Assisted programming system for neural stimulation therapy

Abstract

Disclosed is a neural stimulation system comprising: an implantable device for controllably delivering neural stimuli, and a processor. The device comprises: a pulse generator configured to deliver neural stimuli via one or more stimulus electrode configurations (SECs), each SEC comprising one or more stimulation electrodes of an implanted electrode array, to a neural pathway of a patient, the neural stimuli being configured to evoke neural responses from the neural pathway; and a control unit configured to control the pulse generator to deliver neural stimuli via an SEC of the one or more SECs. The processor is configured to: select, from a predetermined ranked list of possible SECs associated with a predetermined first location on the electrode array, the first possible SEC in rank order that does not contain an unusable electrode; and instruct the control unit to control the pulse generator to deliver the neural stimuli via the selected SEC.

Description

ASSISTED PROGRAMMING SYSTEM FOR NEURAL STIMULATION THERAPY

[0001] The present application claims priority from Australian Provisional Patent Application No. 2024903533 filed on 30 October 2024, the contents of which are incorporated herein by reference in their entirety.TECHNICAL FIELD

[0001] The present invention relates to neural stimulation therapy and in particular to methods and systems for programming a neural stimulation therapy system to suit the needs of a particular patient.BACKGROUND OF THE INVENTION

[0002] There are a range of situations in which it is desirable to apply neural stimuli in order to alter neural function, a process known as neuromodulation. For example, neuromodulation is used to treat a variety of disorders including chronic neuropathic pain, movement disorders, and voiding disorders. A neuromodulation system applies an electrical pulse (stimulus) to neural tissue (fibres, or neurons) in order to generate a therapeutic effect. In general, the electrical stimulus generated by a neuromodulation system evokes a neural response known as an action potential in a neural fibre which then has either inhibitory or excitatory effects on neural networks. Inhibitory effects can be used to modulate an undesired process such as the transmission of pain, or excitatory effects may be used to cause a desired effect such as the contraction of a muscle.

[0003] When used to relieve neuropathic pain originating in the trunk and limbs, the electrical pulse is applied to the dorsal column (DC) of the spinal cord, a procedure referred to as spinal cord stimulation (SCS). Such a system typically comprises an implanted electrical pulse generator, and a power source such as a battery that may be transcutaneously rechargeable by wireless means, such as inductive transfer. An electrode array is connected to the pulse generator, and is implanted adjacent the target neural fibre(s) in the spinal cord, typically in the dorsal epidural space above the dorsal column. An electrical pulse of sufficient intensity applied to the target neural fibres by a stimulus electrode causes the depolarisation of neurons in the fibres, which in turn generates an action potential in the fibres. Action potentials propagate along the fibres in an orthodromic direction (in afferent fibres this means towards the head, or rostral) and in an antidromic direction (in afferent fibres this means towards the cauda, or caudal). Action potentials propagating along A0 (A-beta) fibres being stimulated in this way may inhibit the transmission of pain from a region of the body innervated by the target neural fibres (the dermatome) to the brain. To sustain the pain relief effects, stimuli are applied repeatedly, for example at a stimulus frequency in the range of 30 Hz - 100 Hz.

[0004] For effective and comfortable neuromodulation, it is necessary to maintain stimulus intensity above a recruitment threshold. Stimuli below the recruitment threshold will fail to recruit sufficientneurons to generate action potentials with a therapeutic effect. In some neuromodulation applications, response from a single class of fibre is desired, but the stimulus waveforms employed can evoke action potentials in other classes of fibres which cause unwanted side effects. In pain relief, is therefore necessary to apply stimuli with intensity below a discomfort threshold, above which uncomfortable or painful percepts arise due to over-recruitment of A0 fibres or recruitment of undesired fibre classes. When recruitment is too large, A0 fibres produce uncomfortable sensations. Stimulation at high intensity may even recruit AS fibres, which are sensory nerve fibres associated with acute pain, cold and pressure sensation. It is therefore desirable to maintain stimulus intensity within a therapeutic range between the recruitment threshold and the discomfort threshold.

[0005] The task of maintaining appropriate neural recruitment is made more difficult by electrode migration (change in position over time) or postural changes of the implant recipient (patient), either of which can significantly alter the neural recruitment arising from a given stimulus, and therefore the therapeutic range. The spinal cord itself moves within the cerebrospinal fluid (CSF) with respect to the dura and the electrode array. During postural changes, the distance between the spinal cord and the electrode can change significantly. This effect is so large that postural changes alone can cause a previously comfortable and effective stimulus regime to become either ineffectual or painful.

[0006] Attempts have been made to address such problems by way of feedback or closed-loop control, such as using the methods set forth in International Patent Publication No. WO 2012 / 155188 by the present applicant, the content of which is incorporated herein by reference. Feedback control seeks to compensate for relative nerve / electrode movement by controlling the intensity of the delivered stimuli to maintain neural recruitment at or near a target value. The intensity of a neural response evoked by a stimulus may be used as a feedback variable representative of the amount of neural recruitment. A signal representative of the neural response may be generated by a measurement electrode in electrical communication with the recruited neural fibres, and processed to obtain the feedback variable. Based on the response intensity, the intensity of the applied stimulus may be adjusted to bring the response intensity closer to the target value.

[0007] It is therefore desirable to accurately measure the intensity and other characteristics of a neural response evoked by the stimulus. The action potentials generated by the depolarisation of a large number of fibres by a stimulus sum to form a measurable signal known as an evoked compound action potential (ECAP). Accordingly, an ECAP is the sum of responses from a large number of single fibre action potentials. The ECAP generated from the depolarisation of a group of similar fibres may be sensed by a measurement electrode as a positive peak potential, then a negative peak, followed by a second positive peak. This morphology is caused by the region of activation passing the measurement electrode as the action potentials propagate along the individual fibres.

[0008] Approaches proposed for obtaining a neural response measurement are described by the present applicant in International Patent Publication No. WO 2012 / 155183, the content of which is incorporated herein by reference.

[0009] However, neural response measurement can be a difficult task as a neural response component in the sensed signal will typically have a maximum amplitude in the range of microvolts. In contrast, a stimulus applied to evoke the CAP is typically several volts, and manifests in the measured response as crosstalk of that magnitude. Moreover, stimulus generally results in electrode artefact, which may manifest in the sensed signal as a decaying output of the order of several millivolts after the end of the stimulus. As the CAP signal can be contemporaneous with the stimulus crosstalk or the stimulus artefact, CAP measurements present a difficult challenge of measurement amplifier design. For example, to resolve a 10 pV CAP with 1 pV resolution in the presence of stimulus crosstalk of 5 V requires an amplifier with a dynamic range of 134 dB, which is impractical in implantable devices. In practice, many non-ideal aspects of a circuit lead to artefact, and as these aspects mostly result a time-decaying artefact waveform of positive or negative polarity, their identification and elimination can be laborious.

[0010] Closed-loop neural stimulation therapy is governed by a number of parameters to which values must be assigned to implement the therapy. The effectiveness of the therapy depends in large measure on the suitability of the assigned parameter values to the patient undergoing the therapy. As 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 a large proportion of patients. An important preliminary task, once a neuromodulation device has been implanted in a patient, is therefore to assign values to the therapy parameters that maximise 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 certain test stimuli via the device, recording responses, and based on the recorded responses, inferring or calculating the most effective parameter values for the patient. The resulting parameter values are then formed into a “program” that may be loaded to 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 may be analysed along with subjective responses elicited from the patient. In an effective programming system, the more responses that are analysed, the more effective the eventual assigned parameter values should be.

[0011] However, programming may be costly and time-consuming if unnecessarily prolonged. There is therefore an incentive to minimise the number of test stimuli to be applied and the amount of information to be recorded and analysed in order to produce the assigned values of the therapyparameters. In particular, the size of the therapy parameter search space is such that testing every possible combination of therapy parameters is impractical.

[0012] Moreover, programming workflows are generally conducted 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 may be problematic, particularly when patients lack the capacity to express the sensations they are feeling during the test stimuli. In addition, the subjective responses of the patient, even if clearly expressed, are not always a reliable guide to the device’s effect on the patient. This can lead to inefficient programming and, in a worst case, ineffective assigned values for therapy parameters.

[0013] Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present Background is solely for the purpose of providing a context for the present technology. It is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present technology as it existed before the priority date of each claim of the present disclosure.SUMMARY OF THE INVENTION

[0014] Disclosed herein is an assisted programming system for a neuromodulation device that is configured to assist a clinician to efficiently program the neuromodulation device for a particular patient.

[0015] A first general aspect includes a neural stimulation system comprising: an implantable device for controllably delivering neural stimuli, and a processor. The device comprises: a pulse generator configured to deliver neural stimuli via one or more stimulus electrode configurations (SECs), each SEC comprising one or more stimulation electrodes of an implanted electrode array, to a neural pathway of a patient, the neural stimuli being configured to evoke neural responses from the neural pathway; and a control unit configured to control the pulse generator to deliver neural stimuli via an SEC of the one or more SECs. The processor is configured to: select, from a predetermined ranked list of possible SECs associated with a predetermined first location on the electrode array, the first possible SEC in rank order that does not contain an unusable electrode; and instruct the control unit to control the pulse generator to deliver the neural stimuli via the selected SEC.

[0016] A second general aspect includes a method of programming a neural stimulation device. The method comprises: selecting, from a predetermined ranked list of possible stimulus electrode configurations (SECs) associated with a predetermined first location on an electrode array, the first possible SEC in rank order that does not contain an unusable electrode; and instructing the neural stimulation device to deliver the neural stimuli to a neural pathway of a patient via the selected SEC.

[0017] In some embodiments of the first and second aspects, the device may further comprise: measurement circuitry configured to capture signal windows from signals sensed on the neural pathway by one or more measurement electrode configurations (MECs), each MEC comprising one or more measurement electrodes of the implanted electrode array.

[0018] In some embodiments of the first and second aspects, the processor may further be configured to: select, from a ranked list of possible MECs associated with the selected SEC, the first N possible MECs in rank order that do not contain an unusable electrode, where N is a number greater than one.

[0019] In some embodiments of the first and second aspects, the processor may further be configured to generate the ranked list of possible MECs according to a corresponding predetermined list of ranked rules as applied to the selected SEC.

[0020] In some embodiments of the first and second aspects, the predetermined list of ranked rules may be chosen to balance diversity of possible MECs with a signal-to-noise ratio of each MEC.

[0021] In some embodiments of the first and second aspects, each rule in the predetermined list of ranked rules may specify electrode array offsets of the positions of the recording and reference electrodes of the corresponding MEC relative to a location of the associated SEC on the electrode array.

[0022] In some embodiments of the first and second aspects, the processor may be further configured to select one MEC of the N selected MECs.

[0023] In some embodiments of the first and second aspects, the processor may be further configured to instruct the control unit to control the measurement circuitry to capture signal windows from signals sensed on the neural pathway by the selected MEC.

[0024] In some embodiments of the first and second aspects, the control unit may be further configured to repeatedly: control the pulse generator to deliver a neural stimulus via the selected SEC according to a stimulus parameter; control the measurement circuitry to capture a signal window from a signal sensed on the neural pathway by the selected MEC subsequent to the delivered neural stimulus; measure a characteristic of an evoked neural response in the captured signal window; determine a feedback variable from the measured characteristic of the evoked neural response; and adjust, using a feedback controller, the stimulus parameter so as to maintain the feedback variable at or near a target value.

[0025] In some embodiments of the first and second aspects, the processor may be further configured to identify the unusable electrodes by an electrode impedance check.

[0026] In some embodiments of the first and second aspects, the processor may be further configured to repeat the selecting and instructing for a predetermined second location on the implanted electrode array.

[0027] In some embodiments of the first and second aspects, the ranked lists of possible SECs associated with the second location on the electrode array may be a symmetrically reflected version of the ranked list for the first location.

[0028] According to a third aspect, a neural stimulation system comprising: an implantable device for controllably delivering neural stimuli, the device comprising: a pulse generator configured to deliver neural stimuli via one or more stimulus electrode configurations (SECs), each SEC comprising one or more stimulation electrodes of an implanted electrode array, to a neural pathway of a patient, the neural stimuli being configured to evoke neural responses from the neural pathway; measurement circuitry configured to capture signal windows from signals sensed on the neural pathway by one or more measurement electrode configurations (MECs), each MEC comprising one or more measurement electrodes of the implanted electrode array; and a control unit configured to control the pulse generator to deliver neural stimuli via a selected SEC of the one or more SECs; and a processor configured to: select, from a ranked list of possible MECs associated with the selected SEC, the first N possible MECs in rank order that do not contain an unusable electrode, where N is a number greater than one; and instruct the control unit to control the measurement circuitry to capture signal windows from signals sensed on the neural pathway by one MEC of the N selected MECs.

[0029] According to a fourth aspect, a method of programming a neural stimulation device, the method comprising: selecting, from a ranked list of possible measurement electrode configurations (MECs) associated with a selected stimulus electrode configuration (SEC), the first N possible MECs in rank order that do not contain an unusable electrode, where A is a number greater than one; instructing the neural stimulation device to deliver neural stimuli to a neural pathway of a patient via the selected SEC; and instructing the neural stimulation device to capture signal windows from signals sensed on the neural pathway subsequent to respective neural stimuli by one MEC of the N selected MECs.

[0030] In some embodiments of the third and fourth aspects, the processor may be further configured to generate the ranked list of possible MECs according to a corresponding predetermined list of ranked rules as applied to the selected SEC.

[0031] In some embodiments of the third and fourth aspects, the predetermined list of ranked rules may be chosen to balance diversity of possible MECs with a signal-to-noise ratio of each MEC.

[0032] In some embodiments of the third and fourth aspects, each rule in the predetermined list of ranked rules may specify electrode array offsets of the positions of the recording and reference electrodes of the corresponding MEC relative to a location of the associated SEC on the electrode array.

[0033] In some embodiments of the third and fourth aspects, the processor may be further configured to select the one MEC of the N selected MECs.

[0034] In some embodiments of the third and fourth aspects, the control unit may be further configured to repeatedly: control the pulse generator to deliver a neural stimulus via the selected SEC according to a stimulus parameter; control the measurement circuitry to capture a signal window from a signal sensed on the neural pathway by the selected MEC subsequent to the delivered neural stimulus; measure a characteristic of an evoked neural response in the captured signal window; determine a feedback variable from the measured characteristic of the evoked neural response; and adjust, using a feedback controller, the stimulus parameter so as to maintain the feedback variable at or near a target value.

[0035] According to a fifth aspect, a system comprising: an implantable device comprising: measurement circuitry configured to capture signal windows from signals sensed on a neural pathway by one or more measurement electrode configurations (MECs), each MEC comprising one or more electrodes of an implanted electrode array; and a control unit configured to control the measurement circuitry to capture signal windows from signals sensed on the neural pathway by an MEC of the one or more MECs; and a processor configured to: select, from a ranked list of possible MECs, the first N possible MECs in rank order that do not contain an unusable electrode, where N is a number greater than one; and instruct the control unit to control the measurement circuitry to capture signal windows from signals sensed on the neural pathway by one MEC of the N selected MECs.

[0036] According to a sixth aspect, a method of programming an implantable device, the method comprising:selecting, from a ranked list of possible measurement electrode configurations (MECs), the first N possible MECs in rank order that do not contain an unusable electrode, where TV is a number greater than one; and instructing the neural stimulation device to capture signal windows from signals sensed on the neural pathway subsequent to respective neural stimuli by one MEC of the N selected MECs.

[0037] In some embodiments of the third and fourth aspects, the processor may be further configured to generate the ranked list of possible MECs according to a corresponding predetermined list of ranked rules as applied to the selected SEC.

[0038] In some embodiments of the third and fourth aspects, the predetermined list of ranked rules may be chosen to balance diversity of possible MECs with a signal-to-noise ratio of each MEC.

[0039] In some embodiments of the third and fourth aspects, the processor may be further configured to select the one MEC of the N selected MECs.

[0040] In some embodiments, broken contacts may be identified by an electrode impedance check carried out by an assisted programming module (APM) at the start of the electrode selection step.

[0041] In some embodiments, the ranked list of possible SECs may specify for each possible SEC unique electrode array column and row positions of stimulus and return electrode(s).

[0042] In some embodiments, ranked lists of possible SECs for other locations on the electrode array may be symmetrically reflected versions of the ranked list for one location.

[0043] In some embodiments, the list of ranked rules for generating the ranked list of possible MECs as applied to the selected SEC balance diversity of possible MECs with likelihood of obtaining goodquality ECAPs.

[0044] In some embodiments, the ranked list of possible MECs specifies for each possible MEC unique electrode array row offsets of the positions of the recording and reference electrode of that MEC relative to a location of an associated SEC.

[0045] In some embodiments, the list of ranked rules for generating the ranked list of possible MECs as applied to the selected SEC may specify for each possible MEC unique electrode array row offsets of the positions of the recording and reference electrode of that MEC relative to a location of an associated SEC

[0046] In some embodiments, the array comprises two percutaneous leads.

[0047] In some embodiments, the array is a paddle array.

