Improved analysis of mixed electrophysiological responses to neural stimulation

Abstract

Identifying components of a mixed electrophysiological response to neural stimulation comprises: delivering neural stimuli to neural tissue; capturing an ensemble comprising a plurality of signals representing the mixed electrophysiological response of the neural tissue to the stimuli, each signal captured under different capture conditions; estimating a value of a characteristic of a component of the response by estimating a time delay between an occurrence of a feature template in a first signal from the ensemble and a recurrence of the feature template in a second signal from the ensemble, using the respective capture conditions of the first and second signals; repeating the estimating on a third signal and a fourth signal from the ensemble to form a set of estimated characteristic values, and clustering the estimated characteristic values within the set to obtain at least one cluster centre, wherein each cluster centre corresponds to a component of the response.

Description

IMPROVED ANALYSIS OF MIXED ELECTROPHYSIOLOGICAL RESPONSES TO NEURALSTIMULATION

[0001] The present application claims priority from Australian Provisional Patent Application No 2023902266 filed on 14 July 2023, the contents of which are incorporated herein by reference in their entirety.TECHNICAL FIELD

[0002] The present invention relates to neural stimulation and in particular to analysis of mixed electrophysiological responses to neural stimulation.BACKGROUND OF THE INVENTION

[0003] 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, Parkinson’s disease, and migraine. A neuromodulation device 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 device evokes a neural response known as an action potential in a neural fibre which then has either an inhibitory or excitatory effect. 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.

[0004] 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 device 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 Ap (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 frequency in the range of 30 Hz - 100 Hz.

[0005] 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 sufficient neurons 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, it is therefore desirable to apply stimuli with intensity below a discomfort threshold, above which uncomfortable or painful percepts arise due to over-recruitment of Ap fibres or recruitment of undesired fibre classes. When recruitment is too large, Ap fibres produce uncomfortable sensations. Stimulation at high intensity may even recruit AS (A-delta) fibres, which are sensory nerve fibres associated with acute pain, cold and heat sensation. It is therefore desirable to maintain stimulus intensity within a therapeutic range between the recruitment threshold and the discomfort threshold.

[0006] 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. There is room in the epidural space for the electrode array to move, and such array movement from migration or posture change alters the electrode-to-cord distance and thus the recruitment efficacy of a given stimulus. Moreover, the spinal cord itself can move within the cerebrospinal fluid (CSF) with respect to the dura. During postural changes, the amount of CSF or 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.

[0007] Another control problem facing neuromodulation devices of all types is achieving neural recruitment at a sufficient level for therapeutic effect, but at minimal expenditure of energy. The power consumption of the stimulation paradigm has a direct effect on battery requirements which in turn affects the device’s physical size and lifetime. For rechargeable devices, increased power consumption results in more frequent charging and, given that batteries only permit a limited number of charging cycles, this ultimately reduces the implanted lifetime of the device.

[0008] 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. WO2012 / 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 so as 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 sensed by a measurementelectrode 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.

[0009] 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 measured at 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.

[0010] Approaches proposed for obtaining a neural response measurement are described by the present applicant in International Patent Publication No. WO2012 / 155183, the content of which is incorporated herein by reference.

[0011] Applying neural stimulation therapy to pelvic floor disorders is well known in the field. Pelvic floor disorders include functions that are influenced by sacral nerves. For example, functions such as: urinary incontinence, urinary urge / frequency, urinary retention, pelvic pain, bowel dysfunction (constipation, diarrhea), and sexual dysfunction are some of the functions influenced by the sacral nerves.

[0012] There are many differences between spinal cord stimulation (SCS) and sacral nerve stimulation (SNS). Firstly, the anatomy is different in the spinal cord and the pelvic floor. Secondly, the dimension and the physical properties of leads differ significantly between the two modalities. Further, the target nerve in SNS is a mixed nerve while the target neural pathway in SCS, the dorsal column, is predominantly composed of A|3 fibres. Therefore, rather than having one fibre type (A|3 fibres), in SNS, a multitude of fibre types will be activated by stimulation. Mixed nerves produce a complex electrophysiological response when stimulated. Components of an electrophysiological response to SNS may be:• Evoked neural responses of different fibre types (Aa, A|3, Ay, AS, B, C, ...). These can be “early” (referred to herein as evoked compound action potentials or ECAPs, which propagate along fibres) or “late” (referred to herein as “late responses”, which do not propagate along fibres but essentially appear simultaneously at all locations in the tissue).• Myoelectric responses (referred to as “EMGs”) representing a muscular contraction. These responses do not propagate along nerves but are mid / far-field recordings of the muscular contraction that appear substantially simultaneously at all locations in the tissue. Theseresponses, although they do not themselves propagate, are activated by a propagating Aa efferent ECAP arriving at a certain location in the contracting muscular tissue.

