Spinal cord stimulation system with stimulation modes for adapting customized stimulation parameters

Abstract

A method for programming a stimulator device for a patient using an external device is disclosed. The method provides a graphical user interface (GUI) on the external device that allows the patient to select from a plurality of displayed stimulation modes to program the stimulation provided by one or more electrodes of the stimulator device. The external device stores a model derived for the patient, the model including information indicating a plurality of frequency / pulse width / amplitude coordinates predicted to provide optimal stimulation for the patient. Each stimulation mode corresponds to a subset of coordinates defined according to a plurality of coordinates in the model. Selection of one of the stimulation modes is limited to programming the stimulator device using coordinates within the corresponding subset of coordinates.

Description

Technical Field

[0001] The present application relates to implantable medical devices (IMDs), generally to spinal cord stimulators, and more particularly to methods of controlling such devices. Background Art

[0002] Implantable neurostimulator devices are devices that generate and deliver electrical stimulation to body nerves and tissues for the treatment of various biological disorders, such as pacemakers for treating cardiac arrhythmias, defibrillators for treating cardiac fibrillation, cochlear stimulators for treating deafness, retinal stimulators for treating blindness, muscle stimulators for producing coordinated limb movements, spinal cord stimulators for treating chronic pain, cortical and deep brain stimulators for treating movement 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 present invention within a spinal cord stimulation (SCS) system, such as disclosed in U.S. Patent 6,516,227. However, the present invention may find applicability to any implantable neurostimulator device system.

[0003] SCS systems usually include Figure 1 1 and 1 . An implantable pulse generator (IPG) 10 is shown in FIG. The IPG 10 includes a biocompatible device housing 12 that houses circuitry and a battery 14 required for the operation of the IPG. The IPG 10 is coupled to electrodes 16 via one or more electrode leads 15 that form an electrode array 17. The electrodes 16 are configured to contact the patient's tissue and are carried on a flexible body 18, which also houses a separate lead 20 coupled to each electrode 16. The leads 20 are also coupled to proximal contacts 22, which can be inserted into a lead connector 24 within a header 23 secured to the IPG 10, which header may include, for example, epoxy. Once inserted, the proximal contacts 22 are connected to header contacts within the lead connector 24, which in turn are coupled to circuitry within the housing 12 by feedthrough pins through a housing feedthrough, 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 an IPG is application specific and can therefore vary. The conductive housing 12 may also include an electrode (Ec). In SCS applications, the electrode leads 15 are typically implanted near the dura mater in the patient's spine on both sides of the midline of the spinal cord. The proximal electrodes 22 are tunneled through the patient's tissue to a distant point such as the buttocks where the IPG housing 12 is implanted, at which point they are coupled to the lead connector 24. In other IPG examples designed for direct implantation at a site requiring stimulation, the IPG may be leadless, with 16 electrodes appearing instead on the body of the IPG for contacting the patient's tissue. The IPG leads 15 may be integrated with the housing 12 in other IPG schemes and permanently connected to the housing 12. 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] The IPG 10 may include an antenna 26a that allows it to communicate bidirectionally with a plurality of external devices, such as Figure 4 As shown in Figure 1 The antenna 26a depicted in FIG. 1 is shown as a conductive coil within the housing 12, although the coil 26a may also be present in the head 23. When the antenna 26a is configured as a coil, communication with the external device preferably occurs using near-field magnetic induction. The IPG may also include a radio frequency (RF) antenna 26b. Figure 1 , the RF antenna 26b is shown in the head 23, but it can also be in the housing 12. The RF antenna 26b may include a patch, slot or wire, and can operate as a monopole or dipole. 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, etc.).

[0006] Stimulation in the IPG 10 is usually provided by pulses, such as Figure 2 . The stimulation parameters generally include: the amplitude of the pulse (A; whether current or voltage); the frequency (F) and pulse width (PW) of the pulse; the electrodes 16 (E) that are activated to provide such stimulation; and the polarity (P) of such activated electrodes, i.e., whether the activated electrodes act as anodes (generating (source) current to the tissue) or cathodes (absorbing (sink) current from the tissue). These stimulation parameters considered together include the stimulation program that the IPG 10 can execute to provide therapeutic stimulation to the patient.

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

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

[0009] The IPG 10 includes a stimulation circuit 28 that can be programmed to generate stimulation pulses at electrodes as defined by a stimulation program. The stimulation circuit 28 can, for example, include circuits described in U.S. Patent Application Publications 2018 / 0071513 and 2018 / 0071520 or described in USP 8606362 and 8620436.

[0010] Figure 3An external test stimulation environment is shown that can be used before the IPG 10 is implanted in a patient. During external test stimulation, stimulation can be attempted in the intended implant patient without implanting the 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 in the spine as previously described. The proximal ends of the one or more test leads 15' exit the incision 36 and are connected to an external test stimulator (ETS) 40. The ETS 40 typically mimics the operation of the IPG 10, and can therefore provide stimulation pulses to the patient's tissue, 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 of time (e.g., two weeks), which allows the patient and his or her clinician to experiment with different stimulation parameters to try and find a stimulation program that relieves the patient's symptoms (e.g., pain). If the external trial stimulation proves successful, the trial lead(s) 15' are removed and the complete IPG 10 and lead(s) 15 are implanted as described above; if unsuccessful, only the trial lead(s) 15' are removed.

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

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

[0013] The 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 the IPG 10. The external controller 45 may also include a general-purpose mobile electronic device such as a mobile phone that has been programmed with a medical device application (MDA) that allows it to operate as a wireless controller for the IPG 10 or ETS 40, as described in U.S. Patent Application Publication 2015 / 0231402. The external controller 45 includes a user interface that includes means for inputting commands (e.g., buttons or icons) and a display 46. The user interface of the external controller 45 enables the patient to adjust stimulation parameters, although it may have limited functionality when compared to a more powerful clinician programmer 50, described later.

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

[0015] The external controller 45 may also have control circuitry 48, such as a microprocessor, microcomputer, FPGA, other digital logic structure capable of executing instructions for an electronic device, etc. The control circuitry 48 may, for example, receive patient adjustments to stimulation parameters and create a stimulation program to be wirelessly transmitted to the 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, a portable computer, a laptop, a tablet computer, a mobile smart phone, a personal digital assistant (PDA) type mobile computing device, etc. Figure 4 In FIG. 5 , computing device 51 is shown as a portable computer including typical computer user interface devices such as screen 52, mouse, keyboard, speaker, stylus, printer, etc., which are not all shown for convenience. Figure 4 Also shown are accessory devices for the clinician programmer 50 that are typically specific to its operation as a stimulation controller, such as a communications "wand" 54, and a joystick 58, which may be coupled to appropriate ports on the computing device 51, such as, for example, a USB port 59.

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

[0018] If the IPG 10 or ETS 40 includes an RF antenna 26 b or 42 b, the wand 54, computing device 51, or both may likewise include an RF antenna 56 b to establish communications with the IPG 10 or ETS 40 at greater distances. (The wand 54 may not be necessary in this case.) The clinician programmer 50 may also establish communications with other devices and networks (such as the Internet) wirelessly or via a wired link provided at an Ethernet or network port.

[0019] To program the stimulation program or parameters of the IPG 10 or ETS 40, the clinician interfaces with a clinician programmer graphical user interface (GUI) 64 provided on the display 52 of the computing device 51. As will be appreciated by those skilled in the art, the GUI 64 may be presented by executing clinician programmer software 66 on the computing device 51, which may be stored on the device's non-volatile memory 68. Those skilled in the art will also appreciate that the execution of the clinician programmer software 66 in the computing device 51 may be facilitated by control circuitry 70, such as a microprocessor, microcomputer, FPGA, other digital logic structures capable of executing programs in a computing device, etc. Such control circuitry 70, in addition to executing the clinician programmer software 66 and presenting the GUI 64, may also enable communication via the antenna 56a or 56b to transmit the 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 in . Those skilled in the art will appreciate that the details of GUI 64 will depend on where in the clinician programmer software 66 is executed, which will depend on the GUI selections that the clinician has made. Figure 5A GUI 64 is shown at one point to allow stimulation parameters to be set for a patient and stored as a stimulation program. On the left is a program interface 72 that allows stimulation programs to be named, loaded, and saved for a patient as further described in the '038 disclosure. On the right is a stimulation parameter interface 82 in which specified stimulation parameters (A, D, F, E, P) can be defined for a stimulation program. Values ​​for 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 the clinician can use to increase or decrease these values.

[0021] The stimulation parameters associated with the 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 can be manipulated in the following lead interface 92, which shows the leads 15 (or 15') approximately in their appropriate positions relative to each other, for example, on the left or right side of the spine. The 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 selected electrodes (including the shell electrode Ec) to be designated as anodes, cathodes, or off. The electrode parameter interface 86 also allows the relative strength of the anodic current or cathodic current of the selected electrode to be specified as a percentage X. This is particularly useful if, as described in the '038 disclosure, more than one electrode acts as an anode or cathode at a given time. According to the Figure 2 In the example waveforms shown in FIG. 9 , as shown in lead interface 92, electrode E5 has been selected as the only anode sourcing current, and this electrode receives X=100% of the specified anode current +A. Similarly, electrode E4 has been selected as the only cathode sinking current, and this electrode receives X=100% of the cathode current -A.

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

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

[0024] In one example, a method for programming a stimulator device for a patient is disclosed, which may include: providing a graphical user interface (GUI) that allows the patient to select from a plurality of displayed stimulation modes to program stimulation provided by one or more electrodes of the stimulator device; storing information indicating a plurality of stimulation parameter subsets derived for the patient, wherein each stimulation mode corresponds to one of the stimulation parameter subsets; and based on selection of one of the stimulation modes, limiting programming of the stimulator device to stimulation parameters within the corresponding stimulation parameter subset.

[0025] In one example, each subset of stimulation parameters includes at least two of frequency, pulse width and amplitude. In one example, the stimulation parameters in each subset include lines in a multidimensional space of at least two of frequency, pulse width and amplitude. In one example, the stimulation parameters in each subset include volumes in a multidimensional space of at least two of frequency, pulse width and amplitude. In one example, the stimulation parameters of the selected stimulation mode are configured to provide sub-perceptual stimulation for the patient. In one example, the stimulation parameters of the selected stimulation mode are configured to provide super-perceptual stimulation for the patient. In one example, at least one of the stimulation modes indicates a posture or activity of the patient. In one example, at least one of the stimulation modes indicates a power mode for the stimulator device. In one example, the method also includes providing an automatic option on a GUI, which allows the stimulator device to detect when at least one of the stimulation modes should be entered, wherein the detection of one of the stimulation modes by the stimulator device is limited to programming the stimulator device with the stimulation parameters in the corresponding stimulation parameter subset for one of the detected stimulation modes. In one example, the GUI allows the patient to select at least one stimulation mode to be detected. In one example, the stimulator device includes at least one sensor for detecting when at least one of the stimulation modes will be entered. In one example, the at least one sensor includes an accelerometer. In one example, the at least one sensor includes a clock. In one example, the at least one sensor includes a sensor for detecting the voltage of a battery in the stimulator device. In one example, the at least one sensor includes at least one of the electrodes of the stimulator device. In one example, the method further includes: providing an automatic option on the GUI, which allows an external device to detect when at least one of the stimulation modes should be entered, wherein the detection of one of the stimulation modes by the external device limits the external device to programming the stimulator device with the stimulation parameters in the corresponding stimulation parameter subset for one of the detected stimulation modes. In one example, the external device is configured to detect when at least one of the stimulation modes will be entered by receiving information from another device. In one example, the method further includes providing one or more options on the GUI to allow the patient to program the stimulator device by selecting the stimulation parameters in the stimulation parameter subset corresponding to the selected stimulation mode. In one example, at least one of the one or more options allows the patient to adjust at least two of the frequency, pulse width and amplitude of the stimulation parameters used to program the stimulator device. In one example, in response to providing stimulation to the patient during a testing procedure, measurements obtained from the patient are used to derive a subset of stimulation parameters for the patient.In one example, the GUI is provided on a patient external controller and also includes programming a plurality of displayed stimulation patterns using a clinician programmer.

[0026] In one example, a system is disclosed that may include: a stimulator device configured for implantation in a patient, comprising a plurality of electrodes; and an external device configured to program the stimulator device to provide stimulation at one or more of the plurality of electrodes, wherein the external device stores information indicating a plurality of subsets of stimulation parameters derived for the patient; wherein the external device is configured to: provide a graphical user interface (GUI) configured to allow the patient to select from a plurality of displayed stimulation modes to program the stimulation, wherein each stimulation mode corresponds to one of the subsets of stimulation parameters, and based on selection of one of the stimulation modes, limit programming of the stimulator device to stimulation parameters within the corresponding subset of stimulation parameters.

[0027] In one example, each subset of stimulation parameters includes at least two of frequency, pulse width and amplitude. In one example, the stimulation parameters in each subset include lines in a multidimensional space of at least two of frequency, pulse width and amplitude. In one example, the stimulation parameters in each subset include volumes in a multidimensional space of at least two of frequency, pulse width and amplitude. In one example, the stimulation parameters of the selected stimulation mode are configured to provide sub-perceptual stimulation for the patient. In one example, the stimulation parameters of the selected stimulation mode are configured to provide super-perceptual stimulation for the patient. In one example, at least one of the stimulation modes indicates a posture or activity of the patient. In one example, at least one of the stimulation modes indicates a power mode for the stimulator device. In one example, the external device is also configured to provide an automatic option on the GUI, the automatic option being configured to allow the stimulator device to detect when at least one of the stimulation modes should be entered, wherein the external device is configured to be limited to programming the stimulator device using the stimulation parameters in the corresponding stimulation parameter subset for one of the detected stimulation modes. In one example, the GUI is configured to allow the patient to select at least one stimulation mode to be detected. In one example, the stimulator device includes at least one sensor for detecting when at least one of the stimulation modes will be entered. In one example, the at least one sensor includes an accelerometer. In one example, the at least one sensor includes a clock. In one example, the at least one sensor includes a sensor for detecting the voltage of a battery in the stimulator device. In one example, the at least one sensor includes at least one of the electrodes of the stimulator device. In one example, the external device is further configured to provide an automatic option on the GUI, the automatic option being configured to allow the external device to detect when at least one of the stimulation modes should be entered, wherein the external device is configured to limit the use of stimulation parameters in a corresponding stimulation parameter subset for one of the detected stimulation modes to program the stimulator device. In one example, the external device is configured to detect when at least one of the stimulation modes will be entered by receiving information from another device. In one example, the external device is further configured to provide one or more options on the GUI, the one or more options being configured to allow the patient to program the stimulator device by selecting stimulation parameters in a stimulation parameter subset corresponding to the selected stimulation mode. In one example, at least one of the one or more options is configured to allow the patient to adjust at least two of the frequency, pulse width, and amplitude of the stimulation parameters with which the stimulator device is programmed.In one example, in response to providing stimulation to the patient during a testing procedure, measurements obtained from the patient are used to derive a subset of stimulation parameters for the patient. In one example, the system further includes a clinician programmer, wherein the clinician programmer is configured to program the plurality of displayed stimulation patterns in the external device.

[0028] In one example, a non-transitory computer-readable medium is disclosed that is configured to operate in an external device that is configured to program a stimulator device implantable in a patient to provide stimulation at one or more of a plurality of electrodes, the medium including information indicating a plurality of subsets of stimulation parameters derived for the patient, wherein the medium includes instructions that, when executed on the external device, are configured to: provide a graphical user interface (GUI) on the external device that allows the patient to select from a plurality of displayed stimulation modes to program the stimulation, wherein each stimulation mode corresponds to one of the subsets of stimulation parameters derived for the patient, and based on selection of one of the stimulation modes, limit programming of the stimulator device to stimulation parameters within the corresponding subset of stimulation parameters.

[0029] In one example, a method for programming a stimulator device for a patient is disclosed, which may include: determining a model for the patient, wherein the model includes information indicating predicted stimulation parameters that may be used for the patient; determining information indicating multiple subsets of stimulation parameters using the model, wherein each subset corresponds to one of multiple stimulation modes; and providing a graphical user interface (GUI) to allow the patient to select from the multiple stimulation modes, wherein selection of one of the stimulation modes limits programming of the stimulator device to stimulation parameters within the corresponding subset of stimulation parameters.

[0030] In one example, the stimulation parameters in each subset include a line or volume in a multidimensional space of at least two of frequency, pulse width and amplitude. In one example, in response to providing stimulation to the patient during a test procedure, a model for the patient is determined using a measurement result obtained from the patient. In one example, stimulation is provided to the patient with different pulse widths during the test procedure, and wherein the measurement result includes an indication of a perception threshold at each pulse width, thereby determining the relationship between the pulse width and the patient's perception threshold. In one example, the model is determined by comparing the relationship with another model to determine the predicted stimulation parameters in the model, wherein the other model includes the relationship between frequency, pulse width and perception threshold. In one example, the model and multiple subsets are determined in a clinician programmer communicating with a stimulator device, and the multiple subsets determined are also sent from the clinician programmer to an external device. In one example, the model is determined in a clinician programmer communicating with a stimulator device, and the model is also sent to an external device, wherein multiple subsets are determined in an external device. In one example, the predicted stimulation parameters in the model include a line or volume in a multidimensional space of at least two of frequency, pulse width and amplitude. In one example, at least one subset is determined using the model so that the stimulation parameters of the at least one subset are completely subject to the predicted stimulation parameters in the model. In one example, at least one subset is determined using the model so that the stimulation parameters of the at least one subset are partially subject to the predicted stimulation parameters in the model. In one example, the predicted stimulation parameters in the model include stimulation parameters predicted to provide subperceptual stimulation to the patient. In one example, the model also includes information indicating the sensory abnormality threshold of the patient, wherein at least one stimulation mode provides superperceptual stimulation to the patient by the stimulation parameters in the corresponding subset whose amplitude stimulation parameters exceed the sensory abnormality threshold. In one example, the stimulation parameters of the selected stimulation mode are configured to provide subperceptual stimulation to the patient. In one example, the stimulation parameters of the selected stimulation mode are configured to provide superperceptual stimulation to the patient. In one example, at least one of the stimulation modes indicates the posture or activity of the patient. In one example, at least one of the stimulation modes indicates the power mode for the stimulator device. In one example, the method also includes: providing an automatic option on the GUI, the automatic option allows detection when at least one of the stimulation modes should be entered, wherein the detection of one of the stimulation modes is limited to programming the stimulator device with the stimulation parameters in the corresponding stimulation parameter subset for one of the detected stimulation modes. In one example, the method further includes providing one or more options on the GUI to allow the patient to program the stimulator device by selecting stimulation parameters within the subset of stimulation parameters corresponding to the selected stimulation mode.In one example, at least one of the one or more options allows the patient to adjust at least two of the frequency, pulse width and amplitude of the parameters programmed by the stimulator device. In one example, the stimulation parameters in at least one of the subsets are adjustable. In one example, the GUI is provided on the patient's external controller and also includes programming multiple stimulation modes using a clinician programmer.

