Assistive programming system for neurostimulation therapy

Abstract

Disclosed is an assisted programming system for a neuromodulation device configured to assist a clinician in efficiently programming the neuromodulation device for a particular patient. In particular, the assisted programming system includes a user interface having a plurality of controls, such as switches, each switch control corresponding to a predefined configuration of stimulation electrodes. Activation and deactivation of the switch controls causes stimulation to be delivered via the respective stimulation electrode configuration. Alternatively or additionally, the user interface controls may be tiles, activation of which may reverse the delivery state of stimulation via the respective stimulation electrode configuration.

Description

[Technical field]

[0001] The present invention relates to neural stimulation therapy, and more particularly to methods and systems for programming a neural stimulation therapy system to meet the needs of a particular patient. [Background technology]

[0002] There are various situations in which it is desirable to apply neural stimulation to alter neural function, a process known as neuromodulation. For example, neuromodulation is used to treat various disorders, including chronic neuropathic pain, Parkinson's disease, and migraines. Neuromodulation systems apply electrical pulses (stimuli) to nerve tissue (fibers or nerve cells) to produce a therapeutic effect. In general, the electrical stimulation generated by a neuromodulation system induces a neural response, known as an action potential in the nerve fiber, which then has either an inhibitory or excitatory effect. The inhibitory effect can be used to modulate undesirable processes, such as pain transmission, or the excitatory effect can be used to cause a desired effect, such as muscle contraction.

[0003] When used to reduce neuropathic pain originating from the trunk and extremities, electrical pulses are applied to the dorsal columns (DC) of the spinal cord, a procedure called spinal cord stimulation (SCS). Such systems typically include an implantable electrical pulse generator and a power source, such as a battery, which may be rechargeable percutaneously by wireless means such as inductive transfer. An electrode array is connected to the pulse generator and is implanted in the spinal epidural space, typically above the dorsal columns, adjacent to the target nerve fiber in the spinal cord. Electrical pulses of sufficient intensity applied by the stimulating electrodes to the target nerve fiber cause depolarization of the nerve cells in the fiber, which then generate action potentials in the fiber. The action potentials propagate along the fiber in an anterograde (toward the head or rostral) and retrograde (toward the tail or caudal) direction. The fiber stimulated in this way inhibits the transmission of pain from the area of ​​the body (dermatome) innervated by the target nerve fiber to the brain. To sustain the pain-relieving effect, stimulation is applied repeatedly, for example at frequencies ranging from 30 Hz to 100 Hz.

[0004] For effective and comfortable neuromodulation, it is necessary to maintain the stimulation intensity above the recruitment threshold. Stimulation below the recruitment threshold cannot recruit enough nerve cells to generate action potentials with therapeutic effect. In almost all neuromodulation applications, a response from a single class of fibers is desired, but the stimulation waveform used may induce action potentials in other classes of fibers that cause undesirable side effects. Therefore, for pain relief, it is necessary to apply stimulation at an intensity below the discomfort threshold, above which the perception of discomfort or pain occurs due to over-recruitment of Aβ fibers. If recruitment is too great, Aβ fibers produce unpleasant sensations. High intensity stimulation may even recruit Aδ fibers, the sensory nerve fibers associated with the sensations of sharp pain, cold and pressure. Therefore, it is desirable to maintain the stimulation intensity within the therapeutic range between the recruitment threshold and the discomfort threshold.

[0005] The task of maintaining adequate neural recruitment is made more difficult by electrode migration (changing position over time) and / or postural changes of the implant recipient (patient), either of which can significantly alter the neural recruitment resulting from a given stimulation, and therefore the therapeutic range. The epidural space allows room for the electrode array to move, and such array movement from movement or postural changes alters the electrode-fiber distance and therefore the recruitment efficiency of a given stimulation. Furthermore, the spinal cord itself can move in cerebrospinal fluid (CSF) relative to the dura. During postural changes, the amount of CSF and / or the distance between the spinal cord and the electrodes can change significantly. This effect can be so great that a comfortable and effective stimulation technique can become ineffective or painful, due to postural changes alone.

[0006] Attempts have been made to address such issues by feedback or closed loop control, such as using the method described in the applicant's WO 2012 / 155188. Feedback control attempts to compensate for relative nerve / electrode movement by controlling the intensity of the delivered stimulation to maintain substantially constant neural recruitment. The intensity of the neural response evoked by the stimulation can be used as a feedback variable representative of the amount of neural recruitment. A signal representative of the neural response can be generated by a measurement electrode in electrical communication with the recruited nerve fibers and processed to obtain the feedback variable. Based on the response intensity, the intensity of the applied stimulation can be adjusted to maintain the response intensity within a therapeutic range.

[0007] It is therefore desirable to accurately measure the strength and other characteristics of the neural response evoked by a stimulus. The action potentials generated by the depolarization of many fibers by the sum of the stimuli form a measurable signal known as the evoked compound action potential (ECAP). Thus, the ECAP is the sum of the response from many single fiber action potentials. The ECAP generated from the depolarization of a group of similar fibers may be measured at the measurement electrode as a positive peak potential, then a negative peak, followed by a second positive peak. This morphology is caused by an activation region passing through the measurement electrode as the action potential propagates along the individual fibers.

[0008] The proposed approach for obtaining neural response measurements has been described by the applicant in WO 2012 / 155183, the contents of which are incorporated herein by reference.

[0009] However, neural response measurement can be a challenging task because the observed CAP signal component in the measured response typically has a maximum amplitude in the microvolt range. In contrast, the stimulus applied to elicit a CAP is typically a few volts, which manifests itself in the measured response as crosstalk of that magnitude. Furthermore, the stimulus generally results in electrode artifacts that manifest in the measured response as a decaying output on the order of a few millivolts after the end of the stimulus. CAP measurement presents a difficult challenge for measurement amplifier design because the CAP signal can be simultaneous with stimulus crosstalk and / or stimulus artifacts. For example, resolving a 10 μV CAP with 1 μV resolution in the presence of 5 V of stimulus crosstalk would require an amplifier with a dynamic range of 134 dB, which is impractical in an implantable device. In practice, many non-ideal aspects of the circuitry result in artifacts, and their identification and removal can be tedious, since these aspects result in time-decaying artifact waveforms, mostly of positive or negative polarity.

[0010] Closed-loop neurostimulation therapy is governed by several parameters that must be assigned values ​​in order to implement the therapy. The effectiveness of the therapy depends heavily on the suitability of the assigned parameter values ​​to the patient receiving the therapy. Because patients vary significantly in their physiological characteristics, a "one size fits all" approach to parameter value assignment is likely to result in ineffective therapy for the majority of patients. Thus, once a neuromodulation device is implanted in a patient, an important preliminary task is to assign values ​​to the therapy parameters that will maximize the effectiveness of the therapy that the device delivers to that particular patient. This task is known as programming or fitting the device. Programming generally involves applying specific test stimuli via the device, recording the responses, and inferring or calculating the most effective parameter values ​​for the patient based on the recorded responses. The resulting parameter values ​​are formed into a "program" that can be loaded into the device to manage subsequent therapy. Some of the recorded responses may be neural responses evoked by the test stimuli, providing an objective source of information that can be analyzed along with the subjective responses evoked from the patient. In an effective programming system, the more responses that are analyzed, the more effective the parameter values ​​ultimately assigned should be.

[0011] However, programming can be costly and time-consuming if unnecessarily lengthy. Thus, there is an incentive to minimize the number of test stimuli applied and the amount of information recorded and analyzed to generate assigned values ​​of the treatment parameters. In particular, the size of the treatment parameter search space is such that it is impractical to test all possible combinations of the treatment parameters.

[0012] Furthermore, the programming workflow is generally performed by a trained clinician or engineer who mediates between the patient and the programming system by interpreting the patient's subjective verbal responses. However, this mediation can be problematic, especially when the patient lacks the ability to describe the sensations they are feeling during the test stimulation. Furthermore, the patient's subjective responses, even when clearly expressed, are not always a reliable guide to the effect of the device on the patient. This can lead to inefficient programming and, at worst, invalid values ​​assigned to treatment parameters.

[0013] Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is solely for the purpose of providing a context for the present invention and is not to be construed as an admission that any or all of these matters form part of the prior art or were common general knowledge in the art pertaining to the present invention prior to the priority date of each claim of this application.

[0014] Throughout this specification the word "comprise" or variations thereof will be understood to imply the inclusion of a specified element, integer or step or group of elements, integers or steps, but not the exclusion of any other element, integer or step or group of elements, integers or steps.

[0015] As used herein, a statement that an element can be "at least one" of a list of options should be understood to mean that the element can be any one of the listed options, or any combination of two or more of the listed options. Summary of the Invention [Means for solving the problem]

[0016] Disclosed herein is an assisted programming system for a neuromodulation device configured to assist a clinician in efficiently programming the neuromodulation device for a particular patient. In particular, the assisted programming system includes a user interface having a plurality of controls, such as switches, each switch control corresponding to a predetermined configuration of stimulation electrodes. Activation and deactivation of the switch controls causes stimulation to be delivered via the respective stimulation electrode configuration. Alternatively or additionally, the user interface controls may be tiles, activation of which may reverse the delivery state of stimulation via the respective stimulation electrode configuration.

[0017] According to a first aspect of the present technology, there is provided a nerve stimulation system, comprising: 1. A neurostimulation device for controllably delivering neurostimulation, comprising: a plurality of implantable electrodes including one or more stimulation electrodes; a stimulation source configured to deliver neural stimulation to a neural pathway of the patient via one or more stimulation electrodes; a control unit configured to control the stimulation source to deliver the neurostimulation according to the stimulation intensity parameters; A nerve stimulation device comprising: 1. An external computing device in communication with the neurostimulator, comprising: a display configured to receive user interaction; 1. A processor comprising: rendering a plurality of switch controls on a display, each switch control corresponding to a predetermined configuration of stimulation electrodes; upon receiving activation of one of the switch controls by a user, instructing the control unit to control the stimulation source to deliver neural stimulation via a corresponding predetermined stimulation electrode configuration; upon receiving a deactivation of one of the switch controls by the user, instructing the control unit to control the stimulation source to cease delivery of the neurostimulation via the corresponding predetermined stimulation electrode configuration; a processor configured to: an external computing device including: A neurostimulation system is provided that includes:

[0018] According to a second aspect of the present technology, there is provided an automated method of controlling a neurostimulator to deliver neurostimulation using an external computing device in communication with the neurostimulator, the method comprising: rendering, by a processor of the external computing device, a plurality of switch controls on a display of the external computing device, each switch control corresponding to a predetermined stimulation configuration of the implanted electrodes; and instructing, by the processor, upon receiving activation of one of the switch controls by a user, the neurostimulator to deliver neurostimulation via a corresponding predetermined stimulation electrode configuration; and instructing the neurostimulator, upon receiving a deactivation of one of the switch controls by the user, to cease delivery of the neurostimulation via the corresponding predetermined stimulation electrode configuration. An automated method is provided, comprising:

[0019] In some embodiments, the processor may be further configured to render a tile control in association with each switch control. The processor may be further configured to, upon receiving activation of one of the tile controls by a user while the associated switch control is activated, deactivate the associated switch control, thereby causing the stimulation source to cease delivery of neural stimulation via the corresponding predetermined stimulation electrode configuration.

[0020] In some embodiments, the processor may be further configured, upon receiving a deactivation of one of the tile controls by a user while the associated switch control is deactivated, to activate an associated switch control, thereby causing the stimulation source to deliver neural stimulation via a corresponding predetermined stimulation electrode configuration.

[0021] In some embodiments, the processor may be further configured, upon receiving activation of one of the tile controls by a user while the associated switch control is deactivated, to activate the associated switch control, thereby causing the stimulation source to deliver neural stimulation via the corresponding predetermined stimulation electrode configuration, and upon receiving deactivation of one of the tile controls by the user while the associated switch control is activated, to deactivate the associated switch control, thereby causing the stimulation source to stop delivering neural stimulation via the corresponding predetermined stimulation electrode configuration.

[0022] In some embodiments, the processor may be configured to change the appearance of each tile control depending on its activation state.

[0023] In some embodiments, each tile control may be configured to become active when a user interacts with the tile control, to remain in the active state as long as the user continues to interact with the tile control, and to become inactive as soon as the user stops interacting with the tile control.

[0024] In some embodiments, the processor may be configured to change the appearance of each switch control depending on its activation state.

[0025] In some embodiments, the processor may be configured, upon a user activation of a further control rendered on the display, to record a pre-defined stimulation electrode configuration corresponding to each activated switch control.

[0026] In some embodiments, the processor may be configured to instruct the control unit to control the stimulation source to deliver the neurostimulation by increasing a value of the stimulation intensity parameter while instructing the control unit to control the stimulation source to deliver the neurostimulation in accordance with a ramp value of the stimulation intensity parameter. In some embodiments, the ramp value of the stimulation intensity parameter may cross a stimulation intensity below a predetermined threshold at a faster rate than a stimulation intensity above a predetermined threshold.