[0013] It is, therefore, important to determine the type(s) of fibres recruited due to the stimulation of the sacral nerve, and how the degree of recruitment of each type varies with intensity, in order to provide the best outcome for the patient. In particular, stimulating certain fibre types could result in unpleasant side effects in the patient.

[0014] However, determining the components of an electrophysiological response is conventionally manual and difficult. Engineers / clinicians visually inspect signals obtained from different measurement and stimulus parameters and try to determine whether the electrophysiological response is of neural origin, myoelectric, or a mix of them. This is time consuming, requires a lot of training, and is limited by the natural ability of humans to detect patterns and interpret multi-dimensional relationships.SUMMARY OF THE INVENTION

[0015] The present invention seeks to provide methods and devices for automatically identifying the distinct components of a mixed electrophysiological response of a mixed nerve to neural stimulation, which will overcome or substantially ameliorate at least some of the deficiencies of the prior art, or at least provide an alternative. The disclosed methods and devices estimate a characteristic value of a component of the response across some subset of multiple sensed signals representing the response captured under different conditions. This may be repeated for multiple subsets. Clusters within the resulting ensemble of characteristic values represent respective components of the response. The methods and devices according to the present technology provide insight into the relationships of these distinct components and their respective stimulus and measurement parameters.

[0016] According to a first aspect of the present technology, there is provided a method of identifying one or more components of a mixed electrophysiological response to neural stimulation, the method comprising: delivering one or more neural stimuli to neural tissue of a patient; capturing an ensemble comprising a plurality of signals representing the mixed electrophysiological response of the neural tissue to the one or more neural stimuli, each signal being captured under different capture conditions; estimating a value of a characteristic of a component of the mixed electrophysiological response by estimating a time delay between an occurrence of a feature template in a first signal from the ensemble and a recurrence of the feature template in a second signal from the ensemble, whereinthe estimating a value comprises using the respective capture conditions of the first signal and the second signal; repeating the estimating on at least a third signal and a fourth signal from the ensemble to form a set of estimated characteristic values; and clustering the estimated characteristic values within the set to obtain at least one cluster centre, wherein each cluster centre corresponds to a component of the mixed electrophysiological response.

[0017] According to a second aspect of the present technology, there is provided a neuromodulation device comprising: a stimulus source configured to provide neural stimuli to be delivered via one or more of a plurality of implanted electrodes to neural tissue of a patient, the neural tissue comprising a mixed nerve; measurement circuitry configured to capture signals sensed on the neural tissue via one or more of the plurality of implanted electrodes subsequent to respective neural stimuli; and a control unit configured to: control the stimulus source to provide one or more neural stimuli; control the measurement circuitry to capture an ensemble comprising a plurality of signals representing a mixed electrophysiological response of the neural tissue to the one or more neural stimuli, each signal being captured under different capture conditions; estimate a value of a characteristic of a component of the mixed electrophysiological response by estimating a time delay between an occurrence of a feature template in a first signal from the ensemble and a recurrence of the feature template in a second signal from the ensemble, wherein the estimating comprises using the respective capture conditions of the first signal and the second signal; repeat the estimating on at least a third signal and a fourth signal from the ensemble to form a set of estimated characteristic values; and cluster the estimated characteristic values within the set to obtain at least one cluster centre, wherein each cluster centre corresponds to a component of the mixed electrophysiological response.