[0031] In one example, a system is disclosed that may include: a stimulator device configured to be implanted in a patient, comprising a plurality of electrodes; and at least one external device configured to: determine a model for the patient, wherein the model includes information indicating predicted stimulation parameters that may be used for the patient; determine information indicating a plurality of subsets of stimulation parameters using the model, wherein each subset corresponds to one of a plurality of stimulation modes; and provide a graphical user interface (GUI) configured to allow the patient to select from a plurality of stimulation modes, wherein, based on selection of one of the stimulation modes, the at least one external device is configured to limit programming of the stimulator device to stimulation parameters within the corresponding subset of stimulation parameters.

[0032] In one example, the stimulation parameters in each subset include a line or volume in a multidimensional space of at least two of frequency, pulse width, and amplitude. In one example, at least one external device is configured to determine a model for the patient by receiving a measurement obtained from the patient in response to providing stimulation to the patient during a test procedure. In one example, the at least one external device is configured to provide stimulation to the patient at different pulse widths during the test procedure, and wherein the measurement includes an indication of a perception threshold at each pulse width, wherein the at least one external device is configured to determine a relationship between the pulse width and the patient's perception threshold. In one example, the at least one external device is configured to determine the model of the patient by comparing the relationship with another model to determine a predicted stimulation parameter in the model, wherein the other model includes a relationship between frequency, pulse width, and perception threshold. In one example, at least one external device includes a clinician programmer and a patient external controller, wherein the clinician programmer is configured to determine the model and information indicating a plurality of subsets, wherein the clinician programmer is configured to send the determined plurality of subsets from the clinician programmer to the patient external controller. In one example, at least one external device includes a clinician programmer and a patient external controller, wherein the clinician programmer is configured to determine a model and send the model to the patient external controller, wherein the patient external controller is configured to determine information indicating multiple subsets. In one example, at least one external device includes a clinician programmer and a patient external controller, wherein the clinician programmer is configured to program multiple stimulation patterns in a patient external controller with a GUI. In one example, the predicted stimulation parameters in the model include a line or volume in a multidimensional space of at least two of frequency, pulse width, and amplitude. In one example, the at least one external device is configured to use the model to determine at least one of the subsets, so that the stimulation parameters of the at least one subset are completely constrained by the predicted stimulation parameters in the model. In one example, the at least one external device is configured to use the model to determine at least one of the subsets, so that the stimulation parameters of the at least one subset are partially constrained by the predicted stimulation parameters in the model. In one example, the predicted stimulation parameters in the model include stimulation parameters predicted to provide sub-perceptual stimulation to the patient. In one example, the model also includes information indicating a sensory abnormality threshold of the patient, wherein at least one stimulation mode provides supersensory stimulation to the patient by stimulation parameters in a corresponding subset whose amplitude stimulation parameters exceed the sensory abnormality threshold. In one example, the stimulation parameters of the selected stimulation mode are configured to provide subsensory stimulation to the patient. In one example, the stimulation parameters of the selected stimulation mode are configured to provide supersensory stimulation to the patient. In one example, at least one of the stimulation modes indicates a posture or activity of the patient.In one example, at least one of the stimulation modes indicates a power mode for the stimulator device. In one example, the at least one external device is configured to provide an automatic option on the GUI, the automatic option being configured to allow detection when at least one of the stimulation modes should be entered, wherein the at least one external device is configured to limit the programming of the stimulator device to the stimulation parameters in the corresponding stimulation parameter subset for one of the detected stimulation modes. In one example, at least one external device is configured to provide one or more options on the GUI to allow the patient to program the stimulator device by selecting the stimulation parameters in the stimulation parameter subset corresponding to the selected stimulation mode. In one example, at least one of the one or more options allows the patient to adjust at least two of the frequency, pulse width and amplitude of the parameters programmed to the stimulator device. In one example, at least one external device is configured to allow the user to adjust the stimulation parameters in at least one of the subsets.

[0033] In one example, at least one non-transitory computer-readable medium is disclosed and is configured to operate in at least one external device configured to program a stimulator device implantable in a patient to provide stimulation at one or more of a plurality of electrodes, wherein the at least one medium includes instructions that, when executed on the at least one external device, can be configured to: determine a model for the patient, wherein the model includes information indicating predicted stimulation parameters that may be used for the patient; determine information indicating a plurality of subsets of stimulation parameters using the model, wherein each subset corresponds to one of a plurality of stimulation modes; and provide a graphical user interface (GUI) configured to allow the patient to select from a plurality of stimulation modes, wherein, based on selection of one of the stimulation modes, the at least one external device is configured to limit programming of the stimulator device to stimulation parameters within the corresponding subset of stimulation parameters.

[0034] In one example, a method for programming a stimulator device of a patient using an external device is disclosed, which may include: providing a graphical user interface (GUI) on the external device, the graphical user interface allowing the patient to select from a plurality of displayed stimulation modes to program stimulation provided by one or more electrodes of the stimulator device, wherein the external device stores information indicating a plurality of coordinate subsets, wherein each coordinate in each subset comprises stimulation parameters derived for the patient to provide optimal stimulation for the patient, wherein each stimulation mode corresponds to one of the coordinate subsets, wherein selection of one of the stimulation modes is limited to programming the stimulator device using coordinates within the corresponding coordinate subset.

[0035] In one example, each coordinate includes frequency, pulse width and amplitude. In one example, the coordinates in each subset include lines in the three-dimensional space of frequency, pulse width and amplitude. In one example, the coordinates in each subset include volumes in the three-dimensional space of frequency, pulse width and amplitude. In one example, the method may also include determining a model for the patient, wherein the model includes information indicating a plurality of coordinates, wherein each coordinate in the model includes stimulation parameters predicted to provide optimal stimulation to the patient, wherein a plurality of subsets of coordinates are determined using the model. In one example, in response to providing stimulation to the patient during a test procedure, a model for the patient is determined using measurements obtained from the patient. In one example, stimulation is provided to the patient with different pulse widths during the test procedure, and wherein the measurements include an indication of a perception threshold at each pulse width, thereby determining a relationship between the pulse width and the patient's perception threshold. In one example, the perception threshold includes the lowest amplitude of the stimulation pulse, at which the patient can perceive the stimulation pulse. In one example, the model is determined by comparing the relationship with another model to determine a plurality of coordinates in the model. In one example, another model includes a relationship between frequency, pulse width and perception threshold. In one example, the model and a plurality of subsets are determined in a clinician programmer communicating with a stimulator device. In one example, the method further includes sending the determined multiple subsets from the clinician programmer to an external device. In one example, a model is determined in a clinician programmer that communicates with a stimulator device, and the model is also sent to an external device, wherein multiple subsets are determined in the external device. In one example, multiple coordinates in the model include lines in the three-dimensional space of frequency, pulse width, and amplitude. In one example, multiple coordinates in the model include volumes in the three-dimensional space of frequency, pulse width, and amplitude. In one example, at least one subset of coordinates is determined using the model so that the coordinates of the at least one subset are completely constrained by the multiple coordinates in the model. In one example, at least one subset of coordinates is determined using the model so that the coordinates of the at least one subset are partially constrained by the multiple coordinates in the model. In one example, at least one subset is partially constrained by the multiple coordinates in the model so that only some of the stimulation parameters of the coordinates in at least one subset are equal to the stimulation parameters of the coordinates in the model, but at least one of the stimulation parameters of the coordinates in at least one subset is outside the stimulation parameters of the coordinates in the model. In one example, each coordinate in the model includes stimulation parameters that are predicted to provide optimal subperceptual stimulation to the patient. In one example, the model also includes information indicating a paresthesia threshold of the patient at each of the plurality of coordinates, wherein at least one stimulation mode provides super-perception to the patient by providing coordinates in a corresponding subset where the amplitude stimulation parameter exceeds the paresthesia threshold. In one example, at least one of the stimulation modes is configured to provide sub-perception stimulation to the patient.In one example, at least one of the stimulation modes is configured to provide a patient with extrasensory stimulation. In one example, at least one of the stimulation modes indicates a patient's posture or activity. In one example, at least one of the stimulation modes indicates a power mode for a stimulator device. In one example, the method further includes: providing an automatic option on a GUI that allows the stimulator device to detect when at least one of the stimulation modes should be entered, wherein the detection of one of the stimulation modes by the stimulator device limits the external device to programming the stimulator device with coordinates within a corresponding coordinate subset for one of the detected stimulation modes. In one example, the GUI allows the patient to select at least one stimulation mode that should be detected. In one example, the stimulator device includes at least one sensor for detecting when at least one of the stimulation modes should be entered. In one example, at least one sensor includes an accelerometer. In one example, at least one sensor includes a clock. In one example, at least one sensor includes a sensor for detecting the voltage of a battery in the stimulator device. In one example, at least one sensor includes at least one of the electrodes of the stimulator device. In one example, the method further includes: providing an automatic option on the GUI, the automatic option allowing the external device to detect when at least one of the stimulation modes should be entered, wherein the detection of one of the stimulation modes by the external device limits the external device to programming the stimulator device using the coordinates within the corresponding coordinate subset of one of the detected stimulation modes. In one example, the external device detects when at least one of the stimulation modes should be entered by receiving information from another device. In one example, the method further includes providing one or more options on the user interface to allow the patient to program the stimulator device by selecting the coordinates within the coordinate subset corresponding to the selected stimulation mode. In one example, at least one of the one or more options allows the patient to simultaneously adjust the frequency, pulse width and amplitude of the coordinates for programming the stimulator device. In one example, in response to providing stimulation to the patient during the test procedure, the coordinate subset is derived for the patient using the measurement results obtained from the patient. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 An implantable pulse generator (IPG) that can be used for spinal cord stimulation (SCS) according to the prior art is shown.

[0037] Figure 2 An example of stimulation pulses that may be generated by an IPG according to the prior art is shown.

[0038] Figure 3 The use of an external trial stimulator (ETS) according to the prior art which can be used to provide stimulation prior to IPG implantation is shown.

[0039] Figure 4 Various external devices capable of communicating with and programming stimulation in the IPG and ETS according to the prior art are shown.

[0040] Figure 5 A graphical user interface (GUI) of a clinician programmer external device for setting or adjusting stimulation parameters according to the prior art is shown.

[0041] Figure 6 A sweet spot search for determining effective electrodes for a patient using a movable sub-sensing bipole is shown.

[0042] 7A to 7D A sweet spot search for determining effective electrodes for a patient using a movable super-sensing bipolar is shown.

[0043] Figure 8 A stimulation circuit that may be used in IPG or ETS is shown that is capable of providing multiple independent current controls for independently setting the current at each of the electrodes.

[0044] Fig. 9 A flow chart showing a study conducted on various patients with back pain designed to determine optimal subperceptual SCS stimulation parameters in the frequency range of 1 kHz to 10 kHz.

[0045] FIG. 10A to FIG. 10C Various results are shown as a function of stimulation frequency in the frequency range of 1 kHz to 10 kHz, including: mean optimal pulse width ( Fig. 10A ), average charge per second and optimal stimulation amplitude ( Fig. 10B ), and back pain scores ( Fig. 10C ).

[0046] FIG. 11A to FIG. 11C Also shown is an analysis of the relationship between average optimal pulse width and frequency over the frequency range of 1 KHz to 10 kHz, and identification of statistically-significant regions of optimization for these parameters.

[0047] Fig. 12A Results are shown for patients tested with sub-perceptual therapy at frequencies at or below 1 kHz, and the optimal pulse width range determined at the tested frequencies, as well as the region of optimal pulse width v. frequency for sub-perceptual therapy are shown.

[0048] Fig. 12B Various modeled relationships between average optimum pulse width and frequencies at or below 1 kHz are shown.

[0049] Fig. 12C A study of the duty cycle for the optimum pulse width as a function of frequency at or below 1 kHz is shown.

[0050] Fig.12D The average battery current and battery discharge time at the optimum pulse width as a function of frequency at or below 1 kHz are shown.

[0051] Fig.13A and Fig. 13B Results of additional testing are shown which validate the frequency versus pulse width relationship proposed previously.

[0052] Fig.14 A fitting module is shown that illustrates how the relationships and regions determined for optimal pulse width and frequency (≤ 10 kHz) can be used to set sub-sensory stimulation parameters for IPG or ETS.

[0053] Fig.15 The algorithm used for super-perceptual sweet spot search prior to sub-perceptual treatment is shown, as well as possible optimization of sub-perceptual treatment using a fitting module.

[0054] Fig.16 An alternative algorithm for optimizing sub-perceptual treatment using a fitting module is shown.

[0055] Fig.17 A patient-derived model is shown showing surfaces representing optimal sub-perceptual values ​​of frequency and pulse width, and also includes the patient's perception threshold pth as measured at these frequencies and pulse widths.

[0056] Fig.18A and Fig.18B Plotted perception threshold pth versus pulse width for a number of patients is shown, and it is shown how the results can be curve fit.

[0057] Fig.19 A graph of parameter Z versus pulse width for a patient is shown, where Z comprises the patient's optimal amplitude A expressed as a percentage of the perception threshold pth (ie, Z = A / pth).

[0058] Figures 20A-20F An algorithm is shown for using Figure 17-Figure 19 The modeling information is obtained from the patient's perceptual threshold measurements and used to derive the patient's optimal range of subperceptual stimulation parameters (e.g., F, PW, and A).

[0059] Fig.21 The use of optimal stimulation parameters in a patient external controller is shown, including a user interface that allows the patient to adjust stimulation within a range.

[0060] Figure 22A-22F The effect of statistical variance in modeling is shown, resulting in a volume that may be occupied by the optimal stimulation parameters determined for the patient. A user interface for a patient external controller is also shown to allow the patient to adjust stimulation within this volume.

[0061] Fig.23 A stimulation mode user interface is shown from which the patient can select different stimulation modes to provide stimulation or allow the patient to control stimulation using different subsets of stimulation parameters determined using optimal stimulation parameters.

[0062] Figure 24A-Figure 29B Examples of different subsets of stimulation parameters based on the patient's selection of different stimulation modes are shown. Fig.24A ) shows the frequency and pulse width of a subset, while the graph labeled B (e.g. Fig. 24B ) show the amplitude and perception threshold of the subset. These figures show that the subsets of stimulation parameters corresponding to different stimulation modes can include parameters that are completely constrained by (i.e., completely within) the determined optimal stimulation parameters, or can include parameters that are only partially constrained by the optimal stimulation parameters.

[0063] Fig.30 An automatic mode is shown, where the IPG and / or external controller is used to determine when a particular stimulation mode should be automatically entered based on sensed information.

[0064] Fig.31 Another example of a simulation mode user interface is shown, in which stimulation modes are presented for selection over a two-dimensional representation of stimulation parameters, although a three-dimensional representation indicating subset volumes may also be used.

[0065] Fig.32 A GUI aspect is shown that allows the patient to adjust the stimulation, wherein suggested stimulation areas for the patient are shown in conjunction with the adjustment aspect. DETAILED DESCRIPTION

[0066] Although spinal cord stimulation (SCS) therapy can be an effective means of relieving pain in patients, such stimulation can also cause paresthesia. Paresthesia (sometimes referred to as "supra-perception" therapy) is a sensation that can accompany SCS therapy, such as numbness, tingling, heat, cold, etc. Typically, the effects of paresthesia are mild, or at least not overly concerning to the patient. In addition, for patients whose chronic pain is now under control by SCS therapy, paresthesia is usually a moderate compromise. Some patients even find paresthesia to be comfortable and soothing.

[0067] Nevertheless, at least for some patients, SCS therapy would ideally provide complete pain relief without paresthesias - this is often referred to as "sub-perception" or subthreshold therapy that the patient cannot feel. Effective sub-perception therapy can provide pain relief without paresthesias by emitting stimulation pulses at a higher frequency. Unfortunately, such higher frequency stimulation may require more power, which tends to deplete the battery 14 of the IPG 10. See, for example, U.S. Patent Application Publication 2016 / 0367822. If the battery 14 of the IPG is a primary battery and is not rechargeable, high frequency stimulation means that the IPG 10 will need to be replaced more quickly. Alternatively, if the IPG battery 14 is rechargeable, the IPG 10 will need to be charged more frequently over a longer period of time. Either way, it will cause inconvenience to the patient.

