Improved programming of neurostimulation therapy

Abstract

A neurostimulation system comprising a neuromodulation device for providing neural stimulation and a processor, the neuromodulation device comprising a plurality of electrodes, a stimulation source for providing neural stimulation provided by the electrodes to neural tissue to elicit a neural response, a measurement circuit configured to capture a signal window sensed at the electrodes after each neural stimulation, and a control unit configured to control the stimulation source to provide each neural stimulation according to a stimulation intensity parameter, the processor configured to instruct the control unit to control the stimulation source to provide a plurality of neural stimulations in succession according to a ramp of the stimulation intensity parameter value until a sensory marker, receive the captured signal window after each neural stimulation, detect whether an evoked neural response is present within each signal window, and determine the sensory marker based on the detection.

Description

[Technical field]

[0001] This application claims priority to Australian Provisional Patent Application No. 2022901117, filed on April 28, 2022, the contents of which are incorporated herein by reference in their entirety.

[0002] The present invention relates to neurostimulation therapy, and in particular to determining key parameters of a patient's response to stimulation during such therapy. [Background technology]

[0003] There are a variety of situations in which applying nerve stimulation to alter nerve function, a process known as neuromodulation, is desirable. For example, neuromodulation is used to treat a variety of disorders, including chronic neuropathic pain, Parkinson's disease, and migraines. Neuromodulation devices apply electrical pulses (stimuli) to nerve tissue (fibers, or neurons) to produce a therapeutic effect. In general, the electrical stimulation generated by a neuromodulation device induces a neural response known as an action potential in the nerve fiber, which in turn has either an inhibitory or excitatory effect. The inhibitory effect may be used to modulate an undesirable process, such as pain transmission, or the excitatory effect may be used to cause a desired effect, such as muscle contraction.

[0004] When used to relieve 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 devices typically include an implanted 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 cord adjacent to the target nerve fiber(s), typically in the dorsal epidural space above the dorsal columns. Electrical pulses of sufficient intensity applied to the target nerve fiber by the stimulating electrodes cause neuronal depolarization in the fiber, which in turn generates an action potential in the fiber. The action potential propagates along the fiber in an orthodromic (for afferent fibers this means toward the head, i.e. rostral) and retrograde (for afferent fibers this means toward the tail, i.e. caudal) direction. Stimulation of action potentials traveling along Aβ (A-beta) fibers in this way inhibits the transmission of pain from the area of ​​the body innervated by the target nerve fiber (dermatome) to the brain. To sustain the pain-relieving effect, stimulation is applied repeatedly, for example at frequencies in the range of 30 Hz to 100 Hz.

[0005] For effective and comfortable neuromodulation, it is necessary to maintain a stimulation intensity above the recruitment threshold. Stimulation below the recruitment threshold does not recruit enough neurons to generate therapeutic action potentials. In almost all neuromodulation applications, a response from a single class of fibers is desired, but the stimulation waveform employed may induce action potentials in other classes of fibers, which causes unwanted side effects. For pain relief, it is therefore desirable to apply stimulation at an intensity below the discomfort threshold, above which unpleasant or painful perceptions arise due to over-recruitment of Aβ fibers. When recruitment is too great, Aβ fibers produce unpleasant sensations. High intensity stimulation may even recruit Aδ (A-delta) fibers, the sensory nerve fibers associated with acute pain, cold, and heat sensations. It is therefore desirable to maintain the stimulation intensity within the therapeutic range between the recruitment threshold and the discomfort threshold.

[0006] The task of maintaining adequate neural recruitment is made more difficult by electrode migration (change in 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 provides space for the electrode array to move, and such movement of the array due to movement or postural changes alters the distance from the electrode to the fibers, and therefore the recruitment efficacy of a given stimulation. Additionally, the spinal cord itself can move within the 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 postural changes alone can render a previously comfortable and effective stimulation regime ineffective or painful.

[0007] Another control problem facing all types of neuromodulation devices is achieving neural recruitment at a level sufficient for therapeutic effect, but with minimal energy consumption. The power consumption of the stimulation paradigm has a direct impact on the battery requirements, which affect the physical size and lifespan of the device. In rechargeable devices, increased power consumption leads to more frequent charging, which ultimately reduces the implanted lifespan of the device, given that batteries only allow a limited number of charging cycles.

[0008] Attempts have been made to address such issues by feedback or closed loop control, such as using the method described in the applicant's International Patent Publication No. WO2012 / 155188. Feedback control attempts to counteract relative nerve / electrode movement by controlling the intensity of the applied stimulation to maintain substantially constant neural recruitment. The intensity of the neural response evoked by the stimulation may be used as a feedback variable representative of the amount of neural recruitment. A signal representative of the neural response may be sensed by a measurement electrode in electrical communication with the recruited nerve fibers and processed to obtain a feedback variable. Based on the response strength, the intensity of the applied stimulation may be adjusted to maintain the response strength within a therapeutic range.

[0009] It is therefore desirable to accurately measure the strength and other characteristics of the neural response evoked by stimulation. The action potentials generated by the depolarization of many fibers by stimulation sum to form a measurable signal known as the evoked compound action potential (ECAP). Thus, the ECAP is the sum of the responses from many single fiber action potentials. The ECAP generated from the depolarization of a group of similar fibers can be measured at the measurement electrodes as a positive peak potential, then a negative peak, followed by a second positive peak. This morphology arises when a region of activation passes through the measurement electrodes as the action potential travels along the individual fibers.

[0010] A proposed approach for obtaining neural response measurements is described by the applicant in International Patent Publication No. WO2012 / 155183, the contents of which are incorporated herein by reference.

[0011] Closed-loop neurostimulation therapy is governed by a number of parameters, which 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. As the physiological characteristics of patients vary significantly, a "one-size-fits-all" approach to parameter value assignment may result in an ineffective therapy for a large proportion of patients. Once a neuromodulation device is implanted in a patient, an important preliminary task is therefore to assign values ​​to the therapy parameters that will maximize the effectiveness of the therapy the device will deliver to that particular patient. This task is known as programming or fitting the device. Programming generally involves applying several test stimuli with the device and inferring or calculating the most effective parameter values ​​for the patient based on the recorded responses. The resulting parameter values ​​are then made into a "program" that can be loaded into the device to govern subsequent therapy. Some of the recorded responses may be neural responses evoked by the test stimuli, which provide an objective source of information that can be analyzed along with the subjective responses elicited from the patient. In an effective programming system, the more responses that are analyzed, the more valid the final assigned parameter values ​​will be.

[0012] However, programming can be costly and time consuming if unnecessarily extended. 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 treatment parameters. Furthermore, discomfort thresholds vary widely from patient to patient, from posture to posture for a single patient, and from stimulation electrode to stimulation electrode for a given patient in a given posture. It is difficult to know in advance where the discomfort threshold is for a given patient in a given posture. As a result, a test stimulus of an intensity that is comfortable for one patient may cause severe discomfort for another patient, or for the same patient in a different posture, or for the same patient in the same posture when applied with a different stimulation electrode. This complicates some aspects of programming, including measuring the intensity of the patient's neural response across the entire comfortable range of stimulation intensities with a particular stimulation electrode in a particular posture.

[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 such matter forms part of the prior art or was common general knowledge in the art relevant to the present invention by virtue of existing prior to the priority date of each claim of this application.