[0048] The present technology has been developed primarily for use in or with neuromodulation of the spinal cord and will be described hereinafter mostly with reference to this application. However, it will be appreciated that the present technology is not limited to this particular field of use, and may be applied in other neuromodulation contexts, including but not limited to sacral nerve stimulation,pudendal nerve stimulation, deep brain stimulation, stimulation of other parts of the peripheral and central nervous system. It will further be appreciated that the present technology may be applied for treatment of conditions other than chronic pain, including but not limited to movement disorders, Crohn’s disease, rheumatoid arthritis, diabetes, Reynaud’s phenomenon, pelvic floor disorders, chronic inflammatory conditions, migraine, stroke, or depression.BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Notwithstanding any other implementations which may fall within the scope of the present invention, one or more implementations of the present invention will now be described, by way of example only, with reference to the accompanying drawings, in which:

[0050] Fig. 1 schematically illustrates an implanted spinal cord stimulator, according to one implementation of the present technology;

[0051] Fig. 2a is a block diagram of the stimulator of Fig. 1;

[0052] Fig. 2b schematically illustrates the ventral and dorsal surfaces of a paddle array;

[0053] Fig. 3 is a schematic illustrating interaction of the implanted stimulator of Fig. 1 with a bundle of target nerve fibres;

[0054] Fig. 4a illustrates an idealised activation plot for one posture of a patient undergoing neural stimulation;

[0055] Fig. 4b illustrates the variation in the activation plots with changing posture of the patient;

[0056] Fig. 5 is a schematic illustrating elements and inputs of a closed-loop neural stimulation system, according to one implementation of the present technology;

[0057] Fig. 6 illustrates the typical form of an electrically evoked compound action potential (ECAP) of a healthy subject;

[0058] Fig. 7 is a block diagram of a neural stimulation therapy system including the implanted stimulator of Fig. 1 according to one implementation of the present technology;

[0059] Fig. 8 is a flowchart representing an assisted programming workflow implemented by the assisted programming application according to one implementation of the present technology;

[0060] Fig. 9a illustrates the locations of the stimulus and return electrodes in four candidate stimulus electrode configurations according to one implementation of the present technology suitable for an electrode array consisting of a pair of aligned percutaneous leads;

[0061] Fig. 9b illustrates a list of seven possible SECs for the top location on the left lead of the electrode array of Fig. 9a according to one implementation of the first aspect of the present technology;

[0062] Fig. 9c illustrates the locations of the recording and reference electrodes in the list of eight possible MECs associated with the highest-ranked top-left possible SEC in Fig. 9b;

[0063] Fig. 9d illustrates the locations of stimulus and return electrodes in four candidate stimulus electrode configurations for an electrode array consisting of a paddle implanted in an anterograde orientation according to one implementation of the present technology;

[0064] Fig. 9e illustrates the locations of stimulus and return electrodes in four candidate stimulus electrode configurations for an electrode array consisting of a paddle implanted in a retrograde orientation according to one implementation of the present technology;

[0065] Fig. 9f illustrates the locations of the recording and reference electrodes in the list of six possible MECs associated with the highest-ranked top-left possible SEC in the anterograde orientation of Fig. 9d, generated according to one implementation of the present technology;

[0066] Fig. 9g illustrates the locations of the recording and reference electrodes in the list of six possible MECs associated with the highest-ranked top-left possible SEC in the retrograde orientation of Fig. 9e, generated according to one implementation of the present technology;

[0067] Fig. 10 illustrates a screen of the user interface display during a patient-controlled stimulus ramp stage of the workflow of Fig. 8 according to one implementation of the present technology;

[0068] Fig. 1 la is a flowchart illustrating a data collection and analysis method carried out by the APM and the device during the patient-controlled stimulus ramp stage of the workflow of Fig. 8 according to one implementation of the present technology;

[0069] Fig. 1 lb is a flowchart illustrating a data collection and analysis method carried out by the APM and the device during the patient-controlled stimulus ramp stage of the workflow of Fig. 8 according to an alternative implementation of the present technology;

[0070] Fig. 12 illustrates a screen of the user interface display during a coverage survey stage of the workflow of Fig. 8 according to one implementation of the present technology;

[0071] Fig. 13 shows a fitted logistic growth curve model to a set of value pairs of stimulus current amplitude and ECAP amplitude, alongside a piecewise linear model fit to the same value pairs;

[0072] Fig. 14 illustrates a threshold ramp according to one implementation of the present technology;

[0073] Fig. 15 illustrates a screen of the user interface display during a coverage selection stage of the workflow of Fig. 8 according to one implementation of the present technology;

[0074] Fig. 16 is a flowchart illustrating a method of electrode selection according to one aspect of the present technology;

[0075] Fig. 17 illustrates a screen of the user interface display during a measurement optimisation stage of the workflow of Fig. 8 according to one implementation of the present technology;

[0076] Fig. 18 contains a flowchart illustrating a data collection and analysis method carried out by the APM and the device during the measurement optimisation stage of the workflow of Fig. 8 according to one implementation of the present technology; and

[0077] Figs. 19a to 19f illustrate ramps and down-ramps of stimulus intensity according to one implementation of the present technology.DETAILED DESCRIPTION OF THE PRESENT TECHNOLOGY

[0078] Fig. 1 schematically illustrates an implanted spinal cord stimulator 100 in a patient 108, according to one implementation of the present technology. Stimulator 100 comprises an electronics module 110 housed within a conductive case, implanted at a suitable location. In one implementation, stimulator 100 is implanted in the patient’s lower abdominal area or posterior superior gluteal region. In other implementations, the electronics module 110 is implanted in other locations, such as in a flank or sub-clavicularly. The electronics module 110 is configured to electrically connect to an electrode assembly, typically comprising an electrode array 150 implanted within the epidural space and connected to the module 110 by a suitable lead. The electrode array 150 may comprise one or more electrodes such as electrode pads on a paddle, circular (e.g., ring) electrodes surrounding the body of a percutaneous lead, conformable electrodes, cuff electrodes, segmented electrodes, or any other type of electrodes capable of forming unipolar, bipolar or multipolar electrode configurations for stimulation and measurement. The electrodes may pierce or affix directly to the tissue itself.

[0079] Numerous aspects of the operation of implanted stimulator 100 may be programmable by an external computing device 192, which may be operable by a user such as a clinician or the patient 108. Moreover, implanted stimulator 100 serves a data gathering role, with gathered data being communicated to external device 192 via a transcutaneous communications channel 190. Communications channel 190 may be active on a substantially continuous basis, at periodic intervals, at non-periodic intervals, or upon request from the external device 192. External device 192 may thus provide a clinical interface configured to program the implanted stimulator 100 and recover data stored on the implanted stimulator 100. This configuration is achieved by program instructions collectively referred to as the Clinical Programming Application (CPA) and stored in an instruction memory of the clinical interface.

[0080] Fig. 2a is a block diagram of the stimulator 100. Electronics module 110 contains a battery 112 and a telemetry module 114. In implementations of the present technology, any suitable type of transcutaneous communications channel 190, such as infrared (IR), radiofrequency (RF), capacitive or inductive transfer, may be used by telemetry module 114 to transfer power or data to and from the electronics module 110 via communications channel 190. Module controller 116 has an associated memory 118 storing one or more of clinical data 120, clinical settings 121, control programs 122, andthe like. Controller 116 controls a pulse generator 124 to generate stimuli, such as in the form of pulses, in accordance with the clinical settings 121 and control programs 122. Electrode selection module 126 switches the generated pulses to the selected electrode(s) of electrode array 150, for delivery of the pulses to the tissue surrounding the selected electrode(s). Measurement circuitry 128, which may comprise an amplifier and an analog-to-digital converter (ADC), is configured to process signals comprising neural responses sensed by measurement electrode(s) of the electrode array 150 as selected by electrode selection module 126.

[0081] As mentioned above, the electrode array 150 may comprise one or more circular electrodes surrounding the bodies of one or more percutaneous leads. Alternatively, the electrode array 150 may comprise a two-dimensional array of electrode pads arranged on a paddle. Fig. 2b schematically illustrates the ventral surface 203 and dorsal surface 204 of a paddle array 200, according to one implementation of the present technology.

[0082] The paddle array 200 comprises a paddle body 202 and an array of electrodes attached to, affixed to, or integrated with, the paddle body 202. The paddle array 200 is integrated with a paddle lead 220 which is configured to electrically couple each of the plurality of electrodes of the paddle array 200 to an electronics module, such as electronics module 110. Line 210 indicates the rostro- caudal dimension of the paddle array 200, and line 240 indicates the medial-lateral dimension of the paddle array 200.

[0083] The ventral surface 203 of the paddle body 202 comprises 22 ventral-side electrodes arranged in three evenly-spaced columns along the rostro-caudal direction. The middle column 232 comprises eight electrodes. The left-hand column 230 and the right-hand column 234 each comprise 7 electrodes.

[0084] In the implementation illustrated in Fig. 2b, each of the 22 electrodes on the ventral surface 203 of the paddle body 202 may be configured to be stimulation electrodes or measurement electrodes. As noted previously, an electrode selection module, such as electrode selection module 126, electrically couples electrodes of the paddle array 200 to either the pulse generator 124 or measurement circuitry 128. Alternatively, one or more electrodes of the paddle array 200 may be not connected to either the pulse generator or the measurement circuitry, and may therefore be passive, or unused.

[0085] In the implementation illustrated in Fig. 2b, the three column-wise arrangements of the electrodes positioned on the ventral surface 203 of the paddle array 200, are mutually substantially aligned along the rostro-caudal length of the paddle array 200. That is, the electrodes in each column 230, 232, 234 are positioned in an aligned position, along the rostro-caudal dimension 210, relative to the electrodes of the adjacent column or columns. For example, electrodes 214, 216 and 218 arepositioned substantially in alignment with regard to the rostro-caudal dimension 210 of the paddle array 200, such that these electrodes are positioned at substantially the same distance from the paddle lead 220. The “odd” electrode 219 in the central column 232 is not aligned, in the rostro-caudal dimension, with any other electrode on the paddle body 202. The odd electrode 219 is the closest electrode to the paddle lead 220.

[0086] The dorsal surface 204 of the paddle body 202 comprises two dorsal-side electrodes 206 and 208. In other implementations, the dorsal surface 204 of the paddle body 202 may comprise other numbers of dorsal-side electrodes. Each electrode on the dorsal surface 204 of the paddle body 202 may be configured to be a stimulation electrode, a measurement electrode, or a passive (unused) electrode. Accordingly, the paddle array 200 may be configured to have one or more dorsal-side electrodes acting as measurement electrodes or one or more dorsal-side electrodes acting as stimulation electrodes.

[0087] Fig. 3 is a schematic illustrating interaction of the implanted stimulator 100 with a bundle of target nerve fibres 180 in the patient 108. In the implementation illustrated in Fig. 3 the target fibres 180 may be located in the spinal cord, however in alternative implementations the stimulator 100 may be positioned adjacent any target neural tissue including a peripheral nerve, visceral nerve, sacral nerve, parasympathetic nerve, or a brain structure. Electrode selection module 126 selects a stimulus electrode 2 of electrode array 150 through which to deliver a pulse from the pulse generator 124 to surrounding neural tissue including target fibres 180. A pulse may comprise one or more phases, e.g. a monophasic pulse comprises one phase, and a biphasic stimulus pulse 160 comprises two phases. Electrode selection module 126 also selects a return electrode 4 of the electrode array 150 for stimulus current return in each phase, to maintain a zero net charge transfer. An electrode may act as both a stimulus electrode and a return electrode over a complete multiphasic stimulus pulse. The use of two electrodes in this manner for delivering and returning current in each stimulus phase is referred to as bipolar stimulation. Alternative implementations may apply other forms of bipolar stimulation, or may use a greater number of stimulus or return electrodes, e.g. three electrodes for tripolar stimulation. By contrast, in monopolar stimulation, current is returned through the conductive case of the stimulator 100, which may therefore be configured and function as an electrode though it is not physically part of the electrode array 150. The set of stimulus electrodes and return electrodes (collectively referred to as stimulation electrodes) is referred to as the stimulus electrode configuration (SEC). Electrode selection module 126 is illustrated as connecting to a ground 130 of the pulse generator 124 to enable stimulus current return via the return electrode 4. However, other connections for charge recovery may be used in other implementations.

[0088] Delivery of an appropriate stimulus via electrodes 2 and 4 to the target fibres 180 evokes a neural response 170 comprising an evoked compound action potential (ECAP) which will propagate along the target fibres 180 as illustrated at a rate known as the conduction velocity. The ECAP may be evoked for therapeutic purposes, which in the case of a spinal cord stimulator for chronic pain may be associated with paresthesia at a desired location. To this end, the electrodes 2 and 4 are used to deliver stimuli periodically at any therapeutically suitable stimulus frequency, for example 30 Hz, although other frequencies may be used including frequencies as high as the kHz range. In alternative implementations, stimuli may be delivered in a non-periodic manner such as in bursts, or sporadically, as appropriate for the patient 108. To program the stimulator 100 to the patient 108, a clinician may cause the stimulator 100 to deliver stimuli of various configurations which seek to produce a sensation that may be experienced by the patient as paresthesia. When a stimulus electrode configuration (SEC) is found which evokes paresthesia in a location and of a size which is congruent with the area of the patient’s body affected by pain, the clinician nominates that configuration for ongoing use. The therapy parameters may be loaded into the memory 118 of the electronics module 110 as the clinical settings 121.

[0089] Fig. 6 illustrates the typical form of an ECAP 600 of a healthy subject, as sensed by a single measurement electrode referenced to the system ground 130 or referenced to an indifferent electrode. Such configurations are referred to as single-ended ECAP measurement. The shape and duration of the ECAP 600 shown in Fig. 6 is predictable because it is a result of the ion currents produced by the ensemble of fibres depolarising and generating action potentials (APs) in response to stimulation. The evoked action potentials (EAPs) generated synchronously among a large number of fibres sum to form the ECAP 600. The ECAP 600 generated from the synchronous depolarisation of a group of similar fibres comprises a positive peak Pl, then a negative peak Nl, followed by a second positive peak P2. This shape is caused by the region of activation passing the measurement electrode as the action potentials propagate along the individual fibres.

[0090] The ECAP may be recorded differentially using two measurement electrodes, as illustrated in Fig. 3. Depending on the polarity of recording, a differential ECAP may take an inverse form to that shown in Fig. 6, i.e. a form having two negative peaks Nl and N2, and one positive peak Pl . Alternatively, depending on the distance between the two measurement electrodes, a differential ECAP may resemble the time derivative of the ECAP 600, or more generally the difference between the ECAP 600 and a time-delayed copy thereof.

[0091] The ECAP 600 may be characterised by any suitable character! stic(s) of which some are indicated in Fig. 6. The amplitude of the positive peak Pl is Ap\ and occurs at time Tp . The amplitude of the positive peak P2 is Api and occurs at time Tpi. The amplitude of the negative peak Pl is An\and occurs at time T . The peak-to-peak amplitude is Ap\ + Am. A recorded ECAP will typically have a maximum peak-to-peak amplitude in the range of microvolts and a duration of 2 to 3 ms.

[0092] The stimulator 100 is further configured to measure the intensity of ECAPs 170 propagating along target fibres 180, whether such ECAPs are evoked by the stimulus from electrodes 2 and 4, or otherwise evoked. To this end, any electrodes of the array 150 may be selected by the electrode selection module 126 to serve as measurement electrode 6 and measurement reference electrode 8, whereby the electrode selection module 126 selectively connects the chosen electrodes to the inputs of the measurement circuitry 128. Thus, signals sensed by the measurement electrodes 6 and 8 subsequent to the respective stimuli are passed to the measurement circuitry 128, which may comprise a differential amplifier and an analog-to-digital converter (ADC), as illustrated in Fig. 3. The measurement circuitry 128 for example may operate in accordance with the teachings of the above- mentioned International Patent Application Publication No. WO 2012 / 155183.

[0093] Signals sensed by the measurement electrodes 6, 8 and processed by measurement circuitry 128 are further processed by an ECAP detector implemented within controller 116, configured by control programs 122, to obtain information regarding the effect of the applied stimulus upon the target fibres 180. In some implementations, the sensed signals are processed by the ECAP detector in a manner which measures and stores one or more characteristics from each evoked neural response or group of evoked neural responses contained in the sensed signal. In one such implementation, the characteristics comprise a peak-to-peak ECAP amplitude in microvolts (pV). For example, the sensed signals may be processed by the ECAP detector to determine the peak-to-peak ECAP amplitude in accordance with the teachings of International Patent Publication No. WO 2015 / 074121 by the present applicant, the contents of which are incorporated herein by reference. Alternative implementations of the ECAP detector may measure and store an alternative characteristic from the neural response, or may measure and store two or more characteristics from the neural response.

[0094] Stimulator 100 applies stimuli over a potentially long period such as days, weeks, or months and during this time may store characteristics of neural responses, clinical settings, target response intensity, and other operational parameters in memory 118. To effect suitable SCS therapy, stimulator 100 may deliver tens, hundreds or even thousands of stimuli per second, for many hours each day. Each neural response or group of responses generates one or more characteristics such as a measure of the intensity of the neural response. Stimulator 100 thus may produce such data at a rate of tens or hundreds of Hz, or even kHz, and over the course of hours or days this process results in large amounts of clinical data 120 which may be stored in the memory 118. Memory 118 is however necessarily of limited capacity and care is thus required to select compact data forms for storage into the memory 118, to ensure that the memory 118 is not exhausted before such time that the data isexpected to be retrieved wirelessly by external device 192, which may occur only once or twice a day, or less.

[0095] An activation plot, or growth curve, is an approximation to the relationship between stimulus intensity (e.g. an amplitude of the current pulse 160) and intensity of neural response 170 resulting from the stimulus (e.g. an ECAP peak-to-peak amplitude). Fig. 4a illustrates an idealised activation plot 402 for one posture of the patient 108. The activation plot 402 shows a linearly increasing ECAP amplitude for stimulus intensity values above a threshold 404 referred to as the ECAP threshold. The ECAP threshold exists because of the binary nature of fibre recruitment; if the field strength is too low, no fibres will be recruited. However, once the field strength exceeds a threshold, fibres begin to be recruited, and their individual evoked action potentials are independent of the strength of the field. The ECAP threshold 404 therefore reflects the field strength at which significant numbers of fibres begin to be recruited, and the increase in response intensity with stimulus intensity above the ECAP threshold reflects increasing numbers of fibres being recruited. Below the ECAP threshold 404, the ECAP amplitude may be taken to be zero. Above the ECAP threshold 404, the activation plot 402 has a positive, approximately constant slope indicating a linear relationship between stimulus intensity and the ECAP amplitude. Such a relationship may be modelled in piecewise linear form as:where 5 is the stimulus intensity, d is the ECAP amplitude, Zis the ECAP threshold and S is the slope of the activation plot (referred to herein as the patient sensitivity) above the ECAP threshold T. The sensitivity S and the ECAP threshold T are the key parameters of the activation plot 402. The ECAP threshold is an example of a physiological threshold.

[0096] Fig. 4a also illustrates a discomfort threshold 408, which is a stimulus intensity above which the patient 108 experiences uncomfortable or painful stimulation. Fig. 4 also illustrates a perception threshold 410. The perception threshold 410 is a value of stimulus intensity that corresponds to an ECAP amplitude that is barely perceptible by the patient. There are a number of factors which can influence the position of the perception threshold 410, including the posture of the patient. Perception threshold 410 may be a stimulus intensity that is greater than the ECAP threshold 404, as illustrated in Fig. 4a, if patient 108 does not perceive low levels of neural activation. Conversely, the perception threshold 410 may be a stimulus intensity that is less than the ECAP threshold 404, if the patient has a high perception sensitivity to lower levels of neural activation than can be detected in an ECAP, or if the signal -to-noise ratio of the ECAP is low. The discomfort threshold 408 and perception threshold 410 are examples of perceptual markers for the patient 108.