[0068] In SCS applications, it is desirable to determine a stimulation program that will be effective for each patient. An important part of determining an effective stimulation program is determining the "sweet spot" for stimulation in each patient, i.e., selecting which electrodes should be active (E) and with what polarity (P) and relative amplitude (X%) to recruit and therefore treat the neural site where the pain originates in the patient. Selecting electrodes proximal to this neural site of pain can be difficult to determine, and trials are often performed to select the best combination of electrodes to provide treatment for the patient.

[0069] As described in U.S. Patent Application Serial No. 16 / 419,879, filed on May 22, 2019, when subperceptual therapy is used, selecting electrodes for a given patient may be even more difficult because the patient does not feel the stimulation, and therefore it may be difficult for the patient to feel whether the stimulation is "covering" his pain and therefore whether the selected electrodes are effective. In addition, subperceptual stimulation therapy may require a "washin" period before it can become effective. The wash-in period may take a day or more, and therefore the subperceptual stimulation may not be effective immediately, making electrode selection even more difficult.

[0070] Figure 6 The techniques of the '879 application for sweet spot searching are briefly described, ie, how electrodes may be selected to be proximate to a painful neural site 298 in a patient when sub-sensory stimulation is used. Figure 6 The techniques in are particularly useful in a trial setting after a patient has first been implanted with an electrode array (ie, after receiving their IPG or ETS).

[0071] In the example shown, it is assumed that the pain site 298 is likely to be within a tissue region 299. Such a region 299 can then be derived by the 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 to T10 gap). In the example shown, the region 299 is bounded by electrodes E2, E7, E15, and E10, meaning that electrodes outside of this region (e.g., E1, E8, E9, E16) are unlikely to have an effect on the patient's symptoms. Therefore, these electrodes may not be present in the patient's vertebrae. Figure 6 The dessert described in is selected during the search, as further described below.

[0072] exist Figure 6 In the embodiment of the present invention, a sub-sensing bipolar 297a is selected, where one electrode (e.g., E2) is selected as the anode that will generate positive current (+A) to the patient's tissue, and the other electrode (e.g., E3) is selected as the cathode that will sink negative current (-A) from the tissue. This is similar to the previous description of Figure 2 The content of the description, and biphasic stimulation pulses can be used with effective charge recovery. Because the bipole 297a provides subperceptual stimulation, the amplitude A used during the sweet spot search is titrated down until the patient no longer feels paresthesia. The subperceptual bipole 297a is provided to the patient over a duration of time (such as a few days), which allows the potential effectiveness of the subperceptual bipole to be "washed in" and allows the patient to provide feedback on how well the bipole 297a helps the patient's symptoms. Such patient feedback can include a pain scale ranking. For example, a patient can rank their pain using a numeric rating scale (NRS) or a visual analog scale (VAS) on a scale from 1 to 10, where 1 means no pain or almost no pain, and 10 means the worst pain imaginable. As discussed in the '879 application, such a pain scale ranking can be input into the patient's external controller 45.

[0073] After testing the bipole 297a in this first position, a different combination of electrodes (anodic electrode E3, cathodic electrode E4) is selected that will move the position of the bipole 297 within the patient's tissue. Again, the amplitude of the current A may need to be titrated to an appropriate sub-perceptual level. In the example shown, the bipole 297a is moved down one electrode lead and up the other electrode lead, as shown in the path 296 of finding a combination of electrodes that covers the pain site 298. Figure 6In the example of FIG, given that the pain site 298 is adjacent to electrodes E13 and E14, it can be expected that the bipolar 297a at those electrodes will provide the best relief to the patient, as reflected by the patient's pain score ranking. The specific stimulation parameters selected when forming the bipolar 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 with a telemetry transmitter for execution.

[0074] Although Figure 6 The sweet spot search in can be effective, but it can also take a considerable time when using sub-perceptual stimulation. As already noted, sub-perceptual stimulation is provided for several days at each bipolar 297, and because a large number of bipolar positions are selected, the entire sweet spot search may take up to a month to complete.

[0075] The inventors have determined through testing of SCS patients that even if it is desired to eventually continue to use subperceptual treatment for the patient after the sweet spot search, it is also beneficial to use superperceptual stimulation to select active electrodes for the patient during the sweet spot search. Compared with the use of subperceptual stimulation (which requires a wash-in period at each set of electrodes tested), the use of superperceptual stimulation during the sweet spot search greatly accelerates the determination of effective electrodes for the patient. After the electrodes for the patient are determined using superperceptual treatment, the treatment can be titrated to maintain the subperceptual level of the same electrodes determined for the patient during the sweet spot search. Since it is known that the selected electrodes are recruiting the patient's painful neural sites, it is more likely that the application of subperceptual treatment to those electrodes will have an immediate effect, thereby reducing or potentially eliminating the need for washing in subsequent subperceptual treatment. In summary, when using superperceptual sweet spot search, effective subperceptual treatment can be achieved more quickly for the patient. Preferably, the superperceptual sweet spot search occurs using symmetrical biphasic pulses occurring at low frequencies such as between 40 Hz and 200 Hz in one example.

[0076] According to one aspect of the disclosed technology, a patient will be provided with sub-aware therapy. A sweet spot search for determining electrodes that can be used during sub-aware therapy can precede such sub-aware therapy. In some aspects, when sub-aware therapy is used for a patient, the sweet spot search can use a bipolar 297a ( Figure 6 ), as just described. This can be relevant because a subperceptual sweet spot search can match the ultimate subperceptual treatment that the patient will receive.

[0077] However, the inventors have determined that even if sub-perceptual treatment is ultimately used for the patient, the use of super-perceptual stimulation (i.e., stimulation with accompanying paresthesias) during the sweet spot search may be beneficial. Fig. 7A, where the movable bipole 301a provides a super-sensory stimulation that can be felt by the patient. Figure 6 Providing bipolar 301a as a suprasensory stimulation may simply involve increasing its amplitude (e.g., current A) when compared to the sub-sensory bipolar 297a in FIG. 1 , although other stimulation parameters may also be adjustable—such as by providing a longer pulse width.

[0078] The inventors have determined that even if sub-sensory treatment is ultimately used for the patient, there are benefits to employing super-sensory stimulation during the sweet spot search.

[0079] First, as described above, the use of supersensory therapy by definition allows the patient to feel the stimulation, which enables the patient to provide essentially immediate feedback to the clinician whether the paresthesia appears to cover their pain site 298 well. In other words, it is not necessary to spend time washing in the bipole 301a at each location as it moves along the path 296. Therefore, the appropriate bipole 301a adjacent to the patient's pain site 298 can be established much more quickly, such as within a single clinician's visit, rather than over a period of days or weeks. In one example, when supersensory sweet spot search precedes subsensory therapy, the time required to wash in the subsensory therapy can be 1 hour or less, 10 minutes or less, or even a few seconds or so. This allows the wash-in to occur during a single programming session of programming the patient's IPG or ETS, and does not require the patient to leave the clinician's office.

[0080] Second, the use of supersensory stimulation during the sweet spot search ensures that electrodes are identified that well recruit the pain site 298. As a result, after the sweet spot search is complete and the final subsensory therapy is titrated to the patient, the wash-in of that subsensory therapy may not be performed for a long time because the electrodes required for good recruitment have been confidently identified.

[0081] FIG. 7B to FIG. 7D Other super-sensing bipoles 301b to 301d that can be used are shown, and in particular how a virtual pole can be used to form a virtual bipole by activating three or more of the electrodes 16. Virtual poles are further discussed in U.S. Patent Application Publication 2019 / 0175915, and therefore only briefly described here. To assist in forming a virtual pole, if the stimulation circuit 28 or 44 used in IPG or ETS is capable of independently setting the current at any of the electrodes - this is sometimes referred to as multiple independent current control (MICC), as described below Figure 8 This is further explained.

[0082] When using Virtual Bipolar, the clinician programmer 50 ( Figure 4 ) in the GUI 64( Figure 5) can be used to position 291 (which may not necessarily correspond to the location of the physical electrode 16 Figure 7B ) define the anode pole (+) and cathode pole (-) at these locations 291. The control circuit 70 in the clinician programmer 50 can calculate from these locations 291 and from other tissue modeling information which physical electrodes 16 will need to be selected and at what amplitude to form the virtual anode and virtual cathode at the specified locations 291. As previously described, the amplitude at the selected electrode can be expressed as a percentage X% of the total current amplitude A specified at the GUI 64 of the clinician programmer 50.

[0083] For example, in Figure 7B , the virtual anode pole is positioned at a position 291 between electrodes E2, E3, and E10. The clinician programmer 50 can then calculate, based on this position, 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 this position. Because the location of the virtual anode is closest to electrode E2, this electrode E2 can receive the largest share of the specified anode current +A (e.g., 75%*+A). Electrodes E3 and E10, which are adjacent to the location of the virtual anode pole but farther away, receive a smaller share of the anode current (e.g., 15%*+A and 10%*+A, respectively). Similarly, it can be seen that according to the specified position 291 of the virtual cathode pole adjacent to electrodes E4, E11, and E12, these electrodes will receive an appropriate share 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 be reversed during the second phase 30b of the pulse, as Figure 7B In any case, the use of virtual poles in the formation of the bipole 301b allows shaping of the field in the tissue, and many different combinations of electrodes can be tried during the sweet spot search. In this regard, it is not strictly necessary to move the (virtual) bipole along the path 296 for each electrode in sequence, and the path can be random, perhaps guided by feedback from the patient.

[0084] Figure 7C A useful virtual bipole 301c configuration that can be used during a sweet spot search is shown. This virtual bipole 301c again defines a target anode and cathode whose locations do not correspond to the locations of the physical electrodes. The virtual bipole 301c is formed along the lead (essentially spanning the length of the four electrodes from E1 to E5). This creates a larger field in the tissue that can better recruit the patient's pain site 298. As it moves along the path 296, it is aligned with the Fig. 7A Compared to the smaller bipolar configuration 301a in FIG. 3 , the bipolar configuration 301c may need to be moved to fewer locations, thereby speeding up detection of the pain site 298 . Fig.7Dexist Figure 7C The bipolar configuration of 296 is expanded to create a virtual bipole 301d using electrodes formed on two leads, for example, from electrode E1 to E5 and from electrode E9 to E13. This bipole 301d configuration needs to move only along a single path 296 parallel to the leads because its field is large enough to recruit neural tissue adjacent to both leads. This can further accelerate pain site detection.

[0085] In some aspects, the supersensitive bipolar 301a-301d used during the sweet spot search comprises a symmetrical biphasic waveform having pulse phases 30a and 30b of the same pulse width PW and the same amplitude (with flipped polarity during each phase) that are actively driven (e.g., by the stimulation circuit 28 or 44) (e.g., A 30a =A 30b , and PW 30a =PW 30b This is beneficial because the second pulse phase 30b provides activation charge recovery, where in this case the charge (Q 30a ) is equal to the charge (Q 30b ) so that the pulses are charge balanced. The use of a biphasic waveform is also believed to be beneficial because, as is known, the cathode is heavily involved in the recruitment of neural tissue. When a biphasic pulse is used, the position of the (virtual) anode and cathode will flip during the two phases of the pulse. This effectively doubles the neural tissue recruited for stimulation and therefore increases the likelihood that the pain site 298 will be covered by the bipolar in the correct position.

[0086] The supersensitive bipolar 301a-301b need not, however, include symmetrical biphasic pulses as just described. For example, the amplitude and pulse width of the two phases 30a and 30b may be different while keeping the charge (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 may be charge unbalanced (eg, Q 30a =A 30a *PW 30a >A 30b *PW 30b =Q 30b , or Q 30a =A 30a *PW 30a 30b *PW 30b =Q​30b ). In summary, the pulses in the bipolars 301 to 301d can be bi-phase symmetric (and therefore inherently charge balanced), bi-phase asymmetric but still charge balanced, or bi-phase asymmetric and charge unbalanced.

[0087] In a preferred example, the frequency F of the supersensory pulses 301a to 301d used during the supersensory sweet spot search may be 10 kHz or less, 1 kHz or less, 500 Hz or less, 300 Hz or less, 200 Hz or less, 130 Hz or less, or 100 Hz or less, or a range bounded by two of these frequencies (e.g., 100 to 130 Hz, or 100 to 200 Hz). In a specific example, a frequency of 90 Hz, 40 Hz, or 10 Hz may be used, wherein the pulses comprise biphasic pulses that are preferably symmetrical. However, a single actively driven pulse phase followed by a passive recovery phase may also be used. The pulse width PW may also comprise values ​​in the range of hundreds of microseconds, such as 150 microseconds to 400 microseconds. Because the purpose of the supersensory sweet spot search is simply to determine electrodes that adequately cover the patient's pain, the frequency and pulse width may not be that important at this stage. Once the electrodes are selected for subsensory stimulation, the frequency and pulse width may be optimized, as discussed further below.

[0088] It should be understood that the supersensitive bipoles 301a to 301d used during the sweet spot search are not necessarily the same electrodes selected when subsequently providing sub-sensitive treatment to the patient. Instead, the best position of the bipoles of interest during the search can be used as a basis for revising the selected electrodes. Assuming, for example, that bipole 301a ( Fig. 7A), and it is determined that the bipolar provides the best pain relief when located at electrodes E13 and E14. At that time, sub-perceptual treatment can be attempted with those electrodes E13 and E14 for the patient. Alternatively, it may be wise to modify the selected electrodes to see if the patient's symptoms can be further improved before attempting sub-perceptual treatment. For example, by using a virtual pole as already described, the distance between the cathode and the anode (focus) can be varied. Alternatively, a tripolar (anode / cathode / anode) consisting of electrodes E12 / E13 / E14 or E13 / E14 / E15 can be attempted. See U.S. Patent Application Publication 2019 / 0175915 (discussing tripolar). Or electrodes on different leads can be attempted in conjunction with E13 and E14. For example, because electrodes E5 and E6 are typically adjacent to electrodes E13 and E14, it may be useful to add E5 or E6 as a source of anode current or cathode current (again creating a virtual pole). All of these types of adjustments should also be understood to include adjustments to the "direction" or "location" of applied treatment, even if the center point of stimulation does not change (as can occur, for example, when varying the distance or focus between the cathode and anode).

[0089] In one example about Figure 8 Explain Multiple Independent Current Control (MICC), in Figure 8 FIG. 2 shows a stimulation circuit 28 ( Figure 1 ) or 44( Figure 3 ). The stimulation circuit 28 or 44 can independently control the current or charge at each electrode and use the GUI 64 ( Figure 5 ) allows current or charge to be directed to different electrodes, which is useful, for example, when moving the dipole 301i along the path 296 during a sweet spot search ( 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 Hehui 442 i A digital-to-analog converter (DAC) may be included and may be referred to as a PDAC 440 based on the positive (generated, anode) current and the negative (sunk, cathode) current they source, respectively. i and NDAC 442 i In the example shown, a pair of NDAC 440 i / PDAC442 iDedicated 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 Ci38, which acts as a safety measure to prevent DC current injection into the patient if, for example, there is a circuit fault in the stimulation circuit 28 or 44. PDAC 440 i and DNAC 442 i A voltage source may also be included.

[0090] The PDAC 440 is controlled via the GUI 64 i and NDAC 442 i Appropriate control of allows any of the electrodes 16 and the housing electrode Ec 12 to act as an anode or cathode to generate a current through the patient's tissue. Such control preferably occurs in the form of digital signals Iip and Iin that set the anode current and cathode current at each electrode Ei. If, for example, it is desired to set electrode E1 as an anode with a current of +3 mA, and to set electrodes E2 and E3 to each have a cathode current of -1.5 mA, then control signal I1p will be set to have a 3 mA digital equivalent to cause PDAC 4401 to produce +3 mA, and control signals I2n and I3n will be set to have a 1.5 mA digital equivalent to cause NDAC 4422 and 4423 to each produce -1.5 mA. Note that the definition of these control signals may also occur using programmed amplitudes A and percentages X% set in GUI 64. For example, A may be set to 3 mA, where E1 is designated as an anode with X=100%, and where E2 and E3 are designated as cathodes with X=50%. Alternatively, the control signal may not be set as a percentage, and instead the GUI 64 may simply dictate that current will appear at each electrode at any one point in time.

[0091] 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 a virtual bipolar, which was previously explained as including activating more than two electrodes. The MICC also allows for a finer electric field to be formed in the patient's tissue.

[0092] Other stimulus circuits 28 may also be used to implement the MICC. In an example not shown, the switch matrix may be between one or more PDACs 440 i and electrode node ei 39, and one or more NDAC442 iBetween the electrode nodes. The switch 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 documents: USP 6,181,969, 8,606,362, 8,620,436; and US Patent Application Publications 2018 / 0071513, 2018 / 0071520, and 2019 / 0083796.

[0093] Many of the stimulus circuits 28 or 44 (including the PDAC 440 i and NDAC 442 i , the switch matrix (if present), and the electrode nodes ei 39) can be integrated on one or more application specific integrated circuits (ASICs), as described in the following: U.S. Patent Application Publications 2012 / 0095529, 2012 / 0092031, and 2012 / 0095519. As described in these documents, one or more ASICs may also include: other circuits that can be used in the IPG 10, such as telemetry circuits (for interfacing the disconnect chip with the telemetry antenna of the IPG or ETS), circuits for generating a constant current output voltage (compliance voltage) VH to power the stimulation circuit, various measurement circuits, etc.

[0094] While it is preferred to use a sweet spot search (and in particular a super-perceptual sweet spot search) to determine the electrodes to be used during a subsequent sub-perceptual treatment, it should be noted that this is not strictly necessary. Sub-perceptual treatment may be led by a sub-perceptual sweet spot search, or may not be led by a sweet spot search at all. In short, sub-perceptual treatment as described next does not rely on the use of any sweet spot search.