[0014] Throughout this specification, the word "comprise" or variations such as "comprises" or "comprising" will be understood to imply the inclusion of the stated 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 may be "at least one of" a list of options should be understood to mean that the element may be any one of the listed options, or any combination of two or more of the listed options. [Prior art documents] [Patent documents]

[0016] [Patent Document 1] International Patent Publication No. WO2012 / 155188 [Patent Document 2] International Patent Publication No. WO2012 / 155183 [Patent Document 3] International Patent Publication No. WO2015 / 074121 [Patent Document 4] International Patent Publication No. WO2020 / 124135 [Patent Document 5] International Patent Publication No. WO2015 / 070281 [Patent Document 6] International Patent Publication No. WO2016090436 Summary of the Invention [Means for solving the problem]

[0017] Disclosed herein is a method and system for quickly estimating key parameters of a patient's activation plot without causing discomfort. The disclosed method and system uses measurements of nerve response strength, which is a more reliable and consistent measure of a patient's sensation than stimulation intensity, to estimate key parameters without requiring the patient to report their sensation. Stimulation strength is increased from zero until a nerve response is consistently detected, and the nerve strength at that point is marked as the interim ECAP threshold. A stimulation intensity ramp continues from the interim ECAP threshold to a dynamically determined sensory marker, recording the response strength for each stimulation. An activation plot model is fitted to the measured response strength above and below the interim ECAP threshold. From the fitted model, the key parameters of the activation plot can be obtained.

[0018] According to a first aspect of the present technology, a neurostimulation system is provided, comprising a neuromodulation device for providing neural stimulation and a processor. The neuromodulation device includes a plurality of implantable electrodes including one or more stimulation electrodes and one or more sense electrodes, a stimulation source configured to provide neural stimulation provided by the one or more stimulation electrodes to a patient's neural tissue to elicit a neural response in the neural tissue, a measurement circuit configured to capture a signal window sensed at the one or more sense electrodes after each neural stimulation, and a control unit configured to control the stimulation source to provide each neural stimulation according to a stimulation intensity parameter. The processor is configured to instruct the control unit to control the stimulation source to provide a plurality of neural stimulations in succession according to a ramp of the stimulation intensity parameter value until a sensory marker, receive the captured signal window after each neural stimulation, detect whether an evoked neural response is present within each captured signal window, and determine the sensory marker based on the detection.

[0019] According to a second aspect of the present technology, there is provided an automated method of delivering neural stimulation to neural tissue of a patient, the method including sequentially delivering a plurality of neural stimuli to the neural tissue according to a ramp of stimulation intensity parameter values ​​until a sensory marker, receiving a captured signal window after each delivered neural stimulation, detecting whether an evoked neural response is present in each captured signal window, and determining the sensory marker based on the detection.

[0020] According to a third aspect of the present technology, there is provided a neuromodulation device for providing neural stimulation, the neuromodulation device comprising: a plurality of implantable electrodes including one or more stimulation electrodes and one or more sense electrodes; a stimulation source configured to provide neural stimulation provided by the one or more stimulation electrodes to neural tissue of a patient to elicit a neural response in the neural tissue; a measurement circuit configured to capture a signal window sensed at the one or more sense electrodes after each neural stimulation; and a control unit configured to control the stimulation source to provide each neural stimulation according to a stimulation intensity parameter; and a neural stimulation system comprising: a processor configured to: instruct the control unit to control the stimulation source to provide one or more neural stimulations according to a stimulation intensity parameter value; detect whether an evoked neural response is present within each captured signal window; increment the stimulation intensity parameter value;

[0021] In accordance with a fourth aspect of the present technology, there is provided an automated method of applying neural stimulation to neural tissue of a patient, the method including the steps of applying one or more neural stimuli to the neural tissue in accordance with a stimulation intensity parameter value; detecting whether an evoked neural response is present within each captured signal window; incrementing the stimulation intensity parameter value; repeating the applying, detecting, and incrementing until the stimulation intensity parameter value equals a sensory marker; and determining the sensory marker based on the detection.

[0022] References herein to estimating, determining, comparing, and the like should be understood to refer to automated processes performed on data by a processor operating to execute a predefined procedure suitable for performing the described estimating, determining, and / or comparing step(s). 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 in software (e.g., using instructions tangibly stored on a non-transitory computer readable medium to cause a data processing system to perform the steps described herein), or in a combination of hardware and software. The techniques disclosed 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 that can then 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 other suitable storage device. The computer readable medium can also be distributed over network coupled computer systems so that the computer readable code is stored and / or executed in a distributed fashion.

[0023] One or more implementations of the present invention will now be described with reference to the accompanying drawings. [Brief description of the drawings]

[0024] [Figure 1] FIG. 1 is a schematic diagram of an implanted 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 interaction of the implanted stimulator of FIG. 1 with a nerve. [Figure 4a]FIG. 1 illustrates idealized activation plots for one posture of a patient undergoing neurostimulation. [Figure 4b] FIG. 13 shows the variation of activation plots with changes in patient posture. [Diagram 5] FIG. 1 is a schematic diagram showing elements and inputs of a closed-loop nerve stimulation (CLNS) system, in accordance with one implementation of the present technology. [Figure 6] FIG. 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 implanted stimulator of FIG. 1 in accordance with one implementation of the present technology. [Figure 8A] 1 is a flow chart illustrating a method for estimating key parameters of a patient's response to a stimulus, in accordance with one aspect of the present technology. [Figure 8B] 1 is a flow chart illustrating a method for estimating key parameters of a patient's response to a stimulus, in accordance with one aspect of the present technology. [Figure 9] 9 is a graph 900 illustrating the operation of the method of FIG. [Figure 10] 1 is a flowchart 1000 illustrating a method according to one implementation of the present technology. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0025] FIG. 1 illustrates a schematic of an implanted spinal cord stimulator 100 in a patient 108 according to one implementation of the present technology. The stimulator 100 includes an electronic module 110 implanted in a suitable location. In one implementation, the stimulator 100 is implanted in the patient's lower abdomen or posterior superior gluteal region. In other implementations, the electronic module 110 is implanted in other locations, such as the flank or sub-clavicularly. 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 may 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, split electrodes, or other types of electrodes that can form unipolar, bipolar, or multipolar electrode configurations for stimulation and measurement. The electrodes may penetrate or stick directly to the tissue itself.

[0026] Many aspects of the operation of the implanted 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 implanted stimulator 100 serves as a data collector, with the collected data being communicated to the external device 192 by way of 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. The external device 192 may thus provide a clinical interface configured to program the implanted stimulator 100 and restore data stored in the implanted 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.

[0027] 2 is a block diagram of the stimulator 100. The electronic 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 transfer power and / or data to and from the electronic module 110 via the communication channel 190. The module controller 116 stores one or more of clinical data 120, clinical context 121, control program 122, etc. in an associated memory 118. The controller 116 controls the pulse generator 124 to generate stimulation, such as a form of electrical pulse, according to the clinical context 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 apply the pulse to the tissue surrounding the selected electrode. The measurement circuitry 128 may include an amplifier and / or an analog-to-digital converter (ADC) and is configured to process signals including neural responses detected at the measurement electrodes of the electrode array 150 selected by the electrode selection module 126.