[0097] For effective and comfortable operation of an implantable neuromodulation device such as the stimulator 100, it is desirable to maintain stimulus intensity within a therapeutic range. A stimulus intensity within a therapeutic range 412 is above the ECAP threshold 404 and below the discomfort threshold 408. In principle, it would be straightforward to measure these limits and ensure that stimulus intensity, which may be closely controlled, always falls within the therapeutic range 412. However, the activation plot, and therefore the therapeutic range 412, varies with the posture of the patient 108.

[0098] Fig. 4b illustrates the variation in the activation plots with changing posture of the patient. A change in posture of the patient may cause a change in impedance of the electrode-tissue interface or a change in the distance between electrodes and the spinal cord. While the activation plots for only three postures, 502, 504 and 506, are shown in Fig. 4b, the activation plot for any given posture can lie between or outside the activation plots shown, on a continuously varying basis depending on posture. Consequently, as the patient’s posture changes, the ECAP threshold changes, as indicated by the ECAP thresholds 508, 510, and 512 for the respective activation plots 502, 504, and 506. Additionally, as the patient’s posture changes, the patient sensitivity also changes, as indicated by the varying slopes of activation plots 502, 504, and 506. In general, as the distance between the stimulus electrodes and the spinal cord increases, the ECAP threshold increases and the sensitivity decreases. The activation plots 502, 504, and 506 therefore correspond to increasing distance between stimulus electrodes and spinal cord, and decreasing patient sensitivity.

[0099] To keep the applied stimulus intensity within the therapeutic range as patient posture varies, in some implementations an implantable neuromodulation device such as the stimulator 100 may adjust the applied stimulus intensity based on a feedback variable that is determined from one or more measured ECAP characteristics. In one implementation, the device may adjust the stimulus intensity to maintain the measured ECAP amplitude at or near a target response intensity. For example, the device may calculate an error between a target ECAP amplitude and a measured ECAP amplitude, and adjust the applied stimulus intensity to bring the measured ECAP amplitude closer to the target ECAP amplitude, such as by adding the scaled error to the current stimulus intensity. A neuromodulation device that operates by adjusting the applied stimulus intensity to maintain a feedback variable at or near a target value is said to be operating in closed-loop mode and will also be referred to as a closed-loop neural stimulus (CLNS) device. By adjusting the applied stimulus intensity to maintain the measured ECAP amplitude at or near an appropriate target response intensity, such as an ECAP target 520 illustrated in Fig. 4b, a CLNS device will generally keep the stimulus intensity within the therapeutic range as patient posture varies.

[0100] A CLNS device comprises a pulse generator that takes a stimulus intensity value and converts it into neural stimuli comprising a sequence of electrical pulses according to a predefined stimulation pattern. The stimulation pattern is parametrised by multiple stimulus parameters including stimulus amplitude, pulse width, number of phases, order of phases, number of stimulus electrode poles (two for bipolar, three for tripolar etc.), and stimulus rate or frequency. At least one of the stimulus parameters, for example the stimulus amplitude, is controlled by the pulse generator to implement the received stimulus intensity value. For example, all stimulus parameters may be held constant except stimulus amplitude which is determined in proportion to the received stimulus intensity value. Alternatively, all stimulus parameters may be held constant except pulse width which is varied in proportion to the received stimulus intensity value.

[0101] In an example CLNS system, the user sets a target response intensity, and the CLNS device performs proportional -integral-differential (PID) control. In some implementations, the differential and proportional contributions are disregarded and the CLNS device uses a first order integrating feedback loop. The pulse generator generates a stimulus in accordance with a stimulus intensity parameter, which evokes a neural response in the patient. The intensity of an evoked neural response (e.g. an ECAP) is measured by the CLNS device and compared to the target response intensity.

[0102] The measured neural response intensity, and its deviation from the target response intensity, is used by the feedback loop to determine possible adjustments to the stimulus intensity parameter to maintain the neural response at or near the target response intensity. If the target response intensity is properly chosen, the patient receives consistently comfortable and therapeutic stimulation through posture changes and other perturbations to the stimulus / response behaviour.

[0103] Fig. 5 is a schematic illustrating elements and inputs of a closed-loop neural stimulation system (CLNS) 300, according to one implementation of the present technology. The system 300 comprises a pulse generator 312 which converts a stimulus intensity parameter s, in concert with a set of predefined stimulus parameters, into neural stimuli comprising a sequence of electrical pulses delivered via the stimulation electrodes (not shown in Fig. 5). According to one implementation, the predefined stimulus parameters comprise the number and order of phases, the number of stimulus electrode poles, the pulse width, and the stimulus rate or frequency, and the pulse generator 312 determines the stimulus amplitude in proportion to the stimulus intensity parameter 5.

[0104] The generated stimulus crosses from the electrodes to the spinal cord, which is represented in Fig. 5 by the dashed box 308. The box 309 represents the evocation of a neural response y by the stimulus as described above. The box 311 represents the evocation of an artefact signal a, which is dependent on stimulus intensity and other stimulus parameters, as well as the electrical environment of the measurement electrode. Various sources of measurement noise n may add to the evokedresponse at the summing element 313 before the evoked response is measured, including: electrical noise from external sources such as 50 Hz mains power; electrical disturbances produced by the body such as neural responses evoked not by the device but by other causes such as peripheral sensory input; EEG; EMG; and electrical noise from measurement circuitry 318.

[0105] The neural recruitment arising from the stimulus is affected by mechanical changes, including posture changes, walking, breathing, heartbeat and so on. Mechanical changes may cause impedance changes, or changes in the location and orientation of the nerve fibres relative to the electrode array(s). As described above, the intensity of the evoked response provides a measure of the recruitment of the fibres being stimulated. In general, the more intense the stimulus, the more recruitment and the more intense the evoked response. An evoked response typically has a maximum amplitude in the range of microvolts, whereas the voltage resulting from the stimulus applied to evoke the response is typically several volts.

[0106] Measurement circuitry 318, which may be identified with measurement circuitry 128, amplifies the sensed signal r (potentially including evoked neural response, artefact, and measurement noise) and samples the amplified sensed signal r to capture a “signal window” 319 comprising a predetermined number of samples of the amplified sensed signal r. The ECAP detector 320 processes the signal window 319 and outputs a measured neural response intensity d. In one implementation, the neural response intensity comprises an ECAP amplitude. The measured response intensity d (an example of a feedback variable) is input into the feedback controller 310. The feedback controller 310 comprises a comparator 324 that compares the measured response intensity dto a target ECAP amplitude as set by the target ECAP controller 304 and provides an indication of the difference between the measured response intensity d and the target ECAP amplitude. This difference is the error value, e.

[0107] The feedback controller 310 calculates an adjusted stimulus intensity parameter, s, with the aim of maintaining a measured response intensity d equal to the target ECAP amplitude. Accordingly, the feedback controller 310 adjusts the stimulus intensity parameter 5 to minimise the error value, e. In one implementation, the controller 310 utilises a first order integrating function, using a gain element 336 and an integrator 338, in order to provide suitable adjustment to the stimulus intensity parameter 5. According to such an implementation, the current stimulus intensity parameter .s may be computed by the feedback controller 310 as s = f Kedt (2) where K is the gain of the gain element 336 (the controller gain). This relation may also be represented as8s = Ke (3)where 55 is an adjustment to the current stimulus intensity parameter .s.

[0108] A target ECAP amplitude is input to the comparator 324 via the target ECAP controller 304. In one implementation, the target ECAP controller 304 provides an indication of a specific target ECAP amplitude. In another implementation, the target ECAP controller 304 provides an indication to increase or to decrease the present target ECAP amplitude. The target ECAP controller 304 may comprise an input into the neural stimulus device, via which the patient or clinician can input a target ECAP amplitude, or indication thereof. The target ECAP controller 304 may comprise memory in which the target ECAP amplitude is stored, and from which the target ECAP amplitude is provided to the feedback controller 310.

[0109] A clinical settings controller 302 provides therapy parameters to the system, including the gain K for the gain element 336 and the pulse generator 312. In one example, the clinical settings controller 302 may be configured to adjust the gain K of the gain element 336 to adapt the feedback loop to patient sensitivity. The clinical settings controller 302 may comprise an input into the neural stimulus device, via which the patient or clinician can adjust the therapy parameters. The clinical settings controller 302 may comprise memory in which the therapy parameters are stored, and are provided to components of the system 300.

[0110] In some implementations, two clocks (not shown) are used, being a stimulus clock operating at the stimulus frequency (e.g. 60 Hz) and a sample clock for sampling the measured response r (for example, operating at a sampling frequency of 10 kHz). As the ECAP detector 320 is linear, only the stimulus clock affects the dynamics of the CLNS system 300. On the next stimulus clock cycle, the pulse generator 312 generates a stimulus in accordance with the adjusted stimulus intensity 5. Accordingly, there is a delay of one stimulus clock cycle before the stimulus intensity is updated in light of the error value e.

[0111] Fig. 7 is a block diagram of a neural stimulation system 700. The neural stimulation system 700 is centred on 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 connected wirelessly 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 the home environment by allowing control of the functionality of the neuromodulation device 710, including one or more of the following functions: enabling or disabling stimulation; adjustment of stimulus intensity or target response intensity; and selection of a stimulation control program from the control programs stored on the neuromodulation device 710.

[0112] The charger 750 is configured to recharge a rechargeable power source of the neuromodulation device 710. The recharging is illustrated as wireless in Fig. 7 but may be wired in alternative implementations.

[0113] The neuromodulation device 710 is wirelessly connected to a Clinical System Transceiver (CST) 730. The wireless connection may be implemented as the transcutaneous communications channel 190 of Fig. 1. The CST 730 acts as an intermediary between the neuromodulation device 710 and the Clinical Interface (CI) 740, to which the CST 730 is connected. A wired connection is shown in Fig. 7, but in other implementations, the connection between the CST 730 and the CI 740 is wireless.

[0114] The CI 740 may be implemented as the external computing device 192 of Fig. 1. The CI 740 is configured to program the neuromodulation device 710 and recover data stored on the neuromodulation device 710. This configuration is achieved by program instructions collectively referred to as the Clinical Programming Application (CPA) and stored in an instruction memory of the CI 740.

[0115] The CPA makes use of a user interface (UI) of the CI 740. The UI may comprise a device for displaying information to the user (e.g. a display) and a device for receiving input from the user, such as a touchscreen, movable pointing device controlling a cursor (mouse), keyboard, joystick, touchpad, trackball etc. In the example of a touchscreen, the input device may be combined with the display. Alternatively, the UI of the CI 740 the input device(s) may be separate from the display. Assisted Programming System

[0116] As mentioned above, obtaining patient feedback about their sensations is important during programming of closed-loop neural stimulation therapy, but mediation by trained clinical engineers is expensive and time-consuming. It would therefore be advantageous if patients could program their own implantable device themselves, or with some assistance from a clinician. However, interfaces for current programming systems are non-intuitive and generally unsuitable for direct use by patients because of their technical nature. The Assisted Programming System (APS) described herein is a programming system that is as intuitive for non-technical users as possible while avoiding discomfort to the patient.

[0117] In some implementations, the APS comprises two elements: the Assisted Programming Module (APM), which forms part of the CPA, and the Assisted Programming Firmware (APF), which forms part of the control programs 122 executed by the controller 116 of the electronics module 110. The APF is configured to complement the operation of the APM by responding to commands issued by the APM to the electronics module 110, possibly via a CST such as the CST 730 of Fig. 7, to deliver specified stimuli to the target neural tissue, and by returning, via the CST 730, data comprisingmeasurements of neural responses to the delivered stimuli. The data obtained from the electronics module 110 under the control of the APF is analysed by the APM to determine the clinical settings for the neural stimulation therapy to be delivered by the electronics module 110.

[0118] In other implementations according to the present technology, all the processing of the APS according to the present technology is done by the APF. In other words, the data obtained from the patient is not passed to an APM, but is analysed by the controller 116 of the electronics module 110, configured by the APF, to determine the clinical settings 121 for the neural stimulation therapy to be delivered by the electronics module 110.

[0002] In implementations of the APS in which the APM analyses the data from the patient, the APS instructs the electronics module 110 to capture and return signal windows to the CI 740 via the CST 730. In such implementations, the electronics module 110 captures the signal windows using the measurement circuit 128 and bypasses the ECAP detector 320, storing the data representing the raw signal windows temporarily in memory 118 before transmitting the data representing the captured signal windows to the APM for analysis.

[0119] Fig. 8 is a flow chart representing an assisted programming workflow 800 implemented by the APM, 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, for example the patient, who interacts with the APM for the entirety of the workflow. In some implementations, the patient remains in a fixed predetermined posture throughout the workflow. Having direct patient involvement allows for faster feedback because subjective responses to stimulation do not have to be communicated via a clinician. However, the workflow 800 is just one possible implementation of an APM, and it should be noted that there is no formal requirement for any part of the assisted programming system to include direct patient involvement.

[0120] The workflow 800 has several stages: a Patient-Controlled Stimulus Ramp (PC SR) stage 810, an (optional) Coverage Survey stage 815, a Coverage Selection stage 820, and a Measurement Optimisation (MO) stage 830.

[0121] The PCSR stage 810 is configured to deliver stimuli of a gradually increasing intensity and receive subjective input from the patient as to a maximum value of stimulus intensity (“Max” value) for each of one or more candidate stimulus electrode configurations (SECs). The Max value may be identified with the discomfort threshold 408 of Fig. 4a. Meanwhile, the APM is configured to record sensed signals at each of multiple measurement electrode configurations (MECs) for each candidate SEC and analyse the recorded data, as well as the patient’s Max value for each SEC, to calculate an ECAP Threshold for each SEC. The PCSR stage 810 is described in more detail below.

[0122] The Coverage Survey stage 815 is configured to receive input from the patient concerning their sensations in response to stimuli delivered via each candidate SEC at a comfortable stimulus intensity. The comfortable stimulus intensity is predicted for each candidate SEC based on the Max or ECAP Threshold values derived in the PC SR stage 810. Based on the patient input, the comfortable stimulus intensity at each candidate SEC may be adjusted. In addition, if stimulus delivered via any candidate SEC feels uncomfortable to the patient in an area of the body, the candidate SEC itself may be adjusted and the PCSR stage 810 is repeated for the adjusted SEC. The Coverage Survey stage 815 is described in more detail below.

[0123] 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 Survey stage 815. The comfortable stimulus intensity delivered via each candidate SEC during the Coverage Selection stage 820 is based on the comfortable stimulus intensity derived for that SEC in the PCSR stage 810 and possibly adjusted at the Coverage Survey stage 815. The coverage selection stage 820 allows the patient to test different combinations of candidate SECs before selecting which ones to keep for their final program. The Coverage Selection stage 820 is described in more detail below.

[0124] The Measurement Optimisation (MO) stage 830 is configured to deliver stimulus of a gradually increasing intensity from a primary SEC of the selected SECs, and record sensed signal data at each of multiple measurement electrode configurations (MECs) for the primary SEC. The MO stage 830 is then configured to choose the optimal MEC for the primary SEC based on the response data, calculate physiological characteristics of the patient, and choose optimal therapy parameters for the primary SEC / optimal MEC combination. The selected SECs, including the primary SEC, the optimal MEC, and optimal therapy parameters are referred to as the determined program. The Measurement Optimisation stage 830 is described in more detail below.

[0125] In an alternative implementation of the present technology, the APM is provided with the patient’s selected SECs by a means other than the stages 810 to 820. In such an implementation, the APM implements an “ECAP-only” workflow comprising only the measurement optimisation stage 830.

[0126] Following the workflow 800, if successful, the APS may load the determined program onto the electronics module 110 to govern subsequent neural stimulation therapy. In one implementation, the program comprises clinical settings 121, also referred to as therapy parameters, that are input to the electronics module 110 by, or stored in, the clinical settings controller 302. The patient may subsequently control the electronics module 110 to deliver the therapy according to the determined program using a remote controller for the electronics module 110 such as the remote controller 720 as described above. The determined program may also, or alternatively, be loaded into the instructionmemory of a clinical interface for validation and modification by the CPA. Validation and modification of the determined program may also be carried out by the APS itself. If unsuccessful, the electronics module 110 may be manually programmed.

[0127] In the workflow 800, the APM may use predetermined values of certain therapy parameters.In one implementation, those parameters and values are:• Stimulus frequency: 40 Hz• Pulse width: 240 microseconds• Inter-phase gap: 200 microseconds• Pulse shape: triphasic, with anodic phase first• Signal window length: 60 samples• Sampling frequency: 16 kHz• Inter-stimulus interval: 5 msElectrode selection

[0128] In one implementation of the workflow 800, four candidate stimulus electrode configurations (SECs) are selected in an electrode selection step carried out by the APM before the PCSR stage 810. Each SEC is tripolar, comprising a stimulus electrode that acts primarily as a cathode, sinking stimulus current, with the two return electrodes positioned longitudinally on either side of the stimulus electrode acting primarily as anodes, sourcing return currents. Tripolar stimulus electrode configurations are described in more detail in International Patent Publication no. WO 2017 / 219096 by the present applicant, the entire contents of which are herein incorporated by reference.

[0129] In some implementations, the APM assumes, or is informed by a user or a different module of the CPA, that the electrode array 150 consists of two percutaneous leads implanted approximately symmetrically to left and right (as viewed from behind the patient) of, and parallel to, the patient’s midline, rostro-caudally aligned, as illustrated in Fig. 1 for one lead. In one implementation, each percutaneous lead comprises twelve contacts (electrodes), numbered such that a contact index of zero is the topmost (rostral) contact of a lead and contact index 11 is the bottom-most (caudal) contact of a lead.

[0130] Fig. 9a illustrates the locations of stimulus and return electrodes in four candidate SECs for an electrode array 900 consisting of a pair of aligned percutaneous leads, according to one implementation of the present technology. The left lead 905 and right lead 910 each comprise 12 contacts. The four candidate SECs as illustrated in Fig. 9a are: top left SEC 912 (centred on contact index 1 of left lead 905), top right SEC 914 (centred on contact index 1 of right lead 910), bottom left SEC 916 (centred on contact index 10 of left lead 905) and bottom right SEC 918 (centred on contactindex 10 of right lead 910). Each SEC comprises a central stimulus electrode flanked in the rostro- caudal direction by two return electrodes. In other implementations with a different number of contacts in each lead, the bottom left and bottom right SECs are centred on the second-most caudal contact on the respective leads. In other implementations with bipolar SECs, only a single return electrode flanks the stimulus electrode in each SEC.