[0095] In another aspect of the invention, the inventors have determined through testing of SCS patients that there is a statistically significant correlation between pulse width (PW) and frequency (F) at which an SCS patient will experience a reduction in back pain without paresthesia (subperceptual). Use of this information can help determine what pulse width may be optimal for a given SCS patient based on a particular frequency, and what frequency may be optimal for a given SCS patient based on a particular pulse width. Beneficially, this information suggests that subperceptual SCS stimulation without paresthesia may occur at frequencies of 10 kHz and below. Use of such low frequencies allows subperceptual therapy to be used with much lower power consumption in the patient's IPG or ETS.

[0096] Figures 9 to 11C Results derived from testing patients at frequencies ranging from 1 kHz to 10 kHz are shown. Fig. 9It describes how data was collected from actual SCS patients, and the criteria for patient inclusion in the study. Patients with back pain who had not yet received SCS treatment were first identified. Key patient inclusion criteria included were: persistent low back pain for more than 90 days; NRS pain scale of 5 or higher (NRS described below); stable opioid treatment for 30 days; and baseline Oswestry Disability Index score greater than or equal to 20 and less than or equal to 80. Key patient exclusion criteria included were: back surgery within the previous 6 months; presence of other confounding medical / psychological conditions; and untreated major psychiatric illness or serious medication-related performance problems.

[0097] After such an initial screening, the patient periodically enters a qualitative indicator of their pain (i.e., a pain score) into a portable electronic diary device, which may include a patient external controller 45, and the external controller 45 may in turn transmit its data to a clinician programmer 50 ( Figure 4 Such pain scores may include a numeric rating scale (NRS) score from 1 to 10 and may be entered into an electronic diary three times daily. Fig. 10C As shown in , patients who were not ultimately excluded from the study and who had not yet received subperceptual stimulation treatment had a baseline NRS score of approximately 6.75 / 10, with a standard error SE (sigma / SQRT(n)) of 0.25.

[0098] Back to Fig. 9 , the patient then places the test lead 15'( Figure 3 ) are implanted on the left and right sides of the spine, and external trial stimulation is provided to the patient as previously described. A clinician programmer 50 is used to provide the stimulation program to each patient's ETS 40, as previously described. This is done to confirm that SCS therapy is helpful for a given patient in relieving their pain. If SCS therapy is not helpful for a given patient, the trial leads 15' are removed, and the patient is then excluded from the study.

[0099] Those patients for whom external trial stimulation was helpful eventually receive full implantation of a permanent IPG 10, as previously described. After the healing period, and again using the clinician programmer 50, the "sweet spot" for stimulation is 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 neural sites in the patient 298. The sweet spot search can be performed with the previously described information about Figures 6 to 7D Any of the described approaches may occur, but in a preferred embodiment will include super-sensory stimulation (e.g., such as 7A to 7D) due to the benefits described previously. However, this is not strictly necessary, and sub-sensory stimulation may also be used during the sweet spot search. Fig. 9 In the example of , the sweet spot search occurs at 10kHz, but again the frequency used during the sweet spot search can be varied. Symmetrical biphasic pulses are used during the sweet spot search, but again this is not strictly necessary. The decision on which electrodes should be activated begins by selecting the electrode 16 that exists between thoracic vertebrae T9 and T10. However, electrodes as far away as T8 and T11 can also be activated if necessary. Fluoroscopy images of the leads 15 in each patient are used to determine which electrodes are adjacent to vertebrae T8, T9, T10, and T1.

[0100] During the sweet spot search, bipolar stimulation using only two electrodes was used for each patient, and only adjacent electrodes were used on a single lead 15, similar to Figure 6 and Fig. 7A Thus, a patient's sweet spot may include stimulation of adjacent electrodes E4 (as cathode) and E5 (as anode) on left lead 15, as previously described in Figure 2 (the electrodes may be between T9 and T10), while another patient's sweet spot may include stimulation of adjacent electrodes E9 (as anode) and E10 (as cathode) on the right lead 15 (the electrodes may be between T10 and T11). It is desirable to use only adjacent electrode bipolar stimulation and only between vertebrae T8 to T11 to minimize variability in treatment and symptoms between different patients in a study. However, more complex bipolar stimulation such as that described for FIG. 7B to FIG. 7D Those described can also be used during the sweet spot search. Patients continued in the study if they had sweet spot electrodes at the desired chest location and if they experienced 30% or greater pain relief per NRS score; patients who did not meet these criteria were excluded from further study. While the study initially started with 39 patients, by Fig. 9 So far 19 patients have been excluded from the study, leaving a total of 20 remaining patients.

[0101] The remaining 20 patients then underwent a "washout" period, meaning that their IPG did not provide stimulation for a period of time. In particular, the patient's NRS pain score was monitored until their pain reached 80% of their initial baseline pain. This was to ensure that the benefits of the previous stimulation did not persist into the next analysis period.

[0102] The remaining patients then underwent sub-sensory SCS treatment at different frequencies ranging from 1 kHz to 10 kHz using the previously determined sweet spot activation electrodes. However, this is not strictly necessary because the current at each electrode is also independently controlled to help shape the electric field in the tissue as previously described. Fig. 9 As shown in , each patient was tested using stimulation pulses having frequencies of 10 kHz, 7 kHz, 4 kHz, and 1 kHz. For simplicity, Fig. 9 The frequencies are shown to be tested sequentially for each patient, but in reality the frequencies are applied to each patient in a random order. Once testing at a given frequency is completed, there is a washout period before testing at another frequency begins.

[0103] At each measured frequency, the amplitude (A) and pulse width (PW) of the stimulation are adjusted and optimized for each patient (first pulse phase 30a; Figure 2 ) so that each patient experiences the best possible pain relief without paresthesia (subperception). In particular, using the clinician programmer 50, and keeping the same sweet spot electrode previously determined activated (although again this is not strictly necessary), each patient is stimulated at a low amplitude (e.g., 0), which is increased to the maximum point where paresthesia can be noticed by the patient (perception threshold). The initial stimulation is then selected for the patient at 50% of this maximum amplitude, i.e., so that the stimulation is subperception and therefore without paresthesia. However, other percentages of the maximum amplitude (80%, 90%, etc.) may also be selected, and may vary with patient activity or position, as further described below. In one example, the stimulation circuit 28 or 44 in the IPG or ETS may be configured to receive instructions from the GUI 64 via a selectable option (not shown) to reduce the amplitude of the stimulation pulse to a certain amount or percentage, or to reduce it by a certain amount or percentage for presentation, so that the pulse can be made subperception (if it has not already been made subperception). Other stimulation parameters (e.g., pulse width, charge) may also be reduced to the same effect.

[0104] The patient will then leave the clinician's office and thereafter and in communication with the clinician (or their technician or programmer) will use their external controller 45 ( Figure 4 ) make adjustments to their stimulation (amplitude and pulse width). At the same time, the patient will enter the NRS pain score in their electronic diary (e.g., an external controller), again three times a day. Patient adjustment of amplitude and pulse width is usually an iterative process, but essentially it is based on feedback from the patient to try to adjust the treatment to reduce their pain while still ensuring that the stimulation is sub-perceptual. Testing at each frequency lasted about 3 weeks, and stimulation adjustments may be made every two days or so. At the end of the testing period at a given frequency, the optimal amplitude and pulse width have been determined for each patient and recorded for each patient, together with the patient's NRS pain score for those optimal parameters entered in their electronic diary.

[0105] In one example, the percentage of the maximum amplitude used to provide sub-perceptual stimulation can be selected depending on the patient's activity level or position. In this regard, the IPG or ETS can include a device for determining the patient's activity or position, such as an accelerometer. If the accelerometer indicates a high degree of patient activity or a position where the electrodes are farther 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 degree of activity or a position where the electrodes are closer to the spinal cord (e.g., standing up), the amplitude can be reduced (e.g., to 50% of the maximum amplitude). Although not shown, the GUI 64 ( Figure 5 ) may include setting a percentage of the maximum amplitude at which sensory abnormalities become apparent to the patient, thereby allowing the patient to adjust to sub-perceptual current amplitudes.

[0106] Preferably, multiple independent current controls (MICCs) are used to provide or regulate sub-perceptual therapy, as previously described with respect to Figure 8 as discussed. This allows the current at each electrode to be set independently to facilitate the guidance of current or charge between the electrodes, to help form a virtual bipolar, and more generally to allow the electric field to be shaped in the patient's tissue. In particular, the MICC can be used to direct sub-sensory treatment to different locations in the electrode array and therefore the spinal cord. For example, once a set of sub-sensory stimulation parameters have been selected for a patient, one or more of the stimulation parameters can be changed. Such changes may be warranted or dictated by the treatment location. The patient's physiology may vary at different spinal locations, and the tissue may be more or less conductive at different treatment locations. Therefore, if a sub-sensory treatment location is directed to a new location along the spinal cord (whose position change may include changing the anode / cathode distance or focus), it may be warranted to adjust at least one of the stimulation parameters, such as the amplitude. As previously noted, performing sub-sensory adjustments is facilitated and may occur during a programming session because a substantial wash-in period may be unnecessary.

[0107] Adjustments to sub-perceptual therapy may also include variations in other stimulation parameters, such as pulse width, frequency, and even interphase period (IP) duration ( Figure 2 ). The interphase duration can affect the neural dose, or rate of charge infusion, such that higher sub-perceptual amplitudes will be used with shorter interphase durations. In one example, the interphase duration can be varied between 0 and 3 ms. After the washout period, new frequencies can be tested using the same protocol as described.

[0108] The sub-sensory stimulation pulses used were symmetrical biphasic constant current amplitude pulses having a first pulse phase 30a and a second pulse phase 30b (of the same duration) (see Figure 2). However, constant voltage amplitude pulses may also be used. Pulses of different shapes (triangular, sinusoidal, etc.) may also be used. When providing sub-inductive therapy, pre-pulsing (i.e., providing a small current before providing one or more actively driven pulse phases) may also occur to affect the polarization or depolarization of neural tissue. See, for example, USP 9,008,790.

[0109] FIG. 10A to FIG. 10C Results are shown for patients tested at 10 kHz, 7 kHz, 4 Hz and 1 kHz. Data are shown in each graph as the mean for the 20 remaining patients at each frequency, with error bars reflecting the standard error (SE) between patients.

[0110] by Fig. 10B Initially, the optimized amplitudes A for the 20 remaining patients are shown at the tested frequencies. Interestingly, the optimal amplitude at each frequency is essentially constant, approximately 3 mA. Fig. 10B The amount of energy consumed at each frequency is also shown, more specifically the mean charge per second (MCS) attributable to the pulse in mC / s. The MCS is calculated by taking the optimum pulse width ( Fig. 10A , discussed below) and multiplying it by the optimal amplitude (A) and frequency (F), this MCS value can include nerve dose. MCS is related to the current or power that the battery in the IPG 10 must consume to form the optimal pulse. It is worth noting that the MCS is significantly reduced 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 optimal SCS therapy for relief of back pain without paresthesia can be achieved at lower frequencies like F=1 kHz, with the added benefit of lower power draw being more considerate of the IPG 10's (or ETS 40's) battery.

[0111] Fig. 10A The optimum pulse width is shown as a function of frequency over the tested frequency range of 1 kHz to 10 kHz. As shown, the relationship follows a statistically significant trend: PW = -8.22F + 106 when modeled using linear regression 98a, where pulse width is measured in microseconds and frequency is measured in kilohertz, with a correlation coefficient R 2 When polynomial regression 98b is used for modeling, PW = 0.486F 2 –13.6F+116, again where the pulse width is measured in microseconds and the frequency is measured in kilohertz, with an even better correlation coefficient of R 2= 0.998. Other fitting methods may be used to establish additional information related to the frequency and pulse width at which stimulation pulses are formed to provide pain relief without paresthesia in the frequency range of 1 kHz to 10 kHz.

[0112] Note that the relationship between optimal pulse width and frequency is not simply the expected relationship between frequency and duty cycle (DC) (i.e., the duration of a pulse being "on" divided by its period (1 / F)). In this regard, note that a given frequency has a natural effect on pulse width: it would be expected that a higher frequency pulse would have a smaller pulse width. Thus, for example, a 1 kHz waveform with a 100 microsecond pulse width would be expected to have the same clinical results as a 10 kHz waveform with a 10 microsecond frequency, since both waveforms have a 10% duty cycle. Fig.11A The resulting duty cycle of the stimulation waveform using the optimal pulse width in the frequency range of 1 kHz to 10 kHz is shown. Here, the duty cycle is calculated by considering only the first pulse phase 30a ( Figure 2 ) ; ignoring the duration of the symmetrical second pulse phase. The duty cycle is not constant over the 1kHz to 10kHz frequency range: for example, the optimum pulse width at 1kHz (104 microseconds) is not just 10 times the optimum pulse width at 10kHz (28.5 microseconds). Therefore the optimum pulse width beyond which this is important is only for frequency scaling.

[0113] Fig. 10C The optimal stimulation parameters (optimal amplitude ( Figure 7B ) and pulse width ( Fig. 7A ))). As previously noted, patients in this study, prior to receiving SCS treatment, initially reported pain scores with an average of 6.75. After SCS implantation and during the study, and with amplitude and pulse width optimized during temporary subperceptual treatment, their average pain scores dropped significantly to an average pain score of approximately 3 for all frequencies tested.

[0114] Fig.11A An in-depth analysis of the relationship between the optimum pulse width and frequency over the frequency range of 1kHz to 10kHz is provided. Fig.11AThe graphs in show the mean optimal pulse widths for the 20 patients in the study at each frequency, together with the standard errors resulting from the variation between them. These were normalized at each frequency by dividing the standard error by the optimal pulse width, with the variation ranging between 5.26% and 8.51% at each frequency. Thus, a variation of 5% (below all calculated values) can be assumed to be a statistically significant variation at all frequencies tested.

[0115] Based on this 5% change, the maximum average pulse width (PW+5%) and the minimum average pulse width (PW+5%) can be calculated for each frequency. For example, the optimal average pulse width PW at 1kHz is 104 microseconds, and 5% above this value (1.05*104μs) is 109 microseconds; 5% below this value (0.95*104) is 98.3 microseconds. Similarly, the optimal average pulse width AVG(PW) at 4kHz is 68.0 microseconds, and 5% above this value (1.05*68.0μs) is 71.4 microseconds; 5% below this value (0.95*68.0μs) is 64.6 microseconds. Thus, a statistically significant reduction in pain without paresthesia occurs within or above the linearly defined region 100a where points 102 are (1 kHz, 98.3 μs), (1 kHz, 109 μs), (4 kHz, 71.4 μs), and (4 kHz, 64.6 μs). A linearly defined region 100b around 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), (7 kHz, 48.8 μs). A linearly defined region 100c around 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), (10 kHz, 27.1 μs). Such a region 100 therefore includes information relating to the frequency and pulse width at which stimulation pulses are formed to provide pain relief without paresthesia within the frequency range of 1 kHz to 10 kHz.

[0116] Fig. 11BAn alternative analysis of the resulting relationship between optimal 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 points 102 defining the corners of regions 100a to 100c are only within the range of the SE error bars at each frequency (PW+SE, and PW-SE), although these error bars have different sizes at each frequency. Therefore, a statistically significant reduction in pain without paresthesia occurs within or above the linearly defined region 100a at the points (1kHz, 96.3μs), (1kHz, 112μs), (4kHz, 73.8μs), and (4kHz, 62.2μs). Linearly defined regions 100b and 100c are similar, and because the points 102 defining them are within Fig. 11B This is explained in the diagram at the top of the figure, so it will not be repeated here.

[0117] Fig. 11C Another analysis of the resulting relationship between the optimal pulse width and frequency is provided. In this example, regions 100a to 100c are defined based on a standard deviation (SD) calculated at each frequency, which is greater than the standard error (SE) metric used for that point. Points 102 defining the corners of regions 100a to 100c are located within the range of the SD error bars at each frequency (PW+SD, and PW-SD), although points 102 may also be set within the error bars, similar to the previous description of Fig.11A In any case, a statistically significant reduction in pain without paresthesia occurred within or above the linearly defined region 100a at the points (1 kHz, 69.6 μs), (1 kHz, 138.4 μs), (4 kHz, 93.9 μs), and (4 kHz, 42.1 μs). The linearly defined regions 100b and 100c are similar and are defined by the points 102 at the Fig. 11C This is explained in the diagram at the top of the figure, so it will not be repeated here.

[0118] More generally, although not shown, regions within the frequency range of 1kHz to 10kHz that achieve sub-perceptual therapeutic effects include linearly defined regions 100a (1kHz, 50.0μs), (1kHz, 200.0μs), (4kHz, 110.0μs), and (4kHz, 30.0μs); and / or linearly defined regions 100b (4kHz, 110.0μs), (4kHz, 30.0μs), (7kHz, 30.0μs), and (7kHz, 60.0μs); and / or linearly defined regions 100c (7kHz, 30.0μs), (7kHz, 60.0μs), (10kHz, 40.0μs), and (10kHz, 20.0μs).

[0119] In summary, one or more statistically significant regions 100 can be defined for the following optimal pulse width and frequency data, taken for the patients in the study, to arrive at a combination of pulse width and frequency that reduces pain without paresthesia side effects in the frequency range of 1 kHz to 10 kHz, and different statistical measures of error can be used to so define one or more regions.

[0120] FIG. 12A to FIG. 12D Results of testing other patients with sub-perceptual stimulation therapy at frequencies at or below 1 kHz are shown. Testing of patients typically occurs during a suprasensory sweet spot search (see ) for selecting appropriate electrodes (E), polarity (P), and relative amplitude (X%) for each patient. 7A to 7D ) occurs, although again the subaware electrodes used may vary from those used during the superaware sweet spot search (e.g., using MICC). Although the form of the pulses used during subaware treatment may vary, symmetrical biphasic bipolar is still used to test the patient with subaware stimulation.