[0018] According to a third aspect of the present technology, there is provided a neurostimulation system comprising a neuromodulation device comprising: a stimulus source configured to provide neural stimuli to be delivered via one or more of a plurality of implanted electrodes to neural tissue of a patient, the neural tissue comprising a mixed nerve;measurement circuitry configured to capture signals sensed on the neural tissue via one or more of the plurality of implanted electrodes subsequent to respective neural stimuli; and a control unit configured to: control the stimulus source to provide a neural stimulus; and control the measurement circuitry to capture a signal subsequent to the provided neural stimulus; and a processor configured to: instruct the control unit to control the stimulus source to provide one or more neural stimuli; instruct the control unit to control the measurement circuitry to capture an ensemble comprising a plurality of signals representing a mixed electrophysiological response of the neural tissue to the one or more neural stimuli, each signal being captured under different capture conditions; estimate a value of a characteristic of a component of the mixed electrophysiological response by estimating a time delay between an occurrence of a feature template in a first signal from the ensemble and a recurrence of the feature template in a second signal from the ensemble, wherein the estimating comprises using the respective capture conditions of the first signal and the second signal; repeat the estimating on at least a third signal and a fourth signal from the ensemble to form a set of estimated characteristic values; and cluster the estimated characteristic values within the set to obtain at least one cluster centre, wherein each cluster centre corresponds to a component of the mixed electrophysiological response.

[0019] In some embodiments of the invention, said feature template may be a first portion of the first signal. Such embodiments may further comprise repeating the estimating for at least one further portion of the first signal, wherein said further portion differs from the first portion in at least one of starting position and length.

[0020] In some embodiments of the invention, said feature template is a predetermined sample sequence.

[0021] In some embodiments of the invention, the characteristic may be one of: conduction velocity; and normalised time delay.

[0022] In some embodiments of the invention, detecting the recurrence of the feature template in the second signal may comprise: correlating the feature template with the second signal to obtain a correlation function; and finding a location of a peak of the correlation function.

[0023] Some embodiments of the invention may further comprise filtering the signals of the ensemble into sub-signals before the estimating. In such embodiments, the estimating a value may comprise estimating a time delay between an occurrence of the feature template in a sub-signal of the first signal and a recurrence of the feature template in a sub-signal of the second signal. The filtering in some such embodiments may comprise complementary low-pass and high-pass filtering to obtain a low-pass sub-signal and a high-pass sub-signal. Some embodiments may further comprise decimating the low-pass sub-signal to form a residual low-pass sub-signal. Further embodiments may comprise repeating the complementary low-pass and high-pass filtering and the decimating on the residual low-pass sub-signal. In some embodiments the high-pass filter may be a wavelet filter. In some embodiments of the invention detecting an occurrence of the feature template in a high-pass sub-signal of the first signal may comprise finding a location of a peak of the high-pass sub-signal of the first signal. In some embodiments of the invention, detecting a recurrence of the feature template in a high-pass sub-signal of the second signal may comprise finding a location of a peak of the high- pass sub-signal of the second signal.

[0024] In some embodiments of the invention, the capture conditions may differ with respect to location of a recording electrode via which the signals are sensed. For example the characteristic may be normalised time delay. In some embodiments of the invention, a cluster centre corresponding to a characteristic value near zero corresponds to a non-propagating component of the mixed electrophysiological response.

[0025] In some embodiments of the invention, the capture conditions may differ with respect to location of a stimulus electrode via which the neural stimuli are delivered.

[0026] Some embodiments of the invention may further comprise using a size of a cluster to determine an indicator of confidence in the presence of the corresponding component in the mixed electrophysiological response.

[0027] Some embodiments of the invention may further comprise an external device configured to be in communication with the neuromodulation device. In such embodiments the processor may form part of the external device. In some embodiments of the invention the processor may form part of the neuromodulation device.

[0028] References herein to estimation, determination, comparison and the like are to be understood as referring to an automated process carried out on data by a processor operating to execute a predefined procedure suitable to effect the described estimation, determination and / or comparisonstep(s). 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 embodied as computer-readable code 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 and / or executed in a distributed fashion.BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Notwithstanding any other implementations which may fall within the scope of the present invention, implementations of the invention will now be described, by way of example only, with reference to the accompanying drawings, in which:

[0030] Fig. 1 schematically illustrates an implanted spinal cord stimulator, according to one implementation of the present technology;

[0031] Fig. 2 is a block diagram of the stimulator of Fig. 1;

[0032] Fig. 3 is a schematic illustrating interaction of the implanted stimulator of Fig. 1 with a nerve;

[0033] Fig. 4a illustrates an idealised activation plot for one posture of a patient undergoing neural stimulation;

[0034] Fig. 4b illustrates the variation in the activation plots with changing posture of the patient;