[0028] FIG. 3 is a schematic diagram illustrating the interaction of an implanted stimulator 100 with a nerve 180 in a patient 108. In the implementation shown in FIG. 3, the nerve 180 may be in the spinal cord, but in alternative implementations, the stimulator 100 may be located 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 through which a pulse is delivered 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 current electrode array 150 to which the stimulation current returns at each phase to maintain zero net charge transfer. The electrode serves as both a stimulating electrode and a return electrode throughout the entire multiphasic stimulation pulse. The use of two electrodes in this manner to deliver and return current at each stimulation phase is referred to as bipolar stimulation. Alternative embodiments may apply other forms of bipolar stimulation or may use more stimulation and / or return electrodes. A set of stimulation and return electrodes and their respective polarities is referred to as a stimulation electrode configuration. The electrode selection module 126 is shown connected to ground 130 of the pulse generator 124 to enable stimulation current feedback by the return electrode 4. However, other connections for current feedback may be used in other implementations.

[0029] Proper stimulation of the nerve 180 by the stimulation electrodes 2 and 4 induces a neural response 170, including an evoked compound action potential (ECAP), which travels along the nerve 180 as shown at a speed known as the conduction velocity. The ECAP may be induced for therapeutic purposes, which in the case of a spinal cord stimulator for chronic pain may be to create paresthesia at a desired location. To this end, the stimulation electrodes 2 and 4 are used to periodically stimulate at a frequency suitable for treatment, for example 30 Hz, although other frequencies may be used, including frequencies in the higher kHz range. In alternative implementations, stimulation may be applied aperiodically, such as in bursts or sporadically, depending on the patient 108. To program the stimulator 100 for the patient 108, the clinician may cause the stimulator 100 to deliver various configurations of stimulation that attempt to create sensations experienced by the user as paresthesia. Once a stimulation electrode configuration is found that induces paresthesia at a location, of a magnitude that matches the part of the patient's body affected by pain, and of a nature that is not painful to the patient, the clinician or patient designates that configuration for continued use. The treatment parameters may be loaded into the memory 118 of the stimulator 100 as a clinical context 121 .

[0030] FIG. 6 shows a typical morphology of an ECAP 600 in a healthy subject, recorded with a single measurement electrode referenced to system ground 130. The shape and duration of the single-ended ECAP 600 shown in FIG. 6 is predictable because it is the result of ionic currents created by a collection of fibers depolarizing in response to a stimulus and generating an action potential (AP). Evoked action potentials (EAPs) generated simultaneously among many fibers together form the ECAP 600. The ECAP 600 generated from the simultaneous depolarization of a group of similar fibers includes a positive peak P1, a negative peak N1, followed by a second positive peak P2. This shape results from a region of activation passing the measurement electrode as the action potential propagates along the individual fibers.

[0031] ECAP may be recorded differentially using two measurement electrodes as shown in FIG. 3. Differential ECAP measurements are less susceptible to common mode noise on the surrounding tissue than single-ended ECAP measurements. Depending on the polarity of the recording, the differential ECAP may take the inverse form to that shown in FIG. 6, i.e., with two negative peaks N1 and N2 and one positive peak P1. Alternatively, depending on the distance between the two measurement electrodes, the differential ECAP may resemble the time derivative of ECAP 600, or more generally the difference between ECAP 600 and a time-delayed copy of it.

[0032] The ECAP 600 may be characterized by any suitable characteristics, some of which are shown in FIG. 6. The amplitude of the positive peak P1 is Ap1 and occurs at time Tp1. The amplitude of the positive peak P2 is Ap2 and occurs at time Tp2. The amplitude of the negative peak P1 is An1 and occurs at time Tn1. The peak-to-peak amplitude is Ap1+An1. The recorded ECAP will generally have a maximum peak-to-peak amplitude in the microvolt range and a duration of 2-3 ms.

[0033] The stimulator 100 is further configured to detect the presence and measure the strength of ECAP 170 traveling along the nerve 180, whether such ECAP is induced by stimulation from electrodes 2 and 4 or otherwise. To this end, electrodes of the array 150 may be selected by the electrode selection module 126 to function as the recording electrodes 6 and the reference electrodes 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 after 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 recording electrodes and the reference electrodes are referred to as the measurement electrode configuration. For example, the measurement circuit 128 may operate according to the teachings of the applicant's above-mentioned International Patent Publication No. WO2012 / 155183.

[0034] 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 applying stimulation to the nerve 180. In some implementations, the sensed signals are processed by the ECAP detector to measure and store one or more characteristics from each evoked neural response or 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 International Patent Publication No. WO2015 / 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.

[0035] The stimulator 100 may apply stimulation for potentially extended periods of time, such as days, weeks, or months, and during this time store in memory 118 the characteristics of the neural response, the clinical background, paresthesia target levels, and other operating parameters. To implement a suitable SCS therapy, the stimulator 100 may deliver tens, hundreds, or even thousands of stimuli per second for extended periods of time each day. Each neural response or group of responses generates one or more characteristics, such as a measure of the strength of the neural response. The stimulator 100 may thus 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 may be stored in memory 118. However, the memory 118 is necessarily of limited capacity, and therefore care must be taken to select a compact data format for storage in memory 118 to ensure that the memory 118 is not exhausted by the time the data is expected to be wirelessly retrieved by the external device 192, which may occur only once or twice a day, or even less.

[0036] An activation plot, or growth curve, is an approximation to the relationship between stimulation intensity (e.g., the amplitude of the current pulse 160) and the intensity of the neural response 170 resulting from the stimulation (e.g., the ECAP amplitude). FIG. 4a shows an ideal activation plot 402 for one posture of the patient 108. The activation plot 402 shows an ECAP amplitude that increases linearly for stimulation intensity values ​​above a threshold 404, called the ECAP threshold. The ECAP threshold exists due to the dichotomous nature of fiber recruitment: if the field intensity is too low, no fibers are recruited. However, once the field intensity exceeds the threshold, fibers begin to be recruited and their individual evoked action potentials are independent of the strength of the field. The ECAP threshold 404 thus reflects the field intensity at which a significant number of fibers begin to be recruited, and an increase in response intensity at stimulation intensities above the ECAP threshold represents more and more fibers being recruited. Below the ECAP threshold 404, the ECAP amplitude may 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 may be modeled as follows:

[0037]

number

[0038] where s is the stimulus intensity, y is the ECAP amplitude, T is the ECAP threshold, and S is the slope of the activation plot (referred to herein as patient sensitivity). The slope S and the ECAP threshold T are the key parameters of the activation plot 402.

[0039] FIG. 4a also shows a discomfort threshold 408 (sometimes called a comfort threshold), which is a stimulation intensity above which the patient 108 experiences an unpleasant or painful stimulation. FIG. 4a also shows a perception threshold 410. The perception threshold 410 corresponds to the ECAP amplitude that can be perceived by the patient. There are several factors that can affect the location of the perception threshold 410, including the patient's posture. If the patient 108 does not perceive low levels of neural activation, the perception threshold 410 may correspond to a stimulation intensity greater than the ECAP threshold 404, as shown in FIG. 4a. Conversely, if the patient has a high perceptual sensitivity to lower levels of neural activation than can be detected by ECAP, or if the signal-to-noise ratio of the ECAP is low, the perception threshold 410 may correspond to a stimulation intensity less than the ECAP threshold 404.

[0040] For effective and comfortable operation of an implantable 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 is straightforward to measure these limits and ensure that the stimulation intensity, which can be tightly controlled, is always within the therapeutic range 412. However, the activation plot, and therefore the therapeutic range 412, varies with the posture of the patient 108.