[0027] In some embodiments, the processor may be configured to instruct the control unit to control the stimulation source to stop delivery of the neurostimulation by instructing the control unit to control the stimulation source to deliver the neurostimulation according to a descending ramp value of the stimulation intensity parameter while descending a value of the stimulation intensity parameter, in some embodiments, the descending ramp value of the stimulation intensity parameter may cross a stimulation intensity below a predetermined threshold at a faster rate than a stimulation intensity above the predetermined threshold.

[0028] According to a third aspect of the present technology, there is provided a nerve stimulation system, comprising: 1. A neurostimulation device for controllably delivering neurostimulation, comprising: a plurality of implantable electrodes including one or more stimulation electrodes; a stimulation source configured to deliver neural stimulation to a neural pathway of the patient via one or more stimulation electrodes; a control unit configured to control the stimulation source to deliver the neurostimulation according to the stimulation intensity parameters; A nerve stimulation device comprising: 1. An external computing device in communication with the neurostimulator, comprising: a display configured to receive user interaction; 1. A processor comprising: rendering a plurality of tile controls on a display, each tile control corresponding to a predetermined configuration of stimulation electrodes; upon receiving activation of one of the tile controls by a user, instructing the control unit to reverse a delivery state of the neurostimulation via a corresponding predetermined stimulation electrode configuration; upon receiving a deactivation of one of the tile controls by a user, instructing the control unit to reverse a delivery state of the neurostimulation via the corresponding predetermined stimulation electrode configuration; a processor configured to: an external computing device including: A neurostimulation system is provided that includes:

[0029] According to a fourth aspect of the present technology, there is provided an automated method of controlling a neurostimulator to deliver neurostimulation using an external computing device in communication with the neurostimulator, the method comprising: rendering, by a processor of the external computing device, a plurality of tile controls on a display of the external computing device, each switch control corresponding to a predetermined stimulation configuration of the implanted electrodes; and instructing, by the processor, upon receiving activation of one of the tile controls by a user, the neurostimulator to reverse a delivery state of the neurostimulation via a corresponding predetermined stimulation electrode configuration; and instructing the neurostimulator device, upon receiving a deactivation of one of the tile controls by a user, to reverse a delivery state of the neurostimulation via the corresponding predetermined stimulation electrode configuration. An automated method is provided, comprising:

[0030] In some embodiments, the processor may be configured to change the appearance of each tile control depending on its activation state.

[0031] In some embodiments, each tile control may be configured to become active when a user interacts with the tile control, to remain in the active state as long as the user continues to interact with the tile control, and to become inactive as soon as the user stops interacting with the tile control.

[0032] In some embodiments, the processor may be further configured to render a switch control in association with each tile control.

[0033] In some embodiments, the processor may be further configured to, upon receiving activation of one of the switch controls by a user, instruct the control unit to control the stimulation source to deliver neural stimulation via a corresponding predetermined stimulation electrode configuration, and, upon receiving deactivation of one of the switch controls by the user, instruct the control unit to control the stimulation source to stop delivery of neural stimulation via the corresponding predetermined stimulation electrode configuration.

[0034] In some embodiments, the processor may be further configured to, upon receiving activation of one of the tile controls by a user while the associated switch control is activated, deactivate the associated switch control, thereby causing the stimulation source to stop delivering neural stimulation via the corresponding predetermined stimulation electrode configuration.

[0035] In some embodiments, the processor may be further configured, upon receiving a deactivation of one of the tile controls by a user while the associated switch control is deactivated, to activate an associated switch control, thereby causing the stimulation source to deliver neural stimulation via a corresponding predetermined stimulation electrode configuration.

[0036] In some embodiments, the processor may be further configured, upon receiving activation of one of the tile controls by a user while the associated switch control is deactivated, to activate the associated switch control, thereby causing the stimulation source to deliver neural stimulation via the corresponding predetermined stimulation electrode configuration, and upon receiving deactivation of one of the tile controls by the user while the associated switch control is activated, to deactivate the associated switch control, thereby causing the stimulation source to stop delivering neural stimulation via the corresponding predetermined stimulation electrode configuration.

[0037] In some embodiments, the processor may be configured to change the appearance of each switch control depending on its activation state.

[0038] In some embodiments, the processor may be configured, upon a user activation of a further control rendered on the display, to record a pre-defined stimulation electrode configuration corresponding to each activated switch control.

[0039] In some embodiments, the processor may be configured to instruct the control unit to control the stimulation source to deliver the neurostimulation by increasing the value of the stimulation intensity parameter while instructing the control unit to control the stimulation source to deliver the neurostimulation in accordance with a ramped value of the stimulation intensity parameter.

[0040] In some embodiments, the ramp value of the stimulation intensity parameter may cross stimulation intensities below a pre-defined threshold at a faster rate than stimulation intensities above the pre-defined threshold.

[0041] In some embodiments, the processor may be configured to instruct the control unit to control the stimulation source to stop delivery of the neurostimulation by decreasing the value of the stimulation intensity parameter while instructing the control unit to control the stimulation source to deliver the neurostimulation in accordance with a decreasing ramp value of the stimulation intensity parameter.

[0042] In some embodiments, the descending ramp value of the stimulation intensity parameter may cross stimulation intensities below a predetermined threshold at a faster rate than stimulation intensities above the predetermined threshold.

[0043] References herein to estimating, determining, comparing, and the like should be understood to refer to automated processes performed on data by a processor operative to execute a predefined procedure suitable for performing the described estimating, determining, and / or comparing steps. The techniques disclosed herein may be implemented in hardware (e.g., using a digital signal processor, an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA)) or software (e.g., using instructions tangibly stored in a non-transitory computer readable medium to cause a data processing system to perform the steps described herein) or a combination of hardware and software. The disclosed techniques may also be embodied as computer readable code on a computer readable medium. A computer readable medium may include any data storage device that can store data which can thereafter be read by a computer system. Examples of computer readable media include read only memory ("ROM"), random access memory ("RAM"), magnetic tape, optical data storage device, flash storage device, or any other suitable storage device. The computer readable medium may also be distributed over network coupled computer systems such that the computer readable code is stored and / or executed in a distributed manner.

[0044] One or more implementations of the present invention will now be described with reference to the accompanying drawings. [Brief description of the drawings]

[0045] [Figure 1] 1 illustrates a schematic of an implantable spinal cord stimulator in accordance with one implementation of the present technology. [Diagram 2] FIG. 2 is a block diagram of the stimulator of FIG. 1. [Diagram 3] FIG. 2 is a schematic diagram showing the neural interaction of the implantable stimulator of FIG. 1. [Figure 4a] 1 shows idealized activation plots for one posture of a patient undergoing neurostimulation. [Figure 4b] 1 shows the change in activation plots with changes in patient position. [Diagram 5] FIG. 1 is a schematic diagram showing elements and inputs of a closed-loop nerve stimulation system in accordance with one implementation of the present technology. [Figure 6] 1 shows a typical morphology of electrically evoked compound action potentials (ECAPs) in a healthy subject. [Figure 7] 2 is a block diagram of a neural stimulation therapy system including the implantable stimulator of FIG. 1 in accordance with one implementation of the present technology. [Figure 8] 1 is a flowchart illustrating an assisted programming workflow performed by an assisted programming application in accordance with one implementation of the present technology. [Figure 9] 1 shows the locations of recording and reference electrodes in six candidate measurement electrode configurations according to one implementation of the present technology. [Figure 10] 10 shows a screen shot of a user interface display during a patient-controlled stimulus lamp stage of the workflow of FIG. 8 in accordance with one implementation of the present technology. [Figure 11a] 9 is a flowchart illustrating a data collection and analysis method performed by the APM and device during the patient-controlled stimulus lamp stage of the workflow of FIG. 8 in accordance with one implementation of the present technology. [Figure 11b] 10 is a flowchart illustrating a data collection and analysis method performed by the APM and device during the patient-controlled stimulus lamp stage of the workflow of FIG. 8 in accordance with an alternative implementation of the present technology. [Figure 12] 10 shows a screen shot of a user interface display during the coverage investigation stage of the workflow of FIG. 8 in accordance with one implementation of the present technology. [Figure 13] The fitted logistic growth curve model for a set of value pairs of stimulation current amplitude and ECAP amplitude is shown, along with a piecewise linear model fitted to the same value pairs. [Figure 14] 1 illustrates a threshold ramp in accordance with one implementation of the present technology. [Figure 15] 10 shows a screen shot of a user interface display during the coverage selection stage of the workflow of FIG. 8 in accordance with one implementation of the present technology. [Figure 16]9 shows a screen shot of a user interface display during the measurement optimization stage of the workflow of FIG. 8 in accordance with one implementation of the present technology. [Figure 17] 9 includes a flowchart illustrating a data collection and analysis method performed by the APM and device during the measurement optimization stage of the workflow of FIG. 8, according to one implementation of the present technology. [Figure 18a] 1 illustrates a ramp and down ramp of stimulus intensity according to one implementation of the present technology. [Figure 18b] 1 illustrates a ramp and down ramp of stimulus intensity according to one implementation of the present technology. [Figure 18c] 1 illustrates a ramp and down ramp of stimulus intensity according to one implementation of the present technology. [Figure 18d] 1 illustrates a ramp and down ramp of stimulus intensity according to one implementation of the present technology. [Figure 18e] 1 illustrates a ramp and down ramp of stimulus intensity according to one implementation of the present technology. [Fig. 18f] 1 illustrates a ramp and down ramp of stimulus intensity according to one implementation of the present technology. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0046] FIG. 1 shows a schematic of an implantable spinal cord stimulator 100 in a patient 108 according to one implementation of the present technology. The stimulator 100 includes an electronics module 110 implanted in a suitable location. In one implementation, the stimulator 100 is implanted in the patient's lower abdominal region or posterior upper gluteal region. In other implementations, the electronics module 110 is implanted in other locations, such as the flank or subclavian region. The stimulator 100 further includes an electrode array 150 implanted in the epidural space and connected to the module 110 by a suitable lead. The electrode array 150 can include one or more electrodes, such as electrode pads on a paddle lead, circular (e.g., ring) electrodes surrounding the body of the lead, conformable electrodes, cuff electrodes, segmented electrodes, or any other type of electrodes that can form a unipolar, bipolar, or multipolar electrode configuration for stimulation and measurement. The electrodes can be directly punctured or fixed to the tissue itself.

[0047] Many aspects of the operation of the implantable stimulator 100 may be programmable by an external computing device 192, which may be operable by a user, such as a clinician or patient 108. Additionally, the implantable stimulator 100 serves as a data collector, with collected data being communicated to the external device 192 via a percutaneous communication channel 190. The communication channel 190 may be active substantially continuously, at periodic intervals, at non-periodic intervals, or upon request from the external device 192. Thus, the external device 192 may provide a clinical interface configured to program the implantable stimulator 100 and retrieve data stored in the implantable stimulator 100. This configuration is accomplished by program instructions, collectively referred to as a clinical programming application (CPA), stored in an instruction memory of the clinical interface.

[0048] 2 is a block diagram of the stimulation device 100. The electronics module 110 includes a battery 112 and a telemetry module 114. In an implementation of the present technology, any suitable type of transcutaneous communication channel 190, such as infrared (IR), radio frequency (RF), capacitive and / or inductive transfer, may be used by the telemetry module 114 to transmit power and / or data to and from the electronics module 110 via the communication channel 190. The module controller 116 has an associated memory 118 that stores one or more of clinical data 120, clinical settings 121, control program 122, etc. The controller 116 controls the pulse generator 124 to generate stimulation, such as pulse morphology, according to the clinical settings 121 and the control program 122. The electrode selection module 126 switches the generated pulse to a selected electrode of the electrode array 150 to deliver the pulse to tissue surrounding the selected electrode. Measurement circuitry 128, which may include amplifiers and / or analog-to-digital converters (ADCs), is configured to process signals including neural responses sensed at measurement electrodes of the electrode array 150 selected by the electrode selection module 126.

[0049] FIG. 3 is a schematic diagram illustrating the interaction of the implantable stimulator 100 with a nerve 180 of a patient 108. In the implementation shown in FIG. 3, the nerve 180 may be located in the spinal cord, but in alternative implementations, the stimulator 100 may be placed adjacent to any desired neural tissue, including peripheral nerves, splanchnic nerves, parasympathetic nerves, or brain structures. The electrode selection module 126 selects a stimulating electrode 2 of the electrode array 150 for delivering a pulse from the pulse generator 124 to the surrounding tissue, including the nerve 180. The pulse may include one or more phases, for example, a biphasic stimulation pulse 160 includes two phases. The electrode selection module 126 also selects a return electrode 4 of the electrode array 150 for stimulation current return in each phase to maintain zero net charge transfer. An electrode may act as both a stimulating electrode and a return electrode over a complete multiphasic stimulation pulse. Using two electrodes to deliver and return current in each stimulation phase in this manner is referred to as bipolar stimulation. Alternative embodiments may apply other forms of bipolar stimulation or may use a greater number of stimulating and / or return electrodes, for example, three electrodes for tripolar stimulation. A set of stimulating and return electrodes is referred to as a stimulating electrode configuration (SEC). The electrode selection module 126 is shown as connecting to ground 130 of the pulse generator 124 so that stimulation current can be returned through the return electrode 4. However, in other implementations, other connections for charge collection may be used.