[0035] Fig. 5 is a schematic illustrating elements and inputs of a closed-loop neural stimulation (CLNS) system, according to one implementation of the present technology;

[0036] Fig. 6 illustrates the typical form of an electrically evoked compound action potential (ECAP) of a healthy subject;

[0037] 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;

[0038] Fig. 8 is a block diagram illustrating the data flow of a neural stimulation therapy system such as the system of Fig. 7;

[0039] Fig. 9 is a schematic representation of the functional separation of fibres in the ventral rootlets of a human S2 nerve in cross-section;

[0040] Fig. 10 is a graph showing captured signals representing an electrophysiological response obtained from the S3 sacral nerves of a human patient undergoing SNS therapy;

[0041] Fig. 11 is a flow chart illustrating a method of obtaining an ensemble of signals representing an electrophysiological response captured under different capture conditions, according to one aspect of the present technology;

[0042] Fig. 12 is a graph showing an ensemble of three captured signals representing an electrophysiological response captured at three different measurement electrode configurations;

[0043] Fig. 13 is a flow chart illustrating a method of analysing an ensemble of signals representing an electrophysiological response captured under different capture conditions to identify components of the response, according to one aspect of the present technology;

[0044] Fig. 14a is a graph illustrating estimates of a characteristic of components of the electrophysiological response represented by the ensemble of captured signals of Fig. 12, as obtained using the method of Fig. 13;

[0045] Fig. 14b is a histogram illustrating estimates of a characteristic of components of the electrophysiological response represented by the ensemble of captured signals of Fig. 12, as obtained using the method of Fig. 13;

[0046] Fig. 15 is a graph showing multiple stacked histograms of estimates of component characteristics, as obtained by repeatedly using the method of Fig. 13 at different stimulus intensities; and

[0047] Fig. 16 illustrates a dyadic filter bank with a depth of three levels of decomposition.DETAILED DESCRIPTION OF THE PRESENT TECHNOLOGY

[0048] 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 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 lead, 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.

[0049] 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.

[0050] Fig. 2 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, and the like. Controller 116 is configured by control programs 122, sometimes referred to as firmware, to control a pulse generator 124 to generate stimuli, such as in the form of electrical pulses, in accordance with the clinical settings 121. 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 or an analog-to-digital converter (ADC), is configured to process signals comprising neural responses sensed at measurement electrode(s) of the electrode array 150 as selected by electrode selection module 126.

[0051] 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 astimulus 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. 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 is referred to as the stimulus electrode configuration. 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 current return may be used in other implementations.

[0052] 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 to create paraesthesia at a desired location. To this end, the electrodes 2 and 4 are used to deliver stimuli periodically at any therapeutically suitable 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 is experienced by the user as paraesthesia. When a stimulus electrode configuration is found which evokes paraesthesia in a location and of a size which is congruent with the area of the patient’s body affected by pain and of a quality that is comfortable for the patient, the clinician or the patient nominates that configuration for ongoing use. The therapy parameters may be loaded into the memory 118 of the stimulator 100 as the clinical settings 121.

[0053] Fig. 6 illustrates the typical form of an ECAP 600 of a healthy subject, as recorded at a single measurement electrode referenced to the system ground 130. The shape and duration of the single- ended 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.

[0054] The ECAP may be recorded differentially using two measurement electrodes, as illustrated in Fig. 3. Differential ECAP measurements are less subject to common-mode noise on the surrounding tissue than single-ended ECAP measurements. 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 N1 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.

[0055] 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 Api and occurs at time Tpi. The amplitude of the positive peak P2 is Api and occurs at time Tpi. The amplitude of the negative peak Pl is Am and occurs at time Tm. The peak-to-peak amplitude is Api + 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.

[0056] 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 recording electrode 6 and 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 recording electrode and the reference electrode are referred to as the measurement electrode configuration. The measurement circuitry 128 for example may operate in accordance with the teachings of the above- mentioned International Patent Publication No. WO2012 / 155183.

[0057] 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. W02015 / 074121, 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 measureand store two or more characteristics from the neural response. The parameters of the ECAP detector, together with the measurement electrode configuration, make up the measurement parameters.

[0058] 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 is expected to be retrieved wirelessly by external device 192, which may occur only once or twice a day, or less.

[0059] 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 evoked by the stimulus (e.g. an ECAP 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 as:

[0060] where s is the stimulus intensity, y is the ECAP amplitude, T is the ECAP threshold and S is the slope of the activation plot (referred to herein as 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.