[0041] FIG. 4b illustrates the variation of activation plots with changes in patient posture. A change in patient posture can cause a change in the impedance of the electrode-tissue interface or a change 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, there may be activation plots for a given posture between or beyond those shown, varying continuously with posture. As a result, as patient posture changes, the ECAP threshold changes as shown by the ECAP thresholds 508, 510, 512 of the respective activation plots 502, 504, and 506. In addition, as patient posture changes, the slope of the activation plots also changes as shown by the variation in the slope of the activation plots 502, 504, and 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. The activation plots 502, 504, and 506 thus correspond to an increase in the distance between the stimulating electrode and the spinal cord and a decrease in patient sensitivity.

[0042] To keep the applied stimulation intensity within a threshold range as the patient's posture varies, in some implementations, an implantable neuromodulation device, such as stimulator 100, may adjust the applied stimulation intensity based on a feedback variable determined from one or more measured ECAP characteristics. In one implementation, the device may adjust the stimulation intensity to maintain the measured ECAP amplitude at a target response intensity. For example, the device may 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 a 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 be operating 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 target ECAP amplitude 520 shown in FIG. 4b, a CLNS device generally keeps the stimulation intensity within a therapeutic range as the patient's posture varies.

[0043] The CLNS device includes a stimulator that takes the stimulation intensity value and converts it into a neural stimulus that includes a series of electrical pulses according to a predefined stimulation pattern. The stimulation pattern is parameterized by multiple parameters, including stimulation amplitude, pulse width, number of phases, order of phases, number of stimulation electrode poles (2 for bipolar, 3 for tripolar, etc.), and stimulation rate or frequency. At least one of the stimulation parameters, e.g., stimulation amplitude, is controlled by a feedback loop.

[0044] 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 derivative contribution is ignored, and the CLNS device uses a first order integrating feedback loop. The stimulator generates stimulation according to the stimulation strength parameters, which elicits a neural response in the patient. The strength of the evoked neural response (e.g., ECAP) is detected and its amplitude is measured by the CLNS device and compared to the target response strength.

[0045] 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 chosen appropriately, the patient receives consistent, comfortable, therapeutic stimulation through postural changes and other perturbations to the stimulus / response behavior.

[0046] 5 is a schematic diagram illustrating elements and inputs of a closed-loop nerve stimulation (CLNS) system 300 according to one implementation of the present technology. The system 300 includes a stimulator 312 that converts a stimulation intensity parameter (e.g., stimulation current amplitude) s into a nerve stimulation comprising a series of electrical pulses on a stimulation electrode (not shown in FIG. 5) according to a set of predefined stimulation parameters. According to one implementation, the predefined stimulation parameters include the number and order of phases, the number of stimulation electrode poles, pulse width, and stimulation frequency or frequency.

[0047] The generated stimulus passes from the electrodes to the spinal cord, which is represented in FIG. 5 by the dotted 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, which depends on the stimulus intensity and other stimulation parameters, as well as the electrical environment of the measurement electrodes. Measurement noise n, as well as various sources of artifact a, can multiply the evoked response y in summing element 313 to form a sensed signal r that includes electrical disturbances generated by the body, such as electrical noise from external sources, 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.

[0048] Neural recruitment resulting from stimulation is influenced by mechanical changes, including postural changes, gait, breathing, heartbeat, etc. Mechanical changes can cause impedance changes, i.e., changes in location and orientation of nerve fibers relative to the electrode array(s). As explained above, the strength of the evoked response provides a measure of the recruitment of stimulated fibers. In general, the stronger the stimulation, the more recruited and the greater the strength of the evoked response. Evoked responses typically have a maximum amplitude in the microvolt range, while the voltage resulting from the stimulation applied to elicit a response is typically a few volts.

[0049] The measurement circuit 318, which may be considered identical to the measurement circuit 128, amplifies the sensed signal r (including the evoked neural response, artifacts, and measurement 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. A typical number of samples in the captured signal window is 60. 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.

[0050] 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 make suitable adjustments 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)

[0051] where K is the gain of gain element 336 (the controller gain). This relationship may be expressed as: δs=Ke

[0052] where δs is the adjustment to the current stimulus intensity parameter s.

[0053] 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 CLNS system 300 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, from which the target ECAP amplitude is provided to the feedback controller 310.

[0054] The clinical context controller 302 provides a clinical context to the system 300, including the gain K of the gain element 336 and the stimulation parameters of the stimulator 312. The clinical context controller 302 may be configured to adjust the gain K of the gain element 336 to tune the feedback loop to patient sensitivity. The clinical context controller 302 may include an input to the CLNS system 300, through which a patient or clinician may adjust the clinical context. The clinical context controller 302 may include a memory in which the clinical context is stored and provided to the components of the system 300.

[0055] In some implementations, two clocks (not shown) are used: a stimulation clock running at a stimulation frequency (e.g., 60 Hz) and a sample clock (e.g., running at a sampling frequency of 10 kHz) for sampling the sense signal r. Since the ECAP detector 320 is linear, only the stimulation clock affects the dynamics of the CLNS system 300. At the next stimulation clock cycle, the stimulator 312 outputs stimulation according to the adjusted stimulation strength s. Thus, there is a delay of one stimulation clock cycle before the stimulation strength is updated against the error value e.

[0056] 7 is a block diagram of a neurostimulation system 700. The neurostimulation system 700 is centered around a neuromodulation device 710. In one example, the neuromodulation device 710 may be implemented as the stimulator 100 of FIG. 1 implanted within a patient (not shown). The neuromodulation device 710 is wirelessly connected to a remote controller (RC) 720. The remote controller 720 is a portable computing device that provides the patient with control of their stimulation in a home environment by enabling control of the functions of the neuromodulation device 710, including one or more of the following functions: enabling or disabling stimulation, adjusting stimulation intensity or target neural response intensity, and selecting a stimulation control program from control programs stored in the neuromodulation device 710.

[0057] Charger 750 is configured to recharge the rechargeable power source of neuromodulation device 710. Although recharging is shown as wireless in Figure 7, it may be wired in alternative implementations.

[0058] 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.

[0059] The CI 740 may be implemented as the external computing device 192 of Figure 1. The CI 740 is configured to program the neuromodulation device 710 and to restore data stored in the neuromodulation device 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.

[0060] Assisted Programming System As mentioned above, while programming closed-loop neurostimulation therapy, it is important to obtain patient feedback on the patient's sensations, but intervention by a trained clinical engineer is costly and time-consuming. It would therefore be advantageous if patients could program their own implantable devices themselves, or with some assistance from a clinician. However, the interfaces of current programming systems are not intuitive and are generally unsuitable for direct use by patients due to the technical nature of the interface. It is therefore necessary for CPA to be as intuitive as possible for non-technical users while preventing discomfort to the patient.

[0061] Implementations of an assistive programming system (APS) according to the present technology are generally configured to meet this need. 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 electronic module 110. Data acquired from the patient is analyzed by the APM to determine parameters and context for which neural stimulation therapy is to be 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 a specified stimulation to the patient, and by returning measurements of the neural response to the delivered stimulation via the CST 730.

[0062] In other implementations, all of the processing of the APS according to the present technology is performed by the APF, in other words, data acquired from the patient is not passed to the APM, but is analyzed by the APF to determine parameters and context for the neurostimulation therapy to be delivered by the stimulator 100.