[0121] Fig. 12A The relationship between frequency and pulse width at which patients reported effective subperceptual treatment for frequencies of 1 kHz and below 1 kHz is shown. Note that previously ( Fig. 9 ) can be used when evaluating frequencies at or below 1 kHz, with the frequency adjusted as appropriate.

[0122] As can be seen, at each measured frequency, the optimal pulse width again falls within a certain range. For example, at 800 Hz, patients reported good results when the pulse width fell within the range of 105 to 175 microseconds. The upper end of the pulse width range at each frequency is recorded as PW(high), while the lower end of the pulse width range at each frequency is recorded as PW(low). PW(medium) represents the middle (e.g., average) of PW(high) and PW(low) at each frequency. At each of the measured frequencies, the amplitude (A) of the provided current is titrated down to a sub-perceptual level so that the patient cannot feel the paresthesia. Typically, the current is titrated to 80% of the threshold at which paresthesia can be sensed. Because each patient's anatomy is unique, the sub-perceptual amplitude A can vary from patient to patient. The depicted pulse width data includes the pulse width of only the first phase of the stimulation pulse.

[0123] Table 1 below shows in tabular form the frequency range for frequencies at or below 1kHz. Fig. 12A Optimal width vs. frequency data from , where pulse width is expressed in microseconds:

[0124]

[0125] Table 1

[0126] As previously described for frequencies in the 1kHz to 10kHz range ( FIG. 10A to FIG. 11C ), the data can be broken down to define different regions 300i where effective sub-perceptual treatment below 1 kHz is achieved. For example, regions of effective sub-perceptual treatment can be linearly bounded between the various frequencies and high and low pulse widths that define effectiveness. For example, at 10 Hz, PW(low) = 265 microseconds and PW(high) = 435 microseconds. At 50 Hz, PW(low) = 230 microseconds and PW(high) = 370 microseconds. Thus, the region 300a that provides good sub-perceptual treatment is defined by the linearly bounded regions of points (10 Hz, 265 μs), (10 Hz, 435 μs), (50 Hz, 370 μs), and (50 Hz, 230 μs). Table 2 defines the linearly bounded regions constrained at Fig. 12A Points in each of the regions 300a to 300g are shown in:

[0127] area Defined by point (Hz, μs) 300a (10,265),(10,435),(50,370),(50,230) 300b (50,230),(50,370),(100,325),(100,195) 300c (100,195),(100,325),(200,260),(200,160) 300d (200,160),(200,260),(400,225),(400,140) 300e (400,140),(400,225),(600,200),(600,120) 300f (600,120),(600,200),(800,175),(800,105) 300g (800,105),(800,175),(1000,150),(1000,90)

[0128] Table 2

[0129] The region of subperceptual therapeutic effectiveness at frequencies at or below 1 kHz may be defined in other statistically significant ways, such as those previously described for frequencies in the 1 kHz to 10 kHz range ( Figures 11A-11C ). For example, region 300i can be defined by reference to a pulse width PW(middle) at the middle of each range at each frequency. PW(middle) can include, for example, the average optimal pulse width reported by patients at each frequency, rather than the strict middle of the effective range reported by those patients. PW(high) and PW(low) can then be determined as statistical variances from the average PW(middle) at each frequency, and can be used to set upper and lower boundaries of the effective sub-perceptual region. For example, PW(high) can include the average PW(middle) plus a standard deviation or standard error, or multiples of such statistical measures; PW(low) can also include the average PW(middle) minus the standard deviation or standard error, or multiples of such statistical measures. 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, and PW(low) can include PW(middle) minus a certain percentage. In summary, one or more statistically significant regions 300 can be defined for optimal pulse width and frequency data at frequencies at or below 1 kHz for reducing pain using sub-perceptual stimulation without paresthesia side effects.

[0130] In addition Fig. 12A Shown in the are the average patient pain scores (NRS scores) reported by patients when using the optimal pulse width for different frequencies at or below 1 kHz. Prior to receiving SCS treatment, the patients' initial reported pain scores averaged 7.92. After SCS implantation, and using subperceptual stimulation at the optimal pulse width (with the range shown at each frequency), the patients' average pain scores dropped significantly. At 1 kHz, 200 Hz, and 10 Hz, the average patient-reported pain scores were 2.38, 2.17, and 3.20, respectively. Thus, clinical significance for pain relief was demonstrated when using the optimal width with subperceptual treatment at or below 1 kHz.

[0131] exist Fig. 12B The analysis is performed for frequencies of 1 kHz or less from the perspective of the median pulse width PW(middle) at each frequency (F). Fig. 12A As shown, relationships 310a to 310d follow a statistically significant trend, as shown by Fig. 12B This is demonstrated by the various regression models shown in and summarized in Table 3 below:

[0132]

[0133] Table 3

[0134] Other fitting methods may be used to establish additional information related to the frequency and pulse width at which stimulation pulses are formed to provide subperceptual pain relief without paresthesia.

[0135] Regression analysis can also be used to define statistically relevant regions, such as 300a to 300g, where sub-perceptual treatment is effective at or below 1 kHz. Fig. 12B , but regression may be performed for PW(low)vF to set the lower boundary of the correlation region 300i, and regression may be performed for PW(high)vF to set the upper boundary of the correlation region 300i.

[0136] Notice Fig. 12A The relationship between the optimum pulse width and frequency described in Fig. 12C The expected frequency vs. duty cycle (DC) relationship is shown in Figure 1. This is similar to the case when testing the 1kHz to 10kHz frequency range ( Fig.11A), the duty cycle of the optimal pulse width is not constant at 1kHz and below. Again, the optimal pulse width is only important for frequency scaling. Nevertheless, most pulse widths observed to be optimal at 1kHz and below are greater than 100 microseconds. Such pulse widths are not even possible at higher frequencies. For example, at 10kHz, the phases of the two pulses must fit within a 100 microsecond period, so PWs longer than 100 are not even possible.

[0137] Fig.12D The further benefit of using sub-perceptual therapy at frequencies of 1 kHz and below, namely reduced power consumption, is shown. Two sets of data are plotted. The first set of data includes the optimal pulse width ( ) for the patient using the battery in the patient's IPG or ETS. Fig. 12A ) the average current drawn at each frequency (AVG Ibat), and the current amplitude A required to achieve subperceptual stimulation for that patient (again, this amplitude can vary for each of the patients). At 1 kHz, the average battery current is about 1700 microamps. However, as the frequency is reduced, the average battery current drops to about 200 microamps at 10 Hz. The second data set considers power consumption from a different vantage point, namely the number of days an IPG or ETS with a fully charged rechargeable battery can operate before needing to be recharged (the "discharge time"). Based on the average battery current data, it would be expected that when the average battery current is higher, the discharge time is lower at higher frequencies (e.g., about 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., about 34 days at 10 Hz, depending on various charging parameters and settings). This is important: when using the optimal pulse width, not only can effective sub-apparent therapy be provided at frequencies of 1kHz and below; power consumption is greatly reduced, which can place less stress on the IPG or ETS and allow it to operate for longer periods of time. As noted above, when sub-apparent therapy is conventionally used at higher frequencies, excessive power consumption is a serious problem. NOTE Fig.12D The data in can also be analyzed in terms of mean charges per second (MSC), as described previously for the 1 kHz to 10 kHz data ( Fig. 10B ).

[0138] Fig.13A and Fig. 13B Results of additional testing are shown which confirm the frequency vs. pulse width relationship just proposed. Here, data from 25 patients tested at frequencies of 10 kHz and below using sub-perceptual stimulation are shown. Fig.13ATwo different graphs are shown, showing the results for frequencies of 10k and below (lower graph) and for frequencies of 1kHz and below (upper graph). The mean shows the frequency and pulse width values ​​at which the best sub-perceptual treatment is produced. The upper and lower limits represent the variance of one standard deviation above and below the mean (+STD and –STD). Fig. 13B The curve fit results determined using the average value are shown. The data at 1 kHz and below were fitted using an exponential function and a power function, resulting in the relationship PW = 159e -0.01F +220e -0.00057F and PW = 761–317F 0.10 , both of which fit the data well. The data at 10kHz and below are fitted using a power function, giving PW = -1861 + 2356F -0.024 , again a good fit. The data can also be fit with other mathematical functions.

[0139] Once determined, information 350 relating to the frequency and pulse width for optimal sub-perceptual therapy without paresthesias may be stored in an external device used to program the IPG 10 or ETS 40, such as the clinician programmer 50 or external controller 45 described previously. Fig.14 , wherein the control circuit 70 or 48 of the clinician programmer or external controller is associated with the following: regional information 100i or relationship information 98i for frequencies within the range of 1kHz to 10kHz, and regional information 300i or relationship information 310i for frequencies at or below 1kHz. Such information can be stored in a memory within the control circuit or in a memory associated with the controller. Storing this information with an external device is useful for helping clinicians perform sub-perceptual optimization, as further described below. Alternatively, and although not shown, information related to frequency and pulse width can be stored in the IPG 10 or ETS 40, thereby allowing the IPG or ETS to optimize itself without clinician or patient input.

[0140] The information 350 may be incorporated into the fitting module. For example, the fitting module 350 may be operated as a software module within the clinician programmer software 66, and may be implemented as a software module in the clinician programmer GUI 64 ( Figure 6 The fitting module 350 may also be operated in the control circuit of the IPG 10 or the ETS 40.

[0141] The fitting module 350 can be used to optimize the pulse width when the frequency is known, or vice versa. Fig.14As shown at the top of , a clinician or patient can input a frequency F into a clinician programmer 50 or an external controller 45. The frequency F is passed to a fitting module 350 to determine a pulse width PW for the patient that is statistically likely to provide adequate pain relief without paresthesia. The frequency F can, for example, be input into a relationship 98i or 310i to determine the pulse width PW. Alternatively, the frequency can be compared to a relevant region 100i or 300i within which the frequency falls. Once the correct region 100i or 300i is determined, F can be compared to the data in the region to determine the pulse width PW, which can perhaps be taken as the pulse width between the PW+X and PW-X boundaries at a given frequency, as previously described. Other stimulation parameters (such as amplitude A, activated electrodes E, their relative percentages X%, and electrode polarity P) can be determined in other ways, such as those described below, to arrive at a complete stimulation program (SP) for the patient. Based on the information from Fig. 10B Based on the data, an amplitude close to 3.0 mA may be a logical starting point, as this amplitude has been shown to be preferred by patients in the 1 kHz to 10 kHz range. However, other initial starting amplitudes may be selected, and the amplitude for sub-perceptual treatment may be frequency dependent. Fig.14 The bottom portion of FIG. 60 illustrates the use of the fitting module 350 in the reverse manner (i.e., picking frequency given pulse width). Note that in subsequent algorithms, or even in algorithms used outside of any algorithm, in one example, the system may allow the user to associate frequency and pulse width so that when either frequency or pulse width is changed, the other of the pulse width or frequency is automatically changed to correspond to the optimal setting. In one embodiment, associating frequency with pulse width in this manner may include an optional feature (e.g., in GUI 64) that may be used when sub-perceptual programming is desired, and associating frequency with pulse width may not be selected or selectable for use with other stimulation modes.

[0142] Fig.15 An algorithm 355 that can be used to provide subaural therapy to an SCS patient at frequencies of 10 kHz or less is shown, and summarizes some of the steps discussed above. Steps 320 to 328 describe a superaural sweet spot search. A user (e.g., a clinician) selects electrodes to create a bipolar for the patient (320), such as by using a GUI of a clinician programmer. This bipolar is preferably a symmetrical biphasic bipolar and may include a virtual bipolar, as previously described.

[0143] The bipolar is transmitted to the IPG or ETS with other simulation parameters using a telemetry transmitter for execution (321). Such other stimulation parameters can also be selected in the clinician programmer using a GUI. As a 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 necessary and these values ​​can be modified. At this point, if the bipolar provided by the IPG or ETS is not super-sensory, that is, if the patient does not feel paresthesia, the amplitude A or other stimulation parameters can be adjusted to make it so (322). The effectiveness of the bipolar is then measured by the patient (324) to understand the extent to which the bipolar is covering the patient's painful area. NRS or other score rating systems can be used to determine effectiveness.

[0144] If the bipolar is invalid, or if it still needs to be searched, a new bipolar can be tried (326). That is, the new electrode can preferably be selected in a manner that moves the bipolar to a new position along the path 296, as previously described with respect to 7A to 7D As described above. The new bipole can then be transmitted to the IPG or ETS again with a telemetry transmitter (321) and adjustments made if necessary to render bipolar super-sensory (322). If the bipole is effective, or if the search has been completed and the most effective bipole has been located, the bipole can be optionally modified before sub-sensory treatment (328). Such modifications as described above can involve: selecting other electrodes adjacent to the electrode of the selected bipole to modify the field shape in the tissue to perhaps better cover the patient's pain. Thus, the modification of step 328 can change the bipole used during the search to a virtual bipole, or a tripole, etc.

[0145] Modification of other stimulation parameters may also occur at this point. For example, the frequency and pulse width may be modified. In one example, an operating pulse width may be selected that provides good, comfortable paresthesia coverage (>80%). This may occur by using a frequency of, for example, 200 Hz, and starting with a pulse width of, for example, 120 microseconds. The pulse width may be increased at this frequency until good paresthesia coverage is felt. An amplitude, for example, in the range of 4 mA to 9 mA may be used.

[0146] At this point, the electrodes (E) selected for stimulation, their polarity (P), and the fraction (X%) of current they will receive (and possibly the operating pulse width) are known and will be used to provide sub-perceptual therapy. To ensure that sub-perceptual therapy is provided, the amplitude A of the stimulation is titrated down to a sub-perceptual, non-paresthesia level (330) and transmitted to the IPG or ETS using a telemetry transmitter. As described above, the amplitude A can be set below an amplitude threshold (e.g., 80% of a threshold) where the patient may just begin to experience paresthesias.

[0147] At this point, it may be useful to optimize (332) the frequency and pulse width of the sub-perceptual therapy being provided to the patient. While the frequency (F) and pulse width (PW) used during the sweet spot search may be used for sub-perceptual therapy, it may also be beneficial to additionally adjust these parameters to optimal values ​​based on regions 100i or relationships 98i established at frequencies in the range of 1 kHz to 10 kHz, or regions 300i or relationships 310i established at frequencies at or below 1 kHz. Such optimization may be performed using Fig.14 The fitting module 350 in FIG. 1 and can occur in different ways, and some methods 332a to 332c of optimization are shown in FIG. Fig.15 Option 332a, for example, allows the software in the clinician programmer or IPG or ETS to automatically select the frequency (≤10kHz) and pulse width using the area or relationship data that relates the frequency to the pulse width. Option 332a can use the previously determined working pulse width (328) and select the frequency using the area or relationship. In contrast, option 332b allows the user (clinician) (using the GUI of the clinician program) to specify the frequency (≤10kHz) or pulse width. The software can then use the area or relationship again to select the appropriate value for other parameters (pulse width or frequency (≤10kHz)). Similarly, this option can use the previously determined working pulse width to select the appropriate frequency. Option 332c allows the user to enter the frequency (≤10kHz) and pulse width PW, but in a manner constrained by the area or relationship. Similarly, this option allows the user to enter the working pulse width and frequency suitable for the working frequency, which depends on the area or relationship. The clinician programmer's GUI 64 may not accept input of F and PW that do not fall within the region or along this relationship in this example because such values ​​would not provide optimal sub-perceptual therapy.

[0148] Frequency or pulse width optimization can occur in other ways that more efficiently search the desired portion of parameter space. For example, gradient descent, binary search, simplex method, genetic algorithms, etc. can be used for the search. Machine learning algorithms that have been trained using data from patients can also be considered.

[0149] Preferably, when optimizing frequency (≤ 10 kHz) and pulse width at step 332, these parameters are selected in a manner that reduces power consumption. In this regard, it is preferred 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 therefore increase the discharge time, as previously discussed with respect to Fig. 10B and Fig.12D Reducing the pulse width (if possible) will also reduce the battery drain and increase the discharge time.

[0150] At this point, all relevant stimulation parameters (E, P, X, I, PW, and F (≤10kHz)) are determined and can be sent from the clinician programmer to the IPG or ETS for execution (334) to provide sub-perceptual stimulation therapy to the patient. Adjustment (332) of the optimal pulse width and frequency (≤10kHz) may result in these stimulation parameters providing paresthesia. Therefore, if necessary, the amplitude of current A can be titrated down to sub-perceptual levels again (336). If necessary, the prescribed sub-perceptual therapy can be allowed to wash in for a period of time (338), although as previously described this may not be necessary because the super-perceptual sweet spot search (320-328) has already selected electrodes for situations where the patient's pain site is well recruited.

[0151] If sub-perceptual therapy is not effective, or adjustments may be used, the algorithm may return to step 332 to select a new frequency (≤ 10 kHz) and / or pulse width based on the previously defined zone or relationship.

[0152] It should be pointed out that Fig.15 Not all parts of the steps of the algorithm in need to be performed in an actual implementation. For example, if the effective electrodes (ie, E, P, X) are known, the algorithm can start with sub-perceptual optimization using information related to frequency and pulse width.

[0153] Fig.16 Another approach is shown, in which the fitting module 350 ( Fig.14 ) can be used to determine the optimal sub-perceptual stimulation for a patient at frequencies of 10 kHz or less. Fig.16 In the embodiment of the present invention, the fitting module 350 is again incorporated into or used by the algorithm 150, which again can be executed on the control circuit of the external device as part of its software, or executed in the IPG 10. In the algorithm 105, the fitting module 350 is used to select the initial pulse width given a specific frequency. However, the algorithm 105 is more comprehensive because it will test and optimize the amplitude and also optimize the pulse width at different frequencies. As further described below, the algorithm 105 also optionally helps to select the optimized stimulation parameters that will result in the lowest power requirements of the battery 14 of the IPG. For the algorithm 105 in Fig.16 Some of the steps shown in are optional, and other steps may be added. It is assumed that the sweet spot search for the test patient by the algorithm 105 has occurred, and 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 may also be modified, as described above.