[0131] It is possible that an electrode in an electrode array may be disconnected or “broken” due to manufacturing error or stress during implantation. To identify broken contacts, the APS carries out an electrode impedance check on each electrode in the electrode array 150 at the start of the workflow 1050 before the PCSR stage 1060. In some implementations, the electrode impedance check uses the measurement circuitry 128 to measure a voltage difference between the electrode under test and all the other electrodes shorted together during a stimulus pulse of a predetermined stimulus current amplitude. The electrode impedance is determined as the ratio of the measured electrode voltage difference to the stimulus current amplitude. The impedance check may be performed by configuring the electrode under test as a cathode and all remaining electrodes in parallel as anodes. This ensures the bulk of the measured impedance measured is due to the electrode under test. An electrode is designated as a broken contact if its impedance exceeds a predetermined high-impedance threshold such as 4000 ohms. An electrode is designated as short-circuited to another electrode if its impedance falls below a predetermined low-impedance threshold such as 50 ohms.

[0132] Neither broken contacts nor short-circuited electrodes are usable as either stimulation or measurement electrodes. Additionally, electrodes may be manually designated as unusable via a suitable user interface, for example if their impedance varies significantly between postures. This may occur for example if an electrode is shorted to another electrode in a specific posture but not in another posture. Therefore, according to a first aspect of the present technology, a plurality of possible SECs are defined for each location, rank-ordered in a list of possible SECs at each location (Fig. 9a illustrates the first-ranked possible SEC for each location for the electrode array 900). According to the first aspect of present technology, the APM selects the first possible SEC from the list in rank order that does not contain an unusable electrode (the first “usable” SEC) as the candidate SEC for that location.

[0133] Fig. 9b illustrates a list 920 of seven possible SECs for the top location on the left lead 905 of the electrode array 900 of Fig. 9a according to one implementation of the first aspect of the present technology. The seven possible SECs, labelled 922, 924, 926, 928, 930, 932, and 934, are ranked in order from left to right. For example, if contact number 1 were broken, the first-ranked possible SEC 922, the second-ranked possible SEC 924, and the third-ranked possible SEC 926 would be unusable since they contain contact number 1. Fifth- and seventh- ranked SECs 930 and 934 would also beunusable since they contain contact number 1. Therefore, according to this implementation, the fourth-ranked possible SEC 928 would be selected as the top-left candidate SEC.

[0134] The ranked lists of possible SECs for other locations on the electrode array are symmetrically reflected versions of the list 920 of possible SECs illustrated in Fig. 9b for the top-left location. For example, to obtain the list of possible SECs for the bottom-left location, each possible SEC in the list 920 for the top-left location is simply reflected from top to bottom of the left lead 905. To obtain the list of possible SECs for the top-right location, each possible SEC in the list 920 for the top-left location is replicated on the right lead 910. To obtain the list of possible SECs for the bottom-right location, each possible SEC in the list for the top-right location is reflected from top to bottom of the right lead 910.

[0135] For each candidate SEC, the APM defines N associated candidate measurement electrode configurations (MECs), where Ais a small integer that is greater than one. A measurement electrode configuration comprises two electrodes for differential ECAP recording, as illustrated in Fig. 3. The measurement electrode connected to the positive terminal of the measurement circuitry 318 is referred to as the recording electrode, while the measurement electrode connected to the negative terminal of the measurement circuitry 318 is referred to as the reference electrode. A candidate MEC associated with an SEC may be used to sense signals subsequent to stimuli delivered via that SEC. As with the SECs, the APM selects N candidate MECs from a ranked list of possible MECs on the basis that they do not contain any unusable electrodes. However, since the possible MECs are defined relative to their associated SEC, which is not predetermined but depends on the distribution of unusable electrodes, according to a second aspect of the present technology, the possible MECs are generated according to a list of respective predetermined rules, based on the location of the associated SEC on the electrode array 150. The rules are predetermined to balance diversity of possible MECs, that is, their lack of shared electrodes, with likelihood of obtaining good-quality ECAPs, quantified, for example, by SNR. The SNR of an MEC may be estimated from a computational model of ECAP propagation from the stimulus site to each of the two electrodes of the MEC. The model may assume a fixed level of measurement noise and a fixed delay from the stimulus pulse to the start of the captured signal window. In one implementation of the second aspect suitable for an electrode array 150 that consists of two aligned percutaneous leads, such as the array 900 of Fig. 9a, eight rules are defined, as listed in Table 1 in rank order:Table 1 : Rules to generate possible MECs in a two-percutaneous-lead electrode array according to one implementation of the second aspect.

[0136] The first value (second column) of “RowDelta” in each rule (row) in Table 1 indicates the number of electrodes between the stimulus electrode of the associated SEC and the recording electrode. The second value (third column) of “RowDelta” in each rule indicates the number of electrodes between the stimulus electrode of the SEC and the reference electrode. Note that the position of the return electrodes in the SEC does not affect the associated possible MECs, in this implementation. Note also that the possible MECs are always on the same lead as the associated SEC, in this implementation. To generate the possible MECs for SECs in the top-left and top-right locations, the electrodes are counted in the caudal direction. To generate the possible MECs for SECs in the bottom-left and bottom-right locations, the electrodes are counted in the rostral direction.

[0137] Fig. 9c illustrates the locations of the recording and reference electrodes in the list 940 of eight possible MECs associated with the highest-ranked top-left possible SEC, generated according to the implementation of the present technology represented by Table 1. The electrodes labelled S and R are the stimulus and return electrodes respectively of the highest-ranked top-left possible SEC 922. The electrodes labelled C and F are the recording and reference electrodes respectively of each possible MEC. The eight possible MECs associated with the SEC 922 are ranked in order from left to right. For example, the first-ranked possible MEC 942 associated with the SEC 922 has electrode 5 as the recording electrode (a RowDelta of 4 from the stimulus electrode 1) and electrode 7 as the reference electrode (a RowDelta of 6 from the stimulus electrode 1), in accordance with the first rule in Table 1. The other seven possible MECs for the SEC 922, from left to right, are generated according to the other seven rules in Table 1.

[0138] According to a second aspect of the present technology, the APM selects the first N possible MECs associated with a candidate SEC from a ranked list generated according to the rules, e.g. therules in Table 1, that do not contain any unusable electrodes (the first N usable MECs) as the N candidate MECs associated with the candidate SEC. For example, if N is 6 and none of the contacts 5 to 10 are unusable, the first six possible MECs in Fig. 9c, counting from left to right, are the six candidate MECs for the top-left candidate SEC 922.

[0139] In other implementations, the APM assumes, or is informed of by a user or a different module of the CPA, other configurations for the electrode array 150. One such other configuration is a paddle array, such as the paddle array 200 illustrated in Fig. 2b. Fig. 9d illustrates four candidate SECs suitable for the paddle array 200 according to one such implementation. The paddle array 200 is illustrated in Fig. 9d as having been implanted in an “anterograde” orientation whereby the odd electrode 219 in the central column 232 is at the caudal end of the paddle array 200. The APM assumes, or is informed by a user or a different module of the CPA, that the paddle array 200 has been implanted in this anterograde orientation. The candidate SEC 950 is the top-left SEC and comprises a tripole consisting of a central stimulus electrode, labelled as S, flanked by two return electrodes, labelled as R, occupying the three most rostral electrodes in the left column 230. The candidate SEC 952 is the bottom-left SEC and comprises a tripole consisting of a central stimulus electrode, labelled as S, flanked by two return electrodes, labelled as R, occupying the three most caudal electrodes in the left column 230. The candidate SEC 956 is the bottom-right SEC and comprises a tripole consisting of a central stimulus electrode, labelled as S, flanked by two return electrodes, labelled as R, occupying the three most caudal electrodes in the right column 234. The candidate SEC 954 is the top-right SEC and comprises a tripole consisting of a central stimulus electrode, labelled as S, flanked by two return electrodes, labelled as R, occupying the three most rostral electrodes in the right column 234. The dorsal-side electrodes 206 and 208 are not configured as stimulation electrodes in any candidate SEC in such an implementation, nor are any electrodes in the central column 232, in this configuration.

[0140] Fig. 9e illustrates four candidate SECs suitable for the paddle array 200 according to one such implementation. The paddle array 200 is illustrated in Fig. 9e as having been implanted in a “retrograde” orientation whereby the odd electrode 219 in the central column 232 is at the rostral end of the paddle array 200. The APM assumes, or is informed by a user or a different module of the CPA, that the paddle array 200 has been implanted in the retrograde orientation. The candidate SEC 958 is the top-left SEC and comprises a tripole consisting of a central stimulus electrode, labelled as S, flanked by two return electrodes, labelled as R, occupying the three most rostral electrodes in the left column 230 (which is a different physical column to the left column 230 in Fig. 9d but is so named relative to the patient’s anatomy). The candidate SEC 960 is the bottom-left SEC and comprises a tripole consisting of a central stimulus electrode, labelled as S, flanked by two returnelectrodes, labelled as R, occupying the three most caudal electrodes in the left column 230. The candidate SEC 964 is the bottom-right SEC and comprises a tripole consisting of a central stimulus electrode, labelled as S, flanked by two return electrodes, labelled as R, occupying the three most caudal electrodes in the right column 234. The candidate SEC 962 is the top-right SEC and comprises a tripole consisting of a central stimulus electrode, labelled as S, flanked by two return electrodes, labelled as R, occupying the three most rostral electrodes in the right column 234. As in the anterograde implementation illustrated in Fig. 9d, the dorsal-side electrodes 206 and 208 are not configured as stimulation electrodes in any candidate SEC in such an implementation, nor are any electrodes in the central column 232.

[0141] Even more so than in an electrode array consisting of two aligned percutaneous leads (referred to herein as a two-perc-lead array), it is possible that an electrode in a paddle array 200 may be disconnected or broken due to manufacturing error or stress during implantation. Therefore, according to the first aspect of the present technology, as for the two-perc-lead array, a plurality of possible SECs are defined for each location on a paddle array, rank-ordered in a list of possible SECs at each location (Fig. 9d illustrates the first-ranked possible SEC for each location for the paddle array 200). According to the first aspect of the present technology, the APM selects the first possible SEC from the list in rank order that does not contain an unusable electrode (the first usable SEC) as the candidate SEC for that location.

[0142] The possible SECs at each location on the paddle array 200 correspond to the possible SECs at each location on the two-perc-lead array. For example, in one implementation, the list of seven possible SECs at the top-left location on the paddle array 200 corresponds to the list 920 of seven possible SECs for the top location on the left lead 905 illustrated in Fig. 9b. Likewise, regardless of the implanted orientation of the paddle array 200 (anterograde or retrograde), the ranked lists of possible SECs for other locations on the paddle array 200 are symmetrically reflected versions of the list of possible SECs in Fig. 9b for the top-left location. For example, to obtain the list of possible SECs for the bottom-left location, each possible SEC in the list for the top-left location is simply reflected from top to bottom of the paddle array 200. To obtain the list of possible SECs for the topright location, each possible SEC in the list for the top-left location is reflected from the left column to the right column. To obtain the list of possible SECs for the bottom-right location, each possible SEC in the list for the top-right location is reflected from top to bottom of the paddle array 200. These three symmetrical reflections of the first-ranked possible SEC in the top-left location are illustrated in Fig. 9d for the anterograde orientation of the paddle array 200 and in Fig. 9e for the retrograde orientation of the paddle array 200, and they apply equally to the other possible SECs in the list for each location in the two-perc-lead array as illustrated in Fig. 9b.

[0143] As with the two-perc-lead electrode array 150, for each candidate SEC on a paddle array, the APM defines N associated candidate measurement electrode configurations (MECs). According to implementations of the second aspect, the APM selects candidate MECs from a ranked list of possible MECs on the basis that they do not contain any unusable electrodes. As with the two-perc- lead electrode array 150, the possible MECs are generated according to a ranked list of respective predetermined rules, based on the location of the associated SEC on the paddle array 200. In one implementation of the second aspect suitable for a paddle array such as the paddle array 200 in Fig. 2b, twenty-four unique rules are defined, as listed in Table 2 in rank order:Table 2: Rules to generate possible MECs in paddle array according to one implementation of the second aspect

[0144] The first pair of “RowDelta, ColDelta” values (columns 2 and 3) in each rule (row) in Table 2 defines the row offset and column offset respectively between the stimulus electrode of the associated SEC and the recording electrode of the possible MEC. The second pair of “RowDelta, ColDelta” values (columns 4 and 5) in each rule defines the row offset and column offset between the stimulus electrode of the SEC and the reference electrode of the possible MEC, unless the second value pair is replaced by “Dorsal. Same” or “Dorsal. Opposite”. “Dorsal. Same” defines the reference electrode as the dorsal-side electrode at the same end of the paddle body as the stimulus electrode, while “Dorsal. Opposite” defines the reference electrode as the dorsal-side electrode at the opposite end of the paddle body to the stimulus electrode.

[0145] Some rules do not generate a possible MEC for a particular SEC as there is no electrode in the location defined by the rule. Conversely, some rules will generate two possible MECs, since row offsets can be counted in either direction (rostral or caudal). The potency of a rule is 0 if the rule does not generate a possible MEC, 1 if the rule generates exactly one possible MEC, or 2 if the rule generates two possible MECs. For a given SEC, the potency of a rule will vary between anterograde and retrograde orientations since the location of the odd electrode relative to the stimulus electrode is different between orientations and therefore a different set of electrodes are available. The result is that the list of possible MECs associated with a given SEC is different depending on the orientation of the paddle array.

[0146] Figs. 9f and 9g illustrate this difference. Fig. 9f illustrates the locations of the recording and reference electrodes in a list of six possible MECs associated with the highest-ranked top-left possible SEC 950 in the anterograde orientation, generated according to the implementation of the present technology whose rules are represented by Table 2. The bold-outlined electrodes such as the electrode 970 are the recording electrodes of each possible MEC. The dash-outlined electrodes such as the electrode 972 are the recording electrodes of each possible MEC. The number(s) within the recording electrodes indicate the rank(s) of the possible MEC(s) within the list of six possible MECs for which that electrode is the recording electrode. The number(s) within the reference electrodes indicate the rank(s) of the possible MEC(s) within the list of six possible MECs for which that electrode is the reference electrode. For example, the electrode 970 is the recording electrode for the first, second, and third possible MECs in the list. The electrode 972 is the reference electrode for the second possible MEC in the list. It may be seen that there are three recording electrodes and three reference electrodes, one of which is the dorsal-side electrode 206 closest to the paddle lead 220, and one of which is the odd electrode 219.

[0147] Fig. 9g illustrates the locations of the recording and reference electrodes in a list of six possible MECs associated with the highest-ranked top-left possible SEC 958 in the retrograde orientation, generated according to the implementation of the present technology whose rules are represented by Table 2. The same labelling and numbering scheme is used to indicate the recording and reference electrodes in the list of six possible MECs as in Fig. 9f. For example, the electrode 980 is the recording electrode for the first, second, and fourth possible MECs in the list. The dorsal-side electrode 208 is the reference electrode for the second, third, fifth, and sixth possible MEC in the list. It may be seen that there are four recording electrodes and three reference electrodes, one of which is the dorsal-side electrode 208 furthest from the paddle lead 220, and none of which is the odd electrode 219.

[0148] As for the two-perc-lead array, according to the second aspect of the present technology, the APM selects the first N possible MECs associated with a candidate SEC from a ranked list generated according to the predetermined rules, e.g. the rules in Table 2, that do not contain any unusable electrodes (the first N usable MECs) as the N candidate MECs associated with the candidate SEC for a paddle array. For example, if N is 6, the 6 possible MECs illustrated in Fig. 9f are the 6 candidate MECs associated with the top-left candidate SEC 950 for the paddle array 200 implanted in an anterograde orientation, assuming there are no unusable electrodes in the paddle array 200.

[0149] Fig. 16 is a flowchart illustrating a method 1600 of electrode selection carried out by the APM and the electronics module 110 before the PCSR stage 810 according to one aspect of the present technology. The method 1600 may be carried out once for each location on the electrode array 150. The method 1600 starts at step 1610, which selects the first possible SEC from the list in rank order that does not contain an unusable electrode (the first usable SEC) as the candidate SEC for the location. Step 1620 then generates a ranked list of possible MECs for the SEC selected at step 1610 according to a predetermined list of rules. Finally, step 1630 selects the first N possible MECs from the list generated in step 1620 that do not contain any unusable electrodes (the first N usable MECs) as the N candidate MECs associated with the candidate SEC.

[0150] The above-described scheme of defining possible electrode configurations according to a predetermined ranked list of rules and then selecting one or more configurations in rank order that do not contain any unusable electrodes may be used to select electrodes for purposes other than neural stimulation and measurement of evoked responses. For example, in one implementation, electrodes for sensing non-evoked signals such as spinal ECGs may be selected from an implanted electrode array according to this scheme. In each such implementation, the rules defining the possible configurations will be chosen to balance considerations of diversity and signal quality.Patient Controlled Stimulus Ramp Stage

[0151] In one implementation of the PCSR stage 810, the APM renders on the UI display of the CI 740 a screen 1000 as illustrated in Fig. 10. The screen 1000 comprises a stimulation control 1010 (illustrated as a virtual button), a set of 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 down” the virtual button. In other implementations of the PCSR stage 810, the stimulation control 1010 or the Next control 1040 are hardware controls, such as buttons, forming part of the UI of the CI 740 yet remaining separate from the display.

[0152] Once the stimulation control 1010 is enabled, the instructions 1020 are configured to instruct the patient to activate the stimulation control 1010, e.g. by holding down the virtual button. When the stimulation control 1010 is activated, the APM instructs the electronics module 110 to deliver stimulation via the first of the candidate SECs at a gradually increasing or “ramping” stimulus intensity. The stimulation control 1010 may be animated to dynamically indicate the stimulus intensity, for example by an animated “pie” display as illustrated in Fig. 10. In this example, a sector 1060 that represents the stimulus intensity as a proportion of a predetermined maximum stimulus intensity is filled in a different manner to the remainder of the stimulation control 1010. While the stimulation control 1010 is activated, the sector 1060 grows wider in proportion to the stimulus intensity until it encompasses the entire stimulation control 1010, at which point the stimulus intensity equals the maximum stimulus intensity. This animation indicates to the patient that something is happening when they activate the control 1010, even if they don’t feel stimulation immediately (due to the stimulus intensity being below the perception threshold). The animation also conveys the rate of increase of stimulus intensity to the patient. The animation also indicates the stimulus intensity to a clinician or other skilled user. The animation also reinforces the instructions 1020. That is, even before the patient is able to feel stimulation, the patient can see the sector 1060 increasing when they activate the control 1010 and decreasing when they de-activate it.

