Spinal cord stimulation system with stimulation patterns for adapting custom stimulation parameters

Abstract

A spinal cord stimulation system having a stimulation pattern for accommodating custom stimulation parameters is disclosed. A method for programming a stimulator device of a patient using an external device is also disclosed. The method provides a graphical user interface (GUI) on an external device that allows a patient to select from a plurality of displayed stimulation modes to program 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 indicative of a plurality of frequency / pulse width / amplitude coordinates predicted to provide optimal stimulation for the patient. Each stimulation pattern corresponds to a subset of coordinates defined from a plurality of coordinates in the model. Selection of one of the stimulation patterns is limited to programming the stimulator device with coordinates within the corresponding subset of coordinates.

Description

[0001] This application is a divisional application of the patent application with the application number of "201980024398.3", the application date of "July 2, 2019", and the title of "Spinal Cord Stimulation System with Stimulation Patterns for Adapting to Custom Stimulation Parameters". Technical Field

[0002] This application relates to implantable medical devices (IMDs), generally to spinal cord stimulators, and more particularly, to methods of controlling such devices. Background Art

[0003] Implantable nerve stimulator devices are devices that generate and deliver electrical stimulation to the nerves and tissues of the body for treating various biological disorders, such as pacemakers for treating arrhythmias, defibrillators for treating cardiac fibrillation, cochlear stimulators for treating deafness, retinal stimulators for treating blindness, muscle stimulators for generating coordinated limb movement, spinal cord stimulators for treating chronic pain, cortical and deep brain stimulators for treating movement and psychological disorders, and other nerve stimulators 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 that disclosed in U.S. Patent No. 6,516,227. However, the present invention may find applicability in any implantable nerve stimulator device system.

[0004] An SCS system generally includes an implantable pulse generator (IPG) 10 as shown in Figure 1 The IPG 10 includes a biocompatible device housing 12 that houses the circuitry and 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 that also houses individual leads 20 that are coupled to each electrode 16. The leads 20 are also coupled to proximal contacts 22 that can be inserted into a lead connector 24 within a head 23 that is fixed to the IPG 10, which may include, for example, epoxy resin. Once inserted, the proximal contacts 22 connect to head contacts within the lead connector 24, which in turn are coupled to the circuitry within the housing 12 by feedthrough pins through a housing feedthrough, although these details are not shown.

[0005] In the illustrated IPG 10, there are sixteen lead electrodes (E1 to E16) split between two leads 15, where the head 23 includes a 2x1 array of lead connectors 24. However, the number of leads and electrodes in the IPG is application specific and can thus vary. The conductive housing 12 can also include electrodes (Ec). In SCS applications, the electrode leads 15 are typically implanted near the dura in the patient's spine on the left and right sides of the spinal cord midline. The proximal electrodes 22 tunnel through the patient's tissue to a distant location such as the buttock where the IPG housing 12 is implanted, at which point they are coupled to the lead connectors 24. In other IPG examples designed for direct implantation at the site requiring stimulation, the IPG can be leadless, with 16 electrodes alternatively appearing on the body of the IPG for contacting the patient's tissue. The IPG leads 15 can be integrated with the housing 12 and permanently connected to the housing 12 in other IPG schemes. 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.

[0006] The IPG 10 can include an antenna 26a that allows it to communicate bidirectionally with multiple external devices, as Figure 4 shown. The antenna 26a depicted as Figure 1 a conductive coil within the housing 12, although the coil 26a can also appear in the head 23. When the antenna 26a is configured as a coil, communication with external devices preferably occurs using near-field magnetic induction. The IPG can also include a radio frequency (RF) antenna 26b. In Figure 1 shown, the RF antenna 26b is shown within the head 23, but it can also be within the housing 12. The RF antenna 26b can include a patch, slot, or wire, and can operate as a monopole or dipole. The RF antenna 26b preferably communicates using far-field electromagnetic waves. The RF antenna 26b can operate according to any number of known RF communication standards (such as Bluetooth, Zigbee, WiFi, and MICS, etc.).

[0007] Stimulation in the IPG 10 is typically provided by pulses, as Figure 2 shown. The stimulation parameters typically include: the amplitude (A; either current or voltage) of the pulse; 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 electrode acts as an anode (generating current to the tissue) or a cathode (sinking current from the tissue). These stimulation parameters considered together include the stimulation programs that the IPG 10 can execute to provide therapeutic stimulation to the patient.

[0008] In Figure 2In the example, electrode E5 has been selected as the anode and thus provides a pulse that generates a positive current of magnitude +A to the tissue. Electrode E4 has been selected as the cathode and thus provides a pulse that absorbs a corresponding negative current of magnitude -A 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 the anode at a given time, and more than one electrode can act as the cathode at a given time (e.g., tripolar stimulation, quadripolar stimulation, etc.).

[0009] As Figure 2 The pulses shown in include a first phase 30a, followed immediately by a second phase 30b of 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 (recovered) during the second phase 30b. In the example shown, the first phase 30a and the second phase 30b have the same duration and magnitude (although opposite in polarity), which ensures the same amount of charge during both phases. However, if the integrals of the magnitudes and durations of these two phases are equal in magnitude, the second phase 30b can also balance the charge of the first phase 30a, as is well known. The width PW of each pulse is defined herein as the duration of the first pulse phase 30a, although the pulse width 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.

[0010] The IPG 10 includes a stimulation circuit 28 that can be programmed to generate stimulation pulses at the electrodes as defined by a stimulation program. The stimulation circuit 28 can include, for example, the circuits described in U.S. Patent Application Publications 2018 / 0071513 and 2018 / 0071520 or described in USP 8606362 and 8620436.

[0011] Figure 3Illustrates an external test stimulation environment that can be performed before implanting the IPG 10 into a patient. During external test stimulation, it is possible to attempt to stimulate the intended implant patient without implanting the IPG 10. In contrast, one or more test leads 15' are implanted into the patient's tissue 32 at the target location 34, such as intraspinally as previously described. The proximal end of one or more test leads 15' exits the incision 36 and is connected to an external test stimulator (ETS) 40. The ETS 40 generally mimics the operation of the IPG 10 and can thus 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 generally worn externally by the patient for a short period of time (e.g., two weeks), which allows the patient and their clinician to experiment with different stimulation parameters to attempt to find a stimulation program that relieves the patient's symptoms (e.g., pain). If the external test stimulation proves successful, one or more test leads 15' are removed, and the complete IPG 10 and one or more leads 15 are implanted as described above; if not successful, only one or more test leads 15' are removed.

[0012] Similar to the IPG 10, the ETS 40 can include one or more antennas capable of two-way communication with an external device, as Figure 4 further described. Such antennas can include a near-field magnetic induction coil antenna 42a and / or a far-field RF antenna 42b, as previously described. The ETS 40 can also include a stimulation circuit 44 capable of forming stimulation pulses according to a stimulation program, which circuit can be similar to or include the same stimulation circuit 28 present in the IPG 10. The ETS 40 can also include a battery (not shown) for operating power.

[0013] Figure 4 Illustrates various external devices that can wirelessly transmit data with the IPG 10 and the ETS 40, including the patient's handheld external controller 45 and the clinician programmer 50. Both devices 45 and 50 can be used to send a stimulation program to the IPG 10 or the ETS 40 - that is, to program their stimulation circuits 28 and 44 to produce pulses having the previously described desired shape and timing. Both devices 45 and 50 can also be used to adjust one or more stimulation parameters of the stimulation program currently being executed by the IPG 10 or the ETS 40. Both devices 45 and 50 can also receive information from the IPG 10 or the ETS 40, such as various status information, etc.

[0014] The external controller 45 can be as described, for example, in U.S. Patent Application Publication No. 2015 / 0080982, and can include any dedicated controller configured to work with the IPG 10. The external controller 45 can 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 function as a wireless controller for the IPG 10 or the ETS 40, as described in U.S. Patent Application Publication No. 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 a patient to adjust stimulation parameters, although it may have limited functionality when compared to the more powerful clinician programmer 50, which is described later.

[0015] The external controller 45 can have one or more antennas capable of communicating with the IPG 10 and the ETS 40. For example, the external controller 45 can 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 the ETS 40. The external controller 45 can 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 the ETS 40.

[0016] The external controller 45 can also have a control circuit 48, such as a microprocessor, a microcomputer, an FPGA, or other digital logic structures capable of executing instructions of an electronic device, etc. The control circuit 48 can, for example, receive patient adjustments to stimulation parameters and create a stimulation program that will be wirelessly transmitted to the IPG 10 or the ETS 40.

[0017] The clinician programmer 50 is further described in U.S. Patent Application Publication No. 2015 / 0360038 and is only briefly described here. The clinician programmer 50 can include a computing device 51, such as a desktop computer, a portable computer, a laptop computer, a tablet computer, a mobile smart phone, a personal digital assistant (PDA)-type mobile computing device, etc. In Figure 4 which, the computing device 51 is shown as a portable computer that includes typical computer user interface devices, such as a screen 52, a mouse, a keyboard, speakers, a stylus, a printer, etc., which are not all shown for the sake of simplicity. In Figure 4 accessory devices for the clinician programmer 50 are also shown, which are generally dedicated to its operation as a stimulation controller, such as a communication "stick" 54, and a joystick 58, which can be coupled to an appropriate port on the computing device 51, such as, for example, a USB port 59.

[0018] The antenna used in the clinician programmer 50 for communicating with the IPG 10 or ETS 40 can depend on the type of antenna included in those devices. If the patient's IPG 10 or ETS 40 includes a coil antenna 26a or 42a, the wand 54 can similarly include a coil antenna 56a for establishing near-field magnetic induction communication at close range. In this instance, the wand 54 can be secured adjacent to the patient, such as by placing the wand 54 in a band or sleeve that can be worn by the patient and is near the patient's IPG 10 or ETS 40.

[0019] If the IPG 10 or ETS 40 includes an RF antenna 26b or 42b, the wand 54, the computing device 51, or both can similarly include an RF antenna 56b to establish communication with the IPG 10 or ETS 40 at greater distances. (The wand 54 may not be necessary in this case). The clinician programmer 50 can also establish communication with other devices and networks (such as the Internet) wirelessly or via a wired link provided at an Ethernet or network port.

[0020] To program the stimulation program or parameters of the IPG 10 or ETS 40, the clinician docks with a clinician programmer graphical user interface (GUI) 64 provided on the display 52 of the computing device 51. As will be understood by those skilled in the art, the GUI 64 can be presented by executing clinician programmer software 66 on the computing device 51, which can be stored on a non-volatile memory 68 of the device. Those skilled in the art will also recognize that the execution of the clinician programmer software 66 in the computing device 51 can be facilitated by a control circuit 70 (such as a microprocessor, a microcomputer, an FPGA, or other digital logic structures capable of executing programs in a computing device). Such a control circuit 70 can also implement communication via the antenna 56a or 56b to transmit selected stimulation parameters to the patient's IPG 10 via the GUI 64, in addition to executing the clinician programmer software 66 and presenting the GUI 64.

[0021] In Figure 5 a portion of the GUI 64 is shown by way of example. Those skilled in the art will understand that the details of the GUI 64 will depend on the position of the clinician programmer software 66 in its execution, which will depend on the GUI selections that the clinician has made. Figure 5The GUI 64 at a point is shown to allow setting of stimulation parameters for a patient and storing them as a stimulation program. The program interface 72 is shown on the left, which allows naming, loading, and saving of stimulation programs for a patient as further described in the '038 disclosure. The stimulation parameter interface 82 is shown on the right, where specific stimulation parameters (A, D, F, E, P) can be defined for the stimulation program. Values of the stimulation parameters related to the 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 a clinician can use to increase or decrease these values.

[0022] The stimulation parameters related to 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') in their proper positions relative to each other, e.g., 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 the selected electrode (including the case electrode Ec) to be designated as anode, cathode, or off. The electrode parameter interface 86 also allows the relative intensity of the anode current or cathode 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 example waveform shown in Figure 2 as shown in the lead interface 92, electrode E5 has been selected as the only anode generating current, and this electrode receives the specified anode current +A with X = 100%. Similarly, electrode E4 has been selected as the only cathode absorbing current, and this electrode receives the cathode current -A with X = 100%.

[0023] 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 therefrom will ensure that the IPG 10 and the ETS 40 are programmed such that when biphasic pulses will be used, the stimulation program appears as biphasic pulses. For example, the clinician programming software 66 can automatically determine the duration and amplitude of both pulse phases 30a and 30b (e.g., each having a duration of PW and having opposite polarities +A and -A). The advanced menu 88 can also be used (among other things) to define the relative duration and amplitude of pulse phases 30a and 30b and to allow other more advanced modifications, such as setting the duty cycle (on / off time) of the stimulation pulse and the rise time through which the stimulation reaches its programmed amplitude (A), etc. The mode menu 90 allows the clinician to select different modes for determining stimulation parameters. For example, as described in the '038 disclosure, the mode menu 90 can be used to implement electronic trolling, which includes an automatic programming mode that performs current steering along the electrode array by moving the cathode in a bipolar manner.

[0024] Although the GUI 64 is shown operating in the clinician programmer 50, a user interface of the external controller 45 can also provide similar functionality. Summary of the Invention

[0025] In one example, a method for programming a stimulator device for a patient is disclosed. The method can 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 indicative of a plurality of subsets of stimulation parameters derived for the patient, wherein each stimulation mode corresponds to one of the subsets of stimulation parameters; and based on a selection of one of the stimulation modes, restricting programming of the stimulator device to stimulation parameters within the corresponding subset of stimulation parameters.

[0026] 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 a line in a multi-dimensional space of at least two of frequency, pulse width, and amplitude. In one example, the stimulation parameters in each subset include a volume in a multi-dimensional space of at least two of frequency, pulse width, and amplitude. In one example, the stimulation parameters of the selected stimulation pattern are configured to provide sub-perceptual stimulation to the patient. In one example, the stimulation parameters of the selected stimulation pattern are configured to provide supra-perceptual stimulation to the patient. In one example, at least one of the stimulation patterns indicates the posture or activity of the patient. In one example, at least one of the stimulation patterns indicates a power mode for the stimulator device. In one example, the method further includes providing an automatic option on a GUI that allows the stimulator device to detect when it should enter at least one of the stimulation patterns, wherein the detection by the stimulator device of one of the stimulation patterns is limited to programming the stimulator device with stimulation parameters within the corresponding subset of stimulation parameters for the detected one of the stimulation patterns. In one example, the GUI allows the patient to select at least one stimulation pattern to be detected. In one example, the stimulator device includes at least one sensor for detecting when it will enter at least one of the stimulation patterns. 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 a GUI that allows an external device to detect when it should enter at least one of the stimulation patterns, wherein the detection by the external device of one of the stimulation patterns limits the external device to programming the stimulator device with stimulation parameters within the corresponding subset of stimulation parameters for the detected one of the stimulation patterns. In one example, the external device is configured to detect when it will enter at least one of the stimulation patterns by receiving information from another device. In one example, the method further includes providing one or more options on a 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 pattern. 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 with which the stimulator device is programmed. In one example, in response to providing stimulation to the patient during a test procedure, measurement results obtained from the patient are used to derive a subset of stimulation parameters for the patient.In one example, a GUI is provided on a patient external controller and also includes programming of multiple displayed stimulation patterns using a clinician programmer.

[0027] In one example, a system is disclosed that can include: a stimulator device configured for implantation within a patient and including 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 indicative of 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 patterns to program the stimulation, wherein each stimulation pattern corresponds to one of the subsets of stimulation parameters, and based on a selection of one of the stimulation patterns, limit programming of the stimulator device to stimulation parameters within the corresponding subset of stimulation parameters.

[0028] 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 a line in a multi-dimensional space of at least two of frequency, pulse width, and amplitude. In one example, the stimulation parameters in each subset include a volume in a multi-dimensional space of at least two of frequency, pulse width, and amplitude. In one example, the stimulation parameters of the selected stimulation pattern are configured to provide sub-perceptual stimulation to the patient. In one example, the stimulation parameters of the selected stimulation pattern are configured to provide supra-perceptual stimulation to the patient. In one example, at least one of the stimulation patterns indicates the patient's posture or activity. In one example, at least one of the stimulation patterns indicates a power mode for 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 stimulator device to detect when it should enter at least one of the stimulation patterns, wherein the external device is configured, when one of the stimulation patterns is detected by the stimulator device, to: restrict programming of the stimulator device to use stimulation parameters within the corresponding subset of stimulation parameters for one of the detected stimulation patterns. In one example, the GUI is configured to allow the patient to select at least one stimulation pattern to be detected. In one example, the stimulator device includes at least one sensor for detecting when it will enter at least one of the stimulation patterns. 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 it should enter at least one of the stimulation patterns, wherein the external device is configured, when one of the stimulation patterns is detected, to: restrict programming of the stimulator device to use stimulation parameters within the corresponding subset of stimulation parameters for one of the detected stimulation patterns. In one example, the external device is configured to detect when it will enter at least one of the stimulation patterns 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 within the subset of stimulation parameters corresponding to the selected stimulation pattern. 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 used to program the stimulator device.In one example, in response to providing a stimulus to the patient during a test procedure, measurement results 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.