[0154] The algorithm 105 begins by picking an initial frequency (e.g., F1) within a range of interest (e.g., ≤ 10 kHz). The algorithm 105 then passes that frequency to the fitting module 350, which picks an initial pulse width PW1 using the previously determined relationships and / or regions. For simplicity, Fig.16 The fitting module 350 is shown as a simple lookup table of pulse width and frequency, which may include another form of information related to the frequency and pulse width at which stimulation pulses are formed to provide pain relief without paresthesia. The selection of pulse width using the fitting module 350 may be more sophisticated, as previously described.

[0155] After selecting the pulse width for a given frequency, the stimulation amplitude A is optimized (120). Here, multiple amplitudes are selected and applied to the patient. In this example, the selected amplitudes preferably use the optimal amplitude A determined at each frequency (see, e.g., Fig. 10B ). Therefore, the patient tries amplitudes of A=A2, lower than (A1) and higher than (A3) over a period of time (e.g., every two days). The best of these is picked by the patient. At this point, further adjustments to the amplitude can be tried to try and hone in on the optimal amplitude for the patient. For example, if A2 is preferred, an amplitude slightly higher than this (A2+Δ) and an amplitude slightly lower than this (A2-Δ) can be tried over a period of time. If a lower value of A1 is preferred, an even lower amplitude (A1-Δ) can be tried. If a higher value of A3 is preferred, an even higher amplitude (A3+Δ) can be tried. Finally, iterative testing of such amplitudes arrives at an effective amplitude for the patient that does not cause paresthesia.

[0156] Next, the pulse width can be optimized for the patient (130). As with the amplitude, this can occur by slightly reducing 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 reduced (PW1-Δ) and increased (PW1+Δ) to see if such settings made by the patient are preferred. Further iterative adjustments to the amplitude and pulse width can occur at this point, although this is not shown.

[0157] 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 likely provide effective and non-paresthesia pain relief. However, because each patient is different, the amplitude (120) and pulse width (130) are also adjusted according to the initial values ​​for each patient.

[0158] Thereafter, the optimal stimulation parameters determined for the patient at the tested frequency are stored in the software (135). Optionally, the mean charge per second (MCS) indicating the neural delivery received by the patient, or other information indicating power draw (e.g., mean Ibat, discharge time), is also calculated and stored as well. If yet other frequencies in the range of interest (e.g., F2) have not been tested, they are tested as described above.

[0159] Once one or more frequencies have been tested, stimulation parameters for the patient can be selected (140) using the optimal stimulation parameters (135) previously stored for the patient at each frequency. Because the stimulation parameters are applicable to the patient at each frequency, the selected stimulation parameters can include stimulation parameters that result in the lowest power draw (e.g., lowest) MSC. This is desirable because these stimulation parameters will be easiest on the IPG's battery. It can be expected that the stimulation parameters with the lowest MCS determined by algorithm 105 will include those stimulation parameters 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 undertaken, where the goal of selecting the minimum amplitude is to provide subperceptual pain relief without paresthesia.

[0160] Further investigation revealed Figure 17-22D , the purpose is to provide optimal sub-perceptual modeling that takes into account the perception threshold (pth) as well as the frequency (F) and pulse width (PW). The perception threshold may be an important factor to consider when modeling sub-perceptual stimulation and using such modeling information to determine the optimal sub-threshold stimulation parameters for each patient. The perception threshold pth includes the lowest amplitude (e.g., in mA) at which the patient can feel the effects of paresthesia, and amplitudes below this will cause sub-perceptual stimulation. In fact, different patients will have different perception thresholds. The different perception thresholds are caused in large part because the electrode arrays in some patients may be closer to the spinal nerve fibers than other patients. 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 away from the spinal nerve fibers, the perception threshold pth will be higher. The improved modeling takes into account the understanding of pth, because in addition to the optimal frequency and pulse width, the inclusion of this parameter can also be used to suggest the optimal amplitude A for the patient's sub-perceptual stimulation.

[0161] With this in mind, data was obtained from patients to determine not only the frequencies and pulse widths they found optimal as previously described, but also the perception thresholds at those frequencies and pulse widths. The resulting model 390 is shown in Fig.17 This model 390 was determined based on testing of a sample of patients (N=25), where Fig.17 The average values ​​determined by the three-dimensional regression fit are shown, which produces the model 390 as a surface in the frequency-pulse width-perception threshold space. Fig.17 The data presented were acquired at frequencies of 1 kHz and below. The data at these frequencies are of particular interest because, as already mentioned, lower frequencies are more of a concern for energy usage in IPG or ETS, and therefore, there is a particular need to demonstrate the utility of sub-perceptual stimulation in this frequency range. Fig.17 As can be seen from the equations in Figure 390, by assuming that the frequency varies according to the power function with pulse width (a(PW)b) and perception threshold pth (c(pth)d), the data obtained from the patient is modeled with a good fit. Although these functions provide a suitable fit, other types of mathematical equations can also be used for the fit. Model 390 gives the following result as a surface fit: F(PW,pth) = 4.94x10 8 (PW)-2.749+1.358(pth) 2 Note that frequency, pulse width, and perception threshold are not simply proportionally or inversely related as in model 390, but are instead related by a nonlinear function.

[0162] Fig.18A Further observations noted from the tested patients are shown and provide another modeling aspect that, together with model 390, can be used to determine optimal subthreshold stimulation parameters for the patient. Fig.18A shows how the perception threshold pth of the tested patients varies according to the pulse width, where each patient Fig.18A The analysis of each line shows that the relationship between pth and PW can be well modeled using a power function, ie, pth(PW) = i(PW)j + k, although other mathematical functions can also be used for fitting. Fig.18A 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 frequencies of 150 Hz and higher, using biphasic pulses with active charging). Fig.18A The pulse widths in the were limited to the range of approximately 100 to 400 microseconds. It was reasonable to restrict the analysis to these pulse widths because previous tests (e.g. Fig. 12A ) shows that pulse widths within this range have only subperceptible therapeutic effects at frequencies of 1 kHz and below. Fig.18BAnother example of pth versus pulse width for different patients is shown, and another equation that can be used to model the data is shown. Specifically, the Weiss-Lapicque or intensity-duration equation is used in this example, which relates the amplitude and pulse width required to reach the threshold. The equation takes the form of pth=(1 / a)(1+b / PW), and when the data for different patients are averaged, constants a=0.60 and b=317 have good fit results, where these values ​​represent the average constant parameters extracted from the population data.

[0163] Fig.19 A further observation noted from the patients tested is shown and provides yet another aspect of modeling. Fig.19 Figure 2 shows how the patient's optimal subperceptual amplitude A varies depending on the patient's perception threshold pth and pulse width. Fig.19 In the graph of , the vertical axis plots the parameter Z, which is related to the patient's perception threshold pth and its optimal amplitude A (which will be lower than pth in subperceptual treatment). Specifically, Z is the optimal amplitude expressed as a percentage of pth, i.e., Z = A / pth. Fig.19 As shown, Z varies with pulse width. At smaller pulse widths (e.g., 150 microseconds), Z is relatively low, meaning that the patient's optimal amplitude A is noted to be well 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 optimal amplitude A is noted 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 pulse widths tested, and therefore linear regression is used to determine the relationship between them, yielding Z=0.0017(PW)+0.1524(395). Similarly, Fig.19 The testing in was limited to the approximate range of 100 to 400 microseconds, which was noted to be useful for subperceptual treatments below 1 kHz. It is 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 linear relationship noted. For example, for pulse widths greater than 400 microseconds, Z may flatten out to some value less than 1, and for pulse widths less than 100 microseconds, Z may flatten out to a value greater than 0. Because Z varies with pulse width as the curve fit does, and because Z also varies with the optimal amplitude A and the perceptual threshold pth (Z = A / pth), Fig.19Modeling allows the optimal amplitude A to be modeled as a function of the perception threshold pth and the pulse width PW, i.e., A = pth [0.0017 (PW) + 0.1524] (396). The inventors observed that the optimal amplitude A is generally invariant to changes in frequency and pulse width. However, the perception threshold varies with pulse width. Therefore, Z varies with pulse width, while the optimal amplitude A may not.

[0164] Recognizing and modeling these observations, the inventors have developed an algorithm 400 that can be used to provide personalized sub-perceptual therapy for a particular patient. The algorithm 400 can be largely implemented on the clinician programmer 50 and results in the determination of a range of optimal sub-perceptual parameters (e.g., F, PW, and A) for the patient. Preferably, as a final step of the algorithm 400, the range or volume of optimal sub-perceptual parameters is sent to the patient's external controller 45 to allow the patient to adjust their sub-perceptual therapy within this range or volume.

[0165] from Fig. 20A The algorithm 400 shown at the beginning begins in step 402 by determining the sweet spot in the electrode array on which therapy should be applied for a given patient, i.e., by identifying which electrodes should be activated and with what polarity and percentage (X%). For a given patient, the results of the sweet spot search may be known, and therefore step 402 should be understood to be optional. Step 402 and subsequent steps can be accomplished using the clinician programmer 50.

[0166] At step 404, a new patient is tested by providing a situation pulse, and in algorithm 400, such testing involves measuring the patient's perception threshold pth at various pulse widths using the sweet spot electrode that has been identified at step 402 during the test procedure. Fig.18A and Fig.18B As discussed, testing of different pulse widths may occur at a nominal frequency, such as in the range of 200 to 500 Hz. Determining the PTH at each given pulse width involves applying the pulse width and gradually increasing the amplitude A to the point at which the patient reports feeling stimulation (paresthesia), thereby producing a PTH expressed in amplitude (e.g., milliamperes). Alternatively, determining the PTH at each given pulse width may involve decreasing the amplitude A to the point at which the patient reports no longer feeling stimulation (subthreshold). Fig. 20A 404. Here, it is assumed that the patient in question has a paresthesia 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 between these.

[0167] Next, in step 406, the algorithm 400 in the clinician programmer 50 models the pth v. PW data points measured in step 404 and curve fits them to a mathematical function. This mathematical function may be noted earlier to model pth and PW in other patients well, such as a power function pth(PW)=i(PW) j + k or Weiss-Lapicque equation, as previously discussed Fig.18A and Fig.18B However, any other mathematical function can be used to curve fit the data measurement results of the current patient, such as polynomial functions and exponential functions. In the data shown, the power function models the data well, resulting in pth(PW) = 116.5xPW -0.509 (For simplicity, the constant "k" has been ignored.) The measured data in table 404 and the determined curve fit relationship pth(PW) 406 determined for the patient may be stored in the memory of clinician programmer 50 for use in subsequent steps.

[0168] Next, and refer to Fig. 20B , the algorithm 400 continues by comparing the pth(PW) relationship determined in step 406 with the model 390. This will refer to Fig. 20B The table shown in FIG. 1 is used to illustrate this. In this table, the Fig. 20A As can be seen, discrete pulse width values ​​of interest (100 microseconds, 150 microseconds, etc.) can be used (which may be different from the exact pulse width used during the patient test in step 404). Although Fig. 20B Only six rows of PW v.pth values ​​are shown in the table of , but this could be a longer vector of values ​​where pth is determined in discrete PW steps (such as 10 microsecond steps).

[0169] In step 408, the pth v. PW values ​​(from function 406) are compared to the three-dimensional model 390 to determine the frequency F that is optimal at these various pth v. PW pairings. In other words, the pth and PW values ​​are provided as variables to Fig.17 The surface fitting equation (F(PW,pth)) 390 in FIG. 1 is used to determine the optimal frequencies, which are also shown as filled into Fig. 20B At this point, Fig. 20BThe table in represents a vector 410 that relates to the pulse width and frequency that are optimal for the patient and also includes the perception threshold for the patient at these pulse width and frequency values. In other words, vector 410 represents the values ​​within model 390 that are optimal for the patient. Note that vector 410 for the patient can be represented as a curve along three-dimensional model 390, such as Fig. 20B As shown in .

[0170] Next, and if Fig. 20C As shown in step 412 of , vector 410 can optionally be used to form another vector 413 that contains values ​​of interest or, more realistically, values ​​that can be supported by the IPG or ETS. For example, note that vector 410 for the patient includes frequencies at higher values, such as 1719 Hz, or frequencies at odd values, such as 627 and 197 Hz. It may not be desirable to use frequencies at higher values ​​because such frequencies, even if effective for the patient, may involve excessive power draw. See, e.g. Fig.12D . In addition, the IPG or ETS in question may only be able to provide pulses with frequencies at discrete intervals (such as in increments of 10 Hz). Therefore, in vector 413, a 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 an 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 increments of 10 microseconds). Therefore, although this is not shown in the accompanying drawings, the pulse widths in vector 413 can be adjusted (e.g., rounded) to the nearest supported value.

[0171] Next, and refer to Fig.20D , algorithm 400 determines in step 414 the optimum magnitude of the pulse width and pth value in vector 413 (or vector 410 if vector 413 is not used). This is done by using Fig.19 This occurs using the amplitude function 396 determined earlier in the table, namely A(pth, PW). Using this function, the optimal amplitude A can be determined for each pth, PW pair in the table.

[0172] At this point, in step 416, the optimal subthreshold stimulation parameters F, PW, A 420 are determined as a patient-specific model. The optimal stimulation parameters 420 may not need to include the perception threshold pth: although pth is useful for determining the patient's optimal 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, it may be useful to include pth in the optimal parameters 420 because this may allow the patient to adjust their stimulation to supra-perceptual levels if desired. At this point, the optimal stimulation parameters 420 may then be sent to the IPG or ETS for execution, or as shown in step 422, they may be sent to the patient's external controller 45, as described below.

[0173] Fig.20E and Fig.20F The optimal parameters 420 are depicted in graphical form. Although the optimal 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 graphs: Fig.20E shows the relationship between frequency and pulse width, and Fig.20F shows the relationship between frequency and amplitude. It should also be noted that Fig.20F The paresthesia threshold pth is shown, and the X-axis also shows the Fig.20E The pulse widths corresponding to various frequencies are shown in Table 1. Note that the shape of the data on these plots can vary from patient to patient (e.g., based on Fig. 20A pth measurement results), and may also vary depending on the underlying modeling used (e.g., Figure 17-Figure 19 ). Therefore, the various shapes of the trends shown should not be interpreted as limiting.

[0174] The optimal stimulation parameters 420 determined by the algorithm 400 include a range or vector of values, including those based on modeling ( Figure 17-Figure 19 ) and patient testing ( Fig. 20A , step 404) will result in optimal subthreshold stimulation for that patient. Although for simplicity, in Fig.21 Optimal parameters 420 are shown in tabular form in FIG. 4 , but it should be understood that these optimal parameters (O) can be curve-fitted using equations involving frequency, pulse, and amplitude (i.e., O = f(F, PW, A)). Because each of these coordinates is optimal, it may be reasonable to allow the patient to use it with their IPG or ETS, and as a result, the optimal parameters 420 can be sent from the clinician programmer 50 to the patient external controller 45 ( Figure 4) to allow the patient to choose between them. In this regard, the optimal parameters 420, whether in table form or equation form, can be loaded into the control circuit 48 of the external controller 45.

[0175] Once loaded, the patient can access a menu in the external controller 45 to adjust the therapy provided by the IPG or ETS to be consistent with these optimal parameters 420. For example, Fig.21 A graphical user interface (GUI) of an external controller 45 is shown as displayed on its screen 46. The GUI includes a device that allows the patient to adjust the stimulation simultaneously within the range of the determined optimal stimulation parameters 420. In one example, a slider with a cursor 430 is included in the GUI. The patient can select the cursor 430 and, in this example, move the cursor left or right to adjust the frequency of the stimulation pulses in their IPG or ETS. Moving it to the left reduces the frequency to the minimum value (e.g., 50 Hz) included in the optimal parameters 420. Moving the cursor 430 to the right increases the frequency to the maximum value (e.g., 1000 Hz) included in the optimal parameters. As the cursor 430 moves and the stimulation frequency changes, the pulse width and amplitude will be adjusted simultaneously, as reflected by the optimal 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 effect, cursor 430 allows the patient to browse optimal parameters 420 to find their preferred F / PW / A settings, or simply select stimulation parameters that are still effective but require drawing less 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 relative to each other, but will follow a nonlinear relationship based on the underlying modeling.

[0176] In another example, it may be useful to allow the patient to adjust the stimulation without knowing the stimulation parameters, i.e., without displaying the parameters, which may be too technical for the patient to understand. In this regard, the sliders may 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 the one-dimensional parameter (For example, as shown, 4.2 mA, 413 μs, and 50 Hz may equal 0%). In general, the patient may set the parameters It is understood as an "intensity" or "neural dose" of increasing percentages. In practice, it depends on the optimal stimulation parameters 420 being mapped to In this way, this may be correct.

[0177] It should be understood that while the GUI of the external controller 45 does allow the patient some flexibility to modify the stimulation parameters of their IPG or ETS, it is also simple and advantageously allows the patient to adjust all three stimulation parameters simultaneously using a single user interface element while ensuring that the resulting stimulation parameters will provide optimal subthreshold stimulation.