[0050] Delivery of appropriate stimulation from electrodes 2 and 4 to nerve 180 induces a neural response 170 including an evoked compound action potential (ECAP) that propagates along nerve 180 as indicated by a rate known as conduction velocity. ECAPs can be induced for therapeutic purposes, which in the case of spinal cord stimulators for chronic pain may be to produce paresthesia at a desired location. For this purpose, electrodes 2 and 4 are used to deliver stimulation periodically at any therapeutically appropriate frequency, for example 30 Hz, although other frequencies including frequencies in the kHz range may be used. In alternative implementations, stimulation may be delivered sporadically or in a non-periodic manner such as bursts suitable for the patient 108. To program the stimulator 100 for the patient 108, the clinician can have the stimulator 100 deliver various configurations of stimulation that attempt to produce sensations that the user experiences as paresthesia. If a stimulation electrode configuration (SEC) is found that induces paresthesia in a location and size that matches the area of ​​the patient's body affected by pain, the clinician designates that configuration for continued use. The treatment parameters can be loaded into the memory 118 of the stimulator 100 as clinical settings 121 .

[0051] FIG. 6 shows a typical morphology of an ECAP 600 in a healthy subject, recorded with a single measurement electrode referenced to the system ground 130. The shape and duration of the ECAP 600 shown in FIG. 6 are predictable because they are the result of ionic currents generated by a collection of fibers that depolarize and generate action potentials (APs) in response to a stimulus. The sum of evoked action potentials (EAPs) generated synchronously across a large number of fibers forms the ECAP 600. An ECAP 600 generated from the synchronous depolarization of a group of similar fibers includes a positive peak P1, then a negative peak N1, followed by a second positive peak P2. This phase is caused by an activation region passing the measurement electrode as the action potential propagates along the individual fibers.

[0052] The ECAP can be recorded separately using two measurement electrodes, as shown in Figure 3. Depending on the polarity of the recording, the differential ECAP can take the opposite form to that shown in Figure 6, i.e., with two negative peaks N1 and N2 and one positive peak P1. Instead, depending on the distance between the two measurement electrodes, the differential ECAP can resemble the time derivative of ECAP 600 or more generally the difference between ECAP 600 and its time-delayed copy.

[0053] ECAP 600 may be characterized by any suitable characteristics, some of which are shown in FIG. 6. The amplitude of positive peak P1 is Ap1 and occurs at time Tp1. The amplitude of positive peak P2 is Ap2 and occurs at time Tp2. The amplitude of negative peak P1 is An1 and occurs at time Tn1. The peak-to-peak amplitude is Ap1+An1. Recorded ECAPs typically have maximum peak-to-peak amplitudes in the microvolt range and durations of 2-3 ms.

[0054] The stimulator 100 is further configured to measure the strength of ECAP 170 propagating along the nerve 180, whether such ECAP is induced by stimulation from electrodes 2 and 4 or induced otherwise. To this end, any electrode of the array 150 can be selected by the electrode selection module 126 to function as the measurement electrode 6 and the measurement reference electrode 8, which selectively connects the selected electrodes to the inputs of the measurement circuit 128. Thus, signals sensed by the measurement electrodes 6 and 8 following each stimulation are passed to the measurement circuit 128, which may include a differential amplifier and an analog-to-digital converter (ADC), as shown in FIG. 3. The measurement circuit 128 can operate, for example, according to the teachings of the above-mentioned WO 2012 / 155183.

[0055] The signals sensed by the measurement electrodes 6, 8 and processed by the measurement circuitry 128 are further processed by an ECAP detector implemented in the controller 116 configured by the control program 122 to obtain information regarding the effect of the stimulation applied to the nerve 180. In some implementations, the sensed signals are processed by the ECAP detector in a manner that measures and stores one or more characteristics from each evoked neural response or a group of evoked neural responses contained in the sensed signals. In one such implementation, the characteristics include a peak-to-peak ECAP amplitude in microvolts (μV). For example, the sensed signals may be processed by the ECAP detector to determine a peak-to-peak ECAP amplitude according to the teachings of applicant's International Publication No. WO 2015 / 074121, the contents of which are incorporated herein by reference. Alternative implementations of the ECAP detector may measure and store alternative characteristics from the neural responses or may measure and store more than one characteristic from the neural responses.

[0056] The stimulator 100 can apply stimulation for potentially extended periods of time, such as days, weeks, or months, during which time the characteristics of the neural response, the stimulation settings, paresthesia target levels, and other operating parameters can be stored in the memory 118. To provide an adequate SCS treatment, the stimulator 100 can deliver tens, hundreds, or even thousands of stimuli per second for hours per day. Each neural response or group of responses generates one or more characteristics, such as a measure of the strength of the neural response. Thus, the stimulator 100 can generate such data at rates of tens or hundreds of Hz, or even kHz, and over the course of hours or days, this process results in a large amount of clinical data 120 that can be stored in the memory 118. However, the memory 118 is necessarily limited in capacity, and therefore care must be taken to select a compact data format for storage in the memory 118 and ensure that the memory 118 is not exhausted before the data is expected to be wirelessly retrieved by the external device 192, which may occur no more than once or twice a day.

[0057] An activation plot or growth curve is an approximation of the relationship between stimulation intensity (e.g., the amplitude of the current pulse 160) and the strength of the neural response 170 resulting from the stimulation (e.g., ECAP peak-to-peak amplitude). FIG. 4a shows an idealized activation plot 402 for one posture of the patient 108. The activation plot 402 shows a linear increase in ECAP amplitude for stimulation intensity values ​​above a threshold 404, called the ECAP threshold. The ECAP threshold exists due to the binary nature of fiber recruitment. If the field strength is too low, no fibers are recruited. However, once the field strength exceeds the threshold, fibers begin to be recruited and their individual evoked action potentials are independent of the strength of the field. Thus, the ECAP threshold 404 reflects the field strength at which a significant number of fibers begin to be recruited, and an increase in response strength with stimulation intensity above the ECAP threshold reflects an increase in the number of fibers recruited. Below the ECAP threshold 404, the ECAP amplitude can be considered to be zero. Above the ECAP threshold 404, the activation plot 402 has a positive, approximately constant slope indicating a linear relationship between stimulation intensity and ECAP amplitude. Such a relationship can be modeled as follows:

number

[0058] FIG. 4a also shows discomfort threshold 408, which is the stimulation intensity at which patient 108 experiences unpleasant or painful stimulation. FIG. 4 also shows perception threshold 410. Perception threshold 410 is the stimulation intensity that corresponds to the ECAP amplitude that is perceptible to the patient. There are several factors that may affect the location of perception threshold 410, including the patient's posture. Perception threshold 410 may be a stimulation intensity greater than ECAP threshold 404, as shown in FIG. 4a, when patient 108 does not perceive low levels of neural activation. Conversely, perception threshold 410 may be a stimulation intensity less than ECAP threshold 404 when the patient has high perceptual sensitivity to lower levels of neural activation than can be detected by ECAP or when the signal-to-noise ratio of ECAP is low. Discomfort threshold 408 and perception threshold 410 are examples of perceptual markers for patient 108.

[0059] For effective and comfortable operation of an implanted neuromodulation device such as the stimulator 100, it is desirable to maintain the stimulation intensity within a therapeutic range. A stimulation intensity within the therapeutic range 412 is above the ECAP threshold 404 and below the discomfort threshold 408. In principle, it would be easy to measure these limits and ensure that the stimulation intensity, which can be tightly controlled, always falls within the therapeutic range 412. However, the activation plot, and therefore the therapeutic range 412, changes with the position of the patient 108.

[0060] FIG. 4b shows the change in activation plots with changes in patient posture. Changes in patient posture can cause changes in the impedance of the electrode-tissue interface or changes in the distance between the electrode and the neuron. Although activation plots for only three postures, 502, 504, and 506, are shown in FIG. 4b, the activation plots for any given posture may be between or outside the activation plots shown, on a continuously varying basis with posture. As a result, as the patient posture changes, the ECAP threshold changes as shown by the ECAP thresholds 508, 510, 512 of the respective activation plots 502, 504, 506. Furthermore, as the patient posture changes, the slope of the activation plots also changes as shown by the changing slopes of the activation plots 502, 504, 506. In general, as the distance between the stimulating electrode and the spinal cord increases, the ECAP threshold increases and the slope of the activation plot decreases. Thus, the activation plots 502, 504, and 506 correspond to an increase in the distance between the stimulating electrode and the spinal cord and a decrease in the patient's sensitivity.

[0061] To maintain the applied stimulation intensity within a therapeutic range as the patient's posture changes, in some implementations, an implanted neuromodulation device, such as the stimulation device 100, can adjust the applied stimulation intensity based on a feedback variable determined from one or more measured ECAP characteristics. In one implementation, the device can adjust the stimulation intensity to maintain the measured ECAP amplitude at a target response intensity. For example, the device can calculate the error between the target ECAP amplitude and the measured ECAP amplitude and adjust the applied stimulation intensity to reduce the error as much as possible, such as by adding the scaled error to the current stimulation intensity. A neuromodulation device that operates by adjusting the applied stimulation intensity based on the measured ECAP characteristics is said to operate in a closed-loop mode and is also referred to as a closed-loop neurostimulation (CLNS) device. By adjusting the applied stimulation intensity to maintain the measured ECAP amplitude at an appropriate target response intensity, such as the ECAP target 520 shown in FIG. 4b, a CLNS device generally maintains the stimulation intensity within a therapeutic range as the patient's posture changes.

[0062] The CLNS device includes a stimulator that takes the stimulation intensity values ​​and converts them into neural stimulation comprising a sequence of electrical pulses according to a predetermined stimulation pattern. The stimulation pattern is parameterized by a number of stimulation parameters including stimulation amplitude, pulse width, number of phases, order of phases, number of poles of the stimulation electrodes (two is bipolar, three is tripolar, etc.) and stimulation rate or frequency. At least one of the stimulation parameters, e.g., stimulation amplitude, is controlled by a feedback loop.

[0063] In an exemplary CLNS system, a user (e.g., a patient or clinician) sets a target response strength, and the CLNS device performs proportional-integral-derivative (PID) control. In some implementations, the differential contribution is ignored, and the CLNS device uses a first-order integral feedback loop. The stimulator generates stimulation according to the stimulation strength parameters, which elicit a neural response in the patient. The strength of the evoked neural response (e.g., ECAP) is measured by the CLNS device and compared to the target response strength.

[0064] The measured neural response strength and its deviation from the target response strength are used by a feedback loop to determine possible adjustments to stimulation intensity parameters to maintain the neural response at the target intensity. If the target intensity is properly selected, the patient will receive a consistent and comfortable therapeutic stimulation through postural changes and other perturbations to the stimulation / response behavior.

[0065] 5 is a schematic diagram showing elements and inputs of a closed loop nerve stimulation system (CLNS) 300 according to one implementation of the present technology. The system 300 includes a stimulator 312 that converts stimulation intensity parameters (e.g., stimulation current amplitude) into nerve stimulation comprising a series of electrical pulses on a stimulation electrode (not shown in FIG. 5) according to a set of predetermined stimulation parameters. According to one implementation, the predetermined stimulation parameters include the number and order of phases, the number of poles of the stimulation electrode, pulse width, and stimulation rate or frequency.

[0066] The generated stimulus traverses from the electrodes to the spinal cord, which is represented in FIG. 5 by dashed box 308. Box 309 represents the elicitation of a neural response y by the stimulus as described above. Box 311 represents the elicitation of an artifact signal a that depends on the stimulus intensity and other stimulus parameters as well as the electrical environment of the measurement electrodes. Before the evoked response is measured, various noise sources n may be added to the evoked response y in summing element 313, including electrical noise from external sources such as electrical disturbances generated by the body, such as 50 Hz mains power, neural responses evoked not by the device but by other sources such as peripheral sensory inputs, EEG, EMG and electrical noise from measurement circuitry 318.

[0067] Neural recruitment resulting from stimulation is affected by mechanical changes, including postural changes, gait, breathing, heart rate, etc. Mechanical changes may cause impedance changes or changes in the location and orientation of nerve fibers relative to the electrode array. As noted above, the strength of the evoked response provides a measure of the recruitment of the fibers being stimulated. In general, the stronger the stimulation, the more recruitment and the stronger the evoked response. The evoked response typically has a maximum amplitude in the microvolt range, while the voltage resulting from the stimulation applied to elicit the response is typically several volts.