[0029] In one example, a non-transitory computer-readable medium is disclosed that is 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 includes information indicative of a plurality of subsets of stimulation parameters derived for the patient. 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 patterns to program the stimulation, wherein each stimulation pattern corresponds to one of the subsets of stimulation parameters derived for the patient, and based on a selection of one of the stimulation patterns, limit programming of the stimulator device to stimulation parameters within the corresponding subset of stimulation parameters.

[0030] In one example, a method for programming a patient's stimulator device is disclosed that may include: determining a model for the patient, wherein the model includes information indicative of predicted stimulation parameters available for the patient; using the model to determine information indicative of a plurality of subsets of stimulation parameters, wherein each subset corresponds to one of a plurality of stimulation patterns; and providing a graphical user interface (GUI) to allow the patient to select from the plurality of stimulation patterns, wherein a selection of one of the stimulation patterns limits programming of the stimulator device to stimulation parameters within the corresponding subset of stimulation parameters.

[0031] In one example, the stimulus parameters in each subset include a line or volume in a multi-dimensional space of at least two of frequency, pulse width, and amplitude. In one example, in response to providing a stimulus to a patient during a test procedure, measurements obtained from the patient are used to determine a model for the patient. In one example, during the test procedure, stimuli are provided to the patient at different pulse widths, and wherein the measurements include an indication of the 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 stimulus parameters in the model, wherein the other model includes the relationship between frequency, pulse width, and perception threshold. In one example, the model and the plurality of subsets are determined in a clinician programmer in communication with the stimulator device, and further includes sending the determined plurality of subsets from the clinician programmer to an external device. In one example, the model is determined in a clinician programmer in communication with the stimulator device, and further includes sending the model to an external device, wherein the plurality of subsets are determined in the external device. In one example, the predicted stimulus parameters in the model include a line or volume in a multi-dimensional space of at least two of frequency, pulse width, and amplitude. In one example, the model is used to determine at least one subset such that the stimulus parameters of the at least one subset are fully constrained by the predicted stimulus parameters in the model. In one example, the model is used to determine at least one subset such that the stimulus parameters of the at least one subset are partially constrained by the predicted stimulus parameters in the model. In one example, the predicted stimulus parameters in the model include stimulus parameters predicted to provide sub-perceptual stimulation to the patient. In one example, the model further includes information indicating the paresthesia threshold of the patient, wherein at least one stimulation pattern provides supra-perceptual stimulation to the patient through the stimulus parameters in the corresponding subset where the amplitude stimulus parameter exceeds the paresthesia threshold. In one example, the stimulus parameters of the selected stimulation pattern are configured to provide sub-perceptual stimulation to the patient. In one example, the stimulus parameters of the selected stimulation pattern are configured to provide supra-perceptual stimulation to the patient. In one example, at least one of the stimulation patterns indicates the patient's posture or activity. In one example, at least one of the stimulation patterns indicates the power mode for the stimulator device. In one example, the method further includes: providing an automatic option on the GUI that allows detection when at least one of the stimulation patterns should be entered, wherein detection of one of the stimulation patterns is limited to programming the stimulator device with stimulus parameters within the corresponding subset of stimulus parameters for the detected stimulation pattern. 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 stimulus parameters within the subset of stimulus parameters corresponding to the selected stimulation pattern.In one example, at least one of the one or more options allows a patient to adjust at least two of the frequency, pulse width, and amplitude of the parameters by which the stimulator device is programmed. In one example, the stimulation parameters in at least one of the subsets are adjustable. In one example, a GUI is provided on a patient external controller and also includes programming of multiple stimulation patterns using a clinician programmer.

[0032] In one example, a system is disclosed that may include: a stimulator device configured to be implanted within a patient and including a plurality of electrodes; and at least one external device configured to: determine a model for the patient, wherein the model includes information indicative of predicted stimulation parameters available for the patient; use the model to determine information indicative of a plurality of subsets of stimulation parameters, wherein each subset corresponds to one of a plurality of stimulation patterns; and provide a graphical user interface (GUI) configured to allow a patient to select from among the plurality of stimulation patterns, wherein, based on a selection of one of the stimulation patterns, 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.

[0033] In one example, the stimulation parameters in each subset include a line or volume in a multi-dimensional 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 a patient by receiving measurements 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 measurements include an indication of the perception threshold at each pulse width, and 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 for the patient by comparing the relationship with another model to determine predicted stimulation parameters 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, and 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 the model and send the model to the patient external controller, and wherein the patient external controller is configured to determine information indicating a plurality of 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 a plurality of stimulation patterns in a patient external controller having a GUI. In one example, the predicted stimulation parameters in the model include a line or volume in a multi-dimensional 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 such that the stimulation parameters of the at least one subset are fully 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 such 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 further includes information indicating the patient's paresthesia threshold, and wherein at least one stimulation pattern provides supra-perceptual stimulation to the patient through the stimulation parameters in a corresponding subset where the amplitude stimulation parameter exceeds the paresthesia threshold. In one example, the stimulation parameters of the selected stimulation pattern are configured to provide sub-perceptual stimulation to the patient. In one example, the stimulation parameters of the selected stimulation pattern are configured to provide supra-perceptual stimulation to the patient. In one example, at least one of the stimulation patterns indicates the patient's posture or activity.In one example, at least one of the stimulation patterns 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 a GUI, the automatic option being configured to allow detection when at least one of the stimulation patterns should be entered, wherein the at least one external device is configured to limit programming of the stimulator device to stimulation parameters within a corresponding subset of stimulation parameters for one of the detected stimulation patterns. In one example, at least one external device is configured to provide one or more options on a GUI to allow a patient to program the stimulator device by selecting stimulation parameters within a subset of stimulation parameters corresponding to the selected stimulation pattern. 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 for which the stimulator device is programmed. In one example, at least one external device is configured to allow a user to adjust the stimulation parameters in at least one of the subsets.

[0034] In one example, at least one non - transitory computer - readable medium is disclosed that is configured for operation 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, where the model includes information indicating predicted stimulation parameters available for the patient; use the model to determine information indicating a plurality of subsets of stimulation parameters, where each subset corresponds to one of a plurality of stimulation patterns; and provide a graphical user interface (GUI) configured to allow the patient to select from a plurality of stimulation patterns, wherein, based on the selection of one of the stimulation patterns, 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.

[0035] In one example, a method of programming a patient's stimulator device using an external device is disclosed, the method can include: providing a graphical user interface (GUI) on the external device that allows the patient to select from a plurality of displayed stimulation patterns to program stimulation provided by one or more electrodes of the stimulator device, wherein the external device stores information indicating a plurality of subsets of coordinates, where each coordinate within each subset includes a stimulation parameter derived for the patient to provide optimal stimulation for the patient, where each stimulation pattern corresponds to one of the subsets of coordinates, and where the selection of one of the stimulation patterns limits programming of the stimulator device to the coordinates within the corresponding subset of coordinates.

[0036] In one example, each coordinate includes a frequency, a pulse width, and an amplitude. In one example, the coordinates in each subset include a line in a three-dimensional space of frequency, pulse width, and amplitude. In one example, the coordinates in each subset include a volume in a three-dimensional space of frequency, pulse width, and amplitude. In one example, the method may further include determining a model for a patient, where the model includes information indicating a plurality of coordinates, where each coordinate in the model includes stimulation parameters predicted to provide optimal stimulation for the patient, and where 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, measurements obtained from the patient are used to determine the model for the patient. In one example, stimulation is provided to the patient at different pulse widths during the test procedure, and where the measurements include an indication of the perception threshold at each pulse width, thereby determining the relationship between the pulse width and the patient's perception threshold. In one example, the perception threshold includes the lowest amplitude of a 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, the other model includes the relationship between frequency, pulse width, and perception threshold. In one example, the model and the plurality of subsets are determined in a clinician programmer in communication with the stimulator device. In one example, the method further includes sending the determined plurality of subsets from the clinician programmer to an external device. In one example, the model is determined in a clinician programmer in communication with the stimulator device, and further includes sending the model to an external device, where the plurality of subsets are determined in the external device. In one example, the plurality of coordinates in the model include a line in a three-dimensional space of frequency, pulse width, and amplitude. In one example, the plurality of coordinates in the model include a volume in a three-dimensional space of frequency, pulse width, and amplitude. In one example, at least one subset of coordinates is determined using the model such that the coordinates of the at least one subset are fully constrained by the plurality of coordinates in the model. In one example, at least one subset of coordinates is determined using the model such that the coordinates of the at least one subset are partially constrained by the plurality of coordinates in the model. In one example, at least one subset is partially constrained by the plurality of coordinates in the model such that only some of the stimulation parameters of the coordinates in the at least one subset are equal to the stimulation parameters of the coordinates within the model, but at least one of the stimulation parameters of the coordinates in the at least one subset is outside the stimulation parameters of the coordinates within the model. In one example, each coordinate in the model includes stimulation parameters predicted to provide optimal sub-perceptual stimulation for the patient. In one example, the model further includes information indicating the paresthesia threshold of the patient at each coordinate in the plurality of coordinates, where at least one stimulation pattern provides supra-perception for 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 patterns is configured to provide sub-perceptual stimulation for the patient.In one example, at least one of the stimulation patterns is configured to provide extrasensory stimulation to a patient. In one example, at least one of the stimulation patterns indicates the posture or activity of the patient. In one example, at least one of the stimulation patterns indicates a power mode for the stimulator device. In one example, the method further includes providing an automatic option on the GUI that allows the stimulator device to detect when to enter at least one of the stimulation patterns, wherein detection by the stimulator device of one of the stimulation patterns restricts the external device to programming the stimulator device using coordinates within a corresponding subset of coordinates for the detected one of the stimulation patterns. In one example, the GUI allows the patient to select at least one stimulation pattern to be detected. In one example, the stimulator device includes at least one sensor for detecting when to enter at least one of the stimulation patterns. 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 the 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 that allows the external device to detect when to enter at least one of the stimulation patterns, wherein detection by the external device of one of the stimulation patterns restricts the external device to programming the stimulator device using coordinates within a corresponding subset of coordinates for the detected one of the stimulation patterns. In one example, the external device detects when to enter at least one of the stimulation patterns 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 coordinates within a subset of coordinates corresponding to the selected stimulation pattern. 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 used to program the stimulator device. In one example, in response to providing stimulation to the patient during a test program, measurement results obtained from the patient are used to derive a subset of coordinates for the patient. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 An implantable pulse generator (IPG) available for spinal cord stimulation (SCS) according to the prior art is shown.

[0038] Figure 2 An example of a stimulation pulse that can be generated by the IPG according to the prior art is shown.

[0039] Figure 3 The use of an external trial stimulator (ETS) available for providing stimulation prior to IPG implantation according to the prior art is shown.

[0040] Figure 4 Shows various external devices according to the prior art that are capable of communicating with an IPG and an ETS and programming the stimulation in the IPG and the ETS.

[0041] Figure 5 Shows a graphical user interface (GUI) of a clinician programmer external device for setting or adjusting stimulation parameters according to the prior art.

[0042] Figure 6 Shows a sweet spot search for determining effective electrodes for a patient using a movable sub-perceptual bipolar.

[0043] Figures 7A to 7D Shows a sweet spot search for determining effective electrodes for a patient using a movable supra-perceptual bipolar.

[0044] Figure 8 Shows a stimulation circuit that can be used in an IPG or an ETS and that is capable of providing multiple independent current controls for independently setting the current at each of the electrodes.

[0045] Figure 9 Shows a flow chart of a study conducted on various patients with back pain, the study being designed to determine optimal sub-perceptual SCS stimulation parameters in the frequency range of 1 kHz to 10 kHz.

[0046] Figures 10A to 10C Shows various study results as a function of the stimulation frequency in the frequency range of 1 kHz to 10 kHz, including: average optimal pulse width ( Figure 10A ), average charge per second, and optimal stimulation amplitude ( Figure 10B ), and back pain score ( Figure 10C ).

[0047] Figures 11A to 11C Also shows an analysis of the relationship between the average optimal pulse width and the frequency in the frequency range of 1 kHz to 10 kHz, and the identification of statistically-significant regions for optimizing these parameters.

[0048] Figure 12A Shows the results of patients tested with sub-perceptual therapy at 1 kHz or below 1 kHz, and shows the range of optimal pulse widths determined at the tested frequencies, and the regions of optimal pulse width v. frequency for sub-perceptual therapy.

[0049] Figure 12B Shows various modeled relationships between the average optimal pulse width and the frequency at 1 kHz or below 1 kHz.

[0050] Figure 12C Shows a study of the duty cycle of the optimal pulse width as a function of frequencies of 1 kHz or below 1 kHz.

[0051] Figure 12D Shows the average battery current and battery discharge time at the optimal pulse width as a function of frequencies of 1 kHz or below 1 kHz.

[0052] Figure 13A and Figure 13B Shows the results of additional tests that verify the previously proposed relationship between frequency and pulse width.

[0053] Figure 14 Shows a fitting module that shows how the relationships and regions determined for the optimal pulse width and frequency (≤ 10 kHz) can be used to set sub - perceptual stimulation parameters for IPG or ETS.

[0054] Figure 15 Shows an algorithm used for suprathreshold sweet spot search before sub - perceptual therapy, and a possible optimization of sub - perceptual therapy using the fitting module.

[0055] Figure 16 Shows an alternative algorithm for optimizing sub - perceptual therapy using the fitting module.

[0056] Figure 17 Shows a model derived from a patient that shows a surface representing the optimal sub - perceptual values of frequency and pulse width, and also includes the patient's perception threshold pth measured at these frequencies and pulse widths.

[0057] Figure 18A and Figure 18B Shows the relationship between the perception threshold pth and the pulse width plotted for a number of patients, and shows how the results can be curve - fitted.

[0058] Figure 19 Shows a graph of the relationship between the parameter Z and the pulse width for a patient, where Z includes the patient's optimal amplitude A, expressed as a percentage of the perception threshold pth (i.e., Z = A / pth).

[0059] Figures 20A - 20F Shows an algorithm that is used to use Figures 17 - 19 the modeling information and use the perception threshold measurements made on the patient to derive a range of the patient's optimal sub - perceptual stimulation parameters (e.g., F, PW, and A).

[0060] Figure 21 Shows the use of the optimal stimulation parameters in a patient external controller, including a user interface that allows the patient to adjust the stimulation within a range.

[0061] Figures 22A - 22F Shows the impact of statistical variance in modeling, resulting in that the optimal stimulation parameters determined for a patient may occupy a certain volume. Also shows the user interface of an external controller for the patient to allow the patient to adjust the stimulation within this volume.

[0062] Figure 23 Shows a stimulation mode user interface from which a patient can select different stimulation modes, thereby providing stimulation or allowing the patient to control the stimulation using different subsets of the stimulation parameters determined using the optimal stimulation parameters.

[0063] Figures 24A - 29B Shows an example of different subsets of stimulation parameters based on a patient's selection of different stimulation modes. The figure labeled A (e.g., Figure 24A ) shows the frequency and pulse width of the subset, while the figure labeled B (e.g., Figure 24B ) shows 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 fully constrained by the determined optimal stimulation parameters (i.e., completely within them), or can include parameters that are only partially constrained by the optimal stimulation parameters.

[0064] Figure 30 Shows an automatic mode in which the IPG and / or the external controller are used to determine when a specific stimulation mode should be automatically entered based on sensed information.

[0065] Figure 31 Shows another example of a simulation mode user interface in which the stimulation modes are presented for selection on a two-dimensional representation of the stimulation parameters, although a three-dimensional representation indicating the subset volume can also be used.

[0066] Figure 32 Shows GUI aspects that allow a patient to adjust the stimulation, in which a recommended stimulation area for the patient is shown in combination with the adjustment aspects. Detailed Description

[0067] Although spinal cord stimulation (SCS) therapy can be an effective means of relieving a patient's pain, 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. Generally, the impact of paresthesia is minor, or at least not overly involving the patient. In addition, for patients whose chronic pain is now controlled by SCS therapy, paresthesia is usually a moderate compromise. Some patients even find paresthesia to be comfortable and reassuring.

[0068] Nonetheless, at least for some patients, SCS therapy would ideally provide complete pain relief without paresthesia - which is often referred to as "sub-perception" or sub-threshold therapy that the patient cannot feel. Effective sub-perception therapy can provide pain relief without paresthesia by delivering stimulation pulses at higher frequencies. Unfortunately, such higher frequency stimulation may require more power, which tends to drain 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 non-rechargeable, then high frequency stimulation means that the IPG 10 will need to be replaced more quickly. Alternatively, if the IPG battery 14 is rechargeable, then the IPG 10 will need to be charged more frequently over longer periods of time. Either way, it causes inconvenience to the patient.

[0069] In SCS applications, it is desirable to determine the stimulation program that will be effective for each patient. An important part of determining an effective stimulation program is to determine the "sweet spot" for stimulation in each patient, i.e., which electrodes should be activated (E) and with what polarity (P) and relative amplitude (X%) to recruit and thus treat the nerve site where the pain originates in the patient. Selecting the electrodes adjacent to this nerve site of pain can be difficult to determine, and trials are typically conducted to select the optimal combination of electrodes to provide treatment for the patient.