[0153] In some implementations, the first activation of the stimulation control 1010 at a candidate SEC initiates a “pre-ramp” (described below). The pre-ramp is used to estimate the ECAP threshold / thresh for the candidate SEC, as described below. In such implementations, during the pre-ramp or the subsequent stimulus ramp at the same candidate SEC, the animation may indicate when the stimulus intensity has reached the ECAP threshold. The animation may indicate this by, for example, changing colour, or rendering an indicium on the screen 1000 when the stimulus intensity has reached the ECAP threshold.

[0154] The APM continues to ramp the stimulus intensity as long as the patient continues to activate the stimulation control 1010. In one implementation of the stimulus ramp, the increase in intensity is linear with time, with a predetermined ramp rate. The predetermined ramp rate may be set to 400 microamps / sec to minimise the risk of uncomfortable stimulation.

[0155] When the patient de-activates the stimulation control 1010, e.g. by releasing the virtual button, the APM records the stimulus intensity upon release as the Max value for the current SEC. The APM then ramps down the stimulus intensity. In one implementation, the down-ramp of intensity follows a linear profile, with the rate chosen such that the intensity reaches zero after a predetermined interval, for example three seconds. As the stimulus intensity ramps down, the sector 1060 grows smaller in proportion to the stimulus intensity until it disappears when the stimulus intensity reaches zero.

[0156] The instructions 1020 encourage the patient to continue to activate the stimulation control 1010 for as long as they can tolerate the increasing stimulus intensity, ceasing the activation only when the intensity of stimulus begins to feel uncomfortable. This user interface design takes advantage of the human withdrawal reflex, whereby the patient is likely to instinctively release the button upon receiving uncomfortable stimulation. The design of the PC SR stage 810 therefore minimises the training burden placed on the patient in using the APM. If the patient does not cease to activate the stimulation control 1010 before the stimulus intensity reaches the maximum stimulus intensity (e.g. a stimulus current of 36 mA in one implementation), the APM ceases the stimulus ramp and begins a down-ramp. The stimulus intensity at the point of ceasing the stimulus ramp is recorded as the patient’s discomfort threshold (Max) value for that SEC.

[0157] The progress bar 1050 indicates approximate quantitative progress through the workflow 800. In one implementation, the fraction of the progress bar 1050 that is filled in represents the current ratio of the elapsed time since the start of the workflow 800 to the average time taken to complete the workflow 800, as obtained from the assisted programming of previous patients according to the workflow 800.

[0158] Before and during each stimulus ramp, the APM collects and analyses data as described below. Following a successful stimulus ramp (as defined below), the Next control 1040 is enabled. On activation of the Next control 1040, a new stimulus ramp is carried out for the next candidate SEC. This cycle occurs once for each candidate SEC. Once all the candidate SECs have been used for a stimulus ramp, activation of the Next control 1040 moves the workflow 800 to the coverage selection stage 820.

[0159] Each stimulus ramp in the PCSR stage 810 is implemented by the APF on receipt of a ramp command from the APM. A ramp command specifies a ramp direction (up or down), a ramp rate(absolute change in intensity per unit of time), and an endpoint intensity. In one implementation, once the ramp command is received by the APF, the controller 116 initiates and continues the ramp until either the patient releases the stimulation control 1010, signalled to the APF by a Halt command from the APM, or the endpoint intensity is reached. Once the endpoint is reached, the APM sends a rampdown command to the APF to ramp down the stimulus intensity. Because the purpose of the ramp is to determine the patient’s Max value, the maximum stimulus intensity is deliberately set high, i.e. above the highest expected Max value. This means that if for some reason communications between the APF and the APM are interrupted, the de-activation of the stimulation control 1010 will not be communicated to the APF, so according to this implementation there is a possibility the patient will receive uncomfortably intense stimulation until the APF ramps the stimulus intensity back down.

[0160] In another implementation, the controller 116 interrupts the ramp if the APF receives no communication from the APA within a first timeout period. The controller 116 may then ramp the intensity back down in the continued absence of communication from the APA within a second timeout period. In this implementation, the patient is less likely to receive uncomfortable stimulation if the communication between the APF and the APM is interrupted.

[0161] Figs. 19a to 19f illustrate the operation of this implementation. In Fig. 19a, the ramp 1900 of stimulus intensity versus time is initiated on receipt by the APF of a Ramp command illustrated by the filled star 1905. The ramp 1900 continues as long as communications 1910 (illustrated by unfilled stars in Fig. 8) continue to be received by the APF. (The communications 1910 can be for any purpose, not just related to the PCSR.) The ramp 1900 halts when the APF receives a Halt command, illustrated by the cross 1915, from the APM. The ramp 1900 also halts if the endpoint intensity is reached (not illustrated).

[0162] The ramp 1920 in Fig. 19b occurs when communications are interrupted. After the ramp command and the communication 1925 are received, a first timeout period 1930 elapses with no further communications received by the APF. In one implementation, the first timeout period is one second. The ASPF therefore halts the ramp 1920. After the expiry of a second timeout period 1935 since the halt, the APF ramps down the intensity to zero, regardless of whether further communications, e.g. the communication 1937, are received during the down-ramp. In one implementation, the second timeout period is 0.5 seconds.

[0163] The ramp 1940 in Fig. 19c is halted prematurely for the same reason as in Fig. 19b. However, because the communication 1945 is received before the second timeout period has expired, the downramp does not take place.

[0164] In Fig. 19d, the ramp 1950 is halted prematurely due to the expiry of the first timeout period 1955. As in Fig. 19b, after the expiry of the second timeout period 1960 the APF ramps down the intensity to zero, regardless of the absence of communications from the APM.

[0165] Fig. 19e shows a down-ramp 1970 of intensity by the APF on receipt of a down-ramp command 1975 from the APM. The down-ramp 1970 continues to zero intensity, regardless of whether further communications, e.g. the communication 1980, are received during the down-ramp 1970.

[0166] The down-ramp 1990 in Fig. 19f, like the down-ramp 1970, continues to zero intensity regardless of the absence of communications from the APM during the down-ramp 1990.Data analysis during PC SR stage

[0167] Fig. 1 la is a flowchart illustrating a data collection and analysis method 1100 carried out by the APM and the electronics module 110 during the PC SR stage 810 according to one implementation of the APM. The method 1100 is carried out for each stimulus ramp for each SEC. The method 1100 starts at steps 1110 and 1115. Steps 1110, 1115, and 1125 take place before the APM enables the stimulation control 1010 and therefore before any stimulus is applied. Step 1110 instantiates for each MEC an activation plot (AP) builder, while step 1115 instantiates for each MEC a noise departure detector (NDD). The AP builder and the NDD are described in more detail below.

[0168] At step 1125, the APM instructs the electronics module 110 to capture multiple “zero current” signal windows for each MEC. In one implementation, the electronics module 110 simply captures the signal windows using the measurement circuit 128 and bypasses the ECAP detector 320, storing the raw signal windows temporarily in memory 118 before transmitting the data to the APM. Once this data has been captured and returned to the APM, step 1125 processes these “zero current” signal windows to calibrate each NDD instance.

[0169] The method 1100 then proceeds to step 1120, which enables the stimulation control 1010 to allow the patient to commence the stimulus ramp for the current SEC as described above. During the stimulus ramp, the APM instructs the electronics module 110 to capture and return signal windows at each MEC for each stimulus current amplitude 5. The returned signal windows for each MEC are analysed by the corresponding AP builder, which extracts a detected ECAP amplitude d from each signal window. Once the stimulation control 1010 is de-activated, still at step 1120, each AP builder fits an activation plot to the set of (5, d) value pairs for each MEC. Each AP builder then at step 1130 calculates a growth curve quality index (GCQI) for each fitted AP. AP fitting and the calculation of the GCQI by the AP builder are described in more detail below.

[0170] Step 1135 then chooses the MEC which resulted in the largest GCQI. Step 1140 then calculates an ECAP threshold and a patient sensitivity from the fitted AP corresponding to the chosen MEC. Step 1140 is described in more detail below.

[0171] Step 1145 then tests whether the fitted AP meets certain inclusion criteria. The purpose of the inclusion criteria of step 1145 is to confirm that the fitted activation plot can be trusted. The inclusion criteria may be one or more of:• The fitted AP is based on more than a predetermined number (s, d) value pairs, e.g. 12 value pairs.• The GCQI is greater than a threshold, e.g. 10 dB.• The ECAP threshold calculated from the AP is greater than 0 and less than the Max value recorded for the current SEC at the end of the stimulus ramp.

[0172] If any of the inclusion criteria are not met (“N”), the fitted AP is disregarded, and the APM at step 1150 predicts the ECAP threshold / thresh from the Max value Imax recorded for the current SEC at the end of the stimulus ramp. In one implementation, step 1150 uses a linear prediction model:Ithres ~ 7l ’ ^max (4) where m is a correlation parameter that may be derived from historical patient data. In one implementation, m takes a value between 0.5 and 1.0. In another implementation, m takes a value between 0.6 and 0.9. In one implementation, m takes a value between 0.65 and 0.8. Step 1150 is an example of the prediction of a physiological threshold (the ECAP threshold) from a perceptual marker (the discomfort threshold, Max). The APM then proceeds to step 1155 using the predicted ECAP threshold.

[0173] If all the inclusion criteria tested at step 1145 are met (“Y”), the APM proceeds to step 1155 using the ECAP threshold value obtained at step 1140 from the fitted AP.

[0174] At step 1155, the APM uses the NDD to calculate a detection rate for the MEC chosen at step 1135 over a full range of stimulus intensity. In one implementation, the full range means a stimulus intensity between 1.1 times the ECAP threshold and the Max value. The detection rate is the proportion of stimulus intensity values over the full range for which the NDD returns greater than 50%. Step 1155 may use the signal windows returned during the stimulus ramp for the chosen MEC. Step 1160 then tests whether the detection rate is unusual. In one implementation, an unusual detection rate means a detection rate less than a predetermined fraction, for example 20%. The purpose of this test is to identify if the patient de-activated the stimulation control 1010 prematurely. This may occur if the patient is unfamiliar with the APM or if the patient de-activated the control accidentally.

[0175] If the detection rate is not unusual (“N”), the current SEC is marked as successful, and the method 1100 concludes at step 1165, at which the Next control 1040 is enabled. Otherwise (“Y”), step 1170 tests whether the maximum number of repetitions has been reached. If not (“N”), the APM at step 1175 increments the number of repetitions, and re-starts the method 1100 for the current candidate SEC. If so (“Y”), the current SEC is marked as unsuccessful, and the method 1100 concludes at step 1165, at which the Next control 1040 is enabled. As mentioned above, activation of the Next control 1040 either repeats the method 1100 for the next candidate SEC, or ends the PCSR stage 810 if all candidate SECs have been tested.

[0176] The result of the PCSR stage 810 is a Max value and an ECAP threshold for each candidate SEC marked as successful.

[0177] In other implementations of the PCSR stage 810:• The profile of the stimulus ramp during the activation of stimulation control 1010 may not be linear. One such implementation is a “threshold ramp”, with the threshold being the ECAP threshold, either predicted (as from step 1150) or fitted (as from step 1140). The threshold ramp is described in detail below.• The de-activation of the stimulation control 1010 may cause the stimulation intensity to be reduced exponentially rather than linearly. This handles the scenario where the perception of stimulation startles the patient, causing them to unintentionally release the stimulation control 1010. In such an implementation, the screen 1000 may include an additional user control to return the stimulus intensity all the way to zero in a controlled ramp and allow the patient to ‘lock in’ a Max value so that repetitions of the method 1100 may be handled. In another implementation, a threshold ramp is used for the down-ramp, with the threshold being the ECAP threshold, either predicted (as from step 1150) or fitted (as from step 1140). The threshold ramp is described in detail below.• The stimulus ramp rate may be increased or decreased based on the stimulation control deactivation point if the patient repeats the stimulus ramp for an SEC.• The search space of MECs may be extended from those illustrated in Fig. 9.• ‘Early release’ and ‘missing ECAP’ failure scenarios may be distinguished, and different responses defined for each. For example, the patient may be asked whether they released the button by accident, and the method 1100 repeated as many times as necessary in that scenario.• One or more exclusion criteria, such as the detection of a late response, may be tested at step 1145 and if found to be true, used to exclude the fitted AP.• There may be no maximum number of repetitions tested at step 1170. Instead, a “Y” at step 1160 leads straight to step 1175. If the calculation at step 1155 repeatedly results in an unusualdetection rate for a candidate SEC, the Next control 1040 is therefore never enabled for that candidate SEC no matter how many times the method 1100 is repeated. In such a circumstance, holding down the Next control 1040 marks the candidate SEC as unsuccessful. In this circumstance, the PCSR stage 810 performs an “discomfort” mitigation (see below) and repeats the method 1100. If the Next control 1040 is held down once again for the adjusted candidate SEC, the PCSR stage 810 either repeats the method 1100 for the next candidate SEC, or ends the PCSR stage 810 if all candidate SECs have been tested.

[0178] Fig. 11b is a flowchart illustrating a data collection and analysis method 1100a carried out by the APM and the electronics module 110 during the PCSR stage 810 according to one implementation of the APM. The method 1100a is carried out for each stimulus ramp for each SEC. The method 1100a is similar to the method 1100, in that steps that are the same in the two methods have like labels and as such are not described below. The main difference is that an MEC is not chosen midway through the method 1100a based on its GCQI. Instead, all quantities are computed for each MEC and the number of MECs whose computed quantities meet certain criteria are counted. If the count exceeds one, along with some other criteria, the Next control is enabled. Thus for example, at step 1155a, instead of computing the detection rate only for a chosen MEC, as at step 1155, the detection rate is computed for the current MEC in the list of candidate MECs. Also, at step 1130a, the AP builder for the current MEC calculates a growth curve quality index (GCQI) for the AP fitted at step 1120. Step 1145a then tests whether the fitted AP meets certain inclusion criteria. The purpose of the inclusion criteria of step 1145 is to confirm that the parameters fitted to the AP can be trusted. The inclusion criteria are:• The GCQI is greater than a threshold, e.g. 6 dB.• The ECAP threshold calculated from the AP is not too near the bounds of ECAP threshold within which the parameter fitting of the AP took place.• The sensitivity calculated from the AP is positive.• The standard deviation of the calculated sensitivity is less than a threshold, e.g. 0.5 times the calculated sensitivity.

[0179] If the fitted AP does not meet the inclusion criteria (“N” at step 1145a), the method 1100a gets the next candidate MEC at step 1163 and returns to step 1110 and step 1115.

[0180] If the fitted AP does meet the inclusion criteria (“Y” at step 1145a), the method 1100a checks, at step 1160a, whether the detection rate returned by the NDD for the current MEC at step 1155a is unusual, in the same sense as in step 1160. If the detection rate is not unusual (“N” at step 1160), or the GCQI is greater than 10 dB, the MEC may be deemed “good”. Step 1162 increments the number of “good” MECs at step 1163, and the method 1100a gets the next candidate MEC at step1163 and returns to step 1110 and step 1115. If the detection rate is unusual (“Y” at step 1160), and the GCQI is less than or equal to 10 dB, the method 1100a proceeds directly to step 1163.

[0181] Once all the candidate MECs have been exhausted by step 1163, step 1168 tests whether the number of “good” MECs is greater than one, and the Max value is greater than a threshold, e.g. 1 mA. If so (“Y”), the method 1100a concludes by enabling the Next control at step 1165. If not (“N”), step 1180 waits for the user to “long press” (hold down for a predetermined interval) the Next control to end the method 1100a. If the method 1100a ends in this fashion, the current candidate SEC is marked as unsuccessful, meaning it takes no further part in the workflow 800.Coverage survey stage

[0182] In one implementation of the Coverage Survey stage 815, the APM renders on the UI display of the CI 740 a screen 1200 as illustrated in Fig. 12. The screen 1200 comprises a stimulation control 1210, a set of instructions 1220, a set of options 1230, a Next control 1240, and a progress bar 1250. In other implementations of the Coverage Survey stage 815, the stimulation control 1210 or the Next control 1240 are hardware controls, such as buttons, forming part of the UI of the CI 740 yet remaining separate from the display.

[0183] The screen 1200 is rendered at least once for each successful candidate SEC from the PC SR stage 810 to assess that candidate SEC. The stimulation control 1210 is in the form of a control such as a virtual button that, upon activation and de-activation by the user, toggles stimulation on and off via the current candidate SEC. In one implementation of the coverage survey stage 815, stimulation turns on and off at the current candidate SEC by threshold ramps to and from a comfortable stimulus intensity for the current candidate SEC, as estimated at the PCSR stage 810. The threshold for the threshold ramp may be the ECAP threshold for the current candidate SEC, as estimated at the PCSR stage 810. Threshold ramps are described below.

[0184] An initial comfortable stimulus intensity for each candidate SEC may be predicted at the start of the coverage survey stage 815 for that candidate SEC from the Max value / max and the ECAP threshold thresh that were determined and estimated for the candidate SEC at the PCSR stage 810. In one implementation, the comfortable stimulus intensity / COmf may be calculated as a fixed proportion of the interval between / thresh and / max for the candidate SEC:Icomf ~ Ithres + ^( max ^thres ) (5) where k is a predetermined constant between 0 and 1. This prediction is an example of the prediction of a perceptual marker (the comfortable stimulus intensity) from a physiological threshold (the ECAP threshold).

[0185] In an alternative implementation, the ECAP threshold / thresh is estimated for the candidate SEC at the PCSR stage 810 using a “pre-ramp” (described below). The comfortable stimulus intensity / comf may be calculated directly from / thresh by inverting the linear model of Equation (4) and substituting the result into Equation (5) in place of the Max value / max. In such an implementation, the Max value / max does not need to be determined during the PCSR stage.

[0186] In a further alternative implementation, the comfortable stimulus intensity / COmf may be calculated as follows:clog(5) + d (6)

[0187] where S is the patient sensitivity that was estimated for the candidate SEC at the PCSR stage 810, and c and d are constants, for example with values -173 and -523 respectively. However, if Equation (6) should result in a value of / COmf that exceeds / max or is less than / thresh, then Equation (5) may be used to calculate / COmf instead.

[0188] The instructions 1220 instruct the user to activate the stimulation control 1210 and to select one or more of the options 1230 to provide feedback about their sensations. Each option 1230 corresponds to a line of text next to a 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 Next control 1240 is disabled until stimulation has been tested and least one option is selected. An option may be toggled between selected and deselected by activating (e.g. touching) the control next to its text.