[0068] The measurement circuit 318, which may be identified as the measurement circuit 128, amplifies the sensed signal r (including the evoked neural response, artifacts, and noise) and samples the amplified sensed signal r to capture a "signal window" that includes a predetermined number of samples of the amplified sensed signal r. The ECAP detector 320 processes the signal window and outputs a measured neural response strength d. In one implementation, the neural response strength includes an ECAP amplitude. The measured response strength d is input to the feedback controller 310. The feedback controller 310 includes a comparator 324 that compares the measured response strength d with a target ECAP amplitude set by the target ECAP controller 304 and provides an indication of the difference between the measured response strength d and the target ECAP amplitude. This difference is an error value e.

[0069] The feedback controller 310 calculates an adjusted stimulation intensity parameter s with the goal of maintaining the measured response strength d equal to the target ECAP amplitude. Thus, the feedback controller 310 adjusts the stimulation intensity parameter s to minimize the error value e. In one implementation, the controller 310 utilizes a first order integral function using a gain element 336 and an integrator 338 to provide an appropriate adjustment to the stimulation intensity parameter s. According to such an implementation, the current stimulation intensity parameter s may be calculated by the feedback controller 310 as follows: s = ∫Kedt(2) where K is the gain of gain element 336 (controller gain). This relationship can also be expressed as: δs=Ke where δs is the adjustment to the current stimulus intensity parameter s.

[0070] The target ECAP amplitude is input to the comparator 324 via the target ECAP controller 304. In one embodiment, the target ECAP controller 304 provides an indication of a particular target ECAP amplitude. In another embodiment, the target ECAP controller 304 provides an indication to increase or decrease the current target ECAP amplitude. The target ECAP controller 304 may include an input to the neurostimulator via which a patient or clinician may input the target ECAP amplitude or an indication thereof. The target ECAP controller 304 may include a memory in which the target ECAP amplitude is stored and from which the target ECAP amplitude is provided to the feedback controller 310.

[0071] The clinical setting controller 302 provides therapy parameters to the system, including the gain K of the gain element 336 and stimulation parameters of the stimulator 312. The clinical setting controller 302 may be configured to adjust the gain K of the gain element 336 to adapt the feedback loop to the patient's sensitivity. The clinical setting controller 302 may include an input to the neurostimulator, through which the patient or clinician may adjust the therapy parameters. The clinical setting controller 302 may include a memory in which the therapy parameters are stored and provided to the components of the system 300.

[0072] In some implementations, two clocks (not shown) are used: a stimulus clock running at the stimulus frequency (e.g., 60 Hz) and a sample clock for sampling the measured response r (e.g., running at a sampling frequency of 10 kHz). Since the ECAP detector 320 is linear, only the stimulus clock affects the dynamics of the CLNS system 300. The stimulator 312 outputs a stimulus according to the adjusted stimulus strength s in the next stimulus clock cycle. Thus, there is a delay of one stimulus clock cycle before the stimulus strength is updated in light of the error value e.

[0073] 7 is a block diagram of a neurostimulation system 700. The neurostimulation system 700 is centered around a neuroregulator 710. In one example, the neuroregulator 710 may be implemented as the stimulator 100 of FIG. 1 implanted within a patient (not shown). The neuroregulator 710 is wirelessly connected to a remote control (RC) 720. The remote control 720 is a portable computing device that provides the patient with control of stimulation in a home environment by allowing control of the functions of the neuroregulator 710, including one or more of the following functions: enabling or disabling stimulation, adjusting stimulation intensity or target response intensity, and selecting a stimulation control program from control programs stored in the neuroregulator 710.

[0074] The charger 750 is configured to recharge the rechargeable power source of the neuroregulator 710. Although charging is shown as wireless in Figure 7, it may be wired in alternative implementations.

[0075] The neuromodulation device 710 is wirelessly connected to a Clinical System Transceiver (CST) 730. The wireless connection may be implemented as the percutaneous communication channel 190 of Figure 1. The CST 730 acts as an intermediary between the neuromodulation device 710 and a Clinical Interface (CI) 740 to which the CST 730 is connected. Although a wired connection is shown in Figure 7, in other implementations the connection between the CST 730 and the CI 740 is wireless.

[0076] The CI 740 may be implemented as the external computing device 192 of Figure 1. The CI 740 is configured to program the neuroregulator 710 and to recover data stored in the neuroregulator 710. This configuration is accomplished by program instructions, collectively referred to as a Clinical Programming Application (CPA), stored in an instruction memory of the CI 740.

[0077] The CPA utilizes a user interface (UI) of the CI 740. The UI may include devices for displaying information to a user (e.g., a display) and devices for receiving input from the user, such as a touch screen, a movable pointing device that controls a cursor (mouse), a keyboard, a joystick, a touchpad, a trackball, etc. In the example of a touch screen, the input device may be combined with the display. Alternatively, the UI of the CI 740 of the input device may be separate from the display.

[0078] Assisted Programming System As mentioned above, obtaining patient feedback regarding the patient's sensations during programming of closed-loop neurostimulation therapy is important, but intervention by a trained clinical engineer is expensive and time-consuming. It would therefore be advantageous if patients could program their own implantable devices or with some assistance from a clinician. However, the interfaces of current programming systems are not intuitive and are generally not suitable for direct use by patients due to their technical nature. Therefore, CPA needs to be as intuitive as possible for non-technical users while avoiding discomfort to the patient.

[0079] Implementations of an assistive programming system (APS) according to the present technology are generally configured to meet the above needs. In some implementations, the APS includes two elements: an assistive programming module (APM) that forms part of the CPA, and an assistive programming firmware (APF) that forms part of the control program 122 executed by the controller 116 of the electronics module 110. Data obtained from the patient, both subjective and objective, is analyzed by the APM to determine the clinical settings of the neural stimulation therapy delivered by the stimulator 100. The APF is configured to complement the operation of the APM by responding to commands issued by the APM to the stimulator 100 via the CST 730 to deliver specific stimuli to the patient, and by returning measurements of the neural responses to the delivered stimuli via the CST 730.

[0080] The APS instructs the device 710 via the CST 730 to capture the signal window and return it to the CI 740. In such an implementation, the device 710 captures the signal window using the measurement circuitry 128, bypasses the ECAP detector 320, and temporarily stores data representing the raw signal window in memory 118 before transmitting the data representing the captured signal window to the APS for analysis.

[0081] FIG. 8 is a flow chart depicting an assisted programming workflow 800 implemented by an APM at a high level, according to one implementation of the present technology. In the assisted programming workflow 800, control of the CI 740 is handed over to a user, e.g., the patient, who interacts with the APM for the entire workflow. In some implementations, the patient remains in a fixed, predefined posture throughout the entire workflow. Direct patient involvement allows for faster feedback, as subjective responses to stimuli do not need to be communicated through a clinician. However, it should be noted that the workflow 800 is only one possible implementation of an APM, and there is no formal requirement that any part of the assistive programming system include direct patient involvement.

[0082] The workflow 800 comprises several stages: a patient controlled stimulation lamp (PCSR) stage 810 , an (optional) coverage investigation stage 815 , a coverage selection stage 820 and a measurement optimization (MO) stage 830 .

[0083] The PCSR stage 810 is configured to deliver stimulation of gradually increasing intensity and receive subjective input from the patient regarding a maximum value of stimulation intensity ("Max" value) for each of one or more candidate stimulation electrode configurations (SECs). The Max value can be identified in the discomfort threshold 408 of FIG. 4a. Meanwhile, the APM is configured to record the sensed signal and analyze the recorded data as well as the Max value for each SEC of the patient to calculate the ECAP threshold for each SEC. The PCSR stage 810 is described in more detail below.

[0084] The coverage investigation stage 815 may be configured to receive input regarding the patient's sensations in response to the stimulation delivered through each candidate SEC at a comfortable stimulation intensity. A comfortable stimulation intensity is predicted for each candidate SEC based on the maximum and / or ECAP thresholds derived in the PCSR stage 810. Based on the patient input, the comfortable stimulation intensity at each SEC may be adjusted. Furthermore, if the stimulation delivered through any candidate SEC causes discomfort to the patient in the region of the body, the candidate SEC itself may be adjusted and the PCSR stage 810 is repeated for the adjusted SEC. The coverage investigation stage 815 is described in more detail below.

[0085] The coverage selection stage 820 is configured to receive input from the patient to select one or more of the candidate SECs after any adjustments made by the coverage exploration stage 815. The comfortable stimulation intensity delivered via each candidate SEC is based on the comfortable stimulation intensity derived for that SEC in the PCSR stage 810, and possibly adjusted in the coverage exploration stage 815. The patient may test different combinations of SECs before selecting one to retain. The coverage selection stage 820 is described in more detail below.

[0086] The measurement optimization (MO) stage 830 is configured to deliver stimulation of gradually increasing intensity from the primary SEC of the selected SEC and record sensed signal data at each of a plurality of measurement electrode configurations (MECs) for the primary SEC. The MO stage 830 is then configured to select an optimal MEC for the primary SEC based on the response data, calculate the patient's physiological characteristics, and select optimal treatment parameters for the primary SEC / optimal MEC combination. The selected SEC, including the primary SEC, optimal MEC, and optimal treatment parameters, is referred to as a determined program. The measurement optimization stage 830 is described in more detail below.

[0087] Following the workflow 800, if successful, the APS can load the determined program into the device 710 to manage the subsequent neurostimulation therapy. In one implementation, the program includes clinical settings 121, also referred to as therapy parameters, which are entered or stored in the neuromodulation device 710 by the clinical settings controller 302. The patient can then control the device 710 to deliver therapy according to the determined program using the remote controller 720 as described above. The determined program can also or alternatively be loaded into the CPA for verification and modification. Verification and modification of the determined program can be performed by the APS itself. If unsuccessful, the device 710 can be manually programmed.

[0088] In the workflow 800, the APM may use predefined values ​​for certain treatment parameters. In one implementation, these parameters and values ​​are as follows: Stimulation frequency: 40Hz Pulse width: 240 microseconds Phase gap: 200 microseconds Pulse shape: triphasic, anodic phase first Signal window length: 60 samples Sampling frequency: 16kHz ·Interstimulus interval: 5ms

[0089] In one implementation of workflow 800, four candidate stimulation electrode configurations (SECs) are defined. Each SEC is tripolar and includes a stimulation electrode acting primarily as a cathode, sinking the stimulation current, and two adjacent return electrodes on either side of the stimulation electrode acting primarily as an anode, providing the return current. Tripolar stimulation electrode configurations are described in further detail in applicant's International Publication No. WO 2017 / 219096, the entire contents of which are incorporated herein by reference.

[0090] In some implementations, the APM assumes that the electrode array 150 consists of two leads implanted approximately symmetrically to the left and right of the patient's midline (as viewed from behind the patient), as shown in FIG. 1 for one lead. In one implementation, each lead contains 12 contacts (electrodes), numbered such that contact index zero is the top (rostral) contact of the lead and contact index 11 is the bottom (caudal) contact of the lead. The stimulation electrodes of each of the four candidate SECs are defined as follows: top left (contact index 1, left lead), top right (contact index 1, right lead), bottom left (contact index 10, left lead), and bottom right (contact index 10, right lead). In other implementations with different numbers of contacts on each lead, the bottom left and bottom right stimulation electrodes are defined to be the second most posterior contacts on their respective leads.

[0091] In other implementations, the APM contemplates other configurations for the electrode array 150. One such configuration is a paddle lead. In such implementations, the stimulating electrodes for each of the four candidate SECs may be defined as the top left, top right, bottom left, and bottom right electrodes on the paddle lead.

[0092] For each SEC, the APM defines multiple measurement electrode configurations (MECs). As shown in FIG. 3, the measurement electrode configuration includes two electrodes for differential ECAP recording. The measurement electrode connected to the positive terminal of the measurement circuit 318 is called the recording electrode, and the measurement electrode connected to the negative terminal of the measurement circuit 318 is called the reference electrode. FIG. 9 shows the locations of the recording and reference electrodes in six candidate MECs according to one implementation of the present technology. Each candidate MEC is represented in a row of table 900 under a graphical representation 910 of a 12-contact lead. The electrodes labeled Rec and Ref in each row are the recording and reference electrodes in the corresponding MEC. The electrodes labeled S and R are the stimulating and return electrodes of a tripolar SEC placed as described above at one end of the lead.

[0093] In an alternative implementation of the present technology, the APM includes the patient's selected SECs by means other than steps 810 to 820. In such an implementation, the APM implements a workflow that includes only the measurement optimization step 830.