[0063] In an implementation of the APS in which the APM analyzes data from a patient, the APS instructs the device 710 to capture and return a signal window to the CI 740 via the CST 730. In such an implementation, the device 710 captures the signal window using the measurement circuitry 128, bypassing the ECAP detector 320, and temporarily stores the data representing the raw signal window in memory 118 before transmitting the data representing the captured signal window to the APS for analysis.

[0064] Following this process, the APS may load the determined program onto the device 710 to manage subsequent neurostimulation therapy. In one implementation, the program includes clinical context 121, also referred to as treatment parameters, which are entered into the neuromodulation device by or stored in the clinical context controller 302. The patient may then use the remote controller 720 as described above to control the device 710 to deliver therapy according to the determined program. The determined program may also or alternatively be loaded into the CPA for verification and modification.

[0065] Noise Deviance Detector (NDD) In some implementations, the APS according to the present technology includes a noise deviation detector (NDD). The NDD is a statistical detector of the presence of ECAP in the signal window. The operation of the NDD on the signal window is preferably preceded by an "artifact scrubber" that removes artifacts from the signal window. Such an artifact scrubber is disclosed by the applicant in International Patent Publication No. WO2020 / 124135, the entire contents of which are incorporated herein by reference.

[0066] NDD works by detecting statistically anomalous differences from the expected noise present in a signal window. The degree of difference indicates the likelihood of ECAP within the signal window.

[0067] Calibration of an NDD instance corresponding to a measurement electrode configuration (MEC) may be performed in one or more signal windows 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, and preferably scrubbed of artifacts, and thus may be treated as containing only noise. The calibration involves forming estimates of parameters of a predefined "noise model" (statistical distribution) from samples of one or more "zero current" signal windows. In one implementation, the noise model is Gaussian, and the parameters are the mean of the samples.

[0068]

number

[0069] and standard deviation

[0070]

number

[0071] It is.

[0072] Once calibrated, an NDD instance can be used to measure the number of outliers in a signal window.

[0073]

number

[0074] , i.e., counting the number of samples in the signal window that deviate significantly from the noise model. For a Gaussian noise model, the NDD can be applied to the signal window by

[0075]

number

[0076] is the standard deviation estimate

[0077]

number

[0078] The number of samples that differ by more than n times

[0079]

number

[0080] Count the number of outliers, where n is a small integer.

[0081]

number

[0082] is the time when the signal window averages

[0083]

number

[0084] and standard deviation

[0085]

number

[0086] The number of outliers expected if the data consisted only of noise

[0087]

number

[0088] To obtain a metric r that quantifies the rate at which outliers are present in the signal window relative to the expected rate of outliers in the signal window according to the noise model,

[0089]

number

[0090] and

[0091]

number

[0092] The difference between is divided by the number of samples in the signal window, N.

[0093] It can be shown that for a Gaussian noise model, the NDD can estimate the metric r as follows:

[0094]

number

[0095] where Φ is the standard normal cumulative distribution function.

[0096] 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 may be due to the presence of ECAP within the signal window.

[0097] In one implementation of NDD, n is set to 3. Smaller values ​​of n make the NDD more sensitive, indicating easier deviation from noise, and increasing the rate of Type I errors (false positives). Conversely, higher values ​​for n require larger outliers before r indicates noise deviation, increasing the rate of Type II errors (false negatives).

[0098] In one implementation of NDD, the metric r is defined as a quality indicator Q NDD , i.e.

[0099]

number

[0100] A sigmoid function may be applied to the raw metric r to map it to

[0101] where γ is a parameter that balances type I and type II errors. NDD has a natural interpretation, i.e., Q NDD < 0.5 corresponds to r ≤ 0, indicating that the signal window is most likely noise. Conversely, Q NDD >0.5 indicates a deviation from the noise model believed to represent ECAP. In one implementation, γ is set to 50.

[0102] 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 according to the number of signal windows averaged. In the Gaussian noise model, the standard deviation

[0103]

number

[0104] is divided by the square root of the number of signal windows averaged.

[0105] Estimation of key patient response parameters One specific objective of the APS according to the present technology is to estimate key parameters of the patient's response to stimulation, which in idealized form is represented by the activation plot shown in Figure 4a. The key patient response parameters include the ECAP threshold T and the patient sensitivity S of Equation (1).

[0106] One method of forming an activation plot for a patient in a given posture involves manually increasing the stimulation intensity to a point where the patient first perceives the stimulation (called the perception threshold), then further to a comfortable level, and then further to the discomfort threshold. The perception and discomfort thresholds are used to define a treatment window. However, patients can be unreliable, and patient feedback, even if honest and well-intentioned, is highly subjective and varies with time and medication level, as well as several other factors. Thus, it can be difficult for patients to provide consistent, accurate responses about their sensations during programming.

[0107] CLNS devices such as stimulator 100 can record neural responses (e.g., ECAP), where the same intensity across various postures and movements corresponds to a similar level of paresthesia and similar neural activation (i.e., number of fibers activated), meaning that neural response strength (e.g., ECAP amplitude) is a more reliable and consistent measure of neural activation and therefore patient sensation than stimulation intensity.

[0108] The method and system for programming a CLNS device such as the stimulator 100 according to the present technology does not require the patient to report their sensations, but uses measurements of neural response strength to estimate key parameters. The stimulation strength is increased (monotonically increasing) from zero until an evoked neural response is consistently detected, and the neural strength at that point is marked as the interim ECAP threshold. The stimulation strength ramp continues from the interim ECAP threshold to a determined perceptual marker, and the response strength is recorded for each stimulus. The "perceptual marker" refers to the value of the stimulus that is perceptually significant to the patient. An example of a perceptual marker is the discomfort threshold described above. An activation plot model is fitted to the measured response strength. From the fitted model, the key parameters of the patient's response can be obtained.

[0109] 8A and 8B are flow charts illustrating a method 800 for estimating key parameters of a patient's response to stimulation, according to one aspect of the present technology. Method 800 may form part of the APS described above, and may be performed while the patient is awake, naturally asleep, or sedated, either during implantation or post-operatively.

[0110] Method 800 begins at step 805 with initializing a stimulation intensity s to a low value, e.g., zero, and an increment Δs. The increment Δs may be initialized to reach an expected ECAP threshold after a predetermined number of stimuli or after a predetermined elapsed time (e.g., 10 seconds) at a stimulation frequency. The expected ECAP threshold for a typical patient is 5 mA. Alternatively, the increment Δs may be initialized to a default value, such as 0.1 mA. Step 810 then delivers stimulation at the current stimulation intensity s.

[0111] Step 815 measures the strength d of the neural response in the signal window captured after the stimulus was presented in step 810. Step 815 may use an ECAP detector to measure the neural response strength d. Step 815 records the measured response strength d together with the current stimulus strength s as a pair (s, d). Depending on the form of the ECAP detector, the ECAP detector used in step 815 may return a non-zero value for d even if there is no discernible neural response in the signal window due to the presence of artifact a and non-artifact noise n in the signal window. The measured neural response strength d may even be negative, depending on the form of artifact a.