[0070] As described in the U.S. Patent Application with Serial No. 16 / 419,879, filed on May 22, 2019, when using sub-perception therapy, selecting electrodes for a given patient can be even more difficult because the patient does not feel the stimulation and thus may have difficulty feeling whether the stimulation is "covering" his pain and thus whether the selected electrodes are effective. In addition, sub-perception stimulation therapy may require a "washin" period before it can become effective. The washin period may take a day or more, and thus sub-perception stimulation may not be effective immediately, which makes electrode selection more difficult.

[0071] Figure 6 Briefly describes the technology of the '879 application for sweet spot search, i.e., how electrodes adjacent to the nerve site 298 of pain in a patient can be selected when using sub-perception stimulation. Figure 6 The technology in is particularly useful in a trial setting after a patient is first implanted with an electrode array (i.e., after receiving their IPG or ETS).

[0072] In the example shown, it is assumed that the pain site 298 may be within the tissue region 299. Such a region 299 can then be deduced by a clinician based on the patient's symptoms (e.g., by understanding which electrodes are adjacent to a particular vertebra (not shown), such as within the T9 to T10 interspace). In the example shown, the region 299 is bounded by the electrodes E2, E7, E15, and E10, which means that electrodes outside this region (e.g., E1, E8, E9, E16) are unlikely to have an impact on the patient's symptoms. Therefore, these electrodes may not be selected during the sweet spot search described in Figure 6 as further described below.

[0073] In Figure 6 , the sub-perceptual bipolar 297a is selected, where one electrode (e.g., E2) is selected as the anode that will produce a positive current (+A) to the patient's tissue, and the other electrode (e.g., E3) is selected as the cathode that will absorb a negative current (-A) from the tissue. This is similar to what was described previously with respect to Figure 2 and effective charge recovery can be employed to use biphasic stimulation pulses. Since the bipolar 297a provides sub-perceptual stimulation, the amplitude A used during the sweet spot search is titrated downward until the patient no longer feels paresthesia. This sub-perceptual bipolar 297a is provided to the patient for a period of time (such as several days), which allows the potential effectiveness of the sub-perceptual bipolar to "wash in" and allows the patient to provide feedback on how well the bipolar 297a is helping the patient's symptoms. Such patient feedback can include pain scale rankings. For example, the patient can use a numerical rating scale (NRS) or a visual analog scale (VAS) to rank their pain on a scale from 1 to 10, where 1 indicates no pain or almost no pain, and 10 indicates the most pain imaginable. As discussed in the '879 application, such pain scale rankings can be input into the patient's external controller 45.

[0074] After testing the bipolar 297a in this first position, a different combination of electrodes (anode electrode E3, cathode electrode E4) is selected, which will move the position of the bipolar 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 bipolar 297a is moved down one electrode lead and up the other electrode lead, as shown by the path 296 in the hope of finding a combination of electrodes that covers the pain site 298. In Figure 6In the example of, 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 for 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 devices (such as the patient external controller 45) and wirelessly transmitted to the patient's IPG or ETS via a telemetry transmitter for execution.

[0075] Although Figure 6 the sweet spot search in can be effective, it can also take a fairly long time when using sub-perceptual stimulation. As has been noted, sub-perceptual stimulation is provided at each bipolar 297 for several days, and because a large number of bipolar positions are selected, the entire sweet spot search can take up to a month to complete.

[0076] The inventors have determined via testing of SCS patients that even if it is desired to ultimately continue using sub-perceptual therapy for the patient after the sweet spot search, it is beneficial to use supra-perceptual stimulation during the sweet spot search to select the activation electrodes for the patient. Compared to using sub-perceptual stimulation (which requires a wash-in period at each set of electrodes to be tested), using supra-perceptual stimulation during the sweet spot search significantly accelerates the determination of the effective electrodes for the patient. After determining the electrodes for the patient using supra-perceptual therapy, the therapy can be titrated to a sub-perceptual level that maintains the same electrodes determined for the patient during the sweet spot search. Since it is known that the selected electrodes are recruiting the nerve sites of the patient's pain, applying sub-perceptual therapy to those electrodes is more likely to produce an immediate effect, thereby reducing or potentially eliminating the need for a wash-in for subsequent sub-perceptual therapy. In summary, when using a supra-perceptual sweet spot search, effective sub-perceptual therapy can be achieved more quickly for the patient. Preferably, a supra-perceptual sweet spot search is generated using symmetric biphasic pulses occurring at a low frequency, such as between 40 Hz and 200 Hz in one example.

[0077] According to one aspect of the disclosed technology, a patient will be provided with sub-perceptual therapy. A sweet spot search for determining the electrodes that can be used during sub-perceptual therapy can be performed prior to such sub-perceptual therapy. In some aspects, when using sub-perceptual therapy for a patient, the sweet spot search can use the bipolar 297a ( Figure 6 ) that is sub-perceptual, as just described. This can be relevant because the sub-perceptual sweet spot search can match the final sub-perceptual therapy that the patient will receive.

[0078] However, the inventors have determined that even if sub-perceptual therapy will ultimately be used for the patient, using supra-perceptual stimulation (i.e., stimulation with an accompanying paresthesia) during the sweet spot search can be beneficial. This is in Figure 7Ais shown, where the movable bipolar 301a provides a supra-threshold stimulation that can be felt by the patient. When compared with the sub-threshold bipolar 297a in Figure 6 , providing the bipolar 301a as a supra-threshold stimulation can involve only increasing its amplitude (e.g., current A), although other stimulation parameters can also be adjustable - such as by providing a longer pulse width.

[0079] The inventors have determined that even if a sub-threshold treatment will ultimately be used for the patient, it is beneficial to take supra-threshold stimulation during the sweet spot search.

[0080] First, as described above, using supra-threshold treatment by definition allows the patient to feel the stimulation, which enables the patient to essentially immediately provide feedback to the clinician as to whether the abnormal sensation seems to well cover their pain site 298. In other words, there is no need to spend time ramping up the bipolar 301a at each position while moving along the path 296. Thus, the appropriate bipolar 301a adjacent to the patient's pain site 298 can be established much more quickly, such as within a single clinician visit, rather than over a period of days or weeks. In one example, when supra-threshold sweet spot search precedes sub-threshold treatment, the time required to ramp up the sub-threshold treatment can be 1 hour or less, 10 minutes or less, or even around a few seconds. This allows the ramp-up to occur during a single programming session for programming the patient's IPG or ETS and does not require the patient to leave the clinician's office.

[0081] Second, using supra-threshold stimulation during the sweet spot search ensures that the electrodes that well recruit the pain site 298 are determined. As a result, after the sweet spot search is completed and the final sub-threshold treatment is titrated for the patient, the ramp-up of this sub-threshold treatment may not take a long time because the electrodes required for good recruitment have been surely determined.

[0082] Figures 7B to 7D Other supra-threshold bipolars 301b to 301d that can be used are shown, and in particular how virtual bipolars can be formed using virtual poles by activating three or more of the 16 electrodes is shown. Virtual poles are further discussed in U.S. Patent Application Publication 2019 / 0175915 and are thus only briefly described here. Virtual poles are formed by assistance if the stimulation circuits 28 or 44 used in the IPG or ETS can independently set the current at any of the electrodes - which is sometimes referred to as multiple independent current control (MICC), which is further described below with respect to Figure 8 it.

[0083] When using virtual bipolars, the GUI 64 in the clinician programmer 50 ( Figure 4 ) Figure 5) can be used to define an anode pole (+) and a cathode pole (-) at a location 291( Figure 7B ) that may not necessarily correspond to the location of the physical electrode 16. 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 amplitudes at the specified location 291 to form a virtual anode and a virtual cathode. 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.

[0084] For example, in Figure 7B , the virtual anode pole is located at the location 291 between electrodes E2, E3, and E10. The clinician programmer 50 can then calculate, based on this location, the appropriate share (X%) of the total anode current +A that each of these electrodes will receive (during the first pulse phase 30a) to position the virtual anode at that location. Since 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 smaller shares of the anode current (e.g., 15% * +A and 10% * +A, respectively). Similarly, it can be seen that, depending on the specified location 291 of the virtual cathode pole adjacent to electrodes E4, E11, and E12, these electrodes will receive the 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 flip during the second phase 30b of the pulse, as shown in the waveform in Figure 7B . In any case, the use of virtual poles in the formation of the bipolar 301b allows for field shaping 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) bipolar sequentially along the path 296 for each electrode, and the path can be random, perhaps guided by feedback from the patient.

[0085] Figure 7C shows a useful virtual bipolar 301c configuration that can be used during the sweet spot search. This virtual bipolar 301c again defines a target anode and cathode whose locations do not correspond to the locations of the physical electrodes. The virtual bipolar 301c is formed along the lead (substantially spanning the length of four electrodes from E1 to E5). This creates a larger field in the tissue that can better recruit the patient's pain site 298. When it is moved along the path 296, compared to the smaller bipolar configuration 301a in Figure 7A , this bipolar configuration 301c may need to be moved to fewer locations, thus accelerating the detection of the pain site 298. Figure 7DExtended on the bipolar configuration of Figure 7C to create a virtual bipolar 301d using electrodes formed on two leads, e.g., from electrode E1 to E5 and from electrode E9 to E13. This bipolar 301d configuration requires movement only along a single path 296 parallel to these leads because its field is large enough to recruit neural tissue adjacent to the two leads. This can further accelerate pain site detection.

[0086] In some aspects, the supersensory bipolars 301a to 301d used during sweet spot search include symmetric biphasic waveforms having the same pulse width PW and the same amplitude of pulse phases 30a and 30b actively driven (e.g., by stimulation circuit 28 or 44) (with flipped polarities during each phase) (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 the charge (Q 30a ) provided during the first pulse phase 30a is equal to the charge (Q 30b ) of the second pulse phase 30b, making these pulses charge balanced. The use of biphasic waveforms is also considered beneficial because, as is known, the cathode is greatly involved in neural tissue recruitment. When using biphasic pulses, the positions of the (virtual) anode and cathode will flip during the two phases of the pulse. This effectively doubles the neural tissue targeted for stimulation and thus increases the likelihood that the pain site 298 will be covered by the bipolars at the correct location.

[0087] The supersensory bipolars 301a to 301b, however, do not need to include symmetric biphasic pulses as described above. For example, the amplitudes and pulse widths of the two phases 30a and 30b can be different while maintaining charge (Q) balance for the two phases (e.g., Q 30a = A 30a * PW 30a = A 30b * PW 30b = Q 30b ). Alternatively, the two phases 30a and 30b can be charge unbalanced (e.g., Q 30a = A 30a * PW 30a > A 30b * PW 30b = Q 30b , or Q 30a = A 30a * PW 30a < A 30b * PW 30b = Q30b )。In summary, the pulses in bipolars 301 to 301d can be biphasically symmetric (and thus inherently charge balanced), biphasically asymmetric but still charge balanced, or biphasically asymmetric and charge unbalanced.

[0088] In a preferred example, the frequency F of the supersensory pulses 301a to 301d used during supersensory sweet spot search can be 10 kHz or lower, 1 kHz or lower, 500 Hz or lower, 300 Hz or lower, 200 Hz or lower, 130 Hz or lower, or 100 Hz or lower, or a range bounded by two of these frequencies (e.g., 100 to 130 Hz, or 100 to 200 Hz). In a particular example, frequencies of 90 Hz, 40 Hz, or 10 Hz can be used, where the pulses comprise biphasic pulses that are preferably symmetric. However, a single actively driven pulse phase followed by a passive recovery phase can also be used. The pulse width PW can also include values in the range of hundreds of microseconds, such as 150 microseconds to 400 microseconds. Since the purpose of the supersensory sweet spot search is merely to determine the electrodes that appropriately cover the patient's pain, the frequency and pulse width may not be as important at this stage. Once the electrodes are selected for sub-sensory stimulation, the frequency and pulse width can be optimized, as further discussed below.

[0089] It should be understood that the supersensory bipolars 301a to 301d used during the sweet spot search are not necessarily the same electrodes that are selected for subsequent sub-sensory treatment of the patient. Instead, the optimal location of the bipolars that are of interest during this search can be used as a basis for modifying the selected electrodes. Assume, for example, that bipolar 301a is used during the sweet spot search ( Figure 7A) and it is determined that the bipolar provides optimal pain relief when located at electrodes E13 and E14. At that time, sub-perceptual therapy can be continued for the patient using those electrodes E13 and E14. Alternatively, it may be advisable to modify the selected electrodes before attempting sub-perceptual therapy to see if the patient's symptoms can be further improved. For example, by using virtual poles as already described, the distance (focus) between the cathode and the anode can be varied. Or, a tripolar (anode / cathode / anode) consisting of electrodes E12 / E13 / E14 or E13 / E14 / E15 can be tried. See U.S. Patent Application Publication 2019 / 0175915 (discussing tripolars). Or electrodes on different leads can be tried in combination with E13 and E14. For example, since electrodes E5 and E6 are typically adjacent to electrodes E13 and E14, adding E5 or E6 as a source of anodic or cathodic current can be useful (again creating virtual poles). All of these types of adjustments should also be understood to include "steering" or adjustment of the "location" of the applied therapy, even if the center point of the stimulation does not change (as can occur when, for example, varying the distance or focus between the cathode and the anode).

[0090] In one example regarding Figure 8 illustrating multiple independent current control (MICC), in Figure 8 the stimulation circuit 28 ( Figure 1 ) or 44 ( Figure 3 ) in the IPG or ETS used to form a prescribed stimulation at the tissue of a patient is shown. The stimulation circuit 28 or 44 can independently control the current or charge at each electrode, and using the GUI 64 ( Figure 5 ) allows the current or charge to be directed to different electrodes, which is useful, for example, when moving the bipolar 301i along the path 296 during sweet spot search ( Figures 7A to 7D ). The stimulation circuit 28 or 44 includes one or more current sources 440 i and one or more current sinks 442 i . The source 440 i and the sink 442 i can include digital-to-analog converters (DACs), and can be referred to as PDAC 440 i and NDAC 442 i respectively according to the positive (generated, anodic) current and negative (absorbed, cathodic) current they emit. In the example shown, a pair of NDAC 440 i / PDAC442 iis dedicated to (hard - wired to) a particular electrode node ei 39. Each electrode node ei 39 is preferably connected to an electrode Ei 16 via a DC - blocking capacitor Ci38, which serves as a safety measure to prevent DC current injection into the patient in the event of, for example, a circuit fault in the stimulation circuits 28 or 44. PDAC 440 i and DNAC 442 i may also include a voltage source.

[0091] Via the GUI 64, for PDAC 440 i and NDAC 442 i appropriate control allows either the electrode 16 or the case electrode Ec 12 to act as an anode or a cathode to generate a current through the patient's tissue. Such control preferably appears 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 electrodes E2 and E3 as cathodes each with a current of -1.5 mA, then the control signal I1p will be set to have a 3 - mA digital equivalent to cause PDAC 4401 to generate +3 mA, and the control signals I2n and I3n will be set to have a 1.5 - mA digital equivalent to cause NDAC 4422 and 4423 to each generate -1.5 mA. Note that the definition of these control signals can also appear using the programmed amplitude A and percentage X% set in the GUI 64. For example, A can be set to 3 mA, where E1 is designated as the anode with X = 100%, and where E2 and E3 are designated as cathodes with X = 50%. Alternatively, the control signals can be set without using percentages, and instead the GUI 64 can simply specify the current that will be present at each electrode at any given time point.

[0092] In summary, the GUI 64 can be used to independently set the current at each electrode or direct the current between different electrodes. This is particularly useful in forming virtual bipoles, which were previously explained as including activating more than two electrodes. The MICC also allows for the formation of a finer electric field in the patient's tissue.

[0093] Other stimulation circuits 28 can also be used to implement the MICC. In an example not shown, a switch matrix can be interposed between one or more PDAC 440 i and the electrode nodes ei 39, and between one or more NDAC 442 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 the stimulation circuit can be found in the following documents: USP 6,181,969, 8,606,362, 8,620,436; and U.S. Patent Application Publications 2018 / 0071513, 2018 / 0071520, and 2019 / 0083796.

[0094] Many in the stimulation circuit 28 or 44, including the PDAC 440 i and the NDAC 442 i , the switch matrix (if present), and the electrode node 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 illustrated in these documents, one or more ASICs can also include: other circuits that can be used in the IPG 10, such as telemetry circuits (for interfacing the telemetry antenna that disconnects the chip from the IPG or ETS), circuits for generating the compliance voltage VH that powers the stimulation circuit, various measurement circuits, etc.

[0095] Although it is preferred to use a sweet spot search (and in particular a supersensory sweet spot search) to determine the electrodes to be used during subsequent sub-sensory therapy, it should be noted that this is not strictly necessary. Sub-sensory therapy can be led by a sub-sensory sweet spot search or can be completely unled by a sweet spot search. In summary, the sub-sensory therapy described next does not rely on the use of any sweet spot search.