[0094] Patient-controlled stimulation lamp stages In one implementation of the PCSR stage 810, the APM renders a screen 1000 on the UI display of the CI 740, as shown in Figure 10. The screen 1000 includes a stimulation control 1010 (shown as a virtual button), instructions 1020, a progress bar 1050, and a next control 1040. The stimulation control 1010, once enabled, is configured to remain activated as long as the patient continues to interact with it, for example by "holding" the virtual button. In other implementations of the PCSR stage 810, the stimulation control 1010 and / or the next control 1040 are hardware controls, such as buttons, that form part of the UI of the CI 740 but remain separate from the display.

[0095] When the stimulation control 1010 is enabled, the instructions 1020 are configured to instruct the patient to activate the stimulation control 1010. When the stimulation control 1010 is activated, the APM instructs the device 710 to deliver stimulation via a first of the candidate SECs at a gradually increasing or "climbing" intensity. The stimulation control 1010 may be animated to indicate the time elapsed since activation of the control, for example, by an animated "pie" display as shown in FIG. 10. In this example, a sector 1060 representing the elapsed time is filled differently than the rest of the stimulation control 1010. While the stimulation control 1010 is activated, the sector 1060 widens in proportion to the elapsed time until it encompasses the entire stimulation control 1010. This animation indicates to the patient that something is happening upon activating the control 1010, even if the patient does not immediately feel the stimulation (because the stimulation intensity is below the perception threshold). The animation also communicates to the patient the rate of increase in stimulation intensity. The animation also indicates the stimulation intensity to the clinician or other skilled user. The animation also reinforces the instructions 1020: even before the patient can feel the stimulation, the patient can see that sector 1060 increases when he activates control 1010 and decreases when he deactivates control 1010.

[0096] In some implementations, the first activation of the stimulation control 1010 in a candidate SEC initiates a “pre-ramp” (described below). The pre-ramp begins when the candidate SEC reaches its ECAP threshold I, as described below. thresh In such an implementation, during the pre-ramp and / or subsequent stimulus ramps on the same candidate SEC, an animation can indicate when the stimulus intensity reaches the ECAP threshold. The animation can indicate this, for example, by changing color or rendering on a display.

[0097] The APM continues to increase the stimulation intensity as long as the patient continues to activate the stimulation control 1010. In one implementation of the stimulation ramp, the increase in intensity is linear over time at a predefined ramp rate, which may be set at 400 microamps / second to minimize the risk of unpleasant stimulation.

[0098] When the patient deactivates the stimulation control 1010, for example by releasing a virtual button, the APM records the stimulation intensity at the time of release as the Max value of the current SEC. The APM then reduces the stimulation intensity. In one implementation, the intensity ramp down follows a linear profile at a rate selected such that the intensity reaches zero after a predefined interval, for example 3 seconds.

[0099] The instruction 1020 prompts the patient to continue activating the stimulation control 1010 as long as they are comfortable, and to stop activation only when the stimulation intensity begins to feel uncomfortable. This user interface design takes advantage of the human withdrawal reflex, whereby the patient is more likely to intuitively release the button when experiencing an unpleasant stimulation. Thus, the design of stage 810 minimizes the training burden imposed on the patient when using the APM. If the patient does not stop activating the stimulation control 1010 before the stimulation intensity reaches a hard ceiling (e.g., a pulse amplitude of 36 mA in one implementation), the APM stops the stimulation ramp and begins a down ramp. The stimulation intensity at the time the stimulation ramp is stopped is recorded as the patient's discomfort threshold (Max) value for that SEC.

[0100] The progress bar 1050 indicates approximate quantitative progress through the workflow 800. In one implementation, the portion of the progress bar 1050 that is filled in represents the current ratio of the time elapsed since the start of the workflow 800 to the average time it takes to complete the workflow 800 resulting from previous patient assistance programming according to the workflow 800.

[0101] Before and during each stimulus ramp, the APM collects and analyzes data as described below. Following a successful stimulus ramp (defined below), the next control 1040 is enabled. Upon activation of the next control 1040, a new stimulus ramp is performed for the next candidate SEC. This cycle occurs once per candidate SEC. Once all candidate SECs have been used for the stimulus ramp, activation of the next control 1040 moves the workflow 800 to the coverage selection stage 820.

[0102] Each stimulation ramp in the PCSR stage 810 is implemented by the APF upon receiving a ramp command from the APM. The ramp command specifies the ramp direction (up or down), the ramp rate (absolute change in intensity per unit time) and the end intensity. In one implementation, when the ramp command is received by the APF, the controller 116 starts the ramp and continues ramping until the patient releases the stimulation control 1010, the APF is signaled by a stop command from the APM, or the end intensity is reached. Once the end is reached, the APM sends a ramp down command to the APF to ramp down the stimulation intensity. Since the purpose of the ramp is to determine the Max value of the patient, the end intensity is intentionally set high, i.e., above the highest expected Max value (equal to 36 mA in one implementation). This means that if the communication between the APF and the APM is interrupted for any reason, the deactivation of the stimulation control 1010 is not communicated to the APF, so according to this implementation, the patient may receive an uncomfortably strong stimulation until the APF reduces the stimulation intensity again.

[0103] In another implementation, the controller 116 interrupts the ramp if the APF does not receive communication from the APA within a first timeout period. The controller 116 can then ramp down to a lower intensity if there is a continued absence of communication from the APA within a second timeout period. In this implementation, the patient is less likely to experience unpleasant stimulation if communication between the APF and the APM is interrupted.

[0104] Figures 18a-18f illustrate the operation of this implementation. In Figure 18a, a ramp 1800 of stimulus intensity versus time is initiated upon receipt by the APF of a ramp command, indicated by a star 1805. The ramp 1800 continues as long as communication 1810 (indicated by an unfilled star in Figure 8) continues to be received by the APF. (Communication 1810 can be for any purpose, not just related to the PCSR.) The ramp 1800 stops when the APF receives a pause command, indicated by a cross 1815, from the APM. The ramp 1800 also stops when the endpoint intensity is reached (not shown).

[0105] The ramp 1820 of FIG. 18b occurs when communication is interrupted. After the ramp command and communication 1825 are received, a first timeout period 1830 passes and no further communication is received by the APF. In one implementation, the first timeout period is 1 second. Thus, the ASPF stops the ramp 1820. After the second timeout period 1835 expires after stopping, the APF ramps down to zero intensity, regardless of whether further communication, e.g., communication 1837, is received during the ramp down. In one implementation, the second timeout period is 0.5 seconds.

[0106] The ramp 1840 in Figure 18c is stopped early for the same reasons as in Figure 18b, however the downward ramp is not performed because communication 1845 is received before the second timeout period expires.

[0107] In Figure 18d, the lamp 1850 is shut off early due to the expiration of a first timeout period 1855. As in Figure 18b, after the expiration of a second timeout period 1860, the APF reduces the intensity to zero regardless of the absence of communication from the APM.

[0108] 18e illustrates a downward ramp 1870 in intensity by the APF upon receiving a downward ramp command 1875 from the APM. The downward ramp 1870 continues to go to zero intensity regardless of whether further communications, such as communication 1880, are received during the downward ramp 1870.

[0109] The falling ramp 1890 of FIG. 18f, like the falling ramp 1870, continues to be at zero intensity regardless of whether there is no communication from the APM during the falling ramp 1890.

[0110] Data analysis during the PCSR phase 11a is a flow chart illustrating a data collection and analysis method 1100 performed by the APM and device 710 during the PCSR stage 810 according to one implementation of the APM. The method 1100 is performed per stimulus ramp per SEC. The method 1100 begins with steps 1110 and 1115. Steps 1110, 1115 and 1125 are performed before the APM enables the stimulus control 1010 and therefore before any stimulus is applied. Step 1110 instantiates an activation plot (AP) builder for each MEC, while step 1115 instantiates a noise deviation detector (NDD) for each MEC. The AP builder and NDD are described in more detail below.

[0111] In step 1125, the APM instructs the device 710 to capture multiple "zero current" signal windows for each MEC. In one implementation, the device 710 simply captures the signal windows using the measurement circuitry 128, bypassing the ECAP detector 320, and temporarily stores the raw signal windows in memory 118 before transmitting the data to the APM. Once this data is captured and returned to the APM, step 1125 processes these "zero current" signal windows to calibrate each NDD instance.

[0112] Method 1100 then proceeds to step 1120, whereby stimulation control 1010 allows the patient to initiate a stimulation ramp for the current SEC as described above. During the stimulation ramp, the APM instructs device 710 to capture and return a signal window at each MEC for each stimulation current amplitude s. The returned signal window for each MEC is analyzed by a corresponding AP builder that extracts the detected ECAP amplitude d from each signal window. When stimulation control 1010 is deactivated, still at step 1120, each AP builder fits a model called a logistic growth curve (LGC) to the set of (s, d) value pairs for each MEC. Each AP builder then calculates, at step 1130, a growth curve quality index (GCQI) for each fitted LGC. LGC model fitting and the calculation of GCQI by the AP builder are described in more detail below.

[0113] Step 1135 then selects the MEC that resulted in the largest GCQI. Step 1140 then calculates the ECAP threshold from the fitted LGC corresponding to the selected MEC. Step 1140 is described in more detail below.

[0114] Step 1145 then tests whether the fitted LGC meets certain inclusion criteria. The fitted LGC is based on more than a predetermined number of (s,d) value pairs, for example 12 value pairs. · GCQI is greater than a threshold, e.g. 10dB. · The ECAP threshold calculated from the LGC is greater than 0 and less than the Max value recorded for the current SEC at the end of the stimulation ramp.

[0115] If any of the inclusion criteria are not met ("N"), the fitted LGC is ignored and the APM in step 1150 calculates the Max value I recorded for the current SEC at the end of the stimulation ramp. max From ECAP threshold I thresh In one implementation, step 1150 uses a linear prediction model. Ithresh =m I max (3) where m is a correlation parameter that may be derived from historical patient data. In one implementation, m has a value between 0.5 and 1.0. In another implementation, m has a value between 0.6 and 0.9. In one implementation, m has a value between 0.65 and 0.8. Step 1150 is an example of prediction of a physiological threshold (ECAP threshold) from sensory markers (discomfort threshold, max). The APM then proceeds to step 1155 using the predicted ECAP threshold.

[0116] If all inclusion criteria tested in step 1145 are met ("Y"), the APM proceeds to step 1155 using the ECAP thresholds obtained in step 1140 from the fitted LGC model.

[0117] In step 1155, the APM uses the NDD to calculate the detection rate of the MEC selected in step 1135 over the full range of stimulation intensities. In one implementation, the full range means stimulation intensities between 1.1 times the ECAP threshold and the Max value. The detection rate is the percentage of stimulation intensity values ​​over the full range where the NDD returns more than 50%. Step 1155 can use the signal window returned during the stimulation ramp of the selected MEC. Step 1160 then tests whether the detection rate is abnormal. In one implementation, an abnormal detection rate means a detection rate below a predetermined percentage, for example 20%. The purpose of this test is to identify whether the patient has prematurely deactivated the stimulation control 1010. This can happen if the patient is new to the APM or if the patient has deactivated the control by mistake.

[0118] If the detection rate is not abnormal ("N"), the current SEC is marked as successful and method 1100 ends at step 1165 where the next control 1040 is enabled. If not ("Y"), step 1170 tests whether the maximum number of iterations has been reached. If not ("N"), in step 1175 the APM increments the number of iterations and restarts method 1100 for the current candidate SEC. If so ("Y"), the current SEC is marked as unsuccessful and method 1100 ends at step 1165 where the next control 1040 is enabled. As discussed above, activation of the next control 1040 either repeats method 1100 for the next candidate SEC or ends the PCSR phase 810 if all candidate SECs have been tested.

[0119] The result of the PCSR step 810 is a Max value and an ECAP threshold value for each candidate SEC that is marked as successful.

[0120] In another implementation of the PCSR stage 810, The profile of the stimulus ramp during activation of the stimulus control 1010 need not be linear. One such implementation is a "threshold ramp" where the threshold is a predicted (as from step 1150) or fitted (as from step 1140) ECAP threshold. Threshold ramps are described in more detail below. Deactivation of the stimulation control 1010 may cause the stimulation intensity to decrease exponentially rather than linearly. This addresses scenarios where the perception of stimulation triggers the patient, causing the patient to unintentionally release the stimulation control 1010. In such implementations, the screen 1000 may include an additional user control to return the stimulation intensity completely to zero within the controlled ramp, allowing the patient to "lock in" the Max value, so that iterations of the method 1100 can be processed. In another implementation, a threshold ramp is used for the descending ramp, where the threshold is the predicted (as from step 1150) or fitted (as from step 1140) ECAP threshold. Threshold ramps are described in more detail below. · Stimulation ramp rates may be increased and decreased based on the stimulation control deactivation point if the patient repeats the stimulation ramp during SEC. · The search space of MEC can be expanded from that shown in Figure 9. "Early release" and "missed ECAP" failure scenarios can be distinguished and different responses defined for each. For example, the patient can be asked if they accidentally released the button and the method 1100 can be repeated as many times as necessary for that scenario. One or more exclusion criteria, such as detection of a late response, may be tested in step 1145 and, if found to be true, may be used to exclude the fitted LGC. There may not be a maximum number of iterations tested in step 1170. Instead, a "Y" in step 1160 will proceed directly to step 1175. If the calculation in step 1155 repeatedly results in an abnormal detection rate for a candidate SEC, then the next control 1040 will not be enabled for that candidate SEC, no matter how many times the method 1100 is iterated. In such a situation, pressing and holding the next control 1040 will mark the candidate SEC as failed and either repeat the method 1100 for the next candidate SEC or exit the PCSR phase 810 if all candidate SECs have been tested.