[0112] The ECAP detector used in step 815 (and later step 850 described below) is of the same form as the ECAP detector 320 implemented by the electronic module 110 used during the final treatment. In one implementation, the ECAP detector used in steps 815 and 850 may be the parametric ECAP detector disclosed in the above-mentioned International Patent Publication No. WO2015 / 074121. The parametric ECAP detector disclosed in WO2015 / 074121 has two parameters: correlation delay and length. The ECAP detector in step 815 may be configured with default detector parameters based on typical ECAP signal conduction velocity and known distances between the stimulation and measurement electrodes.

[0113] Step 825 then checks whether a neural response (ECAP) has been detected in the current signal window or in a number of recently processed signal windows. In one implementation, step 825 uses the NDD described above to detect the presence of an ECAP. In one such implementation, step 825 checks whether a quality index Q NDD, i.e., if the NDD detects ECAP in at least 50% of the signal window processed at the current stimulus strength. In another implementation, step 825 uses an ECAP detector based on base element signal separation (BESS). A BESS-based ECAP detector estimates the fundamental component signals (e.g., ECAP and artifacts) of the fundamental ECAP signal window given only the signal window, without knowledge of the exact fundamental ECAP and artifact component signals. Various BESS-based ECAP detectors are described in International Publication No. WO2020 / 124135, mentioned above. If the BESS-based ECAP detector returns a positive value above some predetermined noise threshold for the amplitude d of the ECAP component in the signal window, ECAP has been detected. In one such implementation, step 825 detects ECAP if the BESS-based ECAP detector detects ECAP in at least 50% of the signal window processed at the current stimulus strength.

[0114] In another implementation of step 825, a method for determining the likelihood that a captured signal window contains an ECAP is disclosed in International Patent Publication No. PCT / AU2022 / 051583, the contents of which are incorporated herein by reference. The likelihood returned by such a method may be compared to a threshold value, such as 0.5, to determine whether the captured signal window contains an ECAP.

[0115] If step 825 does not detect a neural response ("N"), then in step 827 method 800 increments the stimulation intensity by a predetermined increment Δs and returns to step 810. If step 825 detects a neural response ("Y"), then step 830 fits a model to the set of pairs (s, d) recorded in multiple iterations of step 815 since the start of method 800 at s=0. The model fitted in step 830 is called a sub-threshold model. The sub-threshold model may be a straight line, in which case step 830 may use linear regression to fit the model. Step 835 then records the current stimulation intensity s as the interim ECAP threshold s0.

[0116] Note that the APS may omit increments in some iterations of step 827 such that multiple successive stimuli are given at the same intensity until the next increment is made. This effectively slows down the rate of increase in stimulation intensity s per unit time. In such implementations, the increment Δs may be predetermined to reach the expected ECAP threshold after a predetermined elapsed time (e.g., 10 seconds).

[0117] Step 840 then calculates a value s for the perceptual marker that indicates the upper limit of the stimulus intensity ramp from the interim ECAP threshold s. max In one implementation, step 840 uses a linear prediction model to obtain the perceptual marker from the interim ECAP threshold s0. s max =m s0(5)

[0118] where m is a correlation parameter that may be derived from historical patient data relating the patient's ECAP threshold to the patient's respective sensory marker. In some implementations where the sensory marker is a discomfort threshold, m has a value between 1.0 and 2.0. In other implementations, m has a value between 1.1 and 1.6. In some such implementations, m has a value between 1.25 and 1.54. Because the sensory markers are likely to vary depending on the patient's sleep state, step 840 may use a different correlation parameter m depending on whether the patient is awake, naturally asleep, or sedated. For a sedated patient, the sensory marker may be the motor threshold at which stimulation activates motor fibers.

[0119] Step 842 then increments the stimulus intensity by a predetermined increment Δs.

[0120] Method 800 then proceeds to step 845, where a stimulus is applied at the current stimulus strength s. Step 850 then measures the strength d of the neural response within the signal window captured after the applied stimulus in step 845 using the same ECAP detector in a manner similar to step 815. Step 850 records the measured response strength d together with the current stimulus strength s as a pair (s, d).

[0121] In an implementation where the ECAP detector used in steps 815 and 850 is the parametric ECAP detector disclosed in the above-mentioned International Patent Publication No. WO2015 / 074121, the APS may dynamically update the parameters (e.g., length and correlation delay) of the parametric ECAP detector for an average signal window obtained by averaging a predetermined number of recent signal windows, e.g., 10 signal windows, in optional step 853. The parametric ECAP detector is applied to measure the ECAP amplitude in the average signal window for all feasible values ​​of correlation delay and length to form a correlation matrix. The correlation delay and length values ​​that maximize the measured ECAP amplitude in the correlation matrix are chosen as optimal. The APS may also retroactively re-measure the response strength d for all signal windows captured since the start of the method 800 using the updated ECAP detector parameters in step 853.

[0122] Step 855 then determines whether the stimulus intensity s corresponds to a perceptual marker s max If not ("N"), step 860 tests whether adverse side effects of increasing stimulation intensity are experienced. In one implementation, an indicator of an adverse side effect is the appearance of a "slow" or "low-speed" response after ECAP within the signal window. Slow responses, which may indicate motor fiber activation, may be detected as described in International Patent Publication No. WO2015 / 070281, the contents of which are incorporated herein by reference. Another indicator of an adverse side effect is a saturation (flattening) of neural response strength with continued increments of stimulation intensity.

[0123] If no side effects are detected in step 860 ("N"), then method 800 increments the stimulation intensity s by an increment Δs in step 862. As with step 827, the APS may omit an increment in some iterations of step 845, such that multiple successive stimuli are delivered at the same intensity until the next increment is made. Method 800 then returns to step 845. However, if the stimulation intensity s is greater than or equal to the sensory marker s max If either the interim ECAP threshold s is reached ("Y" at step 855) or at least one adverse side effect is detected from the increasing stimulation intensity ("Y" at step 860), no further stimulation is administered and method 800 proceeds to step 865. Step 865 fits a model to the set of pairs (s, d) recorded in multiple iterations of step 850 since step 840 where the stimulation intensity s was equal to the interim ECAP threshold s0. The model fitted in step 865 is referred to as a supra-threshold model. The supra-threshold model may be a straight line, in which case step 865 may use linear regression to fit the model.

[0124] In implementations where step 853 remeasures neural response strengths, the subthreshold model fitted to the subthreshold pair (s, d) in step 830 may be refitted in step 865 using the remeasured strengths.

[0125] Step 870 then calculates the value of the stimulus intensity where the subthreshold model fitted in step 830 or refitted in step 865 intersects with the suprathreshold model fitted in step 865. The calculated value of the stimulus intensity is the ECAP threshold T.

[0126] Finally, step 875 calculates the patient sensitivity, S, from the supra-threshold model. In implementations where the supra-threshold model is linear, the sensitivity, S, is the slope of the fitted line.

[0127] 9 is a graph 900 illustrating the operation of method 800. Each point (e.g., point 905) represents a recorded (s, d) pair on an axis representing stimulus strength s and response strength d. Vertical dashed line 910 indicates the interim ECAP threshold s0, where ECAP was first detected. Vertical dashed line 920 indicates the perceptual marker s, derived from the interim ECAP threshold s0. max 9 is shown. Dashed line 930 represents the sub-threshold model as a linear model of the sub-threshold pair. Dashed line 940 represents the supra-threshold model as a linear model of the supra-threshold pair. Point 950 is the intersection of the sub-threshold model 930 and the supra-threshold model 940. The s-coordinate of intersection 950 is the ECAP threshold T. The ECAP threshold T in FIG. 9 is slightly different from the tentative ECAP threshold s0. The slope of the supra-threshold model 940 is the sensitivity S.