[0178] Other stimulation regulation controls may also be provided by the external controller 45. For example, Fig.21 As shown in , another slider may allow the patient to adjust the duty cycle to control the extent to which the pulse will run continuously (100%) or be completely off (0%). An intermediate duty cycle (e.g., 50%) would mean that the pulse would run for a period of time (from seconds to minutes) and would then be off for the same duration. Since "duty cycle" may be a technical concept that patients may not intuitively understand, note that the duty cycle may be labeled in a more intuitive manner. Therefore, and as shown, the duty cycle adjustment may be labeled differently. For example, since a lower duty cycle affects a lower power draw, the duty cycle slider may be labeled as a "power save" function, a "total energy" function, a "total neural charge dose" function, or the like, which may be easier for the patient to understand. The duty cycle may also include a function that is locked into the patient's external controller 45 and is only accessible to the clinician after, for example, entering an 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 subsequent user interface examples, but may be used in such examples as well.

[0179] Figure 22A-22D The practicality of modeling that results in determining the optimal parameters 420 may be imperfect is addressed. For example, the model 390 (frequency as a function of PW and pth (F(PW, pth); Fig.17 ) is averaged across patients and can have some statistical variance. Fig.22A This is briefly illustrated by showing surfaces 390+ and 390- that are higher and lower than the average value reflected in the surface model 390. The surfaces 390+ and 390- may represent some degree of statistical variance or error measure, such as plus or minus one sigma, and in fact may generally include error bars beyond which the model 390 is no longer reliable. These error bars 390+ and 390- (which may not be constant across the surface 390) may also be determined from an understanding of the statistical variance of the various constants assumed during modeling. For example, different confidence measures may be utilized to determine the values ​​a, b, c, and d in the model 390. As shown in FIG. Fig.17 As shown in Figure 1, the constants a, b, and c vary within a 95% confidence interval. For example, the constant "a" can range from 5.53x10 7to 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 ( Fig.18A and Fig.18B ) may have different confidence measures, and the values ​​m and n used to model the relationship between the optional amplitude, pth, and PW may also be different ( Fig.19 ). Over time, as data from more patients is collected, it is expected that the confidence in these models will increase. In this regard, it is noted that the algorithm 400 can be easily updated from time to time with new modeling information by loading the new modeling information into the clinician programmer 50.

[0180] Statistical variance means that the optimal stimulation parameters may not include discrete values, but instead may fall within a volume. Fig.22A 410 (see Fig. 20B ). 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 Fig. 20B Alternatively, for any given variable (such as pulse width), the pth determined for the patient (using the pth(PW) model in step 406) may vary within a range between a statistically significant maximum and minimum value, such as Fig. 22B Model 390 ( Fig.17 ) may also mean that the maximum and minimum frequencies may be determined for each maximum and minimum pth in step 408. Since this is trickled through the algorithm 400, the optimal stimulation parameters 420 may also not have a one-to-one relationship between frequency, pulse width, and amplitude. Instead, and as Fig. 22B As shown in , for any frequency, there may be a range of maximum and minimum optimal pulse widths that are statistically significant, and likewise a range of optimal amplitudes A. Effectively, then, optimal stimulation parameters 420' may define a coordinate volume that is statistically significant in frequency-pulse width-amplitude space rather than in coordinate lines. The paresthesia threshold pth may also vary over a range, and as mentioned previously, it may be useful to include it in the optimal stimulation parameters 420' because pth may help allow the patient to change the stimulation from sub-perception to suprasensory, as discussed in some of the later examples.

[0181] Fig. 22C and Fig.22DThe optimal parameters 420' are depicted in graphical form, showing the statistically relevant range of pulse widths and the statistically relevant range of amplitudes that are appropriate for the patient at each frequency. Although the optimal 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 described in Fig.20E and Fig.20F What happens in: Fig. 22C shows the relationship between frequency and pulse width, and Fig.22D shows the relationship between frequency and amplitude. Also note that Fig.22D The paresthesia threshold pth is shown, which, like the pulse width and amplitude, can vary statistically within a certain range. Also shown are the optimal stimulation parameters 420 for each of the parameters (determined without statistical variance, see Fig.20E and Fig.20F ), and as expected falls within the wider volume of the parameters specified by 420'.

[0182] Where a volume of optimal parameters 420' is defined, it may then be useful to allow the patient to navigate different settings within that volume of optimal parameters 420' using his external controller 45. Fig.22E . Here, the GUI of the external controller 45 does not display a single linear slider, but rather displays a three-dimensional volume representing the volume 420' of the optimal parameters, where the different axes represent the changes that the patient can make in frequency, pulse width, and amplitude. As previously described, the GUI of the external controller 45 allows the patient some flexibility to modify the stimulation parameters of their IPG or ETS, and allows the patient to adjust all three stimulation parameters simultaneously with one adjustment action and using a single user interface element.

[0183] Different GUIs are possible that allow the patient to browse the determined optimal parameter volume 420', and Fig.22F Another example is shown. Fig.22E, two sliders are shown. The first linear slider, controlled by cursor 430a, allows the patient to adjust the frequency based on the frequency reflected in the optimal volume 420'. The 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 optimal parameters 420' and the frequency that has been selected using cursor 430a. For example, if the user chooses to use a frequency F=400Hz, the external controller 45 can consult the optimal parameters 420' to automatically determine the optimal 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 range of allowable pulse width and amplitude that can be selected using cursor 430b will automatically change to ensure that the subthreshold stimulation remains within the volume 420' determined to be statistically useful for the patient.

[0184] Fig.23 Another example is shown where a user may use the derived optimal stimulation parameters to program the settings of their IPG 10 (or ETS).The subsequent example for completeness uses a determined volume 420' of optimal stimulation parameters, but a vector or range 420 of optimal stimulation parameters may also be used.

[0185] Fig.23 A user interface is shown on the screen 46 of the patient's external controller 45 that allows the patient to select from a variety of stimulation modes. Such stimulation modes can include various ways in which the IPG can be programmed to be consistent with the optimal stimulation parameters 420' determined for the patient, such as: an economy mode 500 that provides stimulation parameters with low power draw; a sleep mode 502 that optimizes the stimulation parameters for the patient while sleeping; a sensory mode 504 that allows the patient to feel the stimulation (supersensory); a comfort mode 506 for general daily use; an exercise mode 508 that provides stimulation parameters suitable for when the patient is exercising; and an intense mode 510 that can be used, for example, if the patient is experiencing pain and will benefit from more intense stimulation. Such stimulation modes can be indicative of the patient's posture or activity. For example, the sleep mode 502 provides stimulation optimized for sleep (e.g., when the patient is lying down and not moving significantly), while the exercise mode 508 provides stimulation optimized for exercise (e.g., when the patient is standing up and moving significantly). Although not shown, stimulation modes may also be included that 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 described in the context of an external controller for the patient, it is also recognized that in other examples, another external device that can be used to program the patient's IPG can also be used to select a stimulation mode, such as a clinician programmer 50.

[0186] The patient can select from these stimulation modes, and such selection can program the IPG 10 to provide a subset of stimulation parameters useful for that mode controlled by the optimal stimulation parameters 420'. For certain stimulation modes, the subset of stimulation parameters may be completely constrained by the volume of the optimal stimulation parameters 420' determined for the patient (entirely within it), and will therefore provide the patient with optimal sub-perceptual stimulation treatment. As further explained below, subsets of other modes may be only partially constrained by the optimal stimulation parameters. However, in all cases, the subset is determined using the optimal stimulation parameters (420 or 420'). Preferably, the subset is determined for the patient at the clinician programmer 50 and sent to the patient's external controller 45 and stored therein. Alternatively, the determined optimal stimulation parameters can be sent to the external controller 45, leaving it to the external controller 45 to determine the subset from the optimal stimulation parameters.

[0187] 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 may be warranted because certain stimulation modes may not be relevant to certain patients. In this regard, the clinician can program the patient's external controller 45 to specify the available stimulation modes, such as by entering an appropriate clinician's password. Alternatively, the clinician can program the external controller 45 using a clinician programmer 50 to program the external controller 45.

[0188] An example of a subset of stimulation parameters 425x is shown in Figure 24A-Figure 29B Shown in. Fig.24A and Fig. 24B A subset 425a of stimulation parameter coordinates used when the economy mode 500 is selected is shown, which includes a subset of the optimal stimulation parameters 420' with low power draw. Like the optimal stimulation parameters 420', the subset 425a may include a three-dimensional volume of F, PW, and A parameters, and similarly (compare Fig. 22C and Fig.22D ) uses two two-dimensional graphs to represent subset 425a, where Fig.24A shows the relationship between frequency and pulse width, and Fig.22D The relationship between frequency and amplitude is shown.

[0189] To affect low power draw, the frequencies within subset 425a are low, such as limited to a frequency range of 10 to 100 Hz, even though the optimal stimulation parameters 420' may have been determined over a wider range, such as 10 to 1000 Hz. Furthermore, while the optimal pulse widths within this frequency range may vary more significantly among the optimal stimulation parameters 420', subset 425a may be constrained to the smaller of these pulse widths, such as the lower half of such pulse widths, e.g. Fig.24A Similarly, using a lower pulse width will result in lower power draw. Fig. 24B As shown in FIG. 4 , subset 425a may be constrained to be lower amplitude within optimal stimulation parameters 420′ for the relevant frequency range, again resulting in lower power draw. In short, subset 425a may include a smaller volume of stimulation parameters within the volume of optimal stimulation parameters 420′ that provide adequate subthreshold stimulation for the patient, while providing lower power draw from the IPG battery 14. Not all stimulation parameters associated with the selected stimulation mode ( Fig.23 ) The corresponding subsets 425x all contain stimulation parameters, which must be completely within the determined optimal stimulation parameters 420', as shown in some subsequent examples.

[0190] When the economy mode 500 is selected, the external controller 45 may simply send a single low power optimal parameter (F, PW, A) within the subset 425a to the IPG for execution. However, and more preferably, the user interface will include means for allowing the patient to adjust the stimulation parameters to those within the 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 previously (see Fig.21 ), and a cursor may be included to allow the patient to slide through the parameters in subset 425a. In the example shown, the slider interface 550 may not adjust the pulse width, which is set to a specific value (e.g., 325 μs), but the frequency and amplitude may vary. This is just one example, and in other examples, all three of the frequency, pulse, and amplitude may be changed via sliders, or other parameters may be held constant. Note that a more sophisticated user interface may be used to allow the patient to navigate through subset 425a. For example, although not shown, a user interface element with a higher three-dimensional quality, such as previously described in Fig.22E and Fig.22FThose discussed in can be used to navigate the volume of subset 425a. Parameter interface 560 can also allow the patient to navigate parameters within subset 425a, and is simply shown as having selectable buttons to increase or decrease parameters within determined subset 425a. Parameter interface 560 can also include fields showing current values ​​of frequency, pulse width, and amplitude. Initially, these values ​​can be populated with parameters roughly in the center of determined subset 425a, allowing the patient to adjust stimulation near the center.

[0191] Fig.25A and Fig.25B 4. The selection of sleep mode 502 is shown, and a subset 425b of optimal stimulation parameters 420' that is generated when this selection is selected. In this example, subset 425b is determined using optimal stimulation parameters 420' in a manner such that subset 425b is only partially constrained by optimal stimulation parameters 420'. Subset 425b may include low to medium frequencies within optimal stimulation parameters 420' (e.g., 40 to 200 Hz), and may include medium pulse widths within this frequency range that are allowed by 420', such as by Fig.25A shown.

[0192] Because the intensity of stimulation during sleep may not need to be that high, the amplitudes within subset 425b may fall outside the amplitudes suggested by optimal parameters 420', such as Fig.25B . For example, while optimal parameters 420' may, for example, suggest that for the frequency and pulse width range of interest, the amplitude based on earlier modeling would fall within the range of 3.6 to 4.0 mA, the amplitude within subset 425b in this example is set to an even lower value. Specifically, as shown in slider interface 550, the amplitude may be set between 1.5 mA and 4.0 mA. In order to know where the lower boundary of the amplitude should be set, the modeling information may include an additional model 422, which may be determined separately from the optimal stimulation parameters 420' based on patient testing. In the case of sleep, due to the expected changes in the position of the electrode leads within the patient's spine when the patient lies down, it may be warranted to allow the use of amplitudes lower than those suggested by optimal parameters 420'. Additionally, the patient may be less bothered by pain while sleeping, and thus a lower amplitude may still be reasonably effective. Having said that, subset 425b may also include values ​​(including amplitude) that are completely within and constrained by optimal stimulation parameters 420', similar to Fig.24A and Fig. 24B The values ​​shown for subset 425a in .

[0193] Fig.26A and Fig.26BA selection of a sensory mode 504 is shown, along with the resulting subset 425c that may be used for a given patient during that mode. The purpose of this mode is to allow the patient to sense the stimulation provided by their IPG at their discretion. In other words, the stimulation provided to the patient in this mode is super-sensory. The optimal stimulation parameters 420' preferably define a volume of stimulation parameters in which sub-perceptual stimulation is optimized for the patient. However, as previously described, as part of the determination of the optimal sub-threshold stimulation parameters 420', the perception threshold pth is measured and modeled. As such, the perception threshold pth as determined earlier is useful for selecting an amplitude that the patient will feel during this mode, i.e., an amplitude (particularly pulse width) that is higher than pth for other stimulation parameters. Therefore, the sensory mode 504 is an example in which it is beneficial to include a pth value (or pth range) within the optimal stimulation parameters 420'.

[0194] like Fig.26A As shown in , patients are generally more likely to feel stimulation at lower frequencies, and therefore, the selection of a sensory pattern may constrain stimulation in subset 425c to lower frequencies (e.g., 40 to 100 Hz). Control of pulse width may not be a major issue, and therefore, for this frequency range, pulse width may have a moderate range allowed by 420', as also shown in Fig.26A As shown.

[0195] However, because the patient in this mode wants to feel the stimulation, the amplitudes in subset 425c are set to higher values, such as Fig.26B Specifically, the amplitudes of the relevant frequencies and pulse widths are not only set above the upper limit of the amplitude as determined for the optimal stimulation parameters 420'; they are also set at or above the perception threshold pth. As mentioned previously, the perception threshold pth and the more particularly important range of pth determined for the patient (when taking into account statistical variations) can be included in the optimal stimulation parameters 420' (see Fig.22D ) to produce a useful effect in that mode. Therefore, based on earlier measurements and modeling, subset 425c is defined to set the amplitude within a value or range that should provide extrasensory stimulation. If pth is defined by a range based on statistical variance, the allowed range of amplitudes for the sensory pattern 504 can be set to an upper limit value beyond that range, such as Fig.26B. Thus, while the optimal (sub-perceptual) amplitude (per 420') for the frequency range of interest may range from about 3.7 to 4.5 mA, the amplitudes within subset 425c are set to about 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 supra-perceptual. In this example, note that subset 425c was determined using the optimal stimulation parameters 420', but is only partially constrained by such optimal parameters. Frequency and pulse width are constrained; amplitude is not, because the amplitudes in this subset 425c are set to exceed 420', and more particularly exceed pth.

[0196] Fig.27A and Fig.27B The selection of comfort mode 506 is shown, and the resulting subset 425d of stimulation parameters for that mode. In this mode, the stimulation parameters are set via subset 425d to nominal values ​​within optimal stimulation parameters 420': medium frequencies (such as 200 to 400 Hz), and medium pulse widths for these frequencies (such as 175 to 300 μs as shown in slider interface 550), as shown in FIG. Fig.27A As shown in Fig.27B , the amplitudes within subset 425d may also be mid-amplitudes within optimal stimulation parameters 420' for the frequency and pulse width in question. In this example, the stimulation parameters in subset 425d are completely constrained by optimal stimulation parameters 420', although as mentioned previously, this is not necessarily true for every subset.

[0197] Fig.28A and Fig.28B The selection of an exercise mode 508 and a subset of stimulation parameters 425e associated with the mode are shown. In this mode, medium and high frequencies (e.g., 300-600 Hz) may be used, but for these frequencies, the pulse widths are higher than those specified by the optimal stimulation parameters 420', such as Fig.28A This is because the position of the electrode leads within the patient may vary more as the patient moves, and therefore it may be useful to provide a higher charge injection into the patient, which a higher pulse width can achieve. Fig.28B As shown in , the amplitudes used can span a moderate range of frequencies and pulse widths involved, but higher amplitudes (not shown) beyond 420' can also be used to provide additional charge injection. Subset 425e shows an example in which the frequency and amplitude are constrained by the optimal stimulation parameters 420', while the pulse width is not constrained; thus, subset 425e is only partially constrained by the optimal stimulation parameters 420'. In other examples, subset 425e can be fully constrained within the optimal stimulation parameters 420' determined earlier.

[0198] Fig.29A and Fig.29B Selection of an intense mode 510 of stimulation is shown. In this mode, stimulation is more aggressive and the stimulation parameter subset 425f may occur at a higher frequency (e.g., 500 to 1000 Hz). However, the pulse width and amplitude at these frequencies may be moderate for the frequencies involved, such as Fig.29A and Fig.29B In this example, the subset of stimulation parameters in subset 425f may be completely constrained by (contained in) optimal stimulation parameters 420'. As in the previous examples, the patient may use interfaces 550 or 560 or other interface elements not shown to adjust the stimulation parameters in subset 425x that correspond to the patient's stimulation mode selection ( Fig.23 ). Less preferably, selection of a stimulation mode may cause the external controller 45 to send a single set of stimulation parameters (F, PW, A) determined using the optimal stimulation parameters 420' (or 420).