[0121] FIG. 11b is a flow chart showing a data collection and analysis method 1100a performed by the APM and the device 710 during the PCSR stage 810 according to one implementation of the APM. The method 1100a is performed for each stimulus ramp per SEC. The method 1100a is similar to the method 1100, and steps that are the same in the two methods have similar labels and therefore will not be described below. The main difference is that in the course of the method 1100a, the MEC is not selected based on its GCQI. Instead, all quantities are calculated for each MEC, and the number of MECs whose calculated quantities meet certain criteria is counted. If the count exceeds 1, the next control is enabled, along with some other criteria. Thus, for example, in step 1155a, instead of calculating the detection rate only for the selected MEC, as in step 1155, the detection rate is calculated for the current MEC in the list of candidate MECs. In step 1130a, the AP builder of the current MEC also calculates the growth curve quality index (GCQI) of the LGC fitted in step 1120. Step 1145a then tests whether the fitted LGC meets certain inclusion criteria. The purpose of the inclusion criteria of step 1145 is to ensure that the parameters fitted to the LGC are reliable. The inclusion criteria are as follows: ·GCQI is greater than a threshold, e.g. 6dB. · The ECAP threshold calculated from LGC is not too close to the ECAP threshold boundary where the LGC parameter fitting was performed. The sensitivity calculated from LGC is positive. The standard deviation of the calculated sensitivity is less than a threshold value, for example 0.5 times the calculated sensitivity.

[0122] If the fitted LGC does not meet the inclusion criteria ("N" at step 1145a), the method 1100a obtains the next candidate MEC at step 1163 and returns to steps 1110 and 1115.

[0123] If the fitted LGC meets the inclusion criteria ("Y" at step 1145a), method 1100a checks in step 1160a whether the detection rate returned by the NDD in step 1155a for the current MEC is anomalous, in the same sense as in step 1160. If the detection rate is not anomalous ("N" at step 1160) or the GCQI is greater than 10 dB, the MEC may be considered "good". Step 1162 increments the number of "good" MECs in step 1163, and method 1100a gets the next candidate MEC in step 1163 and returns to steps 1110 and 1115. If the detection rate is anomalous ("Y" at step 1160) and the GCQI is less than or equal to 10 dB, method 1100a proceeds directly to step 1163.

[0124] Once all candidate MECs have been exhausted by step 1163, step 1168 tests whether the number of "good" MECs is greater than 1 and whether the Max value is greater than a threshold, e.g., 1 mA. If so ("Y"), method 1100a ends by activating the next control in step 1165. If not ("N"), step 1180 waits for the user to "long press" (press during a predefined interval) the next control to end method 1100a. If method 1100a ends in this way, the current candidate SEC is marked as failed, meaning that it will not be further involved in workflow 800.

[0125] Coverage survey stage In one implementation of the coverage investigation stage 815, the APM renders a screen 1200 on the UI display of the CI 740, as shown in Figure 12. The screen 1200 includes a stimulus control 1210, instructions 1220, options 1230, a next control 1240, and a progress bar 1250. In other implementations of the coverage investigation stage 815, the stimulus control 1210 and / or the next control 1240 are hardware controls, such as buttons, that form part of the UI of the CI 740 but remain separate from the display.

[0126] The screen 1200 is rendered at least once for each successful candidate SEC from the PCSR stage 810 to perform a coverage study of that SEC. The stimulation controls 1210 are in the form of tiles that, upon activation by the user, switch stimulation on / off over the current candidate SEC. In one implementation of the coverage study stage 815, stimulation is turned on and off at the current candidate SEC by a threshold ramp to a comfortable stimulation intensity for the current candidate SEC and a threshold gradient from the current candidate SEC, as estimated in the PCSR stage 810. The threshold of the threshold ramp is the ECAP threshold of the current candidate SEC, as estimated in the PCSR stage 810. The threshold ramp is described below.

[0127] The initial comfortable stimulation intensity for each candidate SEC is determined by the Max value I estimated for the candidate SEC in the PCSR step 810. max and ECAP threshold I thresh From the above, the candidate SEC can be predicted at the start of the coverage survey. In one implementation, the comfortable stimulation intensity I comf I for candidate SEC thresh and I max It can be calculated as a fixed percentage of the interval between I comf =I thresh +k(I max -I thresh )(4) Here, k is a predetermined constant between 0 and 1. This prediction is an example of prediction of a perceptual marker (comfortable stimulus intensity) from a physiological threshold (ECAP threshold).

[0128] In an alternative implementation, the ECAP threshold I thresh is estimated for the candidate SEC in the PCSR stage 810 using the "pre-ramp" (described below). comf is obtained by inverting the linear model of equation (3) to obtain the Max value I max By substituting into equation (4) instead of I thresh In such an implementation, the Max value I max does not need to be determined during the PCSR stage.

[0129] Instructions 1220 activate the stimulation control 1210 and instruct the user to select one or more of the options 1230 to provide feedback regarding the user's sensations. Each option 1230 corresponds to a line of text next to the circular control. The APM then waits for the patient to select one or more of the options 1230 and activate the next control 1240. The stimulation is tested and the next control 1240 is disabled until at least one option is selected. An option can be selected or deselected by activating the control next to its text.

[0130] In some implementations, for each candidate SEC, option 1230 is not displayed until the user activates the stimulation control that corresponds to that SEC.

[0131] A progress bar 1250 at the bottom of the screen 1200 , similar to the progress bar 1050 , indicates approximate quantitative progress through the workflow 800 .

[0132] When the next control 1240 is activated, the APM responds to the selected option for the current candidate SEC with a "mitigation" selected according to Table 1. A "1" in a column of Table 1 represents selection of the option corresponding to that column, a "0" represents non-selection, and an "X" means the option was selected or not (selection of the option does not affect the selected mitigation).

[0133] [Table 1]

[0134] The relaxation to increase and decrease the comfortable stimulation intensity is 0.05×(I max -I thresh ) However, the decrease and increase in relaxation increase the comfortable stimulus intensity by [I max , I thresh

[0082] If the comfortable stimulation intensity is adjusted according to these mitigating measures, then the coverage investigation stage 815 can be repeated for the adjusted comfortable stimulation intensity.

[0135] The relaxation to move the current candidate SEC is performed by one electrode toward the center of the lead. If the current candidate SEC is moved according to this relaxation strategy, the PCSR (described above) may be repeated for the relocated candidate SEC. The coverage investigation stage 815 is then repeated for the relocated candidate SEC.

[0136] In some implementations, for each candidate SEC, the "too weak" and / or "feels good" options are not enabled until the control 1210 has been activated long enough for the stimulation intensity to increase to a comfortable stimulation intensity. This prevents the patient from responding to the coverage survey with incomplete information.

[0137] If the coverage study is repeated for a candidate SEC, the APM responds to that candidate SEC's selection with the mitigation selected according to Table 2. As in Table 1, a "1" in a column of Table 2 represents selection of the option corresponding to that column, a "0" represents non-selection, and an "X" means whether an option was selected or not (selection of an option does not affect the mitigation selected).

[0138] [Table 2]

[0139] In some implementations of workflow 800, the PCSR may be repeated only once for any candidate SEC (i.e., repeated twice) to reduce the burden on patients of having to undergo repeated PCSR on transferred SECs.

[0140] If in the second iteration of the coverage study for the candidate SEC the patient still has discomfort in a particular area for the candidate SEC, the comfort stimulation intensity for the candidate SEC is decreased (according to the last row of Table 2). In an alternative implementation, the candidate SEC is marked as failed. The coverage study is not repeated for the candidate SEC.

[0141] The coverage exploration phase 815 ends with a set of successful candidate SECs and their respective conceptually comfortable stimulation intensities. If the patient is still uncomfortable in a particular area with a candidate SEC after the second iteration of the coverage exploration phase 815, the patient has the opportunity to discard that candidate SEC during the coverage selection phase 820.

[0142] Noise Deviance Detector (NDD) NDD is a statistical detector of the presence of ECAP within a signal window. The operation of NDD on a signal window is preferably preceded by an "artifact scrubber" that removes artifacts from the signal window. One such artifact scrubber is disclosed in WO 2020 / 124135, the entire contents of which are incorporated herein by reference. NDD works by detecting statistically anomalous differences from the expected noise present in the signal window, which differences are due to the presence of ECAP within the signal window.

[0143] For example, calibration of an NDD instance corresponding to an MEC, performed during step 1125 of method 1100, may be performed on one or more signal windows captured via that MEC that are known to contain no evoked neural responses. In one implementation, such a signal window is a "zero current" signal window captured from an interval where no stimulation is applied, which is preferably scrubbed for artifacts and can therefore be treated as containing only noise. The calibration involves forming estimates of parameters of a predefined "noise model" (statistical variance) from samples within the one or more "zero current" signal windows. In one implementation, the noise model is Gaussian and the parameters are the mean of the samples.

number

number

[0144] Once calibrated, the number of outliers in the signal window

number

number

number

number

number

number

number

number

[0145] It can be shown that for a Gaussian noise model, the NDD can estimate the metric r as follows:

number

[0146] Negative or zero values ​​of the metric r indicate a signal window that is consistent with the noise model, while positive values ​​of r indicate deviations from the noise model that can be attributed to the presence of ECAP within the signal window.

[0147] In one implementation of NDD, n is set to 3. Smaller values ​​of n indicate that the NDD is more sensitive and easier to deviate from noise, increasing the rate of Type I errors (false positives). Conversely, higher values ​​of n require larger outliers before r indicates noise deviation, increasing the rate of Type II errors (false negatives).

[0148] In one implementation of NDD, a sigmoid function is applied to the raw metric r to convert the metric r into a quality indicator Q NDD can be mapped to

number

[0149] In one implementation, NDD may be applied to multiple signal windows after they are averaged together to improve the signal-to-noise ratio. In one such implementation, the number of signal windows averaged is eight. In such an implementation, the parameters of the noise model may be adjusted depending on the number of signal windows averaged. In the Gaussian noise model, the standard deviation

number

[0150] The NDD may be used to estimate the ECAP threshold. In one such implementation, the ECAP threshold is the stimulus intensity at which the NDD returns a quality indicator of 50% (0.5), i.e., the NDD detects ECAP in 50% of the processed signal windows. In one implementation, the ECAP threshold is the quality indicator Q NDD The quality indicator Q can be used to monitor the intensity of the stimulus. NDD The ECAP threshold was reached as soon as consistently exceeded 50%.

[0151] This use of NDD to estimate the ECAP threshold may be employed in alternative implementations of step 1150.

[0152] This use of the NDD may also be employed in alternative implementations of the PCSR stage 810. In such alternative implementations, the ramp rate of the stimulation intensity while the stimulation control 1010 is activated is not predetermined, but is calculated from the results of the "pre-ramp". During the pre-ramp, which begins when the stimulation control 1010 is activated, the NDD is used to estimate the ECAP threshold as described above. The pre-ramp ends by decreasing the stimulation intensity to zero. The value of Max is then predicted from the ECAP threshold estimated during the pre-ramp. This prediction step, which is an example of predicting a sensory marker from a physiological threshold, can be implemented by inverting the linear model of equation (3). The ramp rate is then calculated such that the patient-controlled stimulation ramp reaches the predicted value of Max after a predetermined time. The calculated ramp rate is then used for the patient-controlled stimulation ramp, which is performed as described above upon the next activation of the stimulation control 1010.

[0153] AP Builder As described above, the AP builder fits a model called a logistic growth curve (LGC) to a set of (s, d) value pairs, where d is the measured ECAP amplitude from the signal window and s is the corresponding stimulation current amplitude, e.g., for use in step 1120 of method 1100. The AP builder can also calculate a growth curve quality index (GCQI) of the fitted LGC, e.g., in step 1130 of method 1100.