[0061] Fig. 4a also illustrates a discomfort threshold 408, which is a stimulus intensity above which the patient 108 experiences uncomfortable or painful stimulation. Fig. 4a also illustrates a perception threshold 410. The perception threshold 410 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 correspond to 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 correspond to 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.

[0062] 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.

[0063] 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.

[0064] 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 a target response intensity. For example, the devicemay calculate an error between a target ECAP amplitude and a measured ECAP amplitude, and adjust the applied stimulus intensity to reduce the error as much as possible, such as by adding the scaled error to the current stimulus intensity. A neuromodulation device that operates by adjusting the applied stimulus intensity based on a measured ECAP characteristic is said to be operating in closed- loop mode and will also be referred to as a closed-loop neural stimulation (CLNS) device. By adjusting the applied stimulus intensity to maintain the measured ECAP amplitude at an appropriate target response intensity, such as a target ECAP amplitude 520 illustrated in Fig. 4b, a CLNS device will generally keep the stimulus intensity within the therapeutic range as patient posture varies.

[0065] A CLNS device comprises a stimulator that takes a stimulus intensity value and converts it into a neural stimulus 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 bipolar etc.), and stimulus rate or frequency. At least one of the stimulus parameters, for example the stimulus amplitude, may be controlled by the feedback loop.

[0066] In an example CLNS system, a user (e.g. the patient or a clinician) sets a target response intensity, and the CLNS device performs proportional-integral-differential (PID) control. In some implementations, the differential contribution is disregarded and the CLNS device uses a first order integrating feedback loop. The stimulator produces 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.

[0067] 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 the target intensity. If the target intensity is properly chosen, the patient receives consistently comfortable and therapeutic stimulation through posture changes and other perturbations to the stimulus / response behaviour.

[0068] Fig. 5 is a schematic illustrating elements and inputs of a closed-loop neural stimulation (CLNS) system 300, according to one implementation of the present technology. The system 300 comprises a stimulator 312 which converts a stimulus intensity parameter (for example a stimulus current amplitude) s, in concert with a set of predefined stimulus parameters, to a neural stimulus comprising a sequence of electrical pulses on the stimulus electrodes (not shown in Fig. 5). According to one implementation, the predefined stimulus parameters comprise the number and order of phases, the stimulus electrode configuration (including the number of stimulus electrode poles), the pulse width, and the stimulus rate or frequency.

[0069] 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 electrodes. Various sources of measurement noise «, as well as the artefact a, may add to the evoked response y at the summing element 313 to form the sensed signal r, 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.

[0070] 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.

[0071] Measurement circuitry 318, which may be identified with measurement circuitry 128, amplifies the sensed signal r (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 a peak-to-peak 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 d to 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.

[0072] 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 intensityparameter s. According to such an implementation, the current stimulus intensity parameter s may be determined by the feedback controller 310 as s = f Kedt (2)

[0073] where K is the gain of the gain element 336 (the controller gain). This relation may also be represented as6s = Ke

[0074] where 5s is an adjustment to the current stimulus intensity parameter s.

[0075] A target ECAP amplitude is input to the feedback controller 310 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 CLNS system 300, 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.

[0076] A clinical settings controller 302 provides clinical settings to the system 300, including the feedback controller 310 and the stimulus parameters for the stimulator 312 that are not under the control of the feedback controller 310. In one example, the clinical settings controller 302 may be configured to adjust the controller gain K of the feedback controller 310 to adapt the feedback loop to patient sensitivity. The clinical settings controller 302 may comprise an input into the CLNS system 300, via which the patient or clinician can adjust the clinical settings. The clinical settings controller 302 may comprise memory in which the clinical settings are stored, and are provided to components of the system 300.

[0077] 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 sensed signal r (for example, operating at a sampling frequency of 16 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 stimulator 312 outputs a stimulus in accordance with the adjusted stimulus intensity .v. Accordingly, there is a delay of one stimulus clock cycle before the stimulus intensity is updated in light of the error value e.

[0078] 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). Theneuromodulation 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.

[0079] 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.

[0080] 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.