[0096] In another aspect of the present invention, the inventors have determined via testing of SCS patients that there is a statistically significant correlation between the pulse width (PW) and the frequency (F) at which SCS patients will experience a reduction in back pain without paresthesia (sub-sensory). Using this information can help determine what pulse width is likely to be optimal for a given SCS patient based on a specific frequency and what frequency is likely to be optimal for a given SCS patient based on a specific pulse width. Beneficially, this information suggests that sub-sensory SCS stimulation without paresthesia can occur at frequencies of 10 kHz and below 10 kHz. The use of such low frequencies allows sub-sensory therapy to be used with much lower power consumption in the patient's IPG or ETS.

[0097] Figures 9 to 11C Shows results derived from testing patients at frequencies in the range of 1 kHz to 10 kHz. Figure 9Describes how data was collected from actual SCS patients and the criteria for patient inclusion in the study. First, patients with back pain who had not yet received SCS treatment were identified. The key patient inclusion criteria included: persistent lower back pain for more than 90 days; NRS pain scale of 5 or higher (NRS is described below); stable opioid treatment for 30 days; and a baseline Oswestry Disability Index score greater than or equal to 20 and less than or equal to 80. The key patient exclusion criteria included: having had back surgery within the previous 6 months; having other confounding medical / psychological conditions; and having untreated major mental illness or severe drug-related manifestation problems.

[0098] After such initial screening, patients regularly input qualitative measures of their pain (i.e., pain scores) into a portable electronic diary device, which may include a patient external controller 45, and the external controller 45 may in turn transmit its data to a clinician programmer 50( Figure 4 ). Such pain scores may include a numerical rating scale (NRS) score from 1 to 10 and may be input into the electronic diary three times daily. As Figure 10C shown, the baseline NRS score for patients who were ultimately not excluded from the study and had not yet received sub-perceptual stimulation treatment was approximately 6.75 / 10, with a standard error SE (sigma / SQRT(n)) of 0.25.

[0099] Returning to Figure 9 , the patient then has trial leads 15’( Figure 3 ) implanted on the left and right sides of the spine and is provided with external trial stimulation as previously described. The clinician programmer 50 is used to provide a stimulation program to each patient's ETS 40, as previously described. This is done to confirm that SCS treatment is helpful for a given patient in relieving their pain. If SCS treatment is not helpful for a given patient, the trial leads 15’ are removed and the patient is then excluded from the study.

[0100] Those patients for whom external trial stimulation is helpful ultimately 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 activated (E) and with what polarity (P) and relative amplitude (X%) to recruit and thus treat the nerve site in the patient at site 298. The sweet spot search can occur in any of the ways previously described with respect to Figures 6 to 7D but in the preferred embodiment will include supra-perceptual stimulation (e.g., such as, 7A to 7D) due to the benefits previously described. However, this is not strictly necessary and sub-perceptual stimulation may also be used during the sweet spot search. InFigure 9 In the example of [[ID=]], the sweet spot search appears at 10 kHz, but again, the frequency used during the sweet spot search can vary. Symmetric biphasic pulses are used during the sweet spot search, but again, this is not strictly necessary. Starting from the selection of electrode 16 present between thoracic vertebrae T9 and T10, it is decided which electrodes should be activated. However, electrodes as far as T8 and T11 can also be activated if necessary. Fluoroscopic images of lead 15 within each patient are used to determine which electrodes are adjacent to vertebrae T8, T9, T10, and T1.

[0101] During the sweet spot search, bipolar stimulation using only two electrodes is used for each patient, and only adjacent electrodes are used on a single lead 15, similar to that described in Figure 6 and Figure 7A Thus, the sweet spot for one patient may include stimulation of adjacent electrodes E4 (as the cathode) and E5 (as the anode) on the left lead 15, as shown previously in Figure 2 (the electrodes can be between T9 and T10), while the sweet spot for another patient may include stimulation of adjacent electrodes E9 (as the anode) and E10 (as the cathode) on the right lead 15 (the electrodes can be between T10 and T11). It is desired to use only adjacent electrode bipolar stimulation and only between vertebrae T8 to T11 to minimize the variation in treatment and symptoms between different patients in the study. However, more complex bipolars such as those described regarding Figures 7B to 7D can also be used during the sweet spot search. If patients have sweet spot electrodes at the desired thoracic location and if they experience pain relief of 30% or greater per NRS score, such patients continue in the study; patients not meeting these criteria are excluded from further study. While the study initially started with 39 patients, by Figure 9 19 patients had been excluded from the study, leaving a total of 20 remaining patients.

[0102] The remaining 20 patients then undergo a "washout" period, which means their IPG does not provide stimulation for a period of time. Specifically, the patients' NRS pain scores are monitored until their pain reaches 80% of their initial baseline pain. This is to ensure that the benefits of the previous stimulation do not carry over into the next analysis period.

[0103] Then, the remaining patients undergo sub-perceptual SCS treatment at different frequencies in the range of 1 kHz to 10 kHz using the electrodes activated at the previously determined sweet spot. However, this is not strictly necessary because, as previously mentioned, the current at each electrode is also independently controlled to help shape the electric field in the tissue. As shown in Figure 9 , the patients are each tested with stimulation pulses having frequencies of 10 kHz, 7 kHz, 4 kHz, and 1 kHz. For simplicity,Figure 9 It is shown that these frequencies are tested sequentially for each patient, but in practice these frequencies are applied to each patient in a random order. Once the test at a given frequency is completed, there is a washout period before the test at another frequency is started.

[0104] At each frequency being tested, 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 possible good pain relief without paresthesia (sub-perceptual). 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 (perceptual threshold) at which the patient can notice paresthesia. Then an initial stimulation is selected for the patient at 50% of this maximum amplitude, i.e., such that the stimulation is sub-perceptual and thus without paresthesia. However, other percentages (80%, 90%, etc.) of the maximum amplitude can also be selected and can vary with patient activity or position, as further described below. In one example, the stimulation circuits 28 or 44 in the IPG or ETS can be configured to receive instructions from the GUI 64 via selectable options (not shown) to reduce the amplitude of the stimulation pulses to a certain amount or percentage, or to reduce by a certain amount or percentage for presentation, such that the pulses can be made sub-perceptual (if they are not already sub-perceptual). Other stimulation parameters (e.g., pulse width, charge) can also be reduced to the same effect.

[0105] The patient will then leave the clinician's office and will thereafter communicate with the clinician (or their technician or programmer) to make adjustments to their stimulation (amplitude and pulse width) using their external controller 45 ( Figure 4 ). At the same time, the patient will enter their NRS pain score in their electronic diary (e.g., the external controller), again three times a day. The patient's adjustment of the amplitude and pulse width is generally an iterative process but is essentially an attempt to adjust based on feedback from the patient to adjust the treatment to reduce their pain while still ensuring that the stimulation is sub-perceptual. The test at each frequency lasts for approximately 3 weeks and stimulation adjustments may be made every two days or so. At the end of the test period at a given frequency, the optimal amplitude and pulse width have been determined and recorded for each patient, along with the patient's NRS pain score in their electronic diary for those optimal parameters.

[0106] 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 means for determining the patient's activity or position, such as an accelerometer. If the accelerometer indicates a high level of patient activity or a position where the electrodes will be further away from the spinal cord (e.g., lying down), the amplitude can be increased to a higher percentage to increase the current (e.g., 90% of the maximum amplitude). If the patient is experiencing a lower level of activity or a position where the electrodes will be closer to the spinal cord (e.g., standing), the amplitude can be decreased (e.g., to 50% of the maximum amplitude). Although not shown, the GUI 64 ( Figure 5 ) of the external device can include setting the percentage of the maximum amplitude at a point where the paresthesia becomes obvious to the patient, thereby allowing the patient to adjust the sub-perceptual current amplitude.

[0107] Preferably, multiple independent current control (MICC) is used to provide or adjust sub-perceptual therapy, as previously discussed with respect to Figure 8 . This allows independent setting of the current at each electrode to facilitate the steering of current or charge between electrodes, helps to form a virtual bipolar, and more generally allows shaping of the electric field in the patient's tissue. In particular, MICC can be used to direct sub-perceptual therapy to different locations in the electrode array and thus to the spinal cord. For example, once a set of sub-perceptual stimulation parameters has been selected for a patient, one or more of the stimulation parameters can be changed. Such changes can be warranted or dictated by the treatment location. The patient's physiology can vary at different spinal locations, and the tissue may be more or less conductive at different treatment locations. Thus, if the sub-perceptual treatment location is steered to a new location along the spinal cord (whose location change can include changing the anode / cathode distance or focus), at least one of the stimulation parameters, such as the amplitude, can be adjusted. As previously noted, sub-perceptual adjustment is facilitated and can occur during a programming session because a substantial wash-in period may not be necessary.

[0108] Adjustment of the sub-perceptual therapy can also include varying other stimulation parameters, such as pulse width, frequency, and even the duration of the interphase period (IP) ( Figure 2 ). The interphase duration can affect the neural dose, or the rate of charge infusion, such that a higher sub-perceptual amplitude will be used with a shorter interphase duration. In one example, the interphase duration can vary between 0 and 3 ms. After the washout period, the same protocol can be used to test the new frequency as described.

[0109] The sub-perceptual stimulation pulses used are symmetric biphasic constant current amplitude pulses, having a first pulse phase 30a and a second pulse phase 30b (having the same duration) (see Figure 2)。However, constant voltage amplitude pulses can also be used. Pulses of different shapes (triangular, sine wave, etc.) can also be used. When delivering sub-sensory therapy, pre-pulses (i.e., small currents provided before one or more actively driven pulse phases) can also occur to affect the polarization or depolarization of neural tissue. See, for example, USP 9,008,790.

[0110] Figures 10A to 10C Results of testing patients at 10 kHz, 7 kHz, 4 Hz, and 1 kHz are shown. Data as the average for 20 remaining patients at each frequency are shown in each plot, where error bars reflect the standard error (SE) between patients.

[0111] starting at Figure 10B shows the optimized amplitude A for 20 remaining patients at the frequency being measured. Interestingly, the optimal amplitude at each frequency is essentially constant, approximately 3 mA. Figure 10B Also shown is the amount of energy consumed at each frequency, more particularly showing the mean charge per second (MCS) attributable to the pulses (in mC / s). MCS is calculated by taking the optimal pulse width ( Figure 10A , discussed below) and multiplying it by the optimal amplitude (A) and frequency (F). This MCS value can include the neural dose. MCS is related to the current or power that the battery in the IPG 10 must consume to form the optimal pulses. Notably, MCS decreases significantly at lower frequencies: for example, the MCS at F = 1 kHz is approximately 1 / 3 of its value at higher frequencies (e.g., F = 7 kHz or 10 kHz). This means that the optimal SCS therapy for relieving back pain without paresthesia can be achieved at lower frequencies such as F = 1 kHz, where the additional benefit of lower power draw is more considerate for the battery of the IPG 10 (or the ETS 40).

[0112] Figure 10A Shows the optimal pulse width as a function of frequency in the frequency range of 1 kHz to 10 kHz tested. As shown, this relationship follows a trend with statistical significance: when modeled using linear regression 98a, PW = -8.22F + 106, where the pulse width is measured in microseconds and the frequency is measured in kilohertz, where the correlation coefficient R 2 is 0.974; when modeled using polynomial regression 98b, PW = 0.486F 2 – 13.6F + 116, again where the pulse width is measured in microseconds and the frequency is measured in kilohertz, where the even better correlation coefficient is R 2= 0.998. Other fitting methods can be used to establish the following additional information, which is related to the frequency and pulse width of the stimulation pulses that are formed to provide pain relief without paresthesia in the frequency range of 1 kHz to 10 kHz.

[0113] Note that the relationship between the optimal pulse width and frequency is not simply the expected relationship between frequency and duty cycle (DC) (i.e., the duration for which the pulse is "on" divided by its period (1 / F)). In this regard, note that a given frequency has a natural effect on the pulse width: it would be expected that higher frequency pulses would have smaller pulse widths. Thus, it could be expected, for example, that a 1 kHz waveform with a 100 microsecond pulse width would have the same clinical outcome as a 10 kHz waveform with a 10 microsecond pulse width, since the duty cycle of both waveforms is 10%. Figure 11A Shows the duty cycle resulting from the generation of a stimulation waveform using the optimal pulse width in the frequency range of 1 kHz to 10 kHz. Here, the duty cycle is calculated by considering only the total 'on' time of the first pulse phase 30a ( Figure 2 ); the duration of the symmetric second pulse phase is ignored. This duty cycle is not constant in the 1 kHz to 10 kHz frequency range: for example, the optimal pulse width at 1 kHz (104 microseconds) is not simply 10 times the optimal pulse width at 10 kHz (28.5 microseconds). Thus, the optimal pulse width is significant beyond just scaling with frequency.

[0114] Figure 10C Shows the average patient pain scores at the optimal stimulation parameters (optimal amplitude ( Figure 7B ) and pulse width ( Figure 7A )) for each frequency in the range of 1 kHz to 10 kHz. As previously noted, the patients in this study initially reported an average pain score of 6.75 before receiving SCS treatment. After SCS implantation and during the study, and using the amplitude and pulse width optimized during temporary sub-perceptual treatment, their average pain scores decreased significantly to an average pain score of approximately 3 for all frequencies measured.

[0115] Figure 11A Provides an in-depth analysis of the resulting relationship between the optimal pulse width and frequency in the frequency range of 1 kHz to 10 kHz. In Figure 11AThe figures in [study] show the average optimal pulse width for each of the 20 patients in the study, along with the standard error resulting from the variation between them. These are normalized at each frequency by dividing the standard error by the optimal pulse width, with the range of variation at each frequency being between 5.26% and 8.51%. From this, a 5% variation (lower than all the values calculated) can be assumed to be a variation that is statistically significant at all the measured frequencies.

[0116] Based on this 5% variation, 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 1 kHz is 104 microseconds, and 5% above this value (1.05 * 104 μs) is 109 microseconds; 5% below this value (0.95 * 104) is 98.3 microseconds. Similarly, the optimal average pulse width AVG(PW) at 4 kHz is 68.0 microseconds, and 5% above this value (1.05 * 68.0 μs) is 71.4 microseconds; 5% below this value (0.95 * 68.0 μs) is 64.6 microseconds. Thus, a statistically significant reduction in pain without paresthesia occurs within or above the linearly defined region 100a with points 102 being (1 kHz, 98.3 μs), (1 kHz, 109 μs), (4 kHz, 71.4 μs), and (4 kHz, 64.6 μs). The linearly defined region 100b 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). The 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 thus includes information related to the frequencies and pulse widths at which the stimulating pulses are formed to provide pain relief without paresthesia in the frequency range from 1 kHz to 10 kHz.

[0117] Figure 11BProvides an alternative analysis of the resulting relationship between the optimal pulse width and frequency. In this example, regions 100a to 100c are defined based on the standard error (SE) calculated at each frequency. Thus, the points 102 that define the corners of regions 100a to 100c are only located within the SE error bars (PW + SE, and PW - SE) at each frequency, although these error bars have different sizes at each frequency. Thus, a statistically significant reduction in pain without paresthesia occurs within or above the linearly defined region 100a at the points (1 kHz, 96.3 μs), (1 kHz, 112 μs), (4 kHz, 73.8 μs), and (4 kHz, 62.2 μs). The linearly defined regions 100b and 100c are similar, and since the points 102 that define them are illustrated in the graph at the top of Figure 11B it is not repeated here.

[0118] Figure 11C Provides another analysis of the resulting relationship between the optimal pulse width and frequency. In this example, regions 100a to 100c are defined based on the standard deviation (SD) calculated at each frequency, which is greater than the standard error (SE) metric used for that point. The points 102 that define the corners of regions 100a to 100c are located within the SD error bars (PW + SD, and PW - SD) at each frequency, although the points 102 could also be set within the error bars, similar to what was described previously regarding Figure 11A In any case, a statistically significant reduction in pain without paresthesia occurs 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 since the points 102 that define them are illustrated in the graph at the top of Figure 11C it is not repeated here.

[0119] More generally, although not shown, the regions within the frequency range of 1 kHz to 10 kHz that achieve sub-perceptual efficacy include the linearly defined region 100a (1 kHz, 50.0 μs), (1 kHz, 200.0 μs), (4 kHz, 110.0 μs), and (4 kHz, 30.0 μs); and / or the linearly defined region 100b (4 kHz, 110.0 μs), (4 kHz, 30.0 μs), (7 kHz, 30.0 μs), and (7 kHz, 60.0 μs); and / or the linearly defined region 100c (7 kHz, 30.0 μs), (7 kHz, 60.0 μs), (10 kHz, 40.0 μs), and (10 kHz, 20.0 μs).

[0120] In summary, one or more statistically significant regions 100 can be defined for the following optimal pulse width and frequency data, which are taken for the patients in the study to arrive at a combination of pulse width and frequency that reduces pain in the frequency range of 1 kHz to 10 kHz without paresthesia side effects, and different statistical measures of error can be used to define one or more regions in this way.