[0128] An alternative suprathreshold model that is fitted in step 865 is the logistic growth curve (LGC) model (also called a sigmoid function). In one implementation, the LGC model is a function of four parameters.

[0129]

number

[0130] However, the four parameters are: A, minimum (ECAP amplitude detected in the absence of stimulation) K, the maximum value (the detected ECAP amplitude at which saturation occurs, i.e., an increase in stimulus intensity does not increase the detected ECAP amplitude). M, the current amplitude at the midpoint between A and K B, the steepness of the LGC, proportional to the gradient at the midpoint between A and K.

[0131] In other implementations, fewer parameters may be used for the LGC model, for example an LGC model in which the minimum value A is equal to zero.

[0132] To fit the LGC, parameters A, K, M, and B may be initialized to perceptible starting points A0, K0, M0, and B0. In one implementation, these values ​​may be set to the following: · A0: The average of the response intensities obtained from the lowest few stimulus intensities. · K0: The average of the response intensities obtained from the highest few stimulus intensities. · M0: Stimulation current amplitude at the midpoint between A and K. · B0: may be calculated from the gradient m at the midpoint, obtained from a local linear regression of the pair taken near the midpoint, as B0= m*4 / (K0-A0).

[0133] An optimization algorithm such as Trust Region Reflective (TRF) may then be used to optimize the four parameters A, K, M, and B from their starting points A0, K0, M0, and B0.

[0134] The fitted LGC may be used to estimate the patient sensitivity, as in step 875 of method 800. In one implementation, the patient sensitivity S is the slope of the fitted LGC at its midpoint M, which may be calculated from the steepness B as follows:

[0135]

number

[0136] The point of intersection of the linear sub-threshold model d(s) = m*s + b with the LGC supra-threshold model such as equation (6) can be found by solving the following equation numerically for s:

[0137]

number

[0138] Once the key patient response parameters have been estimated, for example by method 800 , they may be used by the APS to determine the clinical context (treatment parameters) for the CLNS system 300 .

[0139] In one implementation, the measured sensitivity S (possibly adjusted for an effective stimulation frequency) may be used to set the gain K of the gain element 336 to the corresponding SEC. International Patent Publication No. WO2016090436, the contents of which are incorporated herein by reference, describes how the controller gain K may be set based on the measured sensitivity S. In another implementation, the controller gain K may be calculated as follows:

[0140]

number

[0141] however,

[0142] [Number]

[0143] , f c is the loop cutoff frequency, and f s is the stimulation frequency. In one implementation, the loop cutoff frequency is set to 3 Hz to balance the attenuation of noise with the attenuation of postural disturbances such as heart rate.

[0144] In another implementation, the initial target ECAP amplitude may be set based on a measured ECAP threshold T and a measured sensitivity S.

[0145] In another implementation according to the present technology, instead of estimating the primary response parameters in steps 865 through 875, a variation of method 800 outputs a binary indicator in step 865 depending on whether a perceptual marker was reached (i.e., a "Y" result in step 855) or a side effect occurred (i.e., a "Y" result in step 860). If the perceptual marker was reached, the indicator indicates that the perceptual marker was reached without a side effect. If a side effect occurred, the indicator indicates that the side effect occurred before the perceptual marker was reached. The variation of method 800 then stops or continues ramping to another, higher perceptual marker and returns to step 862 to return a similar indicator for the higher perceptual marker.

[0146] 10 is a flow chart illustrating a method 1000 in accordance with one aspect of the present technology. Method 1000 may form part of the APS described above and may be performed while the patient is awake, naturally asleep, or sedated, either during implantation or post-operatively.

[0147] Method 1000 begins at step 1005 by sequentially delivering a plurality of neurostimuli to a patient's nerve fibers according to a ramp of a stimulation intensity parameter value until a sensory marker. Step 1010 receives a captured signal window after each delivered neurostimuli. Step 1020 detects whether an evoked neural response is present within each captured signal window. Step 1030 determines a sensory marker based on the detection. Method 1000 then returns to step 1005 to continue ramping the stimulation intensity parameter until the sensory marker.

[0148] It will be appreciated by those skilled in the art that numerous variations and / or modifications may be made to the invention as shown in the specific embodiments without departing from the spirit or scope of the invention as broadly described, which embodiments are therefore to be considered in all respects as illustrative and not restrictive or restrictive. [Explanation of symbols]

[0149] 100 stimulator 108 patients 110 Electronic Module 112 Battery 114 Telemetry Module 116 Controller 118 Memory 120 Clinical Data 121 Clinical background 122 Control Program 124 Pulse Generator 126 Electrode Selection Module 128 Measurement circuit 130 System Ground 150 Electrode Array 160 Current Pulse 170 ECAP 180 Nerves 190 Transcutaneous Communication Channels 192 External Devices 300 CLNS System 302 Clinical Background Controller 304 Target ECAP Controller Box 308 Box 309 310 Controller Box 311 312 Stimulator 313 Elements 318 Measurement circuit 320 ECAP detector 324 Comparator 336 Gain Elements 338 Integrator 402 Activation Plot 404 ECAP Threshold 408 Discomfort Threshold 410 Perception Threshold 412 Treatment scope 502 Activation Plot 504 Activation Plot 506 Activation Plot 508 ECAP Threshold 510 ECAP Threshold 512 ECAP Threshold 520 Target ECAP amplitude 600 ECAP 700 Neurostimulation System 710 Neuromodulation Device 720 Remote Controller 730 Clinical Background Transceiver 740 Clinical Interface 750 charger 800 ways 805 Steps 810 Steps 815 steps 825 steps 827 steps 830 steps 835 steps 840 steps 842 steps 845 steps 850 steps 853 Steps 855 steps 860 steps 863 steps 865 steps 870 steps 875 steps 900 graphs 905 points 910 Vertical dashed line 920 Vertical dashed line 930 dashed line 940 Suprathreshold Model 950 intersection 1000 ways 1005 Steps 1010 Steps 1020 steps 1030 steps

Claims

1. 1. A neurostimulation system comprising:

1. A neuromodulation device for providing neural stimulation, comprising: a plurality of implantable electrodes including one or more stimulating electrodes and one or more sense electrodes; a stimulation source configured to provide neural stimulation provided by the one or more stimulation electrodes to neural tissue of the patient to elicit a neural response in the neural tissue; a measurement circuit configured to capture a signal window sensed at the one or more sensing electrodes after each neural stimulation; a control unit configured to control the stimulation sources to provide each neural stimulation in accordance with a stimulation intensity parameter; A neuromodulation device comprising:

1. A processor comprising: instructing the control unit to control the stimulation source to sequentially provide a plurality of neural stimuli according to a ramp of stimulation intensity parameter values ​​to a sensory marker; receiving a captured signal window after each neural stimulation; Detecting whether an evoked neural response is present within each captured signal window; determining the sensory marker based on the detection; and A processor configured to: A neurostimulation system comprising:

2. The processor, determining a provisional neural response threshold based on said detecting; determining the sensory marker from the interim neural response threshold; The neurostimulation system of claim 1 , further configured to:

3. The neurostimulation system of claim 2 , wherein the processor is configured to determine the sensory marker from the interim neural response threshold using a linear predictive model.