[0189] In some implementations, for each candidate SEC, the options 1230 are not displayed until after the user has activated the stimulation control corresponding to that SEC.

[0190] The progress bar 1250 at the bottom of the screen 1200, like the progress bar 1050, indicates approximate quantitative progress through the entire workflow 800.

[0191] Once the Next control 1240 is activated, the APM responds to the options selected for the current candidate SEC with a “mitigation” selected according to Table 3. A “1” in a column of Table 3 represents the selection of the option or options corresponding to that column, a “0” represents nonselection, and an “X” means either the option was selected or not (the selection of the option does not affect the chosen mitigation).Table 3: Mitigations in first iteration of Coverage Survey for a candidate SEC

[0192] The mitigations to increase and decrease the comfortable stimulus intensity do so by a small amount, equal to 0.05 x (Jmax— Ithresh) ion. However, the decrease and increase mitigations are not permitted to move the comfortable stimulus intensity outside the therapeutic range defined as [Imax, IthreshQstimulus intensity is adjusted according to these mitigations, the Coverage Survey stage 815 may then be repeated for the adjusted comfortable stimulus intensity.

[0193] The “discomfort” mitigation to adjust the current candidate SEC moves the candidate SEC by one or more electrodes towards the middle of the lead. For example, the current candidate SEC may be moved by three electrodes towards the middle of the lead. If the current candidate SEC is moved according to this mitigation, a PCSR (described above) may be repeated for the adjusted candidate SEC. The Coverage Survey stage 815 is then repeated for the adjusted candidate SEC, if the PCSR stage 1010 was successful for the adjusted candidate SEC.

[0194] In some implementations, for each candidate SEC, the “too weak” or the “feels fine” options are not enabled until the control 1210 has been activated for long enough for the stimulation intensity to ramp up to the comfortable stimulus intensity. This prevents the patient from responding to the Coverage Survey Stage 815 with incomplete information.

[0195] In some implementations, the first option (“ribs or abdomen”) is not enabled for candidate SECs at the caudal end (bottom) of the lead.

[0196] In a repeat iteration of the coverage survey stage 815 for an adjusted candidate SEC, the APM responds to selections for that candidate SEC with a mitigation selected according to Table 4.As in Table 3, a “1” in a column of Table 4 represents the selection of the option corresponding to that column, a “0” represents non-selection, and an “X” means either the option was selected or not(the selection of the option does not affect the chosen mitigation.Table 4: Mitigations in second iteration of Coverage Survey for a candidate SEC

[0197] In some implementations of the workflow 800, a PC SR may only be repeated once (i.e. iterated twice) for any candidate SEC, to reduce the burden on the patient of repeatedly having to undergo PCSRs with adjusted SECs.

[0198] If the patient still feels sensation in ribs or abdomen or other undesirable areas (first or second options) for a candidate SEC at the second iteration of the Coverage Survey Stage 815 for that candidate SEC, the comfortable stimulus intensity for that candidate SEC is decreased (as per the final row of Table 4). In an alternative implementation, that candidate SEC is marked as unsuccessful. The Coverage Survey Stage 815 is not repeated for that candidate SEC.

[0199] The Coverage Survey stage 815 ends with a set of successful candidate SECs and their respective notional comfortable stimulus intensities. If the patient still feels sensation in ribs or abdomen or other undesirable areas (first or second options) for a candidate SEC after the second iteration of the Coverage Survey stage 815, the patient will have the opportunity to discard that candidate SEC during the Coverage Selection stage 820.Noise departure detector (NDD)

[0200] The NDD is a statistical detector of the presence of an ECAP in a signal window. The operation of the NDD on a signal window is preferably preceded by an “artefact scrubber” which removes artefact from the signal window. One such artefact scrubber is disclosed in International Patent Publication no. WO2020 / 124135, the entire contents of which are herein incorporated by reference. The NDD works by detecting a statistically unusual difference from the expected noise present in a signal window, which difference is attributed to the presence of an ECAP in the signal window.

[0201] The calibration of an NDD instance corresponding to an MEC, which occurs for example during step 1125 of the method 1100, may be carried out on one or more signal windows captured via that MEC which are known not to contain evoked neural responses. In one implementation, such signal windows are “zero current” signal windows which are captured from intervals during which no stimulus is being applied, and which have preferably been scrubbed for artefact, and may therefore be treated as comprising only noise. The calibration comprises forming estimates of parameters of a predetermined “noise model” (statistical distribution) from the samples in the one or more “zero current” signal windows. In one implementation, the noise model is Gaussian and the parameters are the mean p. and standard deviation a of the samples.

[0202] Once calibrated, an NDD instance may be applied to a signal window (as in step 1155 of the method 1100) by counting the number k of outliers in the signal window, i.e. the number of samples in the signal window that depart significantly from the noise model. For a Gaussian noise model, the NDD counts the number k of samples that differ from the mean estimate p by more than n times the standard deviation estimate <J, where n is a small integer. The number k of outliers is compared to the number k of samples that would be expected to occur if the signal window consisted solely of noise with mean p and standard deviation a. The difference between k and k is divided by the number of samples N in the signal window to obtain a metric r that quantifies the ratio of outliers present in a signal window relative to the expected ratio of outliers in a signal window that obeys the noise model.

[0203] It may be shown that for Gaussian noise model, the NDD may estimate the metric r as -2* ® <7» where is the standard normal cumulative distribution function.

[0204] A negative or zero value of the metric r indicates a signal window consistent with the noise model, whereas a positive value of r indicates a departure from the noise model. Such a departure is deemed to be due to the presence of an ECAP in the signal window.

[0205] In one implementation of the NDD, n is set to 3. Smaller values of n make the NDD more sensitive, indicating a departure from noise more readily and increasing the rate of Type I errors (false positives). Conversely, high values for n necessitate large outliers before r will indicate a noise departure, increasing the rate of Type II errors (false negatives).

[0206] In one implementation of the NDD, a sigmoid function may be applied to the raw metric r to map the metric r to a quality indicator 0v in the interval [0, 1]:1QnDD =l+exp(-Yr)(8)where y is a parameter that balances the Type I and Type II errors. The quality indicator QNDD has a natural interpretation: QNDD < 0.5 corresponds to r < 0 and indicates that the signal window is most likely noise. Conversely, QNDD > 0.5 indicates a departure from the noise model that is deemed to represent an ECAP. In one implementation, y is set to 50.

[0207] In one implementation, the NDD may be applied to multiple signal windows after they have been averaged together to improve the signal-to-noise ratio. In one such implementation, the number of averaged signal windows is eight. In such implementations, the parameters of the noise model may be adjusted depending on the number of signal windows that are averaged. In the Gaussian noise model, the standard deviation 6 should be divided by the square root of the number of averaged signal windows.

[0208] 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. at which the NDD detects an ECAP in 50% of signal windows processed. In one implementation, the ECAP threshold may be located during a ramp of stimulus intensity while monitoring the quality indicator QNDD. AS soon as the quality indicator QNDD consistently exceeds 50%, the ECAP threshold has been reached.

[0209] This use of the NDD to estimate the ECAP threshold may be employed at an alternative implementation of step 1150.

[0210] This use of the NDD may also be employed in an alternative implementation of the PCSR stage 810. In such an alternative implementation, the ramp rate of stimulus intensity while the stimulation control 1010 is activated is not predetermined, but is calculated from the results of a “preramp”. During the pre-ramp, which commences when the stimulation control 1010 is activated, the NDD is used to estimate the ECAP threshold as described above. The pre-ramp ends by ramping the stimulus intensity down 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 the prediction of a perceptual marker from a physiological threshold, may be implemented by inverting the linear model of Equation (4). A ramp rate is then calculated such that the patient-controlled stimulus ramp would reach the predicted value of Max after a predetermined time. The calculated ramp rate is then used for the patient-controlled stimulus ramp which takes places as described above on the next activation of the stimulation control 1010.AP builder

[0211] As mentioned above, the AP builder, as used for example at step 1120 of the method 1100, fits an activation plot using a model referred to as the Logistic Growth Curve (LGC) to a set of (5, d) value pairs, where d is a measured neural response intensity from a signal window and 5 is the corresponding stimulus intensity parameter. The AP builder may also, for example at step 1130 of the method 1100, calculate a growth curve quality index (GCQI) for a fitted activation plot.

[0212] An important part of the AP builder is an ECAP detector that returns the neural response intensity (e.g. the ECAP amplitude) d from a signal window. In one implementation, the ECAP detector described in the International Patent Publication no. W02024 / 065013 by the present applicant, the contents of which are herein incorporated by reference, may be used by the AP builder to measure the amplitude d of the ECAP in a signal window. Alternatively, the ECAP detector described in the above-mentioned International Patent Publication no. W02015 / 074121 may be used by the AP builder to measure the amplitude d of the ECAP in a signal window. In the latter case, the ECAP detector has two parameters: its correlation delay, and its length (or equivalently its frequency).Other implementations of ECAP detectors may have other adjustable parameters. The optimal values of these parameters are dependent on the SEC and the MEC that gave rise to the signal window and should therefore be customised for each instance of the AP builder, for example the six instances instantiated at step 1110 of the method 1100. In one implementation, the AP builder may customise the ECAP detector parameters on an average signal window obtained by averaging the ten signal windows corresponding to the largest values of stimulus intensity 5. In one implementation, the above-described NDD may first be applied to each signal window before incorporating it into the average signal window. If the NDD indicates that the signal window did not contain a neural response, the signal window is discarded.

[0213] In one example of customising an ECAP detector for a given SEC / MEC combination, suitable for the ECAP detector described in the above-mentioned International Patent Publication no. WO2015 / 074121, the ECAP detector is applied to the average signal window for every feasible value of correlation delay and length to form a correlation matrix. In one example of tuning the parameters of an ECAP detector, the values of correlation delay and length that maximise the measured ECAP amplitude within the correlation matrix are chosen as optimal for that instance of the AP builder. During a stimulus ramp, as the stimulus current increases, the AP builder may dynamically update the optimal values of correlation delay and length using the most recent average signal window. The AP builder may retrospectively recalculate ECAP amplitudes for all signal windows captured since the start of the current stimulus ramp using the currently optimal values.

[0214] In another example of customising an ECAP detector for a given SEC / MEC combination suitable for the ECAP detector described in the above-mentioned International Patent Publication no. W02024 / 065013, the average signal window may be projected onto an artefact basis, and the projection subtracted from the average signal window to obtain a residual that is by definition orthogonal to the artefact basis, as described in the above-mentioned International Patent Publication no. W02024 / 065013. The normalised residual is the ECAP detector that is customised for the SEC / MEC combination.

[0215] Once the ECAP detector has been customised and the set of (5, d) value pairs has been obtained, the AP builder proceeds to fit an LGC model (also referred to as a sigmoid function) to the set of (5, d) value pairs. In one implementation, the LGC model is a four-parameter function:where the four parameters are:• A, the minimum value (the detected ECAP amplitude in the absence of stimulation)• K, the maximum value (the detected ECAP amplitude at which saturation occurs, i.e. increases in stimulus intensity do no increase the detected ECAP amplitude)• A / , the current amplitude at the midpoint between A and K• , the steepness of the LGC, which is proportional to the gradient at the midpoint between A and K.

[0216] In other implementations, fewer parameters may be used for the LGC model, for example an LGC model in which the minimum valued is identically zero. In yet other implementations, other parametrised functions may be fit by the AP builder to the set of (5, d) value pairs.

[0217] To fit the LGC, the parameters A, K, M, and B may be initialised to sensible starting points Ao, Ko, Mo, and Bo. In one implementation, these values may be set to:• Ao: the mean of the ECAP amplitudes obtained from the lowest few stimulus current amplitudes.• Ko the mean of the ECAP amplitudes obtained from the highest few stimulus current amplitudes.• Mo: the stimulus current amplitude at the midpoint between A and K• Bo: may be calculated from the gradient m at the midpoint, obtained from local linear regression of value pairs acquired near the midpoint, as Bo = m*4 / (Ko-Ao).

[0218] An optimisation algorithm such as Trust Region Reflective (TRF) may then be used to optimise the four parameters A, K, M, and B from their starting points Ao, Ko, Mo, and Bo.

[0219] Fig. 13 shows a fitted LGC model 1310 to a set of (5, d) value pairs, alongside a piecewise linear model 1320 fit to the same data. The superior fit of the LGC model to the data at both low and high stimulus current amplitudes is evident.

[0220] The AP builder may also, for example at step 1130 of the method 1100, calculate a growth curve quality index (GCQI) for the fitted LGC model. The GCQI indicates a 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 in Fig. 13 by the arrow 1330) by the standard deviation of the residuals of the fitted LGC.

[0221] The fitted LGC may be used to estimate the ECAP threshold / thresh, as in step 1140 of the method 1100 or step 1840 of the method 1800 (described below). In one implementation, a line is constructed through the midpoint M of the fitted LGC with slope B. The ECAP threshold thresh may be estimated as the stimulus current amplitude 5 at which the constructed line intersects the minimum value A. It may be shown that the resulting ECAP threshold / thresh is given by:2I thresh=~ - (10)D

[0222] The fited LGC may be used to estimate the patient sensitivity S, as in as in step 1140 of the method 1100 or step 1840 of the method 1800. In one implementation, the patient sensitivity S is the slope of the fitted LGC at its midpoint AT, which may be computed from the steepness B as follows:S = ^K - A) (11)

[0223] The fitted LGC may be used to estimate the discomfort threshold, Max, in another example of the prediction of a perceptual marker (the discomfort threshold, Max) from a physiological threshold. In this example, the physiological threshold is the stimulus current amplitude at which the LGC model saturates, i.e. the saturation threshold. In one implementation, saturation may be said to have occurred when d(s) reaches H + U(K-A), where CZis just less than one. The corresponding value .s'sat of the saturation threshold may be computed as:

[0224] The discomfort threshold, Max, may then be estimated from the saturation threshold by a linear predictive model.Threshold ramp

[0225] A threshold ramp is a ramp of stimulus intensity, either up or down, that traverses stimulus intensity values below a predetermined threshold value at a faster rate than the ramp traverses stimulus intensity values above the predetermined threshold value.

[0226] When ramping stimulus intensity up, it is preferred by patients that the ramp feel gradual rather than abrupt. However, it is also generally desirable to produce a user interface that feels responsive to the patient. For example, during the PC SR stage 810, the patient may de-activate the stimulation control 1010, causing the stimulation to turn off. If they do so in response to an uncomfortable stimulus, the responsiveness of the user interface is important. A patient will be more willing to experiment with their comfort limits if stimulation ramps down quickly without producing discomfort.

[0227] Stimulus intensities below the ECAP threshold are generally not perceivable by patients. Therefore, ramping through sub-ECAP-threshold intensities does not improve the patient’s sensation of gradualness and may in fact detract, by taking up unnecessary time, from the patient’s sensation of responsiveness. A threshold ramp may therefore skip over most sub-ECAP-threshold stimulus intensities on either the way up or the way down.

[0228] Fig. 14 illustrates a threshold ramp according to one implementation of the present technology. The profile 1400 represents the time course of stimulus current amplitude according to a threshold ramp up to a target current amplitude 1410. The dotted profile 1420 represents the time course of stimulus current amplitude according to a conventional linear ramp from zero to the targetcurrent amplitude 1410. The instant 1430 represents the time (t = 0) at which the ramp was initiated, e.g. by activation of the stimulation control 1010. The interval 1440 represents the predetermined time that would have been taken by the conventional linear ramp, for example three seconds, to reach the target current amplitude 1410. The ramp rate of the conventional linear ramp profile 1420 is calculated such that the stimulus intensity reaches the target current amplitude 1410 at the end of the interval 1440. The threshold ramp, by contrast, steps comparatively rapidly (e.g. vertically) to a threshold current amplitude 1460. Then during the interval 1450, the threshold ramp linearly increases the stimulus current amplitude at the same rate as the conventional linear ramp. The length of the interval 1450, i.e. the total ramp time, is therefore significantly less than the predetermined time of the interval 1440. The threshold ramp therefore appears more responsive to the patient. Moreover, if the threshold current amplitude 1460 is set slightly below the ECAP threshold, the threshold ramp does not appear any more abrupt than the conventional linear ramp, since the patient is unable to perceive stimulus current amplitudes below the threshold current amplitude 1460.

[0229] In one implementation, the threshold current amplitude 1460 may be obtained by scaling the ECAP threshold by 0.9. This scaling factor provides a balance between having faster overall ramp times and keeping the likelihood of a step to a perceptible current amplitude low.

[0230] A threshold down-ramp according to one implementation is a time-reversed version of the profile 1400 of the threshold ramp illustrated in Fig. 14. In other words, a threshold down-ramp from a starting current amplitude decreases current amplitude linearly at a rate equivalent to a conventional linear down-ramp over the predetermined interval 1440. When the stimulus current amplitude reaches the threshold current amplitude 1460, the stimulus current amplitude steps comparatively rapidly (e.g. vertically) to zero.

[0231] In other implementations of the threshold ramp, the profile of stimulus current amplitude is not piecewise linear as in Fig. 14. Instead, alternative profiles of stimulus intensity may be used. The alternative profiles are also parametrised by a threshold value. In one such implementation, the profile follows a sigmoid function, such as described above, that smoothly and exponentially rises from zero to a midpoint that is computed from the threshold, and decelerates as the stimulus 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 chosen to be less than the average ramp rate of the exponential profile.

[0232] In some implementations, as described above in relation to the Patient-Controlled Stimulus Ramp stage 810, a threshold ramp may be interrupted if the APF receives no communication from the APA within a first timeout period. The controller 116 may then ramp the intensity back down in the continued absence of communication from the APA within a second timeout period. Exampleprofiles of such implementations of a threshold ramp are illustrated in Figs. 19a to 19c. In such implementations, the patient is less likely to receive uncomfortable stimulation if the communication between the APF and the APM is interrupted.Coverage Selection stage

[0233] As mentioned 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 survey stage 815, based on the Max and ECAP threshold values for that candidate SEC. The coverage selection stage 1020 allows the patient to test different combinations of candidate SECs before selecting which ones to keep.

[0234] In one implementation of the coverage selection stage 820, the APM renders on the UI display of the CI 740 a screen 1500 as illustrated in Fig. 15. The coverage selection screen 1500 comprises controls comprising: up to four toggle tiles, e.g. 1510a, 1510b, and 1510c, up to four respective toggle switches, e.g. 1520b and 1520c, a Next control 1540, a progress bar 1550, and a Disable All control 1560.