[0154] An important part of the AP builder is the ECAP detector, which returns the ECAP amplitude d from the signal window. In one implementation, the ECAP detector described in WO 2020 / 124135 above can be used by the AP builder to measure the ECAP amplitude d in the signal window. Alternatively, the ECAP detector described in WO 2015 / 074121 above can be used by the AP builder to measure the ECAP amplitude d in the signal window. In either case, the ECAP detector has two parameters: its correlation delay and its length (or equivalently its frequency). Other implementations of the ECAP detector may have other adjustable parameters. The optimal values ​​of these parameters depend on the SEC and MEC that gave rise to the signal window and should therefore be adjusted for each instance of the AP builder, for example the six instances instantiated in step 1110 of method 1100. In one implementation, the AP builder can adjust the ECAP detector parameters on an average signal window obtained by averaging the 10 signal windows corresponding to the maximum value of the stimulation current amplitude s. In one implementation, the NDD described above may be first applied to each signal window before combining them into an average signal window. If the NDD indicates that the signal window did not contain a neural response, the signal window is discarded.

[0155] The ECAP detector is applied to the average signal window for each possible value of correlation delay and length to form a correlation matrix. In one example of tuning the parameters of the ECAP detector, the correlation delay and length values ​​that maximize the measured ECAP amplitude in the correlation matrix are selected as optimal for that instance of the AP builder. During the stimulation ramp, as the stimulation current increases, the AP builder can dynamically update the optimal values ​​of correlation delay and length using the latest average signal window. The AP builder can retroactively recalculate the ECAP amplitude for all signal windows captured since the start of the current stimulation ramp using the current optimal values.

[0156] Once the ECAP detector is tuned and a set of (s,d) value pairs is obtained, the AP builder proceeds to fit an LGC model (also called a sigmoid function) to the set of (s,d) value pairs. In one implementation, the LGC model is a four-parameter function.

number

[0157] In other implementations, an LGC model with fewer parameters may be used, for example an LGC model with the minimum value A identically zero. In yet other implementations, other parameterized functions may be fitted by the AP builder to the set of (s, d) value pairs.

[0158] To fit the LGC, parameters A, K, M, and B may be initialized to sensible starting points A0, K0, M0, and B0. In one implementation, these values ​​may be set as follows: · A0: average of the ECAP amplitudes obtained from the lowest few stimulation current amplitudes. · K0: average of the ECAP amplitudes obtained from several maximal stimulation current amplitudes. M0: Stimulation current amplitude at the midpoint between A and K · B0: It can be calculated from the slope m of the midpoint, obtained from a local linear regression of the value pairs obtained near the midpoint, B0=m*4 / (K0-A0).

[0159] An optimization algorithm such as Trust Region Reflection (TRF) can then be used to optimize the four parameters A, K, M, and B from their starting points A0, K0, M0, and B0.

[0160] 13 shows the LGC model 1310 fitted to a set of (s,d) value pairs, alongside a piecewise linear model 1320 fitted to the same data. The excellent fit of the LGC model to the data at both low and high stimulation current amplitudes is evident.

[0161] The AP builder may also calculate a growth curve quality index (GCQI) of the fitted LGC model, for example, at step 1130 of method 1100. The GCQI indicates the signal-to-noise ratio (SNR) of the fitted LGC. In one implementation, the AP builder may calculate the GCQI by dividing the peak-to-peak amplitude of the fitted LGC (e.g., as indicated by arrow 1330 in FIG. 13) by the standard deviation of the remaining of the fitted LGC.

[0162] The fitted LGC is then adjusted to the ECAP threshold I, as in step 1140 of method 1100 or step 1740 of method 1700 (described below). thresh In one implementation, a line is constructed through the midpoint M of the fitted LGC with slope B. The ECAP threshold I thresh can be estimated as the stimulation current amplitude s at which the constructed line intersects the minimum A. The resulting ECAP threshold I thresh It can be shown that is given by:

number

[0163] The fitted LGC can be used to estimate the patient sensitivity, as in step 1740 of method 1700. In one implementation, the patient sensitivity S is the slope of the fitted LGC at its midpoint M, which can be calculated from the steepness B as follows:

number

[0164] In another example of predicting a perceptual marker (discomfort threshold, max) from a physiological threshold, the fitted LGC can be used to estimate the discomfort threshold Max. In this example, the physiological threshold is the stimulation current amplitude at which the LGC model saturates, i.e., the saturation threshold. In one implementation, saturation can be said to occur when d(s) reaches A+U(KA), where U is less than 1. The corresponding value of the saturation threshold s sat can be calculated as follows:

number

[0165] The discomfort threshold Max can then be estimated from the saturation threshold by a linear prediction model.

[0166] Threshold Ramp A threshold ramp is a ramp, either ascending or descending, of stimulus intensity that crosses stimulus intensity values ​​below a predefined threshold at a faster rate than it crosses stimulus intensity values ​​above the predefined threshold.

[0167] It is preferable for the patient to feel a gradual rather than abrupt increase in stimulation intensity. However, it is generally also desirable to generate a user interface that feels responsive to the patient. For example, during the PCSR stage 810, the patient can deactivate the stimulation control 1010 and turn off the stimulation. When doing so in response to an uncomfortable stimulation, the responsiveness of the user interface is important. The patient would prefer to experiment at the limits of comfort if the stimulation were to ramp down rapidly without causing discomfort.

[0168] Stimulation intensities below the ECAP threshold are generally not perceptible to the patient. Thus, ascending through sub-ECAP threshold intensities does not improve the patient's sense of responsiveness and may actually impair the patient's sense of responsiveness by taking unnecessary time. Thus, the threshold ramp may skip most of the sub-ECAP threshold stimulation intensities either in the ascending or descending direction.

[0169] FIG. 14 illustrates a threshold ramp according to one implementation of the present technology. Profile 1400 represents the time course of stimulation current amplitude due to a threshold rise to a target current amplitude 1410. Dotted profile 1420 represents the time course of stimulation current amplitude due to a conventional linear ramp from zero to the target current amplitude 1410. Instant 1430 represents the time (t=0) at which the ramp is initiated, for example, by activation of the stimulation control 1010. Interval 1440 represents a predetermined time that would have taken, for example, 3 seconds, with a conventional linear ramp to reach the target current amplitude 1410. The ramp rate of the conventional linear ramp profile 1420 is calculated such that the stimulation intensity reaches the target current amplitude 1410 at the end of the interval 1440. In contrast, the threshold ramp steps relatively quickly (e.g., vertically) to the threshold current amplitude 1460. Then, during interval 1450, the threshold ramp linearly increases the stimulation current amplitude at the same rate as the conventional linear ramp. Thus, the length of interval 1450, i.e., the total ramp time, is significantly shorter than the predetermined time of interval 1440. Thus, the threshold ramp appears more responsive to the patient. Furthermore, if the threshold current amplitude 1460 is set just below the ECAP threshold, the patient will not be able to perceive a stimulation current amplitude below the threshold current amplitude 1460, and therefore the threshold ramp will appear less abrupt than a conventional linear ramp.

[0170] In one implementation, the threshold current amplitude 1460 may be obtained by scaling the ECAP threshold by a factor of 0.9. This scaling factor provides a balance between having a faster overall ramp time and keeping the probability of a step to a perceptible current amplitude low.

[0171] A threshold down ramp according to one implementation is a time-reversed version of the threshold ramp profile 1400 shown in Figure 14. In other words, a threshold down ramp from a starting current amplitude linearly decreases the current amplitude at a rate comparable to a conventional linear down ramp over a predefined interval 1440. Once the stimulation current amplitude reaches the threshold current amplitude 1460, the stimulation current amplitude steps relatively quickly (e.g., vertically) to zero.

[0172] In other implementations of the threshold ramp, the profile of the stimulation current amplitude is not piecewise linear as in FIG. 14. Instead, alternative profiles of stimulation intensity may be used. The alternative profiles are also parameterized by the threshold. In one such implementation, the profile follows a sigmoid function as described above, rising smoothly exponentially from zero to a midpoint calculated from the threshold, and then decelerating as the stimulation current amplitude approaches the target current amplitude. Another such implementation is an exponential profile below the threshold, followed by a linear profile above the threshold. The ramp rate of the linear profile is selected to be less than the average ramp rate of the exponential profile.

[0173] In some implementations, as described above in connection with the patient-controlled stimulation ramp stage 810, the threshold ramp can be interrupted if the APF does not receive communication from the APA within a first timeout period. The controller 116 can then ramp down to lower the intensity in the continued absence of communication from the APA within a second timeout period. An exemplary profile of such an implementation of the threshold ramp is shown in Figures 18a-18c. In such implementations, the patient is less likely to experience unpleasant stimulation if communication between the APF and the APM is interrupted.

[0174] Coverage Selection Phase As described above, the coverage selection stage 820 is configured to receive input from the patient to select one or more of the successful candidate SECs from the coverage exploration stage 815 based on the maximum and ECAP thresholds for that candidate SEC. The patient may test various combinations of candidate SECs before selecting the ones to retain.

[0175] In one implementation of the coverage selection stage 820, the APM renders a screen 1500 on the UI display of the CI 740, as shown in Figure 15. Screen 1500 includes controls including up to four toggle tiles, e.g., 1510a, 1510b, and 1510c, up to four toggle switches, e.g., 1520b and 1520c, a next control 1540, a progress bar 1550, and a disable all control 1560.

[0176] Toggle switches 1520b and 1520c are associated with respective toggle tiles 1510b and 1510c. However, in FIG. 15, toggle tile 1510a does not have a toggle switch associated with it because a switch corresponding to a tile is not rendered until the tile is activated once. At the state of coverage selection phase 830 shown in FIG. 15, tile 1510a is not yet activated, so no switch is associated with tile 1510a. However, tiles 1510b and 1510c are activated, so tiles 1510b and 1510c have associated switches 1520b and 1520c.

[0177] In other implementations of the coverage selection stage 820, one or more of the controls are hardware controls, such as buttons or switches, that form part of the UI of the CI 740 but remain separate from the display. The UI also includes instructions 1530.

[0178] Each toggle control pair, e.g., tile 1510b and switch 1520b, corresponds to one of the successful candidate SECs after coverage investigation stage 815. (As an example, only three control pairs are shown in FIG. 15 because the fourth candidate SEC was marked as failed during PCSR stage 810.) The four (tile, switch) control pairs can be activated and deactivated independently. The state of the stimulus on a SEC (on or off) corresponds to the state of the corresponding toggle switch (activated or deactivated). Stimulus pulses from all "on" SECs at a given time are interleaved in a predefined time order, staggered in time by the inter-stimulus interval.

[0179] Each toggle tile is configured to remain activated as long as the patient continues to interact with it, e.g., by "holding" the toggle tile, and is deactivated when the patient stops interacting with the toggle tile, e.g., by "releasing" the toggle tile. The toggle tiles take on a different appearance when activated, e.g., by being filled with a different color. In contrast, each toggle switch cannot be "depressed," but flips its state from inactive to active or active to inactive each time the patient interacts with the toggle switch. The toggle switches take on a different appearance when activated, e.g., by filling a disk representing the toggle switch.

[0180] In one implementation, the toggle tile has an inverted behavior, whereby the state of the stimulation indicated by the state of the toggle switch is always inverted as long as the toggle tile is activated, e.g., pressed down. For example, when a toggle switch is activated, activating the corresponding tile deactivates the toggle switch to stop the stimulation, and deactivating the tile activates the toggle switch to resume the stimulation. Conversely, when a toggle switch is deactivated, pressing and holding the corresponding tile activates the toggle switch to start the stimulation, and releasing the tile deactivates the toggle switch to stop the stimulation. The stimulation is always on when the switch is activated and always off when the switch is deactivated. Thus, the appearance of the toggle switch provides a visual cue that indicates the state of the stimulation on the corresponding SEC.

[0181] Table 3 summarizes the effect of activating and deactivating toggle tiles and toggle switches on stimuli from corresponding candidate SECs according to this implementation of the coverage selection stage 820. Blank cells represent actions that cannot occur.

[0182] [Table 3]

[0183] In another implementation, when a toggle switch is activated, activating the corresponding tile deactivates the toggle switch to stop stimulation, and deactivating the tile does not further change the state of stimulation. Conversely, when a toggle switch is deactivated, pressing and holding the corresponding tile activates the toggle switch to start stimulation, and releasing the tile deactivates the toggle switch to stop stimulation. Table 4Table 3 summarizes the effect of activating and deactivating toggle tiles and toggle switches on stimulation from corresponding candidate SECs according to this implementation of the coverage selection stage 820.

[0184] [Table 4]

[0185] Under the implementation summarized in Table 4, when the stimulation control is deactivated, the behavior of stopping stimulation is maintained as during the PCSR and coverage check phases.

[0186] The progress bar 1550 , like the progress bars 1050 and 1250 , indicates approximate quantitative progress through the workflow 800 .

[0187] Disable all control 1560 disables all stimulation and deactivates all toggle switches 1520b etc.

[0188] Instructions 1530 inform the patient that activating the toggle tile ("press and hold") will cause them to feel a stimulation in one of four locations.

[0189] In an alternative implementation of the coverage selection stage 820, there are no toggle tiles, only toggle switches.