[0081] 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.Dataflow

[0082] Fig. 8 is a block diagram illustrating the data flow 800 of a neural stimulation therapy system such as the system 700 of Fig. 7 according to one implementation of the present technology. Neuromodulation device 804, once implanted within a patient, applies stimuli over a potentially long period such as weeks or months and records neural responses, clinical settings, target response intensity, and other operational parameters, discussed further below. Neuromodulation device 804 may comprise a Closed-Loop Neural Stimulation (CLNS) device, in that the recorded neural responses are used in a feedback arrangement to control clinical settings on a continuous or ongoing basis. To effect suitable SCS therapy, neuromodulation device 804 may deliver tens, hundreds or even thousands of stimuli per second, for many hours each day. The feedback loop may operate for most or all of this time, by obtaining sensed signals subsequent to every stimulus, or at least obtaining such sensed signals regularly. Each sensed signal generates a feedback variable such as a measure of the amplitude of the evoked neural response, which in turn results in the feedback loop changing atleast one stimulus parameter for a following stimulus. Neuromodulation device 804 thus produces 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. This is unlike past neuromodulation devices such as open-loop SCS devices which lack any ability to record any neural response.

[0083] When brought in range with a receiver, neuromodulation device 804 transmits data, e.g. via telemetry module 114, to a clinical programming application (CPA) 810 installed on a clinical interface. In one implementation, the clinical interface is the CI 740 of Fig. 7. The data can be grouped into two main sources: (1) Data collected in real-time during a programming session; (2) Data downloaded from a stimulator after a period of non-clinical use by a patient. CPA 810 collects and compiles the data into a clinical data log file 812.

[0084] All clinical data transmitted by the neuromodulation device 804 may be compressed by use of a suitable data compression technique before transmission by telemetry module 114 or before storage into the memory 118 to enable storage by neuromodulation device 804 of higher resolution data. This higher resolution allows neuromodulation device 804 to provide more data for postanalysis and more detailed data mining for events during use. Alternatively, compression enables faster transmission of standard-resolution clinical data.

[0085] The clinical data log file 812 is manipulated, analysed, and efficiently presented by a clinical data viewer (CDV) 814 for field diagnosis by a clinician, field clinical engineer (FCE) or the like. CDV 814 is a software application installed on the Clinical Interface (CI). In one implementation, CDV 814 opens one Clinical Data Log file 812 at a time. CDV 814 is intended to be used in the field to diagnose patient issues and optimise therapy for the patient. CDV 814 may be configured to provide the user or clinician with a summary of neuromodulation device usage, therapy output, and errors, in a simple single-view page immediately after log files are compiled upon device connection.

[0086] Clinical Data Uploader 816 is an application that runs in the background on the CI, that uploads files generated by the CPA 810, such as the clinical data log file 812, to a data server. Database Loader 822 is a service which runs on the data server and monitors the patient data folder for new files. When Clinical Data Log files are uploaded by Clinical Data Uploader 816, database loader 822 extracts the data from the file and loads the extracted data to Database 824.

[0087] The data server further contains a data analysis web API 826 which provides data for third- party analysis such as by the analysis module 832, located remotely from the data server. The ability to obtain, store, download and analyse large amounts of neuromodulation data means that the present technology can: improve patient outcomes in difficult conditions; enable faster, more cost effective and more accurate troubleshooting and patient status; and enable the gathering of statistics acrosspatient populations for later analysis, with a view to diagnosing aetiologies and predicting patient outcomes.Mixed nerve response analysis

[0088] Almost all major nerves in the periphery are of mixed nature, meaning that the nerve contains fibres of various types and functions that run together. The peripheral nerves bundle together at various stages and form the spinal nerves (such as the S3 sacral nerve, which is the main target for SNS). Before joining the spinal cord, the spinal nerves split up into the ventral and dorsal roots. In simplified terms, the ventral roots contain mostly a variety of efferent fibres, and the dorsal roots contain mostly a variety of afferent fibres. Mixed nerves therefore can contain both afferent and efferent fibre types. Mixed nerves are heterogenous collections of fascicles, and it has been shown that the fascicles bundle nerve fibres that serve similar functions and share common physiological properties. This separation of function can be observed from the rootlets which form the dorsal and ventral roots of each spinal nerve.