[0235] Toggle switches 1520b and 1520c are associated with respective toggle tiles 1510b and 1510c to form control pairs. However, toggle tile 1510a has no associated toggle switch in Fig. 15. This is because, according to some implementations, the switch corresponding to a tile is not rendered until the tile has been activated once for a predetermined minimum duration, e.g. five seconds. In the state of the coverage selection stage 820 illustrated in Fig. 15, tile 1510a has not yet been activated, so tile 1510a has no associated switch. However, tiles 1510b and 1510c have been activated, so tiles 1510b and 1510c have associated switches 1520b and 1520c.

[0236] In other implementations of the coverage selection stage 820, one or more of the controls are hardware controls, such as buttons or switches, forming part of the UI of the CI 740 yet remaining separate from the display. The UI also comprises instructions 1530.

[0237] Each toggle control pair, e.g. the tile 1510b and the switch 1520b, corresponds to one of the successful candidate SECs after the coverage survey stage 815. (As an example, only three control pairs are shown in Fig. 15, as the fourth candidate SEC was marked as unsuccessful during the PCSR stage 810.) The four (tile, switch) control pairs may be activated and de-activated independently. The state of stimulation on an SEC (active or inactive) corresponds to the state of the corresponding toggle switch (activated or de-activated). The stimulus pulses from all the active SECs at a given time are delivered interleaved in a predetermined time order, staggered in time by the inter-stimulus interval. In some implementations, the toggle control pairs are physiologically ordered. That is to say, the position in which each toggle control pair appears on the coverage selection screen 1500 corresponds to the physical position of its corresponding SEC on the electrode array 150, and therefore to therelative location on the body where paresthesia induced by stimulation controlled by that control pair may be felt. In one such implementation, suitable for the situation described above in which the four candidate SECs are defined as top left, top right, bottom left, and bottom right of a pair of parallel implanted leads: the top left tile 1510a corresponds to the top-left candidate SEC, the top right tile 1510b corresponds to the top-right candidate SEC, the bottom left tile 1510c corresponds to the bottom-left candidate SEC, and the bottom right tile (disabled in Fig. 15) corresponds to the bottom right candidate SEC. Physiological ordering may assist the patient in recalling the effect of each control pair without having to interact with it, and therefore may contribute to a more efficient coverage selection stage 820.

[0238] Each toggle tile is configured to remain activated as long as the patient continues to interact with it, for example by “holding down” the toggle tile, and becomes de-activated when the patient ceases to interact with it, for example by “releasing” the toggle tile. The toggle tile may take on a different appearance when it is activated, for example by being filled in a different colour. By contrast, each toggle switch cannot be “held down”, but inverts its state from de-activated to activated or from activated to de-activated each time the patient interacts with the toggle switch. The toggle switch takes on a different appearance when it is activated, for example by filling in the disk representing the toggle switch.

[0239] In one implementation, the toggle tiles have an inverting behaviour, whereby for as long as the toggle tile is being activated, e.g. held down, the state of stimulation, which is always indicated by the state of the toggle switch, is inverted. For example, if a toggle switch is activated, activating the corresponding tile de-activates the toggle switch and stops stimulation, and de-activating the tile activates the toggle switch and restarts stimulation. Conversely, if a toggle switch is de-activated, activating the corresponding tile activates the toggle switch and starts stimulation, and de-activating the tile de-activates the toggle switch and stops stimulation. The stimulation is always on if the switch is activated, and always off if the switch is de-activated. The appearance of a toggle switch therefore offers a visual cue to indicate the state of stimulation on the corresponding SEC.

[0240] Table 5 summarises the effect of activating and de-activating the toggle tile and the toggle switch on the stimulation from the corresponding candidate SEC according to this implementation of the coverage selection stage 820. Blank cells represent actions that cannot occur.Table 5: State transition table for one implementation of coverage selection stage

[0241] In another implementation, if the toggle switch is activated, activating the corresponding tile de-activates the toggle switch and stops stimulation, and de-activating the tile does not further change the state of stimulation. Conversely, if the toggle switch is de-activated, activating the corresponding tile activates the toggle switch and starts stimulation, and de-ctivating the tile de-activates the toggle switch and stops stimulation. Table 6 summarises the effect of activating and de-activating the toggle tile and the toggle switch on the stimulation from the corresponding candidate SEC according to this implementation of the coverage selection stage 820.Table 6: State transition table for alternative implementation of coverage selection stage

[0242] Under the implementation summarised in Table 6, the behaviour of stopping stimulation when a stimulus control is de-activated, as during the PCSR and coverage survey stages, is maintained.

[0243] The progress bar 1550, like the progress bars 1050 and 1250, indicates approximate quantitative progress through the entire workflow 800.

[0244] The Disable All control 1560 disables all stimulation and de-activates all toggle switches 1520b etc.

[0245] The instructions 1530 inform the patient that when they activate (“hold down”) a toggle tile, they will feel stimulation in one of four locations.

[0246] In an alternative implementation of the coverage selection stage 820, there are no toggle tiles, only toggle switches.

[0247] In one implementation of the coverage selection stage 820, stimulation turns on and off at a candidate SEC by threshold ramps to and from the comfortable stimulus intensity for the candidate SEC that resulted from the Coverage Survey stage 815. The threshold for the threshold ramp is the ECAP threshold for the candidate SEC that was estimated at the PCSR stage 810. Threshold ramps are described above.

[0248] The Next control 1540 is enabled after at least one toggle switch has been activated. In some implementations, an additional criterion for enabling the Next control 1540 is that stimulationaccording to the final selected coverage needs to have been active for a minimum duration, for example five seconds. Once the patient activates the Next control 1540, the APM records the currently activated candidate SECs as the selected SECs, and stimulation is stopped on all SECs.

[0249] In an alternative implementation of the coverage selection stage 820, there are no toggle switches, only toggle tiles.

[0250] In such an implementation, the Next control 1540 is enabled after at least one toggle tile has been activated. Once the patient activates the Next control 1540, the APM records the currently activated candidate SECs as the selected SECs, and stimulation is stopped on all SECs.

[0251] In another implementation of the coverage selection stage 820, there are no tiles or switches. Instead, the coverage selection screen displays a list of all possible combinations of the candidate SECs. For example, in an implementation in which there are four successful candidate SECs, the coverage selection screen displays the fifteen (15) possible combinations of the successful candidate SECs. The patient may select any one of the combinations in the list, which turns on stimulation at all of the SECs in the selected combination at their respective comfortable stimulus intensities (and turns off stimulation at any previously selected combination). The selected SECs at the end of the coverage selection stage 820 according to this implementation are those SECs in the selected combination when the Next control 1540 is activated.Measurement Optimisation Stage

[0252] As mentioned above, the Measurement Optimisation (MO) stage 830 is configured to deliver stimulus of a gradually increasing intensity from a primary SEC of the selected SECs, and record sensed signal data at each of multiple measurement electrode configurations for the primary SEC. The MO stage 830 is then configured to choose the optimal MEC for the primary SEC, calculate physiological characteristics of the patient based on the neural responses extracted from signal windows recorded via the optimal MEC, and choose optimal therapy parameters for the primary SEC / optimal MEC combination.

[0253] The primary SEC in the determined program is the selected SEC from which neural responses are measured to drive the feedback loop to adjust the stimulus current amplitude of the primary SEC in accordance with the system 300 as described above. Neural responses evoked by the non-primary selected SECs are not recorded or analysed. Instead, the stimulus current amplitudes of the non-primary SECs are adjusted by the controller 116 so they remain in fixed ratios with the stimulus current amplitude of the primary SEC. The ratios to which the non-primary selected SECs are fixed may be saved in the determined program as the ratios of their respective comfortable stimulus intensities to the comfortable stimulus intensity of the primary SEC.

[0254] In one implementation of the MO stage 830, the APM displays on the UI display of the CI 740 a screen 1700 as illustrated in Fig. 17. The screen 1700 comprises some information 1720, a progress bar 1750, and a Stop Stimulation control 1710. The screen 1700 is displayed while some neural stimulation is delivered, and the collected signal windows are analysed as described below. In one implementation, activation of the Stop Stimulation control 1710 at any time during the MO stage stops the stimulation. The screen 1700 is then replaced with an exit screen (not shown) informing the patient that manual programming is required. The MO stage 830 ends and the APM then halts without loading a program to the electronics module 110.

[0255] The progress bar 1750, like the progress bars 1050, 1250, and 1550, indicates approximate quantitative progress through the entire workflow 800.

[0256] Once the data collection and analysis of the MO stage 830 are complete, the APM displays one of two screens depending on the success of the data analysis. If the data analysis was successful, the screen contains a Finish control. Instructions on the screen inform the patient that the programming was successful. When the patient activates the Finish control, the MO stage 830 ends.

[0257] If the data analysis was unsuccessful, the screen contains a Finish control. Instructions on the screen inform the patient that the programming was unsuccessful, and that manual programming is required. When the patient activates the Finish control, the MO stage 830 ends and the APM halts without loading a program to the electronics module 110.Data Analysis during the MO stage

[0258] Fig. 18 contains a flowchart illustrating a data collection and analysis method 1800 carried out by the APM and the electronics module 110 during the MO stage 830 according to one implementation of the APM. The method 1800 starts at step 1815, where the APM selects a primary SEC from among the selected SECs from the coverage selection stage 820. In one implementation, step 1815 selects as the primary SEC the remaining selected SEC (if there is one) with the smallest comfortable stimulus intensity. Meanwhile, step 1810 instantiates an AP builder for each MEC corresponding to the current primary SEC, as in step 1110. The MECs corresponding to one SEC are illustrated in Fig. 9. At the next step 1825, the APM instructs the electronics module 110 to commence a stimulus ramp for the current primary SEC. The stimulus ramp commences at a stimulus intensity of zero and increases via discrete steps to a maximum stimulus intensity determined by the Max value for the primary SEC selected at step 1815. In one implementation, there are 10 evenly-spaced steps to a maximum stimulus intensity that is 90% of the Max value for the primary SEC.

[0259] In an alternative implementation of step 1825, the electronics module 110 may increase the stimulus intensity in constant-ratio steps, i.e. each increment comprises multiplying the previous stimulus current amplitude by a constant ratio. This is equivalent to a ramp with an exponential ratherthan a linear profile. In an exponential ramp, the discrete steps are more widely spaced as the maximum stimulus intensity is approached. In one example, if the ECAP threshold is set to 0.7 times the Max value as described above, a constant ratio of 1.025 will provide ten steps of exponential increase between the ECAP threshold and 90% of Max.

[0260] In an alternative implementation of step 1825, rather than using a ramp, the electronics module 110 may vary the stimulus intensity non-monotonically between the zero and the maximum stimulus intensity. For example, the variation may be random. Such an approach may lead to faster convergence by the AP builder to the fitted LGC.

[0261] During the stimulus ramp, at step 1820, the APM instructs the electronics module 110 to capture and return signal windows for each stimulus current amplitude 5 at each MEC. The returned signal windows for each MEC are analysed by the corresponding AP builder at step 1820, which extracts a detected ECAP amplitude d from each signal window. In one implementation, multiple signal windows (e.g. sixteen windows) are analysed for each MEC at each stimulus current amplitude 5 during the ramp. Each AP builder tunes the parameters, e.g. length and correlation delay, of its ECAP detector during the step 1820 using the captured signal windows as described above.

[0262] To complete step 1820, each AP builder fits an AP to the set of (5, d) value pairs for the corresponding MEC as described above. Meanwhile, at step 1845, the APM instructs the electronics module 110 to ramp down the stimulus intensity. In one implementation, step 1845 uses a threshold ramp as described above, using the ECAP threshold as the threshold of the threshold ramp.

[0263] Each AP builder then at step 1830 calculates the GCQI of the AP fit for the corresponding MEC as described above. At step 1835, the APM chooses the MEC corresponding to the fitted AP with the highest GCQI. The APM then at step 1840 calculates the ECAP threshold and patient sensitivity S from the fitted AP as described above.

[0264] Step 1850 then determines whether the chosen MEC meets certain exclusion criteria indicative of poor quality. In one implementation, the exclusion criteria are:• The chosen GCQI is less than a threshold, e.g. 10 dB.• The calculated ECAP threshold is outside a predetermined range. In one implementation, the range is from the first percentile to the 99th percentile of the distribution of ECAP thresholds obtained from existing patient data.• The calculated sensitivity is outside a predetermined range. In one implementation, the range is from the first percentile to the 99th percentile of the distribution of patient sensitivities obtained from existing patient data.

[0265] If any of the exclusion criteria are met (“Y”), the current primary SEC is marked as unsuccessful. The APM at step 1860 determines whether there are any remaining selected SECs thathave not been tested. If so (“Y”), step 1870 restarts the method 1800. If not (“N”), the final step 1880 ends the MO stage 830, and the workflow 800 is deemed unsuccessful.

[0266] If none of the exclusion criteria tested at step 1850 are met (“N”), the current primary SEC is marked as be the primary SEC for the program, and the chosen MEC is marked as the optimal MEC for the primary SEC. Step 1855 then calculates the gain K of the gain element 336 of the system 300 from the patient sensitivity S calculated at step 1840. In one implementation, step 1855 calculates the gain K aswhere m = fcis a loop cutoff frequency, and fsis the stimulus frequency. In one implementation, Js the loop cutoff frequency is set to 3 Hz to balance the attenuation of noise with the attenuation of postural disturbances such as heartbeat.

[0267] Step 1865 calculates other therapy parameters for the CLNS system 300. In one implementation, the therapy parameters are:• A target ECAP amplitude. This may be calculated using equation (9) as the value of ECAP amplitude d on the fitted AP corresponding to the comfortable stimulus intensity 5 = Tcomf.• A maximum stimulus intensity. This may be set to the Max value for the primary SEC.• A maximum target ECAP amplitude. This may be set to the value of ECAP amplitude d on the fitted AP corresponding to the Max value for the primary SEC.

[0268] Step 1875 saves the determined program, comprising the selected SECs, including the primary SEC, the optimal MEC, the Max, ECAP threshold, and sensitivity, and calculated therapy parameters. The MO stage 830 ends, and the workflow 800 is deemed successful.

[0269] In an alternative implementation of the MO stage 830, there is no primary SEC. Instead, each selected SEC runs its own independent feedback loop via its own dedicated MEC, assuming an MEC of sufficient quality may be found. A modified method 1800 is carried out for each selected SEC. The modified method 1800 has no step 1815, nor does it have steps 1860 and 1870. Instead, if one of the exclusion criteria is met at step 1850, the modified method 1800 ends unsuccessfully at step 1880.INTERPRETATION

[0270] The technology disclosed herein may be implemented in hardware (e.g., using digital signal processors, application specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs)), or in software (e.g., using instructions tangibly stored on non-transitory computer-readable media for causing a data processing system to perform the steps described herein), or in a combination of hardware and software. The disclosed technology can also be implemented as computer-readablecode on a computer-readable medium. The computer-readable medium can include any data storage device that can store data which can thereafter be read by a computer system. Examples of the computer-readable medium include read-only memory ("ROM"), random-access memory ("RAM"), magnetic tape, optical data storage devices, flash storage devices, or any other suitable storage devices. The computer-readable medium can also be distributed over network-coupled computer systems so that the computer-readable code is stored or executed in a distributed fashion. The present technology is not limited to any particular programming language or operating system.Wireless

[0271] In the context of the present disclosure, the term “wireless” and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communications channels, etc., that may communicate data through the use of modulated electromagnetic radiation through a non-solid medium. The term does not imply that the associated devices do not contain any wires, although in some embodiments they might not. In the context of the present disclosure, the term “wired” and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communications channels, etc., that may communicate data through the use of modulated signals propagating through a conductive medium. The term does not imply that the associated devices are coupled by electrically conductive wires.

[0272] Wireless communication standards that can be accommodated include IEEE 802.11 wireless LANs and links, Bluetooth, and wireless Ethernet. The technology disclosed herein may be implemented using devices conforming to other network standards and for other applications, including, for example other WLAN standards and other wireless standards such as MICS.Processes

[0273] Unless specifically stated otherwise, as apparent from the following discussions, it is appreciated that throughout the specification discussions utilizing terms such as “processing”, “computing”, “comparing”, “estimating”, “calculating”, “determining”, “analysing” or the like, refer to the action or processes of a computer or computing system, or similar electronic computing device, that manipulate or transform data represented as physical, such as electronic, quantities into other data similarly represented as physical quantities, or to otherwise execute a predefined procedure suitable to effect the described actions.Processor

[0274] In a similar manner, the term “processor” may refer to any device or portion of a device that processes electronic data, e.g., from registers or memory, to transform that electronic data into other electronic data that, e.g., may be stored in registers or memory. A “computer” or a “computing device” or a “computing machine” or a “computing platform” may include one or more processors.

[0275] The methods described herein are, in one embodiment, performable by one or more processors that accept computer-readable (also called machine-readable) code containing a set of instructions that when executed by one or more of the processors cause the one or more processors to carry out at least one of the methods described herein. Any processor capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken are included within the meaning of the term “processor”. Thus, one example is a typical processing system that includes one or more processors. The processing system further may include a memory subsystem including main RAM or a static RAM, or ROM.Networked or Multiple Processors

[0276] In alternative embodiments, the one or more processors operate as respective standalone device(s) or may be connected, e.g., networked to other processor(s), in a networked deployment. The one or more processors may operate in the capacity of a server or a client machine in serverclient network environment, or as a peer machine in a peer-to-peer or distributed network environment. The one or more processors may form a web appliance, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine.

[0277] Note that while some diagram(s) only show(s) a single processor and a single memory that carries the computer-readable code, those in the art will understand that many of the components described above are included, but not explicitly shown or described in order not to obscure the inventive aspect. For example, while only a single machine may be illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methods discussed herein.Additional Implementations

[0278] Thus, one implementation of each of the methods described herein is in the form of a computer-readable medium carrying a set of instructions, e.g., a computer program that are for execution on one or more processors. Thus, as will be appreciated by those skilled in the art, aspects of the present technology may be implemented as a method, an apparatus such as a special purpose apparatus, an apparatus such as a data processing system, or a computer-readable medium. The computer-readable medium carries computer-readable code including a set of instructions that when executed on one or more processors cause the processor or processors to implement a method. Accordingly, aspects of the present technology may take the form of a method, an entirely hardware implementation, an entirely software implementation or an implementation combining software and hardware aspects. Furthermore, the present technology may take the form of a carrier medium (e.g., a computer program product) carrying computer-readable program code embodied in the medium.Carrier Medium

[0279] The software may further be transmitted or received over a network via a network interface device. While the carrier medium is shown in an example embodiment to be a single medium, the term “carrier medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) that store the one or more sets of instructions. A carrier medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media.Means For Carrying out a Method or Function

[0280] Furthermore, some of the implementations are described herein as a method or combination of elements of a method that can be implemented by a processor of a processor device, computer system, or by other means of carrying out the function. Thus, a processor with the necessary instructions for carrying out such a method or element of a method forms a means for carrying out the method or element of a method. Furthermore, an element described herein of an apparatus is an example of a means for carrying out the function performed by the element.