[0121] Figures 12A to 12D The results of treating other patients with sub-perceptual stimulation at frequencies of 1 kHz or below are shown. Testing of patients typically occurs after a suprathreshold sweet spot search (see Figures 7A to 7D ) to select the appropriate electrodes (E), polarities (P), and relative amplitudes (X%) for each patient, although the sub-perceptual electrodes used again may vary according to those used during the suprathreshold sweet spot search (e.g., using MICC). Although the form of the pulses used during sub-perceptual treatment may vary, symmetric biphasic bipolar is still used to test patients with sub-perceptual stimulation.

[0122] Figure 12A The relationship between the frequencies and pulse widths at which patients reported effective sub-perceptual treatment for frequencies of 1 kHz and below is shown. Note that the same patient selection and testing criteria described previously ( Figure 9 ) can be used when evaluating frequencies at 1 kHz or below, where the frequency is adjusted as appropriate.

[0123] 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 denoted as PW(high), and the lower end of the pulse width range at each frequency is denoted as PW(low). PW(mid) represents the middle (e.g., average) of PW(high) and PW(low) at each frequency. At each frequency of the measured frequencies, the amplitude (A) of the current provided is titrated down to the sub-perceptual level so that the patient cannot feel paresthesia. Typically, the current is titrated to 80% of the threshold at which paresthesia can be sensed. Since the anatomy of each patient 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.

[0124] Table 1 below presents in tabular form the optimal width and frequency data in Figure 12A for frequencies of 1 kHz or below, where the pulse width is expressed in microseconds:

[0125]

[0126] Table 1

[0127] As described previously for the analysis of frequencies in the range of 1 kHz to 10 kHz ( Figures 10A to 11C ), the data can be decomposed into defined regions 300i where effective sub-perceptual therapy below 1 kHz is achieved. For example, the regions of effective sub-perceptual therapy can be linearly bounded between the respective frequencies defining effectiveness and the high and low pulse widths. 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 providing good sub-perceptual therapy is defined by the linearly bounded region of the points (10 Hz, 265 μs), (10 Hz, 435 μs), (50 Hz, 370 μs), and (50 Hz, 230 μs). Table 2 defines the points linearly constraining each of the regions 300a to 300g shown in Figure 12A :

[0128] Region Bounded by the 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)

[0129] Table 2

[0130] The regions of sub-perceptual therapy effectiveness at 1 kHz or below 1 kHz can be defined in other statistically significant ways, such as those described previously for frequencies in the range of 1 kHz to 10 kHz ( Figures 11A - 11C ). For example, the region 300i can be defined by referring to the pulse width PW(med) at the middle of the respective ranges at each frequency. PW(med) can include, for example, the average optimal pulse width reported by the patients at each frequency, rather than the exact middle of the effective ranges reported by those patients. PW(high) and PW(low) can then be determined as the statistical variance from the average PW(med) at each frequency and can be used to set the upper and lower boundaries of the effective sub-perceptual regions. For example, PW(high) can include the average PW(med) plus the standard deviation or standard error, or a multiple of such statistical measures; PW(low) can similarly include the average PW(med) minus the standard deviation or standard error, or a multiple 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(med) plus a certain percentage, while PW(low) can include PW(med) minus a certain percentage. In summary, one or more statistically significant regions 300 can be defined for the optimal pulse width and frequency data at frequencies of 1 kHz or below 1 kHz where sub-perceptual stimulation is used to reduce pain without paresthesia side effects.

[0131] In addition, shown in Figure 12A are the average patient pain scores (NRS scores) reported by patients when using the optimal pulse width for different frequencies at 1 kHz or below 1 kHz. Before receiving SCS treatment, the average pain score initially reported by the patients was 7.92. After SCS implantation and using sub-perceptual stimulation at the optimal pulse width (with the ranges shown at each frequency), the average pain score of the patients decreased significantly. At 1 kHz, 200 Hz, and 10 Hz, the average pain scores reported by the patients were 2.38, 2.17, and 3.20, respectively. Thus, the clinical significance regarding pain relief is demonstrated when using the optimal width with sub-perceptual treatment at 1 kHz or below 1 kHz.

[0132] In Figure 12B the optimal pulse width and frequency data in Figure 12A were analyzed from the perspective of the middle pulse width PW(med) at each frequency (F) for frequencies at 1 kHz or below 1 kHz. As shown, the relationships 310a to 310d follow a trend with statistical significance, as demonstrated by the various regression models shown in Figure 12B and summarized in Table 3 below:

[0133]

[0134] Table 3

[0135] Other fitting methods can be used to establish the following additional information, which is related to the frequencies and pulse widths at which stimulation pulses are formed to provide sub-perceptual pain relief without paresthesia.

[0136] Regression analysis can also be used to define statistically relevant regions, such as 300a to 300g, where sub-perceptual treatment is effective at 1 kHz or below 1 kHz. For example, and although not shown in Figure 12B a regression can be performed for PW(low) v. F to set the lower boundary of the relevant region 300i, and a regression can be performed for PW(high) v. F to set the upper boundary of the relevant region 300i.

[0137] Note Figure 12A that the relationship between the optimal pulse width and frequency depicted in Figure 12C is not simply the expected relationship between frequency and duty cycle (DC) as shown in Figure 11A)Similarly, the duty cycle of the optimal pulse width is not constant at 1 kHz and below 1 kHz. Again, the optimal pulse width is beyond what is significant for mere frequency scaling. Nevertheless, most of the pulse widths observed to be optimal at 1 kHz and below 1 kHz are greater than 100 microseconds. Such pulse widths are not even possible at higher frequencies. For example, at 10 kHz, two pulse phases must fit within a 100 microsecond time period, so it is not even possible for the PW to be longer than 100.

[0138] Figure 12D Shows more benefits achieved using sub-perceptual therapy at frequencies of 1 kHz and below 1 kHz, namely reduced power consumption. Two sets of data are plotted. The first data set includes the average current (AVG Ibat) drawn by the battery in the patient's IPG or ETS using the optimal pulse width for that patient ( Figure 12A ) at each frequency, and the current amplitude A required to achieve sub-perceptual stimulation for that patient (again, this amplitude can vary for each in the patient). At 1 kHz, the average battery current is approximately 1700 microamps. However, as the frequency decreases, this average battery current drops to approximately 200 microamps at 10 Hz. The second data set considers power consumption in terms of a different benefit point, namely the number of days an IPG or ETS with a fully charged rechargeable battery can operate before it needs to be recharged ("discharge time"). Based on the average battery current data, it would be expected that when the average battery current is high, the discharge time is lower at higher frequencies (e.g., approximately 3.9 days at 1 kHz, depending on various charging parameters and settings), and when the average battery current is low, the discharge time is higher at lower frequencies (e.g., approximately 34 days at 10 Hz, depending on various charging parameters and settings). This is important: when using the optimal pulse width, not only can effective sub-perceptual therapy be provided at frequencies of 1 kHz and below 1 kHz; the power consumption is significantly reduced, which can place less stress on the IPG or ETS and allow it to operate for a long time. As pointed out above, when sub-perceptual therapy is used conventionally at higher frequencies, excessive power consumption is a serious problem. Note Figure 12D The data in can also be analyzed in terms of the average charge per second (MSC), as described previously for the data from 1 kHz to 10 kHz ( Figure 10B ).

[0139] Figure 13A and Figure 13B Shows the results of additional tests that verified the relationship between frequency and pulse width just presented. Here, data from 25 patients tested using sub-perceptual stimulation at frequencies of 10 kHz and below are shown. Figure 13ATwo different graphs are shown, which show the results for frequencies of 10 kHz and below (lower graph) and 1 kHz and below (upper graph). The mean shows the frequency and pulse width values at which optimal sub-perceptual therapy is produced. The upper and lower limits represent the variance of one standard deviation above and below the mean (+STD and –STD). Figure 13B Shows the curve fitting results determined using the mean. The data at 1 kHz and below were fitted using an exponential function and a power function, resulting in the relationships PW = 159e -0.01F +220e -0.00057F and PW = 761 – 317F 0.10 , both of which fit the data well. The data at 10 kHz and below were fitted using a power function, resulting in PW = -1861 + 2356F -0.024 , again fitting well. The data can also be fitted to other mathematical functions.

[0140] Once determined, the information 350 related to the frequency and pulse width for optimal sub-perceptual therapy without paresthesia can be stored in an external device for programming the IPG 10 or ETS 40, such as the clinician programmer 50 or external controller 45 described previously. This is shown in Figure 14 where the control circuit 70 or 48 of the clinician programmer or external controller is associated with: area information 100i or relationship information 98i for frequencies in the range of 1 kHz to 10 kHz, and area information 300i or relationship information 310i for 1 kHz or below 1 kHz. Such information can be stored in a memory within the control circuit or a memory associated with the controller. Storing this information using an external device is useful for assisting the clinician in sub-perceptual optimization, as further described below. Alternatively, and although not shown, the information related to frequency and pulse width can be stored in the IPG 10 or ETS 40, thus allowing the IPG or ETS to optimize itself without clinician or patient input.

[0141] The information 350 can be incorporated into a fitting module. For example, the fitting module 350 can operate as a software module within the clinician programmer software 66 and may perhaps be implemented as an option selectable within an advanced menu 88 or mode menu 90 selectable in the clinician programmer GUI64( Figure 6 ). The fitting module 350 can also operate in the control circuit of the IPG 10 or ETS 40.

[0142] The fitting module 350 can be used to optimize the pulse width when the frequency is known, or vice versa. As Figure 14As shown at the top, a clinician or patient can input a frequency F into the clinician programmer 50 or the external controller 45. This frequency F is passed to the fitting module 350 to determine a pulse width PW for the patient that statistically has the potential to provide adequate pain relief without paresthesia. The frequency F can be input, for example, into relationship 98i or 310i to determine the pulse width PW. Alternatively, the frequency can be compared to the associated region 100i or 300i into 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 may be 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, activation electrode E, their relative percentage X%, and electrode polarity P) can be determined in other ways such as those described below to arrive at a complete stimulation program (SP) for the patient. Based on data from Figure 10B An amplitude approaching 3.0 mA can be used as a logical starting point because it is shown that this amplitude is preferred by the patient in the range of 1 kHz to 10 kHz. However, other initial starting amplitudes can also be selected, and the amplitude for sub-perceptual therapy can depend on the frequency. Figure 14 The use of the fitting module 350 is shown at the bottom in the reverse manner (i.e., selecting a frequency given a pulse width). Note that in subsequent algorithms, and even in algorithms used outside of any algorithm, in one example, the system can allow the user to associate a frequency and a pulse width so that when the frequency or the pulse width changes, the other of the pulse width or the frequency is automatically changed to correspond to the optimal setting. In one embodiment, associating the frequency with the pulse width in this manner can include an optional feature (e.g., in the GUI64) that can be used when sub-perceptual programming is desired, and associating the frequency with the pulse width may not be selectable or may not be available for use with other stimulation modes.

[0143] Figure 15 An algorithm 355 is shown that can be used to provide sub-perceptual therapy to an SCS patient at frequencies of 10 kHz or lower, and summarizes some of the steps discussed above. Steps 320 to 328 describe a supra-perceptual sweet spot search. The user (e.g., a clinician) selects electrodes, for example, by using the GUI of the clinician programmer to create a bipolar for the patient (320). This bipolar is preferably a symmetric biphasic bipolar and can include a virtual bipolar, as previously described.

[0144] The bipolar, along with other analog parameters, is transmitted to the IPG or ETS using a telemetry transmitter for execution (321). Such other stimulation parameters can also be selected in the clinician programmer using the 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 supersensory, i.e., if the patient does not feel paresthesia, the amplitude A or other stimulation parameters can be adjusted to make it so (322). The patient then measures the effectiveness of the bipolar (324) to see to what extent the bipolar is covering the patient's pain site. The NRS or other scoring system can be used to determine effectiveness.

[0145] If the bipolar is ineffective, or if it still needs to be searched for, a new bipolar can be tried (326). That is, a new electrode can preferably be selected in such a way as to move the bipolar to a new position along path 296, as previously described with respect to Figures 7A to 7D The new bipolar can then be transmitted again to the IPG or ETS using the telemetry transmitter (321) and adjusted if necessary to render the bipolar supersensory (322). If the bipolar is effective, or if the search has been completed and the most effective bipolar has been located, the bipolar can optionally be modified prior to sub-sensory therapy (328). Such modifications as described above can involve: selecting other electrodes adjacent to the selected bipolar electrodes to modify the field shape in the tissue to potentially better cover the patient's pain. Thus, the modification in step 328 can change the bipolar used during the search to a virtual bipolar, or a tripolar, etc.

[0146] Modification of other stimulation parameters can also occur at this point. For example, the frequency and pulse width can be modified. In one example, a working pulse width can be selected that provides good, comfortable paresthesia coverage (>80%). This can occur by using a frequency of, for example, 200 Hz and starting with a pulse width of, for example, 120 microseconds. The pulse width can be increased at this frequency until good paresthesia coverage is felt. An amplitude in the range of, for example, 4 mA to 9 mA can be used.

[0147] At this point, the electrodes (E) selected for stimulation, their polarities (P), and the fraction (X%) of the current they will receive (and possibly the working pulse width) are known and will be used to provide sub-sensory therapy. To ensure that sub-sensory therapy is provided, the amplitude A of the stimulation is titrated down to a sub-sensory, non-paresthesia level (330) and transmitted to the IPG or ETS using the telemetry transmitter. As described above, the amplitude A can be set below the amplitude threshold (e.g., 80% of the threshold), where the patient can just begin to feel paresthesia.

[0148] At this point, it may be useful to optimize (332) the frequency and pulse width of the sub-perceptual therapy provided to the forward patient. While the frequency (F) and pulse width (PW) used during the sweet spot search can be used for sub-perceptual therapy, it is also beneficial to adjust these parameters to optimal values based on the region 100i or relationship 98i established at frequencies in the range of 1 kHz to 10 kHz, or the region 300i or relationship 310i established at frequencies of 1 kHz or below 1 kHz. Such optimization can use Figure 14 the fitting module 350 in Figure 15 and can occur in different ways, and some methods 332a to 332c of optimization are shown in

[0149] Frequency or pulse width optimization can occur in other ways that more effectively search the desired part of the 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 the patient can also be considered.

[0150] Preferably, when optimizing the frequency (≤10 kHz) and pulse width at step 332, these parameters are selected in a way that reduces power consumption. In this regard, it is preferred to select the lowest frequency because this will reduce the mean charge per second (MCS), reduce the average current drawn from the battery in the IPG or ETS, and thus increase the discharge time, as previously discussed with respect to Figure 10B and Figure 12D Reducing the pulse width (if possible) will also reduce battery draw and increase the discharge time.

[0151] At this point, all relevant stimulation parameters (E, P, X, I, PW, and F (≤ 10 kHz)) are determined and can be sent from the clinician programmer to the IPG or ETS for execution (334) to provide sub-perceptual stimulation therapy to the patient. Adjustment (332) of the possible optimal pulse width and frequency (≤ 10 kHz) can cause these stimulation parameters to provide paresthesia. Thus, if necessary, the amplitude of current A can be titrated down again to the sub-perceptual level (336). If necessary, a prescribed sub-perceptual therapy can be allowed to have a wash-in period for a period of time (338), although as previously described this may not be necessary because the supra-perceptual sweet spot search (320 - 328) has selected the electrodes for the case of well-recruiting the patient's pain site.

[0152] If the sub-perceptual therapy is ineffective, or adjustment is available, the algorithm can return to step 332 to select a new frequency (≤ 10 kHz) and / or pulse width according to the previously defined region or relationship.

[0153] It should be noted that Figure 15 not all parts of the steps of the algorithm in need to be executed in actual implementation. For example, if the effective electrodes (i.e., E, P, X) are known, the algorithm can start with sub-perceptual optimization using information related to the frequency and pulse width.

[0154] Figure 16 Another way is shown, in which the fitting module 350 ( Figure 14 ) can be used to determine the optimal sub-perceptual stimulation for the patient at a frequency of 10 kHz or less. In Figure 16 , the fitting module 350 is incorporated into or used by the algorithm 150 again, which can again be executed as part of its software on the control circuit of the external device, or executed in the IPG 10. In the algorithm 105, the fitting module 350 is used to pick an initial pulse width at a given 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 pick the optimized stimulation parameters that will result in the lowest power requirement of most concern to the battery 14 of the IPG. Some of the steps shown for the algorithm 105 in Figure 16 are optional, and other steps can also be added. It is assumed that the sweet spot search for testing the patient has occurred through the algorithm 105, 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 because these electrode parameters can also be modified as described above.

[0155] Algorithm 105 begins by picking an initial frequency (e.g., F1) within a range of interest (e.g., ≤10 kHz). Algorithm 105 then transmits this frequency to fitting module 350, which picks an initial pulse width PW1 using previously determined relationships and / or regions. For simplicity, in Figure 16 fitting module 350 is shown as a simple look-up table of pulse width versus frequency, which may include another form of information related to the frequencies and pulse widths at which stimulation pulses are formed to provide pain relief without paresthesia. The selection of the pulse width using fitting module 350 can be more refined, as previously described.