[0199] 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, although this may also be the case, it is not strictly necessary that the stimulation parameters in a given subset are unique to the subset or the stimulation mode represented by the subset. In addition, the boundaries of the individual subsets 425x may be adjustable. For example, although not shown, the external controller 45 may have an option to change the boundaries of the individual subsets. Using such an option, a patient or clinician can, for example, change one or more of the stimulation parameters (e.g., frequency) in the subset (e.g., by increasing the frequency within subset 425a from 10 Hz to 100 Hz to 10 Hz to 150 Hz). Adjustment of subset 425x may also be affected in response to certain feedback, such as a patient's pain level that may be input into the external device 45, or detection of patient activity or posture. More complex adjustments can be locked to the patient and only accessible to the clinician, such as by providing such accessibility through a password entered into the external controller 45. Behind such password protection, subset 425x may be adjustable and / or may enable other stimulation modes (e.g., beyond Fig.23 ) are only accessible to clinicians. As previously described, clinicians can also use clinician programmer 50 to make such clinician adjustments.

[0200] Subset 425x may also be automatically updated from time to time. This may be advantageous because as data is collected for more patients, the underlying modeling that led to the generation of optimal stimulation parameters 420' may change or become better informed. It may also be learned later that different stimulation parameters may better produce the desired effect of the stimulation pattern, and thus warrant adjustment of which parameters are included in the subset. Different stimulation patterns that are provided for different reasons or to produce different effects may also become apparent later, and thus such new patterns and their corresponding subsets may be programmed into external controller 45 later and updated in the future. Fig.23 The stimulation mode user interface is presented to the patient. Updating of subsets and / or stimulation modes can occur wirelessly by connecting the external controller 45 to a clinician's programmer or to a network such as the Internet. It should be understood that the disclosed stimulation modes and subsets of stimulation parameters 425x corresponding to such modes are merely exemplary and that a different mode or subset may be used.

[0201] Reference again Fig.23 , the stimulation mode user interface may include an option 512 to allow the patient or clinician to define a custom mode of stimulation. The custom mode 512 may allow the user to select the frequency, pulse width, and amplitude, or define a subset at least partially defined by the optimal stimulation parameters 420'. Selection of this option may provide a user interface that allows the patient to navigate different stimulation parameters within the optimal stimulation parameters 420', such as previously described in Fig.22E and Fig.22F If the patient finds stimulation parameters that appear to work well as a simulation mode through this option, the user interface may allow the stimulation mode to be stored for future use. For example, and with reference to Fig.22E , the patient may have found stimulation parameters within the optimal stimulation parameters 420' that are beneficial when the patient is walking. Such parameters can then be saved by the patient and appropriately labeled, such as Fig.22D This newly saved stimulation pattern can then be presented to the patient as a selectable stimulation pattern ( Fig.23 ). Logic in the external controller 45 may additionally define a subset 425 of stimulation parameters (e.g., 425g) through which the patient may navigate when later selecting the user-defined stimulation mode. Subset 425g may include, for example, stimulation parameters that constrain the patient's selected parameters (e.g., + / - 10% of the patient's selected frequency, pulse width, and amplitude), but are still constrained in whole or in part by selectable stimulation parameters 420'.

[0202] like Fig.23As shown in FIG. 5 , the stimulation mode user interface may also include an option 514 for automatically selecting and adjusting a stimulation mode for the patient based on various factors that the IPG 10 may detect. Fig.30 514. Preferably, the selection of the automatic mode 514 allows the patient to select 570 which of the stimulation modes he wants to detect and will be automatically used by his IPG 10. In the depicted example, the user has selected the sleep mode 502, the comfort mode 506, and the exercise mode 508. The IPG 10 will attempt to automatically detect when these stimulation modes should be entered, and in this regard the IPG 10 may include a stimulation mode detection algorithm 610. As shown, the algorithm may be programmed into the control circuit 600 of the IPG 10. The control circuit may include a microprocessor, a microcomputer, an FPGA, other digital logic structures, etc., which are capable of executing instructions of an electronic device. 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 the user selecting 570 the stimulation mode of interest.

[0203] The algorithm 610 can receive different inputs related to detecting stimulation patterns, and therefore receive a subset 425x that should be used for a patient at any given time. For example, the algorithm 610 can receive inputs from various sensors such as an accelerometer 630 that indicate the patient's posture and / or activity level. The algorithm 610 can also receive inputs from various other sensors 620. In one example, the sensor electrodes 620 can include electrodes Ex of the IPG 10, which can sense various signals related to the stimulation pattern determination. For example, and as discussed in USP 9,446,243, the signals sensed at the electrodes can be used to determine the (complex) impedance between the various pairs of electrodes, which can be related to various impedance features indicating the patient's posture or activity in the algorithm 610. The signals sensed at the electrodes can include those generated by the stimulation, such as induced compound action potentials (ECAP). As disclosed in U.S. patent application serial number 16 / 238,151 filed on January 2, 2019, a review of the various features of the detected ECAP can be used to determine the patient's posture or activity. The signals sensed at the electrodes may also include stimulation artifacts resulting from stimulation, as disclosed in U.S. Provisional Patent Application Serial No. 62 / 860,627 filed on June 12, 2019, which may also indicate the patient's posture or activity. The signals sensed at the electrodes may also be used to determine the patient's heart rate, as disclosed in U.S. Patent Application Serial No. 16 / 282,130 filed on February 21, 2019, which may also be related to the patient's posture or activity.

[0204] The algorithm 610 may receive other information relevant to determining the stimulation mode. For example, the clock 640 may provide time information to the algorithm 610. This may be relevant to determining or confirming whether the patient is participating in activities that occur during certain times of the day. For example, it may be expected that the patient may sleep at night, or exercise in the morning or afternoon. Although not shown, the user interface may allow programming of the time range of the expected activities, such as whether the patient prefers to exercise in the morning or afternoon. The algorithm 610 may also receive input from the battery 14, such as the current state of the battery's voltage Vbat, which may be provided by any number of voltage sensors, such as an analog-to-digital converter (ADC; not shown). For example, this may be useful in deciding when the economy mode 500 or other power-based stimulation mode should be automatically entered (i.e., if Vbat is low).

[0205] In any case, the stimulation pattern detection algorithm 610 can wirelessly receive an indication that the automatic mode 514 has been selected, as well as 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 entered, and will therefore enable the use of the subset 425x corresponding to the detected stimulation pattern at the appropriate time. For example, in Fig.30 In the example of FIG. 6 , the algorithm 610 may use the accelerometer 630, the sensor 620, and the clock 640 to determine that a person is still at night, lying on his back or prone, and / or that his heart rate is slow, and therefore determines that the person is currently sleeping. The algorithm 610 may then automatically activate the sleep mode 502 and activate the subset 425b corresponding to the mode ( Figure 25A-25B ) within the stimulation parameters. In addition, the IPG 10 may send a notification of the current stimulation mode determination back to the external controller 45, which may be displayed at 572. This is useful to allow the patient to review that the algorithm 610 has correctly determined the stimulation mode. In addition, notifying the external controller 45 of the current determined mode may allow the external controller 45 to use an appropriate subset 425x of the mode to allow the patient to adjust the stimulation. That is, the external controller 45 may use the determined mode (sleep) to adjust the ( Figure 25A-25B ) is constrained to be a corresponding subset of the pattern (425b).

[0206] If the algorithm 610 uses one or more of its inputs to determine that a person is changing position rapidly, standing upright, and / or that his heart rate is high, it can be determined that the person is currently exercising, which is the stimulation mode of interest selected by the patient. The algorithm 610 can then automatically activate the exercise mode 508 and activate the subset 425e corresponding to that mode ( Figure 25A-25B ) within the stimulation parameters. Again, the IPG 10 may send a notification of the current stimulation mode determination back to the external controller 45 to adjust ( Figure 28A-28B) to the corresponding subset of that mode (425e). If the algorithm 610 cannot determine that the patient is sleeping or exercising, it can default to the selection of comfort mode 506 and provide stimulation, notifications, and constraint adjustments accordingly (subset 425d, Figure 27A-27B ).

[0207] The external controller 45 may also be useful in determining the relevant stimulation mode to be used during automatic mode selection. Fig.30 , but the external controller 45 may include a sensor, 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 its battery voltage from the IPG 10, and wirelessly receive 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' responsive to such inputs. The algorithm 610' may replace the algorithm 610 in the IPG 10, or may supplement the information determined from the algorithm 610 to improve the stimulation pattern determination. In short, and as facilitated by two-way wireless communication between the external controller 45 and the IPG 10, the stimulation pattern detection algorithm may be effectively split between the external controller and the IPG 10 in any desired manner.

[0208] 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 (such as a smart watch or smart phone) worn by the patient. Such a smart device 612 contains sensors indicating motion (e.g., accelerometers) and may also include biosensors (heart rate, blood pressure), which can help understand different patient states and therefore different stimulation modes should be used. More generally, other sensors 614 can also provide relevant information to the external controller 45. Such other sensors 614 can include other implantable devices that detect various biological states (glucose, hearing rate, etc.) of IPG patients. Such other sensors 614 can provide other information. For example, since it has been shown that cold or bad weather can affect the stimulation treatment of IPG patients, the sensor 614 may include a weather sensor that provides weather information to the external controller 45. Please note that the sensor 614 may not need to communicate directly with the external controller 45. Information from such a sensor 614 can be sent by a network (e.g., the Internet) and provided to the external controller 45 via various gateway devices (routers, WiFi, Bluetooth antennas, etc.).

[0209] Fig.31Another example of a user interface on a patient external controller 45 is shown that allows the patient to select from different stimulation modes. In this example, different stimulation modes (consistent with the optimal 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 versus frequency or pulse width versus amplitude) may be used. However, please note that these X and Y axes may not be labeled, nor may specific pulse width or frequency values ​​be labeled, if the goal is to provide the patient with a simple user interface that is not hindered by technical information that the patient may not understand.

[0210] Labeled in this two-dimensional representation are the different stimulation modes discussed previously, with boundaries showing the range of the subset 425x for each stimulation mode. Using this representation, the patient can position a cursor 430 to select a particular stimulation mode, and in doing so, select a frequency and pulse width and its corresponding subset 425x. Because the subsets 425x can overlap, selections on a particular frequency and pulse width can select more than one stimulation mode and more than one subset 425x, thereby allowing the patient to browse more than one subset of stimulation parameters. Because the amplitude is not represented in the two-dimensional representation, the amplitude can be automatically adjusted to an appropriate value if a stimulation mode / subset 425x or a particular frequency / pulse width is selected. Alternatively, a separate slider can be included to allow the patient to additionally adjust the amplitude for each of the stimulation modes according to the subset 425x. As explained above, the amplitude can be completely constrained within the optimal stimulation parameters 420' by the selected mode / subset, or can be allowed to range beyond 420' (e.g., Fig.25B , Fig.26B ). In a more complex example, the representation may include a three-dimensional space (F, PW, A) in which the patient may move a cursor 430, similar to Fig.22E Shown in FIG. 4 is a three-dimensional subset 425x with a displayed stimulation pattern.

[0211] Fig.32 Another GUI aspect is shown that allows the patient to adjust stimulation according to the model developed for the patient. In these examples, a suggested stimulation area 650 for the patient is shown, which is overlaid on a user interface element that otherwise allows the user to adjust stimulation. Fig.32 The example in Fig.21 and Fig.31, but may also be applied to other user interface examples. In these examples, the suggested stimulation areas 650 provide a visual indicator for the patient where the patient may wish to select (e.g., using cursor 430) stimulation settings that are consistent with the optimal stimulation parameters 420 or 420' or subset 425x. These areas 650 can be determined in different ways. They can be mathematically determined using the optimal stimulation parameters 420 or 420' or subset 425x, such as by determining the center or "center of mass" of these areas. They can also be determined with particular emphasis on providing the patient with stimulation parameters with appropriate amplitude, intensity, or total charge. This may be particularly useful if the patient's previous selections have been far from such ideal values. Areas 650 can also be determined during a fitting procedure by determining an area or volume within the optimal stimulation parameters 420 or 420' or subset 425x that the patient likes best.

[0212] In addition, the area 650 can be determined for the patient over time based on the previously selected stimulation parameters. Therefore, the area 650 can be associated with the settings that the patient uses most often. In an improved example, the patient can also provide feedback related to the location of the determined area 650. For example, the external device 45 may include option 652 to allow the patient to provide an indication of their symptoms (e.g., pain) using the rating scale shown. Over time, the external controller can track the pain level entered at 652 and associate it with the selected stimulation parameters, and draw or update the area 650 to the appropriate position of the stimulation adjustment that covers the patient has experienced the best symptom relief. Again, a weighted mathematical analysis of the stimulation parameters relative to their pain level, or a centroid method, can be used.

[0213] It should be noted that the use of the disclosed technology should not necessarily be limited to the specific frequencies tested.Other data suggest the applicability of the disclosed technology to provide pain relief without paresthesia at frequencies as low as 2 Hz.

[0214] 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 for presenting and operating GUI 64), which can be expressed in a formula and stored as instructions in a computer-readable medium associated with such devices, such as stored in a magnetic memory, an optical memory, or a solid-state memory. The computer-readable medium with such stored instructions can also include a device that can be read by a clinician programmer or an external controller, such as in a memory stick or a removable disk, and can reside elsewhere. For example, the computer-readable medium can be associated with a server or any other computer device, thereby allowing instructions to be downloaded to a clinician programmer system or an external system or to an IPG or ETS via, for example, the Internet.

Claims

1. A system comprising: a stimulator device configured for implantation in a patient, comprising a plurality of electrodes; as well as an external device configured to program the stimulator device to provide stimulation at one or more of the plurality of electrodes, wherein the external device stores information indicative of a plurality of subsets, wherein each of the plurality of subsets comprises a plurality of sets of stimulation parameters derived for a patient, wherein each set of stimulation parameters comprises at least two of frequency, pulse width, and amplitude; Wherein, the external device is configured as: providing a graphical user interface (GUI) configured to allow the patient to select from a plurality of displayed stimulation patterns to program the stimulation, wherein each stimulation pattern corresponds to one of the subsets, and wherein at least one of the stimulation patterns is indicative of a posture or activity of the patient, and Based on selection of one of the stimulation modes, programming of the stimulator device is restricted to sets of stimulation parameters within the corresponding subset.

2. The system according to claim 1, wherein: Each set of stimulation parameters includes frequency, pulse width and amplitude.

3. The system according to claim 1, wherein: The multiple sets of stimulation parameters in each subset include lines in the multi-dimensional space of at least two of frequency, pulse width, and amplitude.

4. The system according to claim 1, wherein: The multiple sets of stimulation parameters in each subset include volumes in the multi-dimensional space of at least two of frequency, pulse width, and amplitude.

5. The system according to claim 1, wherein: The sets of stimulation parameters of the selected stimulation mode are configured to provide sub-perceptual stimulation to the patient.

6. The system according to claim 1, wherein: The sets of stimulation parameters of the selected stimulation mode are configured to provide suprasensory stimulation to the patient.

7. The system according to claim 1, wherein: At least one of the stimulation modes indicates a power mode for the stimulator device.

8. The system according to claim 1, wherein: The external device is also configured to provide an automatic option on the GUI, the automatic option being configured to allow the stimulator device to detect when at least one of the stimulation modes should be entered, wherein the external device, when one of the stimulation modes is detected by the stimulator device, is configured to: limit programming of the stimulator device to multiple sets of stimulation parameters within a corresponding subset for one of the detected stimulation modes.

9. The system according to claim 8, wherein: The GUI is configured to allow the patient to select at least one stimulation pattern to be tested.

10. The system according to claim 8, wherein: The stimulator device comprises at least one sensor for detecting when at least one of the stimulation modes is to be entered.

11. The system according to claim 8, wherein: The at least one sensor comprises an accelerometer.

12. The system according to claim 8, wherein: The at least one sensor includes a clock.

13. The system according to claim 8, wherein: The at least one sensor includes a sensor that detects the voltage of a battery in the stimulator device.

14. The system according to claim 8, wherein: The at least one sensor comprises at least one of the electrodes of the stimulator device.

15. The system of claim 1, wherein: The external device is also configured to provide an automatic option on the GUI, the automatic option being configured to allow the external device to detect when at least one of the stimulation modes should be entered, wherein the external device, upon detecting one of the stimulation modes, is configured to limit programming of the stimulator device to multiple sets of stimulation parameters within a corresponding subset for one of the detected stimulation modes.

16. The system of claim 15, wherein: The external device is configured to detect when at least one of the stimulation modes is to be entered by receiving information from another device.

17. The system of claim 16, wherein: The external device is further configured to provide one or more options on the GUI, the one or more options configured to allow the patient to program the stimulator device by selecting stimulation parameters within a subset corresponding to a selected stimulation mode.

18. The system of claim 17, wherein: At least one of the one or more options is configured to allow the patient to adjust at least two of frequency, pulse width, and amplitude of stimulation parameters with which the stimulator device is programmed.

19. The system of claim 1, wherein: The sets of stimulation parameters are derived for the patient using measurements obtained from the patient in response to providing stimulation to the patient during a testing procedure.

20. The system of claim 1, further comprising a clinician programmer, wherein the clinician programmer is configured to program the plurality of displayed stimulation patterns in the external device.

21. A non-transitory computer readable medium configured to operate in an external device configured to program a stimulator device implantable in a patient to provide stimulation at one or more of a plurality of electrodes, the medium comprising information indicative of a plurality of subsets of stimulation parameters, wherein: Each of the plurality of subsets comprises a plurality of stimulation parameters derived for the patient, wherein each set of stimulation parameters comprises at least two of frequency, pulse width, and amplitude, wherein the medium comprises instructions that, when executed on the external device, are configured to: providing a graphical user interface (GUI) on the external device, the graphical user interface allowing the patient to select from a plurality of displayed stimulation patterns to program the stimulation, wherein each stimulation pattern corresponds to one of the subsets, and wherein at least one of the stimulation patterns is indicative of a posture or activity of the patient, and Based on selection of one of the stimulation modes, programming of the stimulator device is restricted to sets of stimulation parameters within the corresponding subset.

Images