[0190] In one implementation of the coverage selection stage 820, stimulation is turned on and off in the candidate SEC by a threshold ramp between the comfortable stimulation intensity for the candidate SEC obtained from the coverage exploration stage 815. The threshold of the threshold ramp is the ECAP threshold of the candidate SEC estimated in the PCSR stage 810. The threshold ramp is described above.

[0191] The next control 1540 is active as long as at least one toggle switch is activated. In some implementations, an additional criterion for activating the next control 1540 is that stimulation with the final selected coverage must be active for a minimum period, e.g., 5 seconds. When the patient activates the next control 1540, the APM records the currently activated candidate SEC as the selected SEC and stimulation is stopped in all SECs.

[0192] In an alternative implementation of the coverage selection stage 820, there are no toggle switches, only toggle tiles.

[0193] In such an implementation, as long as at least one toggle tile is activated, the next control 1540 is enabled. When the patient activates the next control 1540, the APM records the currently activated candidate SEC as the selected SEC and stimulation is stopped in all SECs.

[0194] Measurement optimization stage As described above, the measurement optimization (MO) stage 830 is configured to deliver stimulation of gradually increasing intensity from the primary SEC of the selected SEC and record sensed signal data at each of a plurality of measurement electrode configurations for the primary SEC. The MO stage 830 is then configured to select an optimal MEC for the primary SEC, calculate physiological characteristics of the patient based on neural responses extracted from signal windows recorded via the optimal MEC, and select optimal treatment parameters for the primary SEC / optimal MEC combination.

[0195] The primary SECs in the determined program are selected SECs whose neural responses are measured to drive a feedback loop to adjust the stimulation current amplitude of the primary SECs according to system 300 as described above. Neural responses evoked by non-primary selected SECs are not recorded or analyzed. Instead, the stimulation current amplitude of the non-primary SECs is adjusted by controller 116 to remain at a constant ratio with the stimulation current amplitude of the primary SEC. The ratio at which the non-primary selected SECs are fixed may be stored in the determined program as a ratio of their respective comfortable stimulation intensity to the comfortable stimulation intensity of the primary SEC.

[0196] In one implementation of the MO phase 830, the APM displays a screen 1600 on the UI display of the CI 740, as shown in FIG. 16. The screen 1600 includes some information 1620, a progress bar 1650, and a stimulation stop control 1610. The screen 1600 is displayed while some neurostimulation is delivered, and collected signal windows are analyzed as described below. In one implementation, activating the stimulation stop control 1610 at any point during the MO phase stops the stimulation. The screen 1600 is then replaced with an exit screen (not shown) that informs the patient that manual programming is required. The MO phase 830 ends, after which the APM stops without loading a program into the device 710.

[0197] The progress bar 1650 , like the progress bars 1050 , 1250 , and 1550 , indicates approximate quantitative progress through the workflow 800 .

[0198] Upon completion of the data collection and analysis of the MO phase 830, the APM displays one of two screens depending on the success of the data analysis. If the data analysis is successful, the screen includes finish controls. Instructions on the screen inform the patient that programming was successful. Once the patient initiates the finish controls, the MO phase 830 ends.

[0199] If the data analysis fails, the screen includes an edit control. On-screen instructions inform the patient that programming has failed and that manual programming is required. If the patient activates the edit control, the MO step 830 ends and the APM stops without loading a program onto the device 710.

[0200] Data analysis during the MO phase FIG. 17 includes a flow chart illustrating a data collection and analysis method 1700 performed by the APM and the device 710 during the MO phase 830 according to one implementation of the APM. The method 1700 starts at step 1715, where the APM selects a primary SEC from among the SECs selected from the coverage selection phase 820. In one implementation, step 1715 selects the remaining selected SEC (if any) with the smallest comfortable stimulation intensity as the primary SEC. Meanwhile, step 1710 instantiates an AP Builder for each MEC corresponding to the current primary SEC, similar to step 1110. The MEC corresponding to one SEC is shown in FIG. 9. In the next step 1725, the APM commands the device 710 to start a stimulation ramp for the current primary SEC. The stimulation ramp starts with a stimulation intensity of zero and increases through discrete steps up to a maximum stimulation intensity determined by the Max value of the primary SEC selected in step 1715. In one implementation, there are 10 equally spaced steps up to a maximum stimulation intensity that is 90% of the Max value of the primary SEC.

[0201] In an alternative implementation of step 1725, the device 710 can increase the stimulation intensity in fixed ratio steps, i.e., each increment involves multiplying the previous stimulation current amplitude by a fixed ratio. This corresponds to an exponential ramp rather than a linear profile. In an exponential ramp, the discrete steps are more widely spaced as the maximum stimulation intensity is approached. In one example, if the ECAP threshold is set to 0.7 times the Max value as described above, a fixed ratio of 1.025 provides a 10-step exponential increase between the ECAP threshold and 90% of Max.

[0202] In an alternative implementation of step 1725, rather than using a ramp, the apparatus 710 can vary the stimulus intensity non-monotonically between zero and a maximum stimulus intensity. For example, the variation can be random. Such an approach can result in faster convergence to the fitted LGC by the AP builder.

[0203] During the stimulation ramp, in step 1720, the APM instructs device 710 to capture and return a signal window for each stimulation current amplitude s at each MEC. The returned signal window for each MEC is analyzed by a corresponding AP builder in step 1720 to extract a detected ECAP amplitude d from each signal window. In one implementation, multiple signal windows (e.g., 16) are analyzed for each MEC at each stimulation current amplitude s during the ramp. Each AP builder uses the captured signal windows as described above to adjust the parameters of its ECAP detector during step 1720, such as length and correlation delay.

[0204] To complete step 1720, each AP builder fits an LGC to the set of (s,d) value pairs of the corresponding MEC, as described above. Meanwhile, in step 1745, the APM instructs the device 710 to reduce the stimulus intensity. In one implementation, step 1745 uses a threshold ramp as described above, using the ECAP threshold as the threshold of the threshold ramp.

[0205] Then, in step 1730, each AP builder calculates the GCQI of the LGCs that fit the corresponding MEC, as described above. In step 1735, the APM selects the MEC that corresponds to the fitted LGC with the highest GCQI. Then, in step 1740, the APM calculates the ECAP thresholds and patient susceptibility S from the fitted LGCs, as described above.

[0206] Step 1750 then determines whether the selected MEC meets certain exclusion criteria indicative of poor quality. In one implementation, the exclusion criteria are as follows: The selected GCQI is below a threshold, for example 10 dB. The calculated ECAP threshold is outside a predetermined range. In one implementation, the range is from the 1st to the 99th percentile of the distribution of ECAP thresholds obtained from existing patient data. The calculated sensitivity is outside a predefined range. In one implementation, the range is from the 1st to the 99th percentile of the distribution of patient sensitivities obtained from existing patient data.

[0207] If any of the exclusion criteria are met ("Y"), the current primary SEC is marked as failed. In step 1760, the APM determines whether there are any remaining selected SECs that have not been tested. If so ("Y"), step 1770 restarts method 1700. If not ("N"), final step 1780 ends the MO phase 830 and workflow 800 is considered to have failed.

[0208] If none of the exclusion criteria tested in step 1750 are met ("N"), the current primary SEC is marked as the primary SEC of the program and the selected MEC is marked as the MEC that is best suited to the primary SEC. Step 1755 then calculates the gain K of the gain element 336 of the system 300 from the patient sensitivity S calculated in step 1740. In one implementation, step 1755 calculates the gain K as follows:

number

number

[0209] Step 1765 calculates other treatment parameters of the CLNS system 300. In one implementation, the treatment parameters include: Target ECAP amplitude. This is the comfortable stimulation intensity s=I comf can be calculated using equation (7) as the value of the ECAP amplitude d on the fitted LGC corresponding to Maximum stimulation intensity. This can be set to the Max value of the primary SEC. Maximum target ECAP amplitude. This can be set to the value of the ECAP amplitude d on the fitted LGC that corresponds to the Max value of the primary SEC.

[0210] Step 1775 saves the determined program including the selected SEC, including the primary SEC, optimal MEC, Max, ECAP threshold and sensitivity, and the calculated treatment parameters. The MO stage 830 ends and the workflow 800 is considered successful.

[0211] In an alternative implementation of MO stage 830, there is no primary SEC. Instead, each selected SEC runs its own independent feedback loop via its own dedicated MEC, assuming a MEC of sufficient quality can be found. Modified method 1700 is performed for each selected SEC. Modified method 1700 does not have step 1715, nor does it have steps 1760 and 1770. Instead, if one of the exclusion criteria is met in step 1750, modified method 1700 ends in failure in step 1780.

[0212] Those skilled in the art will appreciate that numerous variations and / or modifications may be made to the invention as illustrated in the specific embodiments without departing from the spirit or scope of the invention as broadly described. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive or restrictive.

[0213] [Table 5]

[0214] [Table 6] [Explanation of symbols]

[0215] 112 Power supply 114 Telemetry Module 116 Control device 120 Clinical Data 121 Clinical Setting 122 Control Program 124 Pulse Generator 126 Electrode Selection Module 128 Measurement circuit

Claims

1. 1. A neurostimulation system comprising:

1. A neurostimulation device for controllably delivering neurostimulation, comprising: a plurality of implantable electrodes including one or more stimulation electrodes; a stimulation source configured to deliver neural stimulation to a neural pathway of the patient via the one or more stimulation electrodes; a control unit configured to control the stimulation source to deliver the neurostimulation in accordance with a stimulation intensity parameter; a nerve stimulation device including: an external computing device in communication with the neurostimulator, a display configured to receive user interaction; 1. A processor, comprising: rendering a plurality of switch controls on the display, each switch control corresponding to a predetermined stimulation electrode configuration of the one or more stimulation electrodes; upon receiving activation of one of the switch controls by a user, instructing the control unit to control the stimulation source to deliver the neurostimulation via the corresponding predetermined stimulation electrode configuration; upon receiving a deactivation of one of the switch controls by a user, instructing the control unit to control the stimulation source to stop delivery of the neural stimulation via the corresponding predetermined stimulation electrode configuration; a processor configured to: an external computing device including 1. A neurostimulation system comprising:

2. The neurostimulation system of claim 1 , wherein the processor is further configured to render a tile control associated with each switch control.

3. 3. The neurostimulation system of claim 2, wherein the processor is further configured to, upon receiving activation of one of the tile controls by a user while the associated switch control is activated, deactivate the associated switch control, thereby causing the stimulation source to stop delivering the neurostimulation via the corresponding predetermined stimulation electrode configuration.

4. 4. The neurostimulation system of claim 3, wherein the processor is further configured to, upon receiving deactivation of one of the tile controls by a user while the associated switch control is deactivated, activate the associated switch control, thereby causing the stimulation source to deliver the neurostimulation via the corresponding predetermined stimulation electrode configuration.

5. The processor: upon receiving activation of one of the tile controls by a user while the associated switch control is deactivated, activating the associated switch control, thereby causing the stimulation source to deliver the neurostimulation via the corresponding predetermined stimulation electrode configuration; upon receiving a deactivation of one of the tile controls by a user while the associated switch control is activated, deactivating the associated switch control, thereby causing the stimulation source to cease delivering the neurostimulation via the corresponding predetermined stimulation electrode configuration; The neurostimulation system of any one of claims 2 to 4, further configured to:

6. The neurostimulation system of any one of claims 2 to 5, wherein the processor is configured to change the appearance of each tile control depending on its activation state.

7. Each tile control: an activated state when the user interacts with the tile control; remains active as long as the user continues to interact with the tile control; and The neurostimulation system of any one of claims 2 to 6, configured to become deactivated as soon as the user stops interacting with the tile control.

8. The neurostimulation system of any one of claims 1 to 7, wherein the processor is configured to change the appearance of each switch control depending on its activation state.

9. 9. The neurostimulation system of claim 1, wherein the processor is configured, upon user activation of further controls rendered on the display, to record the predetermined stimulation electrode configuration corresponding to each activated switch control.

10. 10. The neurostimulation system of claim 1, wherein the processor is configured to instruct the control unit to control the stimulation source to deliver the neurostimulation by increasing the value of the stimulation intensity parameter while instructing the control unit to control the stimulation source to deliver the neurostimulation in accordance with a ramp value of the stimulation intensity parameter.

11. 11. The neurostimulation system of claim 10, wherein the ramp value of the stimulation intensity parameter crosses stimulation intensities below a predetermined threshold at a faster rate than stimulation intensities above the predetermined threshold.

12. 12. The neurostimulation system of claim 1, wherein the processor is configured to instruct the control unit to control the stimulation source to stop delivering the neurostimulation by instructing the control unit to control the stimulation source to decrease the value of the stimulation intensity parameter while delivering the neurostimulation in accordance with a decreasing ramp value of the stimulation intensity parameter.

13. 13. The neurostimulation system of claim 12, wherein the descending ramp value of the stimulation intensity parameter traverses stimulation intensities below a predetermined threshold at a faster rate than stimulation intensities above the predetermined threshold.

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