[0156] After selecting the pulse width for a given frequency, the stimulation amplitude A(120) is optimized. Here, multiple amplitudes are selected and applied to the patient. In this example, the selected amplitudes are preferably determined using the optimal amplitude A determined at each frequency (see, e.g., Figure 10B ). Thus, the patient tries amplitudes of A = A2, lower (A1), and higher (A3) over a period of time (e.g., every two days). The best of these is picked by the patient. At this point, further adjustment of the amplitude can be attempted to refine and hone in on the optimal amplitude for the patient. For example, if A2 is preferred, amplitudes slightly higher (A2 + Δ) and slightly lower (A2 - Δ) can be tried over a period of time. If a lower value of A1 is preferred, 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, such iterative testing of amplitudes arrives at an effective amplitude for the patient that does not cause paresthesia.

[0157] Next, the pulse width (130) can be optimized for the patient. As with the amplitude, this can occur by slightly decreasing or increasing the previously selected pulse width (350). For example, at a frequency of F1 and an initial pulse width of PW1, the pulse width can be decreased (PW1 - Δ) and increased (PW1 + Δ) to see if such settings are preferred by the patient. Further iterative adjustment of the amplitude and pulse width can occur at this point, although this is not shown.

[0158] 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 desired that these values will likely provide effective and paresthesia-free pain relief. Nevertheless, because each patient is different, the amplitude (120) and pulse width (130) are also adjusted based on the initial values for each patient.

[0159] Thereafter, the optimal stimulation parameters determined for the patient at the measured frequency are stored in the software (135). Optionally, the average charge per second (MCS) indicative of the neuromedication received by the patient, or other information indicative of power draw (e.g., average Ibat, discharge time) is also calculated and stored. If yet another frequency (e.g., F2) within the range of interest has not yet been tested, it is tested as described above.

[0160] Once one or more frequencies have been tested, the optimal stimulation parameters (135) previously stored for the patient at each frequency can be used to select the stimulation parameters (140) for the patient. Since the stimulation parameters are suitable for the patient at each frequency, the selected stimulation parameters can include the stimulation parameters that result in the lowest power draw (e.g., lowest) MSC. This is desirable because these stimulation parameters will be the easiest on the IPG's battery. It is expected that the stimulation parameters with the lowest MCS determined by algorithm 105 will include those taken at the lowest frequency. However, each patient is different and thus this may not be the case. Once the stimulation parameters have been selected, further amplitude optimization (150) can be taken, where the aim is to select the minimum amplitude to provide sub-perceptual pain relief without paresthesia.

[0161] The results of the further investigation are shown in Figures 17 - 22D which aims to provide an optimal sub-perceptual model that takes into account the perception threshold (pth) as well as the frequency (F) and pulse width (PW). When modeling sub-perceptual stimulation and using such modeling information to determine the optimal sub-threshold stimulation parameters for each patient, the perception threshold can be an important factor to consider. The perception threshold pth includes the lowest amplitude (e.g., in mA) at which the patient can feel the effect of paresthesia, and amplitudes below this cause sub-perceptual stimulation. In fact, different patients will have different perception thresholds. Different perception thresholds result in large part because the electrode array in some patients may be closer to the spinal nerve fibers than in other patients. Thus, such patients will experience perception at a lower amplitude, i.e., for these patients, the pth will be lower. If the electrode array in other patients is farther from the spinal nerve fibers, the perception threshold pth will be higher. The improved model takes into account the understanding of pth because, in addition to the optimal frequency and pulse width, the inclusion of this parameter can be used to suggest the optimal amplitude A for the patient's sub-perceptual stimulation.

[0162] With this in mind, data is obtained from the patient 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 Figure 17 which is determined based on tests performed on a patient sample (N = 25), whereFigure 17 shows the average values determined by three-dimensional regression fitting, which results in model 390 as a surface in the frequency-pulse width-perception threshold space. As Figure 17 the data represented were obtained at frequencies of 1 kHz and below. The data at these frequencies are of particular interest because, as already mentioned, lower frequencies are more concerned with energy use in the IPG or ETS, and thus, it is particularly necessary to demonstrate the utility of sub-perceptual stimulation in this frequency range. As can be seen from Figure 17 the equation in, the data obtained from the patient were modeled with a good fit by assuming that the frequency varies according to a power function with respect to the pulse width (a(PW)b) and the perception threshold pth (c(pth)d). Although these functions provide a suitable fit, other types of mathematical equations can also be used for fitting. Model 390 as a surface fit results in the following: F(PW,pth) = 4.94x10 8 (PW)-2.749 + 1.358(pth) 2 . Note that the frequency, pulse width, and perception threshold are not simply proportionally related or inversely related in model 390, but instead are related by a non-linear function.

[0163] Figure 18A shows further observations noted from the measured patients and provides another modeling aspect that, together with model 390, can be used to determine the optimal sub-threshold stimulation parameters for the patient. Figure 18A shows how the perception threshold pth of the measured patients varies according to the pulse width, where each patient is represented by a different line in the Figure 18A figure. Analysis of each line shows that the relationship between pth and PW can be well modeled using a power function, i.e., pth(PW) = i(PW)j + k, although other mathematical functions can also be used for fitting. Figure 18A The data were obtained for each patient at a nominal frequency (such as 200 to 500 Hz), and further analysis confirmed that the results do not vary greatly with frequency (at least at frequencies of 150 Hz and higher, using biphasic pulses with active charging). Figure 18A The pulse width in is limited to a range of approximately 100 to 400 microseconds. Limiting the analysis to these pulse widths is reasonable because previous tests (e.g., Figure 12A ) showed that pulse widths in this range have a unique sub-perceptual therapeutic effect at frequencies of 1 kHz and below. Figure 18BShows another example of pth versus pulse width for different patients and shows another equation that can be used to model the data. Specifically, in this example, the Weiss-Lapicque or strength-duration equation is used, which relates the amplitude and pulse width required to reach a threshold. The equation takes the form pth = (1 / a)(1 + b / PW), and when averaging the data for different patients, the constants a = 0.60 and b = 317 give a good fit, where these values represent average constant parameters extracted from population data.

[0164] Figure 19 Shows further observations noted from the patient being tested and provides yet another aspect of modeling. In fact, Figure 19 Shows how the optimal sub-perceptual amplitude A of a patient varies according to the patient's perceptual threshold pth and pulse width. In Figure 19 the graph, the vertical axis plots the parameter Z, which is related to the patient's perceptual threshold pth and their optimal amplitude A (which will be below pth in sub-perceptual therapy). Specifically, Z is the optimal amplitude expressed as a percentage of pth, i.e., Z = A / pth. As Figure 19 shown, Z varies with pulse width. At a smaller pulse width (e.g., 150 microseconds), Z is relatively low, which means the patient's optimal amplitude A is noted to be significantly below their perceptual threshold (e.g., A = 40% of pth). At a longer pulse width (e.g., 350 microseconds), Z is higher, which means the patient's optimal amplitude A is noted to be closer to their perceptual threshold (e.g., A = 70% of pth). As noted from testing various patients, Z and PW generally have a linear relationship over the tested pulse widths, and thus linear regression is used to determine the relationship between them, yielding Z = 0.0017(PW) + 0.1524(395). Similarly, Figure 19 the tests in Figure 19The modeling 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 have observed that the optimal amplitude A is generally invariant to changes in frequency and pulse width. However, the perception threshold varies with the pulse width. Thus, Z varies with the pulse width, while the optimal amplitude A may not.

[0165] Recognizing these observations and modeling them, 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 the 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.

[0166] From Figure 20A The algorithm 400, shown starting at

[0167] begins in step 402 by determining the sweet spot in the electrode array on which the therapy should be applied for a given patient, i.e., by identifying which electrodes should be activated and with what polarities and percentages (X%). For a given patient, the results of the sweet spot search may be known, and thus step 402 should be understood as optional. Step 402 and subsequent steps can be accomplished using the clinician programmer 50.

[0167] At step 404, a new patient is tested by providing conditioning pulses, and in the algorithm 400, this testing involves measuring the patient's perception threshold pth at various pulse widths using the sweet spot electrodes that have been identified at step 402 during the test procedure. As previously discussed with respect to Figure 18A and Figure 18B the testing at different pulse widths can occur at a nominal frequency (such as in the range of 200 to 500 Hz). Determining pth at each given pulse width involves applying the pulse width and gradually increasing the amplitude A to the point where the patient reports feeling the stimulus (paresthesia), thereby yielding pth expressed in amplitude (e.g., milliamps). Alternatively, determining pth at each given pulse width can involve decreasing the amplitude A to the point where the patient reports no longer feeling the stimulus (sub-threshold). The testing 404 for a particular patient is shown in Figure 20A both graphically and in tabular form. Here, it is assumed that the patient under discussion has a paresthesia threshold pth of 10.2 mA at a pulse width of 120 microseconds. At a pulse width of 350 microseconds, pth is 5.9 mA, and other values are in between.

[0168] Next, in step 406, algorithm 400 in 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 have been noted earlier to model pth and PW well in other patients, such as the power function pth(PW) = i(PW) j + k or the WeissLapicque equation, as previously discussed regarding Figure 18A and Figure 18B However, any other mathematical function can be used to curve-fit the data measurements 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 for the patient can be stored in the memory of clinician programmer 50 for use in subsequent steps.

[0169] Next, and referring to Figure 20B , algorithm 400 continues to compare the pth(PW) relationship determined in step 406 with model 390. This will be illustrated with reference to the table shown in Figure 20B . In this table, the values of pth and PW determined by pth(PW)(406) as determined in Figure 20A are filled in. 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 widths used during the patient test in step 404). Although only six rows of PW v. pth values are shown in the table in Figure 20B , this may be a longer vector of values where pth is determined in discrete PW steps (such as 10 microsecond steps).

[0170] In step 408, the pth v. PW values (from function 406) are compared with three-dimensional model 390 to determine the optimal frequency F at these various pth v. PW pairings. In other words, the pth and PW values are provided as variables into the surface fitting equation (F(PW, pth))390 in Figure 17 to determine the optimal frequencies, which are also shown as filled in the chart in Figure 20B . At this point,[[]] Figure 20B ​The table in represents vector 410, which 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 that are optimal for the patient within model 390. Note that vector 410 for the patient can be represented as a curve along the three-dimensional model 390, as Figure 20B shown in .

[0171] Next, and as Figure 20C shown in step 412 of , vector 410 can optionally be used to form another vector 413 that contains values of interest or more practically 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, even if effective for the patient, such frequencies would involve excessive power draw. See, for example, Figure 12D . Further, the IPG or ETS under discussion may only provide pulses at frequencies with discrete intervals (such as in 10 Hz increments). Thus, in vector 413, the frequencies of interest or supported frequencies (such as 1000 Hz, 400 Hz, 200 Hz, 100 Hz, etc.) are 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 it easier to populate vector 413. Nevertheless, vector 413 includes substantially the same information as vector 410, although at the desired frequencies. It is realized that the IPG or ETS may only support certain pulse widths (e.g., in 10 microsecond increments). Thus, although not shown in the figures, the pulse widths in vector 413 can be adjusted (e.g., rounded) to the closest supported value.

[0172] Next, and with reference to Figure 20D , algorithm 400 determines the optimal amplitude of the pulse width and pth values in vector 413 (or vector 410 if vector 413 is not used) in step 414. This occurs by using the amplitude function 396, i.e., A(pth,PW), determined earlier in Figure 19 . Using this function, the optimal amplitude A can be determined for each pth, PW pair in the table.

[0173] 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 optimal subthreshold amplitude A for the patient (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 can allow the patient to adjust their stimulation to a supra-perceptual level if desired. At this point, the optimal stimulation parameters 420 can then be sent to the IPG or ETS for execution, or as shown in step 422, they can be sent to the patient's external controller 45, as described below.

[0174] Figure 20E and Figure 20F The optimal parameters 420 are depicted graphically. 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: Figure 20E shows the relationship between frequency and pulse width, and Figure 20F shows the relationship between frequency and amplitude. It should also be noted that Figure 20F shows the paresthesia threshold pth, and also shows on the X-axis the pulse widths corresponding to various frequencies from Figure 20E . Note that the shape of the data on these graphs can vary from patient to patient (e.g., based on Figure 20A pth measurements), and can also change depending on the underlying modeling used (e.g., Figures 17 - 19 ). Thus, the various shapes of the trends shown should not be construed as restrictive.

[0175] The optimal stimulation parameters 420 determined by the algorithm 400 include a range or vector of values, including frequency / pulse width / amplitude coordinates based on modeling ( Figures 17 - 19 ) and patient testing ( Figure 20A , step 404) that will result in optimal subthreshold stimulation for that patient. Although for simplicity, the optimal parameters 420 are shown in tabular form in Figure 21 , it should be understood that these optimal parameters (O) can be curve-fitted using equations that include 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 them 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 tabular form or equation form, can be loaded into the control circuit 48 of the external controller 45.

[0176] Once loaded, the patient can access the menu in the external controller 45 to adjust the therapy provided by the IPG or ETS in accordance with these optimal parameters 420. For example, Figure 21 A graphical user interface (GUI) of the external controller 45 as shown on its screen 46 is illustrated. The GUI includes means for allowing the patient to simultaneously adjust the stimulation within the range of the determined optimal stimulation parameters 420. In one example, the GUI includes a slider with a cursor 430. The patient can select the cursor 430 and, in this example, 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 included in the optimal parameters (e.g., 1000 Hz). 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 fact, the cursor 430 allows the patient to browse through the optimal parameters 420 to find their preferred F / PW / A settings, or simply select stimulation parameters that are still effective but require less power to be drawn from the IPG or ETS (e.g., at a lower frequency). Note that the frequency, pulse width, and amplitude may not be adjusted proportionally or inversely relative to each other, but will follow a non-linear relationship according to the underlying modeling.

[0177] In another example, it may be useful to allow the patient to adjust the stimulation without knowing the stimulation parameters, i.e., without the parameters being displayed, which may be too technical for the patient to understand. In this regard, the slider can be marked with more general parameters, such as a parameter that the patient can adjust, such as between 0% and 100%. The three-dimensional simulation parameters A, PW, and F can be mapped to this one-dimensional parameter (e.g., as shown, 4.2 mA, 413 μs, and 50 Hz can be equal to 0%). Generally, the patient can understand the parameter as a kind of "intensity" or "nerve dose" with an increasingly higher percentage. In fact, depending on the way the optimal stimulation parameters 420 are mapped to this, this may be correct.

[0178] It should be understood that although 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.

[0179] Other stimulation adjustment controls may also be provided by the external controller 45. For example, as Figure 21 shown, 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 will run for a period of time (from seconds to minutes) and then be off for the same duration. Since "duty cycle" may be a technical concept that is not intuitive for the patient, note that the duty cycle can be labeled in a more intuitive way. Thus, and as shown, the duty cycle adjustment can be labeled differently. For example, since a lower duty cycle affects lower power draw, the duty cycle slider can be labeled as a "power saving" function, a "total energy" function, a "total nerve charge dose" function, or the like, which may make it easier for the patient to understand. The duty cycle can also include a function locked to the patient's external controller 45 and be accessible to the clinician only 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, the duty cycle adjustment is not shown in the subsequent user interface examples but can also be used in such examples.

[0180] Figures 22A - 22D Addresses the practicality that the modeling leading to the determination of the optimal parameters 420 may not be perfect. For example, the model 390 (modeling the frequency as a function of PW and pth (F(PW,pth); Figure 17 )) is averaged from various patients and can have some statistical variance. This is simply illustrated in Figure 22A by showing surfaces 390+ and 390- that are higher and lower than the average reflected in the surface model 390. The surfaces 390+ and 390- can represent a measure of statistical variance or error, such as plus or minus one sigma, and can actually generally include error bars beyond which the model 390 will no longer be reliable. These error bars 390+ and 390- (which may not be constant across the entire surface 390) can also be determined from an understanding of the statistical variance of the various constants assumed during the modeling. For example, different confidence measures can be used to determine the values a, b, c, and d in the model 390. As Figure 17 shown, the constants a, b, and c vary within a 95% confidence interval. For example, the range of the constant "a" can be from 5.53x10 7to 9.32x10 8 , as shown. (Here, it is assumed that the constant d is only 2 and does not change). Similarly, the values used to model the relationship between pth and pulse width ( Figure 18A and Figure 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 ( Figure 19 ). As time goes by, as more patient data is collected, it can be expected that the confidence of these models will increase. In this regard, note that by loading new modeling information into the clinician programmer 50, the algorithm 400 can be easily updated with new modeling information from time to time.