[0281] Those of skill would further appreciate that the various illustrative logical blocks, modules, and algorithm steps described in connection with the implementations disclosed herein may be implemented as electronic hardware, computer software running on a special purpose machine that is programmed to carry out the operations described in the present disclosure, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the exemplary implementations.Implementations

[0282] Reference throughout the present disclosure to “one implementation” or “an implementation” means that a particular feature, structure or characteristic described in connection with the implementation is included in at least one implementation of the present technology. Thus, appearances of the phrases “in one implementation” or “in an implementation” in various places throughout the present disclosure are not necessarily all referring to the same implementation, but may refer to different implementations. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner, as would be apparent to one of ordinary skill in the art from this disclosure, in one or more implementations.

[0283] Similarly, it should be appreciated that in the above description of example implementations of the present technology, various features are sometimes grouped together in a single implementation, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects may lie in less than all features of a single foregoing disclosed implementation. Thus, the claims following the Detailed Description of the Present Technology are hereby expressly incorporated into this Detailed Description of the Present Technology, with each claim standing on its own as a separate implementation of the present technology.

[0284] Furthermore, while some implementations described herein include some, but not other features included in other implementations, combinations of features of different implementations are meant to be within the scope of the present technology, and form different implementations of the present technology, as would be understood by those in the art. For example, in the following claims, any of the claimed implementations can generally be used in any combination.

[0285] As used herein in the specification and claims, including as used in the examples and unless otherwise expressly specified, all numbers may be read as if prefaced by the word "about" or "approximately," even if the term does not expressly appear. The phrase "about" or "approximately" may be used when describing magnitude or position to indicate that the value or position described is within a reasonable expected range of values or positions. For example, a numeric value may have a value that is + / - 0.1% of the stated value (or range of values), + / - 1% of the stated value (or range of values), + / - 2% of the stated value (or range of values), + / - 5% of the stated value (or range of values), + / - 10% of the stated value (or range of values), etc. Any numerical values given herein should also be understood to include about or approximately that value, unless the context indicates otherwise. For example, if the value " 10" is disclosed, then "about 10" is also disclosed. Any numerical range recited herein is intended to include all sub-ranges subsumed therein. It is also understood that each value between two particular values is also disclosed. For example, if 10 and 15 are disclosed, then 11, 12, 13, and 14 are also disclosed.Different Instances of Objects

[0286] As used herein, unless otherwise specified the use of the ordinal adjectives “first”, “second”, “third”, etc., to describe a common object, merely indicates that different instances of like objects are being referred to, and is not intended to imply that the objects so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner.Specific Details

[0287] In the description provided herein, numerous specific details are set forth. However, it is understood that implementations of the present technology may be practiced without these specific details. In other instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of the present technology.Terminology

[0288] Throughout the present disclosure, the terms "a" and "an" mean "one or more", unless expressly specified otherwise.

[0289] Throughout the present disclosure, the word "comprise", or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a 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.

[0290] Throughout the present disclosure, a statement that an element may be “at least one of’ or “one or more of’ a list of options is to be understood to mean that the element may be any one of the listed options, or may be any combination of two or more of the listed options.

[0291] Throughout the present disclosure, the word “or” is to be read inclusively rather than exclusively, except where otherwise indicated.

[0292] Neither the title nor any abstract of the present disclosure should be taken as limiting in any way the scope of the claimed invention.

[0293] Where the preamble of a claim recites a purpose, benefit or possible use of the claimed invention, it does not necessarily limit the claimed invention to having only that purpose, benefit or possible use.

[0294] In the present specification, terms such as "part", "component", "means", "section", or "segment" may refer to singular or plural items and are terms intended to refer to a set of properties, functions, or characteristics performed by one or more items having one or more parts. It is envisaged that where a "part", "component", "means", "section", "segment", or similar term is described as consisting of a single item, then a functionally equivalent object consisting of multiple items is considered to fall within the scope of the term; and similarly, where a "part", "component", "means", "section", "segment", or similar term is described as consisting of multiple items, a functionally equivalent object consisting of a single item is considered to fall within the scope of the term. The intended interpretation of such terms described in this paragraph should apply unless the contrary is expressly stated or the context requires otherwise.

[0295] The term "connected" or a similar term, should not be interpreted as being limited to direct connections only. Thus, the scope of the expression “an item A connected to an item B” should notbe limited to items or systems wherein an output of item A is directly connected to an input of item B. It means that there exists a path between an output of A and an input of B which may be a path including other items or means. "Connected", or a similar term, may mean either that two or more elements are in direct physical or causal contact, or that two or more elements are not in direct contact with each other yet still co-operate or interact with each other.

[0296] It will be appreciated by persons skilled in the art that numerous variations or modifications may be made to the present technology as shown in the specific implementations without departing from the spirit or scope of the invention as broadly described. For example, any formulas given above are merely representative of procedures that may be used. Functionality may be added or deleted from the block diagrams and operations may be interchanged among functional blocks. Steps may be added or deleted to methods described within the scope of the present technology. The disclosed implementations are, therefore, to be considered in all respects as illustrative and not limiting or restrictive.

[0297] The features described in relation to one or more aspects of the present technology are to be understood as applicable to other aspects of the present technology. More generally, combinations of the steps in the method(s) of the present technology or the features of the system(s) or device(s) of the present technology described elsewhere in the present disclosure, including in the claims, are to be understood as falling within the scope of the disclosure of the present disclosure.INDUSTRIAL APPLICABILITY

[0298] It is apparent from the above that the arrangements described are applicable to the health care industries.LABEL LIST stimulator 100 ECAP 170 patient 108 target fibres 180 electronics module 110 communications channel 190 battery 112 external computing device 192 telemetry module 114 paddle array 200 controller 116 paddle body 202 memory 118 ventral surface 203 clinical data 120 dorsal surface 204 clinical settings 121 dorsal - side electrode 206 control programs 122 dorsal - side electrode 208 pulse generator 124 line 210 electrode selection module 126 electrode 214 measurement circuit 128 electrode 216 ground 130 electrode 218 electrode array 150 odd electrode 219 pulse 160 paddle lead 220column 230 bottom right SEC 918 central column 232 list 920 right - hand column 234 SEC 922 line 240 SEC 924CLNS system 300 SEC 926 clinical settings controller 302 SEC 928 target ECAP controller 304 SEC 930 box 308 SEC 932 box 309 SEC 934 controller 310 list 940 box 311 MEC 942 stimulator 312 candidate SEC 950 element 313 candidate SEC 952 measurement circuitry 318 candidate SEC 954ECAP detector 320 candidate SEC 956 comparator 324 candidate SEC 958 gain element 336 candidate SEC 960 integrator 338 candidate SEC 962 activation plot 402 candidate SEC 964ECAP threshold 404 electrode 970 discomfort threshold 408 electrode 972 perception threshold 410 electrode 980 therapeuti c range 412 screen 1000 activation plot 502 control 1010 activation plot 504 instructions 1020 activation plot 506 next control 1040ECAP threshold 508 progress bar 1050ECAP threshold 510 sector 1060ECAP threshold 512 method 1100ECAP target 520 step 1110ECAP 600 step 1115 neural stimulation system 700 step 1120 device 710 step 1125 remote controller 720 step 1130 clinical System Transceiver 730 step 1135 clinical interface 740 step 1140 charger 750 step 1145 workflow 800 step 1150PCSR stage 810 step 1155 coverage survey Stage 815 step 1160 coverage selection stage 820 step 1163 measurement optimisation stage 830 step 1165 electrode array 900 step 1168 left lead 905 step 1170 right lead 910 step 1175 top left SEC 912 method 1100a top right SEC 914 step 1130a b ottom 1 eft SEC 916 step 1145astep 1155a progress bar 1750 step 1160a method 1800 screen 1200 step 1810 stimulation control 1210 step 1815 instructions 1220 step 1820 set of options 1230 step 1825 next control 1240 step 1830 progress bar 1250 step 1835LGC model 1310 step 1840 linear model 1320 step 1845 arrow 1330 step 1850 profile 1400 step 1855 target current amplitude 1410 step 1860 profile 1420 step 1865 instant 1430 step 1870 interval 1440 step 1875 interval 1450 step 1880 threshold current amplitude 1460 ramp 1900 coverage selection screen 1500 star 1905 tile 1510a communications 1910 tile 1510b cross 1915 tile 1510c ramp 1920 switch 1520b communi cati on 1925 switch 1520c first timeout period 1930 instructions 1530 second timeout period 1935 next control 1540 communication 1937 progress bar 1550 ramp 1940 control 1560 communi cati on 1945 method 1600 ramp 1950 step 1610 first timeout period 1955 step 1620 second timeout period 1960 step 1630 down - ramp 1970 screen 1700 down - ramp command 1975 stimulation control 1710 communi cati on 1980 information 1720 down - ramp 1990

Claims

CLAIMS:

1. A neural stimulation system comprising: an implantable device for controllably delivering neural stimuli, the device comprising: a pulse generator configured to deliver neural stimuli via one or more stimulus electrode configurations (SECs), each SEC comprising one or more stimulation electrodes of an implanted electrode array, to a neural pathway of a patient, the neural stimuli being configured to evoke neural responses from the neural pathway; and a control unit configured to control the pulse generator to deliver neural stimuli via an SEC of the one or more SECs; and a processor configured to: select, from a predetermined ranked list of possible SECs associated with a predetermined first location on the electrode array, the first possible SEC in rank order that does not contain an unusable electrode; and instruct the control unit to control the pulse generator to deliver the neural stimuli via the selected SEC.

2. The system of claim 1, wherein the device further comprises: measurement circuitry configured to capture signal windows from signals sensed on the neural pathway by one or more measurement electrode configurations (MECs), each MEC comprising one or more measurement electrodes of the implanted electrode array.

3. The system of claim 2, wherein the processor is further configured to: select, from a ranked list of possible MECs associated with the selected SEC, the first N possible MECs in rank order that do not contain an unusable electrode, where A is a number greater than one.

4. The system of claim 3, wherein the processor is further configured to generate the ranked list of possible MECs according to a corresponding predetermined list of ranked rules as applied to the selected SEC.

5. The system of claim 4, wherein the predetermined list of ranked rules is chosen to balance diversity of possible MECs with a signal-to-noise ratio of each MEC.

6. The system of claim 4, wherein each rule in the predetermined list of ranked rules specifies electrode array offsets of the positions of the recording and reference electrodes of the corresponding MEC relative to a location of the associated SEC on the electrode array.

7. The system of any one of claims 3 to 6, wherein the processor is further configured to select one MEC of the N selected MECs.

8. The system of claim 7, wherein the processor is further configured to instruct the control unit to control the measurement circuitry to capture signal windows from signals sensed on the neural pathway by the selected MEC.

9. The system of claim 8, wherein the control unit is further configured to repeatedly: control the pulse generator to deliver a neural stimulus via the selected SEC according to a stimulus parameter; control the measurement circuitry to capture a signal window from a signal sensed on the neural pathway by the selected MEC subsequent to the delivered neural stimulus; measure a characteristic of an evoked neural response in the captured signal window; determine a feedback variable from the measured characteristic of the evoked neural response; and adjust, using a feedback controller, the stimulus parameter so as to maintain the feedback variable at or near a target value.

10. The system of any one of claims 1 to 9, wherein the processor is further configured to identify the unusable electrodes by an electrode impedance check.

11. The system of any one of claims 1 to 10, wherein the processor is further configured to repeat the selecting and instructing for a predetermined second location on the implanted electrode array.

12. The system of claim 11, wherein the ranked lists of possible SECs associated with the second location on the electrode array is a symmetrically reflected version of the ranked list for the first location.

13. A method of programming a neural stimulation device, the method comprising: selecting, from a predetermined ranked list of possible stimulus electrode configurations(SECs) associated with a predetermined first location on an electrode array, the first possible SEC in rank order that does not contain an unusable electrode; and instructing the neural stimulation device to deliver the neural stimuli to a neural pathway of a patient via the selected SEC.

14. The method of claim 13, further comprising: instructing the neural stimulation device to capture signal windows from signals sensed on the neural pathway subsequent to respective neural stimuli by a measurement electrode configuration (MEC).

15. The method of claim 14, further comprising:selecting, from a ranked list of possible MECs generated from the selected SEC, the first TV possible MECs in rank order that do not contain an unusable electrode, where Ais a number greater than one; and instructing the neural stimulation device to capture the signal windows from signals sensed via the N selected MECs.

16. The method of claim 15, further comprising generating the ranked list of possible MECs according to a corresponding predetermined list of ranked rules as applied to the selected SEC.

17. The method of any one of claims 15 to 16, further comprising selecting one MEC of the N selected MECs.

18. The method of claim 17, further comprising capturing signal windows from signals sensed on the neural pathway by the selected MEC.

19. The method of claim 18, further comprising repeatedly, by the neural stimulation device: delivering a neural stimulus via the selected SEC; measuring a characteristic of an evoked neural response in a captured signal window subsequent to the neural stimulus; determining a feedback variable from the measured characteristic of the evoked neural response; and adjusting a stimulus parameter so as to maintain the feedback variable at or near a target value.

20. A neural stimulation system comprising: an implantable device for controllably delivering neural stimuli, the device comprising: a pulse generator configured to deliver neural stimuli via one or more stimulus electrode configurations (SECs), each SEC comprising one or more stimulation electrodes of an implanted electrode array, to a neural pathway of a patient, the neural stimuli being configured to evoke neural responses from the neural pathway; measurement circuitry configured to capture signal windows from signals sensed on the neural pathway by one or more measurement electrode configurations (MECs), each MEC comprising one or more measurement electrodes of the implanted electrode array; and a control unit configured to control the pulse generator to deliver neural stimuli via a selected SEC of the one or more SECs; and a processor configured to:select, from a ranked list of possible MECs associated with the selected SEC, the first N possible MECs in rank order that do not contain an unusable electrode, where TV is a number greater than one; and instruct the control unit to control the measurement circuitry to capture signal windows from signals sensed on the neural pathway by one MEC of the N selected MECs.

21. The system of claim 20, wherein the processor is further configured to generate the ranked list of possible MECs according to a corresponding predetermined list of ranked rules as applied to the selected SEC.

22. The system of claim 21, wherein the predetermined list of ranked rules is chosen to balance diversity of possible MECs with a signal-to-noise ratio of each MEC.

23. The system of claim 21, wherein each rule in the predetermined list of ranked rules specifies electrode array offsets of the positions of the recording and reference electrodes of the corresponding MEC relative to a location of the associated SEC on the electrode array.

24. The system of any one of claims 20 to 23, wherein the processor is further configured to select the one MEC of the N selected MECs.

25. The system of any one of claims 20 to 24, wherein the control unit is further configured to repeatedly: control the pulse generator to deliver a neural stimulus via the selected SEC according to a stimulus parameter; control the measurement circuitry to capture a signal window from a signal sensed on the neural pathway by the selected MEC subsequent to the delivered neural stimulus; measure a characteristic of an evoked neural response in the captured signal window; determine a feedback variable from the measured characteristic of the evoked neural response; and adjust, using a feedback controller, the stimulus parameter so as to maintain the feedback variable at or near a target value.

26. A method of programming a neural stimulation device, the method comprising: selecting, from a ranked list of possible measurement electrode configurations (MECs) associated with a selected stimulus electrode configuration (SEC), the first N possible MECs in rank order that do not contain an unusable electrode, where N is a number greater than one; instructing the neural stimulation device to deliver neural stimuli to a neural pathway of a patient via the selected SEC; and instructing the neural stimulation device to capture signal windows from signals sensed on the neural pathway subsequent to respective neural stimuli by one MEC of the N selected MECs.

27. The method of claim 26, further comprising generating the ranked list of possible MECs according to a corresponding predetermined list of ranked rules as applied to the selected SEC.

28. The method of any one of claims 26 to 27, further comprising selecting the one MEC of the N selected MECs.

29. The method of any one of claims 26 to 28, further comprising repeatedly, by the neural stimulation device: delivering a neural stimulus via the selected SEC; measuring a characteristic of an evoked neural response in a captured signal window subsequent to the neural stimulus; determining a feedback variable from the measured characteristic of the evoked neural response; and adjusting a stimulus parameter so as to maintain the feedback variable at or near a target value.

30. A system comprising: an implantable device comprising: measurement circuitry configured to capture signal windows from signals sensed on a neural pathway by one or more measurement electrode configurations (MECs), each MEC comprising one or more electrodes of an implanted electrode array; and a control unit configured to control the measurement circuitry to capture signal windows from signals sensed on the neural pathway by an MEC of the one or more MECs; and a processor configured to: select, from a ranked list of possible MECs, the first N possible MECs in rank order that do not contain an unusable electrode, where N is a number greater than one; and instruct the control unit to control the measurement circuitry to capture signal windows from signals sensed on the neural pathway by one MEC of the N selected MECs.

31. The system of claim 30, wherein the processor is further configured to generate the ranked list of possible MECs according to a corresponding predetermined list of ranked rules as applied to the selected SEC.

32. The system of claim 31, wherein the predetermined list of ranked rules is chosen to balance diversity of possible MECs with a signal-to-noise ratio of each MEC.

33. The system of any one of claims 30 to 32, wherein the processor is further configured to select the one MEC of the N selected MECs.

34. A method of programming an implantable device, the method comprising:selecting, from a ranked list of possible measurement electrode configurations (MECs), the first N possible MECs in rank order that do not contain an unusable electrode, where TV is a number greater than one; and instructing the neural stimulation device to capture signal windows from signals sensed on the neural pathway subsequent to respective neural stimuli by one MEC of the N selected MECs.

35. The method of claim 34, further comprising generating the ranked list of possible MECs according to a corresponding predetermined list of ranked rules as applied to the selected SEC.

36. The method of claim 35, wherein the predetermined list of ranked rules is chosen to balance diversity of possible MECs with a signal-to-noise ratio of each MEC.

37. The method of any one of claims 34 to 36, further comprising selecting the one MEC of theN selected MECs.

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