determine the optimal stimulation parameter 750 for different patient postures. This can be useful because it allows the optimal stimulation parameter 750 to be adjusted as the patient changes posture. This is similar to what was described above regarding the selection of different subsets of patient postures 425 depending on the currently detected patient posture: when a new patient posture is detected, a new, optimal stimulation parameter 750 associated with the detected posture can be applied.

[0342] Preferably, when selecting the optimal stimulation parameter 750 for the patient, the fitting algorithm 740 uses the previously determined optimal stimulation parameter 420 or 420' for that patient. However, this is not strictly necessary, and Figure 46 An alternative fitting algorithm 740' is shown. As previously described, training data 810 can be applied to the patient's fitting information 760 to determine the fitting variable J. However, the fitting variable J is used to select the best optimized stimulation parameters 750 for the patient from a general model 830. Model 830 may not be specific to the patient providing the fitting information 760 and may represent a general modeling of preferred stimulation parameters, such as those noted based on empirical data to provide beneficial results on a larger subset of patients. Model 830 may include a range or volume of stimulation parameters providing subsensory stimulation, although this is not strictly necessary, and model 830 may also include a range or volume of stimulation parameters providing supersensory stimulation. For example, model 830 may include previously discussed... Figures 10A-13B Discussion area 100 or relationship 98, reference Figure 18 The model 390 discussed, or other models developed in the future that indicate beneficial stimulation parameters. Even if the fitting algorithm 740' does not select the best optimized stimulation parameter 750 from the optimized stimulation parameters 420 or 420' determined to be useful for a particular patient, it is anticipated that as more patients are successfully treated, the model 830 and training data 810 will evolve over time to allow the use of the fitting information 760 for a given patient to predict the best optimized stimulation parameter 750 for that patient.

[0343] Various aspects of the disclosed technology include processes that can be implemented in an IPG or ETS, or in an external device (such as a clinician programmer or external controller), to present and operate a GUI 64, which can be expressed as formulas and stored as instructions in a computer-readable medium associated with such a device, such as being stored in magnetic storage, optical storage, or solid-state storage. The computer-readable medium having such stored instructions may also include devices readable by a clinician programmer or external controller, such as in a memory stick or removable disk, and may reside elsewhere. For example, the computer-readable medium may be associated with a server or any other computer device, thus allowing instructions to be downloaded to the clinician programmer system or external system, or to the IPG or ETS, via, for example, the Internet. Methods involving the use of the disclosed subject matter also include aspects of the applicant's invention.

[0344] Although specific embodiments of the invention have been shown and described, it should be understood that the above discussion is not intended to limit the invention to these embodiments. It will be apparent to those skilled in the art that various changes and modifications can be made without departing from the spirit and scope of the invention. Therefore, the invention is intended to cover alternatives, modifications, and equivalents that may fall within the spirit and scope of the invention as defined by the claims.

Claims

1. A system for delivering stimulation pulses to a patient, comprising: Stimulatory devices that can be implanted in a patient and include multiple electrodes; as well as An external device, programmed using a model, wherein the external device is configured to target the patient and determine, according to the model, first stimulation parameters defining the first stimulation pulse. The external device is further configured to determine a modulation function to be applied to the first stimulation parameters to form a modulated stimulation pulse, wherein the modulation function modulates the charge delivered to the patient by the modulated stimulation pulse as a function of time. The external device is configured to send information to the stimulator device such that the stimulator device provides the modulated stimulation pulse at one or more of the electrodes.

2. The system according to claim 1, wherein, The model is derived for the patient based on data obtained from the patient.

3. The system according to claim 1 or 2, wherein, The model is configured to determine the first stimulation parameters such that the modulated stimulation pulses provide subsensory stimulation to the patient.

4. The system according to claim 1 or 2, wherein, The external device is configured to apply the modulation function to the first stimulation parameter.

5. The system according to claim 4, wherein, The information sent to the stimulator device includes the first stimulation parameters and an on / off schedule or duty cycle, wherein the on / off schedule or duty cycle modulates the charge delivered to the patient by the modulated stimulation pulses as a function of time.

6. The system according to claim 5, wherein, The modulated stimulation pulses at one or more of the electrodes comprise: stimulation pulses formed at one or more electrodes according to a first stimulation parameter modulated by a duty cycle or an on / off schedule.

7. The system according to claim 4, wherein, The information sent to the stimulator device includes modified stimulation parameters, wherein the modulated stimulation parameters determine the modified stimulation pulse according to the modulation function.

8. The system according to claim 7, wherein, The modulated stimulation parameters are determined based on the model.

9. The system according to claim 8, wherein, The information sent to the stimulator device includes modulated stimulation parameters, wherein the modulated stimulation parameters vary as a function of time according to the modulation function.

10. The system according to claim 1 or 2, wherein, The stimulator device is configured to apply the modulation function to the first stimulation parameter to provide the modulated stimulation pulse.

11. The system according to claim 10, wherein, The information sent to the stimulator device includes the modulation function and the first stimulation parameter.

12. The system according to claim 11, wherein, The modulation function includes an on / off schedule or duty cycle, wherein the on / off schedule or duty cycle modulates the charge delivered to the patient by the modulated stimulation pulse as a function of time.

13. The system according to claim 12, wherein, The modulated stimulation pulse includes: stimulation pulses formed at one or more electrodes according to a first stimulation parameter modulated by the duty cycle or on / off schedule.

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