[0181] Statistical variance means that the optimal stimulation parameters may not include discrete values, but instead may fall within a volume. This is shown in Figure 22A with respect to the vector 410 determined for the patient (see Figure 20B ). Given the statistical variance, the vector 410 can include a rigid line within the volume 410'. In other words, there may not be a one-to-one correspondence between PW, pth, and F, as is the case with the vector 410 in Figure 20B . Instead, for any given variable (such as the pulse width), the pth determined for the patient (using the pth(PW) model in step 406) can vary within a range between a statistically significant maximum and minimum, as shown in Figure 22B . The statistical variation in the model 390 ( Figure 17 ) may also mean that the maximum and minimum frequencies can be determined for each maximum and minimum pth in step 408. Since this trickles through the algorithm 400, the optimal stimulation parameters 420 may also not have a one-to-one correspondence between frequency, pulse width, and amplitude. Instead, and as shown in Figure 22B , for any frequency, there may be a range of statistically significant maximum and minimum optimal pulse widths, and similarly a range of optimal amplitudes A. Then, effectively, the optimal stimulation parameters 420' can be defined as a coordinate volume with statistical significance in the frequency - pulse width - amplitude space rather than on a coordinate line. The paresthesia threshold pth may also vary within a range, and as mentioned earlier, 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-perceptual to supra-perceptual, as discussed in some of the later examples.

[0182] Figure 22C and Figure 22DGraphically depicts the optimal parameter 420’, showing the statistically relevant range of pulse widths suitable for the patient at each frequency and the statistically relevant range of amplitudes. Although the optimal parameter 420’ in this example includes a three-dimensional coordinate volume (F, PW, and A), for ease of illustration, they are depicted in two two-dimensional graphs, similar to what appeared previously in Figure 20E and Figure 20F : Figure 22C Shows the relationship between frequency and pulse width, and Figure 22D Shows the relationship between frequency and amplitude. Also note that Figure 22D Shows the paresthesia threshold pth, which, like 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 in the absence of statistical variance, see Figure 20E and Figure 20F ), and as expected, they fall within the broader volume of parameters specified by 420’.

[0183] In the case of defining the volume of the optimal parameter 420’, it may be useful to then allow the patient to navigate different settings within the volume of the optimal parameter 420’ using his external controller 45. This is shown in an example in Figure 22E Here, the GUI of the external controller 45 does not display a single linear slider, but rather a three-dimensional volume representing the volume of the optimal parameter 420’, where the different axes represent the changes the patient can make in frequency, pulse width, and amplitude. As previously mentioned, the GUI of the external controller 45 allows the patient a certain degree of flexibility to modify the stimulation parameters of his IPG or ETS, and allows the patient to simultaneously adjust all three stimulation parameters with a single adjustment action and using a single user interface element.

[0184] It is possible to allow the patient to browse different GUIs that determine the volume of the optimal parameter 420’, and Figure 22F shows another example. In Figure 22EIn it, two sliders are shown. The first linear slider is controlled by cursor 430a, allowing the patient to adjust the frequency according to 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 ranges of the pulse width and amplitude are constrained by the optimal parameters 420' and the frequency selected using cursor 430a. For example, if the user selects to use a frequency F = 400 Hz, the external controller 45 can consult the optimal parameters 420' to automatically determine the optimal ranges of the 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 ranges of the pulse width and amplitude that can be selected using cursor 430b will automatically change to ensure that the sub-threshold stimulation remains within the volume 420' determined to be statistically useful for the patient.

[0185] Figure 23 Another example is shown where the user can use the exported optimal stimulation parameters to program the settings of their IPG 10 (or ETS). The subsequent examples for completeness use the determined volume 420' of the optimal stimulation parameters, but vectors or ranges 420 of the optimal stimulation parameters can also be used.

[0186] Figure 23 A user interface on the screen 46 of the patient's external controller 45 is shown, which 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, which provides stimulation parameters with low power draw; a sleep mode 502, which optimizes the stimulation parameters for the patient when sleeping; a sensory mode 504, which allows the patient to feel the stimulation (hyperesthesia); a comfort mode 506 for general daily use; an exercise mode 508, which provides stimulation parameters suitable for when the patient is exercising; and an intense mode 510, which can be used, for example, if the patient is experiencing pain and would benefit from a more intense stimulation. Such stimulation modes can indicate 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 can 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 the patient's external controller, it is also realized in other examples that another external device that can be used to program the patient's IPG can also be used to select the stimulation mode, such as the clinician programmer 50.

[0187] The patient can select from these stimulation patterns, and such selection can program the IPG 10 to provide a subset of stimulation parameters useful for that pattern controlled by the optimal stimulation parameters 420'. For some stimulation patterns, the subset of stimulation parameters can be completely volume-constrained (entirely within) by the optimal stimulation parameters 420' determined for the patient, and thus will provide the patient with optimal sub-perceptual stimulation therapy. As further explained below, subsets of other patterns can 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.

[0188] The number of stimulation patterns available for the patient to select on the external controller 45 can be limited or programmed by the clinician. This may be warranted because some stimulation patterns may not be relevant to some patients. In this regard, the clinician can program the patient's external controller 45 to specify the available stimulation patterns, such as by entering an appropriate clinician password. Alternatively, the clinician can use the clinician programmer 50 to program the external controller 45.

[0189] An example of a subset of stimulation parameters 425x is shown in Figures 24A - 29B the Figure 24A and Figure 24B show a subset of stimulation parameter coordinates 425a used when the economy mode 500 is selected, which includes a subset of the optimal stimulation parameters 420' with low power draw. Like the optimal stimulation parameters 420', the subset 425a can include a three-dimensional volume of F, PW, and A parameters, and similarly (compare Figure 22C and Figure 22D ) two two-dimensional plots are used to represent the subset 425a, where Figure 24A shows the relationship between frequency and pulse width, and Figure 22D shows the relationship between frequency and amplitude.

[0190] 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 if the optimal stimulation parameters 420’ may have been determined over a wider range such as 10 to 1000 Hz. Additionally, while the optimal pulse widths within this frequency range may vary more significantly among the optimal stimulation parameters 420’, subset 425a can be constrained to the lower of these pulse widths, such as the lower half of such pulse widths, as Figure 24A shown. Similarly, using lower pulse widths will result in lower power draw. Additionally, as Figure 24B shown, subset 425a can be constrained to lower amplitudes within the optimal stimulation parameters 420’ for the relevant frequency range, again resulting in lower power draw. In short, subset 425a can include a smaller volume of stimulation parameters within the volume of the optimal stimulation parameters 420’ that provide sufficient sub-threshold stimulation to the patient while providing lower power draw from the IPG battery 14. Not all subsets 425x corresponding to the selected stimulation mode ( Figure 23 ) contain stimulation parameters that must be exactly within the determined optimal stimulation parameters 420’, as shown in some of the subsequent examples.

[0191] When the economy mode 500 is selected, the external controller 45 can simply send a single low-power optimal parameter (F, PW, A) within subset 425a to the IPG for execution. However, and more preferably, the user interface will include means to allow the patient to adjust the stimulation parameters to those within subset 425a. In this regard, the user interface can include a slider interface 550 and a parameter interface 560. The slider interface 550 can be as previously described (see Figure 21 ), and can include a cursor to allow the patient to slide through the parameters within subset 425a. In the example shown, the slider interface 550 may not adjust the pulse width that is set to a specific value (e.g., 325 μs), but the frequency and amplitude can vary. This is merely an example, and in other examples, all three of the frequency, pulse, and amplitude can be changed by the slider, or other parameters can be held constant. Note that more complex user interfaces can be used to allow the patient to browse subset 425a. For example, although not shown, user interface elements with higher three-dimensional quality, such as those previously in Figure 22E and Figure 22FThe volumes discussed in can be used to navigate subset 425a. The parameter interface 560 can also allow a patient to navigate parameters within subset 425a and is simply shown as having selectable buttons to increase or decrease the parameters within the determined subset 425a. The parameter interface 560 can also include fields that display the current values of frequency, pulse width, and amplitude. Initially, these values can be populated with parameters roughly at the center of the determined subset 425a, allowing the patient to adjust the stimulation near that center.

[0192] Figure 25A and Figure 25B The selection of sleep mode 502 is shown, as well as a subset 425b of optimal stimulation parameters 420' that results when that selection is made. 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 can include mid - low frequencies (e.g., 40 to 200 Hz) within optimal stimulation parameters 420' and can include medium pulse widths allowed by 420' within that frequency range, as shown by Figure 25A shown.

[0193] Because the intensity of stimulation may not need to be as high during sleep, the amplitude within subset 425b may fall outside of the amplitude suggested by optimal parameters 420', as shown in Figure 25B For example, although optimal parameters 420' may suggest, for the frequency and pulse width ranges of interest, that the amplitude based on earlier modeling would fall within the range of 3.6 to 4.0 mA, the amplitude within subset 425b is set to an even lower value in this example. Specifically, as shown in slider interface 550, the amplitude can be set between 1.5 mA and 4.0 mA. To know where to set the lower boundary of the amplitude, the modeling information can include an additional model 422, which can be determined separately from optimal stimulation parameters 420' based on patient testing. In the case of sleep, due to the expected change in the position of the electrode leads within the patient's spine when the patient lies down, it is ensured that amplitudes lower than those suggested by optimal parameters 420' are allowed. Additionally, the patient may be less bothered by pain while sleeping, and thus lower amplitudes can still be reasonably effective. That being said, subset 425b can also include values (including amplitude) that are entirely within and constrained by optimal stimulation parameters 420', similar to the values shown for subset 425a in Figure 24A and Figure 24B shown for subset 425a.

[0194] Figure 26A and Figure 26BShows the selection of the sensory mode 504, and the resulting subset 425c available to a given patient during that mode. The purpose of this mode is to allow the patient to sense the stimulation provided by their IPG according to their judgment. In other words, the stimulation provided to the patient in this mode is supra-perceptual. The optimal stimulation parameters 420' preferably define the volume of the stimulation parameters, where sub-perceptual stimulation is optimized for the patient. However, as described previously, 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 during this mode for selecting the amplitude that the patient will sense, i.e., the amplitude (especially the pulse width) that is above pth for other stimulation parameters. Thus, the sensory mode 504 is an example where it is beneficial to include the pth value (or pth range) within the optimal stimulation parameters 420'.

[0195] As Figure 26A shown, patients generally find it easier to sense the stimulation at lower frequencies, and thus, the selection of the sensory mode can constrain the stimulation in subset 425c to lower frequencies (e.g., 40 to 100 Hz). The control of the pulse width may not be a major issue, and thus, for this frequency range, the pulse width may have a medium range allowed by 420', as also Figure 26A shown.

[0196] However, since the patient in this mode wants to sense the stimulation, the amplitude within subset 425c is set to a higher value, as Figure 26B shown. Specifically, the amplitude of the relevant frequency and pulse width is not only set to be above the upper limit of the amplitude determined for the optimal stimulation parameters 420'; they are also set to be at or above the perception threshold pth. As mentioned earlier, the perception threshold pth and the more particularly important range of pth determined for the patient (when considering statistical variations) can be included in the optimal stimulation parameters 420' (see Figure 22D ) to produce a useful effect in this mode. Thus, based on earlier measurements and modeling, subset 425c is defined as setting the amplitude at a value or range that should provide supra-perceptual stimulation. If pth is defined by a range according to statistical variance, the allowed range of the amplitude of the sensory mode 504 can be set to a value above the upper limit of that range, as [[ID=268As shown. Thus, while the optimal (sub-perceptual) amplitude in the frequency range of interest (per 420’) may be in the range of approximately 3.7 to 4.5 mA, the amplitudes within subset 425c are set to approximately 5.8 to 7.2 mA, exceeding the upper limit of the pth range, to ensure that the stimulation delivered to the patient under discussion is supra-perceptual. In this example, note that subset 425c is determined using the optimal stimulation parameters 420’, but is only partially constrained by such optimal parameters. The frequency and pulse width are constrained; the amplitude is not, as the amplitudes in this subset 425c are set to exceed 420’, and more particularly, exceed the pth.

[0197] ​ and ​ shows the selection of the comfort mode 506 and the resulting subset 425d of the stimulation parameters for this mode. In this mode, the stimulation parameters are set to nominal values within the optimal stimulation parameters 420’ via subset 425d: a medium frequency (such as 200 to 400 Hz), and a medium pulse width for these frequencies (such as 175 to 300 μs as shown in the slider interface 550), as Figure 27A shown in. As Figure 27B shown in, the amplitudes within subset 425d can likewise be medium amplitudes within the optimal stimulation parameters 420’ for the frequencies and pulse widths under discussion. In this example, the stimulation parameters in subset 425d are fully constrained by the optimal stimulation parameters 420’, although as mentioned earlier, not every subset has to be so.

[0198] Figure 28A and Figure 28B shows the selection of the exercise mode 508 and the subset 425e of the stimulation parameters associated with this mode. In this mode, it is ensured that medium to high frequencies (e.g., 300 - 600 Hz) are used, but for these frequencies, the pulse width is higher than those specified by the optimal stimulation parameters 420’, as Figure 28A shown in. This is because when the patient moves, the position of the electrode leads within the patient may vary more, and thus it may be useful to provide a higher charge injection into the patient, and a higher pulse width can achieve it. As Figure 28B shown in, the amplitudes used can span a medium range for the frequencies and pulse widths involved, but higher amplitudes (not shown) exceeding 420’ can also be used to provide additional charge injection. Subset 425e shows an example where the frequency and amplitude are constrained by the optimal stimulation parameters 420’, while the pulse width is not; thus, subset 425e is only partially constrained by the optimal stimulation parameters 420’. In other examples, subset 425e can be fully constrained within the previously determined optimal stimulation parameters 420’.

[0199] Figure 29A and Figure 29B shows the selection of an intense mode 510 of stimulation. In this mode, the stimulation is more aggressive, and the subset 425f of stimulation parameters can occur at higher frequencies (e.g., 500 to 1000 Hz). However, the pulse width and amplitude at these frequencies may be moderate for the frequencies involved, as shown respectively in Figure 29A and Figure 29B . In this example, the subset of stimulation parameters in subset 425f can be fully constrained (included therein) by the optimal stimulation parameters 420'. As in the previous example, the patient can use interface 550 or 560 or other interface elements not shown to adjust the stimulation corresponding to the patient's stimulation mode selection in subset 425x ( Figure 23 ). Less preferably, the selection of the stimulation mode can 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).

[0200] Note that the stimulation parameters in subset 425x can overlap; some F, PW, and A values in one subset (e.g., 425a) may also be present in another subset (e.g., 425b). In other words, while this may also be the case, it is not strictly necessary for the stimulation parameters in a given subset to be unique to that subset or the stimulation mode represented by that subset. Additionally, the boundaries of the respective subsets 425x can be adjustable. For example, although not shown, the external controller 45 can have an option to change the boundaries of the respective subsets. Using such an option, the 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). In response to certain feedback, such as the patient pain level that may be input into the external device 45, or the detection of the patient's activity or posture, may also affect the adjustment of subset 425x. More complex adjustments can be locked to the patient and only accessible to the clinician, for example, by entering a password in the external controller 45 to provide such accessibility. Behind this password protection, subset 425x can be adjustable, and / or other stimulation modes (e.g., beyond those shown in Figure 23 ) can be made accessible only to the clinician. As previously mentioned, the clinician can also make such clinician adjustments using the clinician programmer 50.

[0201] The subset 425x can also be updated automatically from time to time. This can be advantageous because as more patient data is collected, the underlying modeling that leads to the generation of the optimal stimulation parameters 420' may change or become better informed. It will also be appreciated later that different stimulation parameters may better produce the desired effects of the stimulation pattern and thus which parameters are included in the adjustment subset can be ensured. Different stimulation patterns provided for different reasons or producing different effects may also become apparent later and thus such new patterns and their corresponding subsets can be programmed into the external controller 45 at a later time and presented to the patient in the Figure 23 stimulation pattern user interface. The update of the subset and / or the stimulation pattern can occur wirelessly by connecting the external controller 45 to the clinician's programmer or to a network such as the Internet. It should be understood that the disclosed stimulation patterns and the subsets of the stimulation parameters 425x corresponding to such patterns are merely exemplary and different patterns or subsets can be used.

[0202] Referring again to Figure 23 , the stimulation pattern user interface can include an option 512 to allow the patient or clinician to define a custom pattern of stimulation. This custom pattern 512 can allow the user to select the frequency, pulse width, and amplitude, or to define a subset that is at least partially defined by the optimal stimulation parameters 420'. The selection of this option can provide a user interface that allows the patient to navigate different stimulation parameters within the optimal stimulation parameters 420', such as those previously shown in Figure 22E and Figure 22F . If the patient finds stimulation parameters through this option that seemingly can effectively operate as a simulation pattern, the user interface can allow the storage of the stimulation pattern for future use. For example, and referring to Figure 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, as shown at the user interface element 580 in Figure 22D . The newly saved stimulation pattern can then be presented to the patient as an optional stimulation pattern ( Figure 23 ). The logic in the external controller 45 can additionally define a subset 425 (e.g., 425g) of the stimulation parameters through which the patient can navigate when the user-defined stimulation pattern is selected later. The subset 425g can include, for example, stimulation parameters that limit the patient's selected parameters (e.g., + / - 10% of the frequency, pulse width, and amplitude selected by the patient), but are still all or partially constrained by the optional stimulation parameters 420'.