4. The neurostimulation system of claim 2 or 3, wherein the processor is configured to determine the interim neural response threshold as the lowest stimulation intensity parameter at which the detection detects an evoked neural response within the captured signal window.

5. 5. The neurostimulation system of claim 1, wherein the processor is configured to detect whether an evoked neural response is present within a captured signal window by detecting a statistically anomalous difference from noise expected to be present within the signal window.

6. 6. The neurostimulation system of claim 5, wherein the processor is configured to detect whether an evoked neural response is present within a captured signal window by first removing artifacts from the signal window.

7. The neurostimulation system of any one of claims 2 to 4, wherein the processor is further configured to measure the strength of the evoked neural response within each captured signal window, thereby forming a stimulation intensity parameter-response intensity pair for each neurostimulation.

8. The neurostimulation system of claim 7 , wherein the processor is further configured to fit a subthreshold model to stimulation intensity parameter-response intensity pairs with stimulation intensity values ​​up to the interim neuronal response threshold.

9. The neurostimulation system of claim 8 , wherein the processor is further configured to fit a suprathreshold model to stimulation intensity parameter-response intensity pairs having stimulation intensity values ​​above the interim neuronal response threshold.

10. 10. The neurostimulation system of claim 9, wherein the processor is further configured to determine key parameters of the response of the neural tissue to neurostimulation from the suprathreshold model.

11. 11. The neurostimulation system of claim 10, wherein the primary parameter is a neural response threshold, and the processor is configured to determine the neural response threshold from a point of intersection of the subthreshold model and the suprathreshold model.

12. 11. The neurostimulation system of claim 10, wherein the primary parameter is sensitivity, and the processor is configured to determine the primary parameter by measuring the sensitivity from the suprathreshold model.

13. 13. The neurostimulation system of claim 7, wherein the processor is further configured to measure the strength of the evoked neural response within each captured signal window using a parametric evoked compound action potential (ECAP) detector including one or more parameters.

14. The neurostimulation system of claim 13 , wherein the processor is further configured to update one or more parameters of the parametric ECAP detector during the ramp in stimulation intensity parameter value.

15. 15. The neurostimulation system of claim 14, wherein the processor is further configured to re-measure the measured strength of the evoked neural response using the updated parameters of the parametric ECAP detector.

16. 16. The neurostimulation system of claim 1, wherein the processor is further configured to stop the ramping of the stimulation intensity parameter value upon detecting an adverse side effect of the neurostimulation.

17. The neurostimulation system of claim 1 , wherein the processor is part of a neuromodulation device.

18. 18. The neurostimulation system of claim 1 , further comprising an external computing device in communication with the neuromodulation device.

19. The neurostimulation system of claim 18 , wherein the processor is part of the external computing device.

20. 1. An automated method for providing neurostimulation to neural tissue of a patient, comprising: sequentially applying a plurality of neural stimuli to the neural tissue according to a ramp of stimulation intensity parameter values ​​to a sensory marker; receiving a captured signal window after each applied neural stimulation; detecting whether an evoked neural response is present within each captured signal window; determining the sensory marker based on the detection; An automated method comprising:

21. determining a provisional neural response threshold based on said detection; determining the sensory marker from the interim neural response threshold; 21. The method of claim 20, further comprising:

22. 22. The method of claim 21, wherein the determining the sensory marker from the interim neural response threshold uses a linear predictive model.

23. 23. The method of claim 21 or 22, wherein the determining the interim neural response threshold comprises determining the lowest stimulation intensity parameter at which the detection detects an evoked neural response within the captured signal window.

24. 24. The method of any one of claims 20 to 23, wherein detecting whether an evoked neural response is present within a captured signal window comprises detecting a statistically anomalous difference from noise expected to be present within the signal window.

25. 25. The method of claim 24, wherein the step of detecting whether an evoked neural response is present within a captured signal window comprises the step of first removing artifacts from the signal window.

26. 24. The method of claim 21, further comprising measuring the strength of the evoked neural response within each captured signal window, thereby forming a stimulation intensity parameter-response intensity pair for each neural stimulation.

27. 27. The method of claim 26, further comprising fitting a subthreshold model to stimulation intensity parameter-response intensity pairs with stimulation intensity values ​​up to the interim neural response threshold.

28. 28. The method of claim 27, further comprising fitting a suprathreshold model to stimulation intensity parameter-response intensity pairs having stimulation intensity values ​​above the provisional neural response threshold.

29. 30. The method of claim 28, further comprising determining key parameters of the response of the neural tissue to neural stimulation from the suprathreshold model.

30. 30. The method of claim 29, wherein the primary parameter is a neural response threshold, and wherein determining the primary parameter comprises determining the neural response threshold from a point of intersection of the subthreshold model and the suprathreshold model.

31. 30. The method of claim 29, wherein the primary parameter is sensitivity, and wherein the determining the primary parameter comprises measuring the sensitivity from the suprathreshold model.

32. 32. The method of any one of claims 26 to 31, further comprising measuring the strength of the evoked neural response within each captured signal window using a parametric ECAP detector including one or more parameters.

33. 33. The method of claim 32, further comprising updating one or more parameters of the parametric ECAP detector during the ramp in stimulus intensity parameter value.

34. 34. The method of claim 33, further comprising re-measuring the measured strength of the evoked neural response using the updated parameters of the parametric ECAP detector.

35. 35. The method of any one of claims 20 to 34, further comprising stopping the ramping of stimulation intensity parameter value upon detecting an adverse side effect of the neurostimulation.

36. 1. A neurostimulation system comprising:

1. A neuromodulation device for providing neural stimulation, comprising: a plurality of implantable electrodes including one or more stimulating electrodes and one or more sense electrodes; a stimulation source configured to provide neural stimulation provided by the one or more stimulation electrodes to neural tissue of the patient to elicit a neural response in the neural tissue; a measurement circuit configured to capture a signal window sensed at the one or more sensing electrodes after each neural stimulation; a control unit configured to control the stimulation sources to provide each neural stimulation in accordance with a stimulation intensity parameter; A neuromodulation device comprising:

1. A processor comprising: instructing the control unit to control the stimulation source to provide one or more neural stimuli according to a stimulation intensity parameter value; Detecting whether an evoked neural response is present within each captured signal window; incrementing the stimulation intensity parameter value; repeating the commanding, detecting, and incrementing until the stimulus intensity parameter value is equal to a sensory marker; determining the sensory marker based on the detection; and A processor configured to: A neurostimulation system comprising:

37. 37. The neurostimulation system of claim 36, wherein the control unit is further configured to measure a strength of an evoked neural response within each captured signal window after the neural stimulation, thereby forming a stimulation intensity parameter value-response strength pair for each neural stimulation.

38. 1. An automated method for providing neurostimulation to neural tissue of a patient, comprising: applying one or more neural stimuli to the neural tissue according to a stimulation intensity parameter value; detecting whether an evoked neural response is present within each captured signal window; incrementing the stimulation intensity parameter value; repeating the applying, detecting, and incrementing until the stimulus intensity parameter value is equal to a sensory marker; determining the sensory marker based on the detection; An automated method comprising:

39. 39. The method of claim 38, further comprising measuring the strength of the evoked neural response within each captured signal window after the neural stimulation, thereby forming a stimulation intensity parameter value-response strength pair for each neural stimulation.

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