[0203] As in Figure 23As shown, the stimulation mode user interface may also include option 514 that automatically selects and adjusts a stimulation mode for a patient based on various factors that the IPG 10 can detect. In Figure 30 The selection of this automatic mode 514 is shown in more detail in. Preferably, the selection of the automatic mode 514 allows the patient 570 to select which one 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, this algorithm can 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 the electronic device. Alternatively, the algorithm 610 in the IPG 10 may attempt to detect and adjust the stimulation of all stimulation modes (e.g., 500 - 510) supported by the system without the user selecting 570 the stimulation mode of interest.

[0204] The algorithm 610 may receive different inputs related to detecting the stimulation mode and thus receive a subset 425x that should be used for the patient at any given time. For example, the algorithm 610 may receive inputs from various sensors such as an accelerometer 630 indicating the patient's posture and / or activity level. The algorithm 610 may also receive inputs from various other sensors 620. In one example, the sensor electrodes 620 may include the electrodes Ex of the IPG 10, which may sense various signals related to the determination of the stimulation mode. 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 various pairings of the electrodes, which can be correlated with various impedance characteristics indicating the patient's posture or activity in the algorithm 610. The signals sensed at the electrodes may include those signals generated by the stimulation, such as evoked compound action potentials (ECAP). As disclosed in U.S. Patent Application Serial No. 16 / 238,151 filed on January 2, 2019, the review of various characteristics 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 generated by the stimulation, as disclosed in U.S. Provisional Patent Application Serial No. 62 / 860,627 filed on June 12, 2019, which can also indicate the patient's posture or activity. The signals sensed at the electrodes can 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 can also be related to the patient's posture or activity.

[0205] Algorithm 610 may receive other information related to determining a stimulation pattern. For example, clock 640 may provide time information to algorithm 610. This may be related to determining or confirming whether the patient is engaged 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 expected activities, such as whether the patient prefers to exercise in the morning or afternoon. Algorithm 610 may also receive an input from 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 automatic economy mode 500 or other power-based stimulation modes should be entered (i.e., if Vbat is low).

[0206] In any case, the stimulation pattern detection algorithm 610 may wirelessly receive an indication of the selected automatic mode 514 and any selected mode 570 of interest to the patient. Then, algorithm 610 may use its various inputs to determine when those modes should be entered, and thus will enable the use of the subset 425x corresponding to the detected stimulation pattern at the appropriate time. For example, in Figure 30 the example of, algorithm 610 may use accelerometer 630, sensor 620, and clock 640 to determine that a person is stationary, supine or prone at night, and / or that his heart rate is slow, and thus determine that the person is currently sleeping. Algorithm 610 may then automatically activate the sleep mode 502 and activate the use of the stimulation parameters within the subset 425b ( Figures 25A - 25B ) corresponding to that mode. In addition, IPG 10 may send a notification of the current stimulation pattern determination back to the external controller 45, which may be displayed at 572. This is useful for allowing the patient to review that algorithm 610 has correctly determined the stimulation pattern. In addition, notifying the external controller 45 of the currently determined mode may allow the external controller 45 to use the appropriate subset 425x of that mode to allow the patient to adjust the stimulation. That is, the external controller 45 may use the determined mode (sleep) to constrain the adjustment ( Figures 25A - 25B ) to the corresponding subset (425b) of that mode.

[0207] If algorithm 610 uses one or more of its inputs to determine that a person is changing position rapidly, upright, and / or that his heart rate is high, it may be determined that the person is currently exercising, which is a stimulation pattern of interest selected by the patient. At that time, algorithm 610 may automatically activate the exercise mode 508 and activate the use of the stimulation parameters within the subset 425e ( Figures 25A - 25B ) corresponding to that mode. Again, IPG 10 may send a notification of this current stimulation pattern determination back to the external controller 45 to constrain the adjustment ( Figures 28A - 28B) is constrained to the corresponding subset (425e) of the pattern. If algorithm 610 cannot determine whether the patient is sleeping or exercising, it can default to the selection of comfort mode 506 and provide stimulation, notifications, and constraint adjustment accordingly (subset 425d, Figures 27A - 27B ).

[0208] External controller 45 may also be useful in determining the relevant stimulation pattern to use during automatic mode selection. In this regard, although not shown in Figure 30 , external controller 45 may include sensors for determining patient activity or posture, such as an accelerometer. External controller 45 may also include a clock and may wirelessly receive information about its battery voltage from IPG 10 and information about the signals detected at the electrodes of the IPG from sensor 620. Thus, external controller 45 may also include a stimulation pattern detection algorithm 610' responsive to such inputs. This algorithm 610' may replace algorithm 610 in IPG 10 or may supplement the information determined from algorithm 610 to improve stimulation pattern determination. In short, and as facilitated by the two-way wireless communication between external controller 45 and IPG 10, the stimulation pattern detection algorithm may be effectively split between the external controller and IPG 10 in any desired manner.

[0209] In addition, external controller 45 may receive relevant information from a variety of other sensors to determine which stimulation pattern should be input. For example, external controller 45 may receive information from an external device 612 worn by the patient, such as a smartwatch or smartphone. Such a smart device 612 includes sensors indicating movement (e.g., an accelerometer) and may also include biosensors (heart rate, blood pressure), which can help understand different patient states and thus different stimulation patterns should be used. More generally, other sensors 614 may also provide relevant information to external controller 45. Such other sensors 614 may include other implantable devices that detect various biological states (glucose, auditory rate, etc.) of the IPG patient. Such other sensors 614 may provide other information. For example, since it has been shown that cold or inclement weather can affect the stimulation treatment of IPG patients, sensor 614 may include a weather sensor that provides weather information to external controller 45. Note that sensor 614 may not need to communicate directly with external controller 45. Information from such sensors 614 may be sent by a network (e.g., the Internet) and provided to external controller 45 via various gateway devices (routers, WiFi, Bluetooth antennas, etc.).

[0210] Figure 31Another example of the user interface on the patient external controller 45 is shown, which allows the patient to select from different stimulation modes. In this example, the different stimulation modes (consistent with the optimal stimulation parameters 420' determined for the patient) are shown in a two-dimensional representation. In the example shown, the two-dimensional representation includes a graph of pulse width (Y-axis) versus frequency (X-axis), but any two stimulation parameters (amplitude and frequency or pulse width and amplitude) can also be used. However, note that if the goal is to provide a simple user interface that is not hindered by technical information that the patient may not understand, the X and Y axes may not be labeled, nor the specific pulse width or frequency values.

[0211] Marked in this two-dimensional representation are the different stimulation modes discussed previously, where the boundaries show the range of the subset 425x of each stimulation mode. Using this representation, the patient can position the cursor 430 to select a specific stimulation mode, and in so doing, select the frequency and pulse width and their corresponding subset 425x. Since the subsets 425x can overlap, a selection at a specific frequency and pulse width can select more than one stimulation mode and more than one subset 425x, thus allowing the patient to browse more than one subset of stimulation parameters. Since the amplitude is not represented in the two-dimensional representation, the amplitude can be automatically adjusted to an appropriate value when a stimulation mode / subset 425x or a specific frequency / pulse width is selected. Alternatively, 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 fully constrained within the optimal stimulation parameters 420' by the selected mode / subset, or can be allowed to range beyond 420' (e.g., Figure 25B , Figure 26B ). In a more complex example, the representation can include a three-dimensional space (F, PW, A) in which the patient can move the cursor 430, similar to Figure 22E as shown, with a three-dimensional subset 425x of the stimulation modes displayed.

[0212] Figure 32 Another GUI aspect is shown, which allows the patient to adjust the stimulation according to a model developed for the patient. In these examples, a recommended stimulation region 650 for the patient is shown, which is overlaid on the user interface elements that otherwise allow the user to adjust the stimulation. Figure 32 The example in Figure 21 and Figure 31Modifications to the graphical user interface shown, but can also be applied to other user interface examples. In these examples, the proposed stimulation region 650 provides a visual indicator to the patient where they may wish to select (e.g., using cursor 430) a stimulation setting consistent with the optimal stimulation parameters 420 or 420’ or subset 425x. These regions 650 can be determined in different ways. They can be determined mathematically using the optimal stimulation parameters 420 or 420’ or subset 425x, such as by determining the center or “centroid” of these regions. They can also be determined by placing a particular emphasis on providing the patient with stimulation parameters having an appropriate amplitude, intensity, or total charge. This can be particularly useful if the patient’s previous selections have been away from such ideal values. Region 650 can also be determined during a fitting procedure by determining the region or volume within the optimal stimulation parameters 420 or 420’ or subset 425x that the patient prefers most.

[0213] In addition, region 650 can be determined for the patient over time based on previously selected stimulation parameters. Thus, region 650 can be related to the settings most frequently used by the patient. In an improved example, the patient can also provide feedback related to the location of region 650. For example, the external device 45 can 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 levels entered at 652 and the selected stimulation parameters and correlate them, and plot or update region 650 to the appropriate location covering the stimulation adjustments where the patient has experienced optimal symptom relief. Again, a mathematical analysis of the weighting of the stimulation parameters relative to their pain levels, or the centroid method, can be used.

[0214] It should be noted that the use of the disclosed techniques should not necessarily be limited to the specific frequencies tested. Other data suggest the applicability of the disclosed techniques in providing pain relief without paresthesia at frequencies as low as 2 Hz.

[0215] Aspects of the disclosed techniques include processes that can be implemented in the IPG or ETS, or in an external device (such as a clinician programmer or external controller for presenting and operating the GUI 64), which can be formulated and stored as instructions in a computer-readable medium associated with such a device, such as being stored in magnetic memory, optical memory, or solid-state memory. A computer-readable medium having such stored instructions can also include a device readable by the clinician programmer or external controller, such as on a memory stick or removable disk, and can reside elsewhere. For example, the computer-readable medium can be associated with a server or any other computer device, thus allowing the instructions to be downloaded to the clinician programmer system or external system or to the IPG or ETS via, for example, the Internet.

Claims

1. A system, comprising: A stimulator device configured to be implanted within a patient and including 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; Characterized in that the external device is configured to: Provide a graphical user interface GUI on the external device, which 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, Wherein at least one of the stimulation modes indicates the patient's posture, the patient's activity, or a power mode for the stimulator device, Wherein the external device stores information indicating a plurality of subsets of coordinates, wherein each coordinate in each subset includes stimulation parameters derived for the patient to provide optimal stimulation for the patient, Wherein each stimulation mode corresponds to one of the subsets of coordinates, Wherein the selection of one of the stimulation modes is restricted to programming the stimulator device using the coordinates within the corresponding subset of coordinates.

2. The system according to claim 1, wherein Each coordinate includes frequency, pulse width, and amplitude.

3. The system according to claim 1, wherein, The coordinates in each subset include a line or volume in a three-dimensional space of frequency, pulse width, and amplitude.

4. The system according to claim 1, wherein, The external device is further configured to determine 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 for the patient, and wherein the plurality of subsets of coordinates are determined using the model.

5. The system according to claim 4, wherein In response to providing stimulation to the patient during a test procedure, measurements obtained from the patient are used to determine a model for the patient.

6. The system according to claim 5, wherein the model and the plurality of subsets are determined in a clinician programmer in communication with the stimulator device.

7. The system according to claim 6, further comprising sending the determined plurality of subsets from the clinician programmer to the external device.

8. The system according to claim 4, wherein the model is determined in a clinician programmer in communication with the stimulator device, and further comprising sending the model to the external device, wherein the plurality of subsets are determined in the external device.

9. The system according to claim 4, wherein the plurality of coordinates in the model include a line or volume in a three-dimensional space of frequency, pulse width, and amplitude.

10. The system according to claim 9, wherein Use the model to determine at least one subset of coordinates such that the coordinates of the at least one subset are completely constrained by the plurality of coordinates in the model.

11. The system according to claim 9, wherein, Use the model to determine at least one subset of coordinates such that the coordinates of the at least one subset are partially constrained by the plurality of coordinates in the model.

12. The system according to claim 9, wherein each coordinate in the model includes stimulation parameters predicted to provide optimal sub-perceptual stimulation for the patient.

13. 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 including information indicative of 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, the graphical user interface allowing 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. At least one of the stimulation modes indicates the patient's posture, the patient's activity, or a power mode for the stimulator device. The external device stores information indicating a plurality of subsets of coordinates, where each coordinate in each subset includes stimulation parameters derived for the patient to provide optimal stimulation for the patient. Each stimulation mode corresponds to one of the subsets of coordinates. The selection of one of the stimulation modes is restricted to programming the stimulator device using the coordinates within the corresponding subset of coordinates.

14. A system, comprising: A stimulator device configured to be implanted in a patient, including a plurality of electrodes; And At least one external device configured to Determine a model for the patient, where the model includes information indicating predicted stimulation parameters available for the patient; Use the model to determine information indicating a plurality of subsets of stimulation parameters, where 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 the plurality of stimulation modes, where, based on the selection of one of the stimulation modes, the at least one external device is configured to restrict the programming of the stimulator device to the stimulation parameters within the corresponding subset of stimulation parameters.

15. The system according to claim 14, where the stimulation parameters in each subset include a line or volume in a three-dimensional space of at least two of frequency, pulse width, and amplitude.

16. The system according to claim 14, wherein The at least one external device is configured to determine the model for the patient by receiving measurement results obtained from the patient in response to providing stimulation to the patient during a test procedure.

17. The system according to claim 16, wherein, The at least one external device is configured to provide stimulation to the patient at different pulse widths during the test procedure, and where the measurement results include an indication of the perception threshold at each pulse width, and where the at least one external device is configured to determine the relationship between the pulse width and the patient's perception threshold.

18. The system according to claim 17, where the at least one external device is configured to determine the model for the patient by comparing the relationship with another model to determine the predicted stimulation parameters in the model, where the another model includes the relationship between frequency, pulse width, and perception threshold.

19. The system according to claim 14, wherein, The at least one external device includes a clinician programmer and a patient external controller, where the clinician programmer is configured to determine the model and the information indicating the plurality of subsets, and where the clinician programmer is configured to send the determined plurality of subsets from the clinician programmer to the patient external controller.

20. The system according to claim 14, wherein The 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 send the model to the patient external controller, and wherein the patient external controller is configured to determine information indicative of the plurality of subsets.

21. The system according to claim 14, wherein The at least one external device includes a clinician programmer and a patient external controller, wherein the clinician programmer is configured to program the plurality of stimulation patterns in the patient external controller having the GUI.

22. The system according to claim 14, wherein the predicted stimulation parameters in the model include a line or a volume in a multi-dimensional space of at least two of frequency, pulse width, and amplitude.

23. The system according to claim 14, wherein, The at least one external device is configured to use the model to determine at least one of the subsets such that the stimulation parameters of the at least one subset are fully constrained by the predicted stimulation parameters in the model.

24. The system according to claim 14, wherein, The at least one external device is configured to use the model to determine at least one of the subsets such that the stimulation parameters of the at least one subset are partially constrained by the predicted stimulation parameters in the model.

25. The system according to claim 14, wherein, The predicted stimulation parameters in the model include stimulation parameters predicted to provide sub-perceptual stimulation to the patient, and wherein the stimulation parameters of the selected stimulation pattern are configured to provide sub-perceptual stimulation to the patient.

26. The system according to claim 14, wherein At least one of the stimulation patterns indicates the posture or activity of the patient, or indicates a power mode for the stimulator device.

27. At least one non-transitory computer-readable medium configured to operate in at least one external device configured to program a stimulator device implantable within 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, are configured to: Determine a model for the patient, wherein the model includes information indicative of predicted stimulation parameters available for the patient; Use the model to determine information indicative of a plurality of subsets of stimulation parameters, wherein each subset corresponds to one of a plurality of stimulation patterns; and And Provide a graphical user interface GUI configured to allow the patient to select from the plurality of stimulation patterns, wherein, based on the selection of one of the stimulation patterns, 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.

28. A system comprising: A stimulator device configured for implantation within a patient, including a plurality of electrodes; And At least one external device configured to Determine a model for the patient, wherein the model includes information indicative of a set of predicted stimulation parameters available for the patient; Determine information indicative of a plurality of subsets, wherein each subset corresponds to one of a plurality of stimulation patterns and includes a set of a plurality of stimulation parameters within the model; and Provide a graphical user interface (GUI) configured to allow the patient to select from the plurality of stimulation modes, wherein, based on the selection of one of the stimulation modes, the at least one external device is configured to limit the programming of the stimulator device to a plurality of stimulation parameters within a corresponding subset.

29. At least one non-transitory computer-readable medium 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 the plurality of electrodes, wherein the at least one medium includes instructions that, when executed on the at least one external device, are configured to: Determine a model for the patient, wherein the model includes information indicative of a set of predicted stimulation parameters available for the patient; Determine information indicative of a plurality of subsets, wherein each subset corresponds to one of the plurality of stimulation modes and includes a plurality of sets of stimulation parameters within the model; and Providing a graphical user interface (GUI) configured to allow the patient to select from the plurality of stimulation patterns, wherein, Based on the selection of one of the stimulation modes, the at least one external device is configured to limit the programming of the stimulator device to a plurality of sets of stimulation parameters within a corresponding subset.

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