[0089] Fig. 9 is a schematic representation of the functional separation of fibres in the ventral root of a human S2 nerve in cross-section. The root consists of 2 rootlets. Three different nerve distribution patterns arise: the somatic type (labelled S) with predominantly large, thickly myelinated fibres and an absence of parasympathetic fibres; the vegetative type (labelled V) with an abundance of parasympathetic fibres; and the mixed type (labelled M). Note the topographic aggregation of the fascicles of vegetative and somatic types. The vegetative type fascicles with a predominance of parasympathetic fibres are concentrated in the right rootlet of the ventral root. In contrast, purely somatic fascicles are found in the left rootlet. It appears that the nerve fibres do not simply follow a random distribution, but rather some sort of functional organization.

[0090] Stimulation of any given subsection of a mixed nerve, for example by applying stimuli only from one side of the nerve at an amplitude which only recruits fibres in fascicles proximal to the stimulus electrode, will therefore activate a particular portion of fibres that serve a distinct function. The present technology recognises that targeting the appropriate fibres of a mixed nerve is of great importance for many neuromodulation applications, including SNS.

[0091] However, such targeting is in essence impossible from a purely anatomical approach, because the course of each fascicle in a mixed nerve varies along the nerve. The fascicles can cross, merge, and split, so that a desired fibre type takes significantly varying positions within the nerve at different parts of the nerve. Further, inter-patient variability of the content and disposition of fascicles within a nerve exists. These changes occur on scales which are significantly smaller than a typical stimulus electrode spacing, as typical implanted electrode arrays utilise electrodes which are 3mm long, andare 4mm apart from each other, i.e. positioned on a 7mm pitch. However, in the space of 7mm along a nerve, any given nerve fascicle could take any or all positions within the bundle making it impossible to effectively selectively target that fascicle if based only on surrounding anatomical orientations. Simplistically utilising smaller electrodes would not resolve these uncertainties.

[0092] Nevertheless, the present technology recognises that nerve fibre types can be identified based on their physiological properties, such as myelination state and diameter. These properties result in differences in electrophysiological properties that allow the classification of stimulated nerve fibre types through estimates of those electrophysiological properties. Examples of such electrophysiological properties include the conduction velocity of the evoked action potentials, refractory period, and strength-duration curve properties. For example, a linear relationship exists between the diameter of a myelinated fibre of a particular type and its conduction velocity. A range of nerve fibre type classification systems exist, however, regardless of nomenclature or classification used throughout the present disclosure, it is to be understood that it is the measures of differences in electrophysiological properties which permit identification of fibre types, irrespective of nomenclature.

[0093] Monopolar or single-ended recording is preferred in the case of stimulating anatomies with mixed nerves as there is a need to analyse the electrophysiological response in its entirety. A single- ended signal sensed via an ‘indifferent’ reference electrode that senses an insubstantial amount of the electrophysiological response allows identification of the activated components of the response more clearly than that of a signal sensed via a reference electrode that is somewhat responsive to the stimulus. Techniques for selecting an indifferent reference electrode are disclosed in International Patent Publication no. WO2023 / 235926 by the present applicant, the contents of which are herein incorporated by reference.

[0094] Fig. 10 is a graph showing captured signals representing an electrophysiological response obtained from the S3 sacral nerves of a human patient undergoing SNS therapy. The responses of Fig. 10 were obtained by stimulating from standard cylindrical (ring) electrodes, therefore preferentially activating the fibres on the side of the nerve which was in closest proximity to the electrode. In Fig. 10 each of the three signals were sensed via respective different measurement electrode configurations. The measurement electrode configurations respectively comprise consecutive recording electrodes along the lead with increasing distance from the stimulus electrode on the same lead (6 mm, 12 mm and 18 mm from the stimulus electrode, respectively) and a common indifferent reference electrode, so that neural responses propagating as action potentials along a nerve occur later in time on the signals from more distant recording electrodes. By contrast, nonpropagating components are not spaced apart in time across the respective signals, allowing neuralresponses to be distinguished from other electrophysiological activity. In Fig. 10, electrode 4 was used for stimulation, so that the signal 1010 (labelled as CH3) was recorded closest to the stimulus electrode and the signal 1020 labelled as CHI was recorded furthest from the stimulus electrode.

[0095] In Fig. 10 we observe an A|3 ECAP in the timeframe around 1-2 ms, and a myoelectric response between 4 and 10 ms. The ECAP in the timeframe around 1-2 ms may be specifically identified as an A|3 ECAP because of the finite conduction velocity and the distance of each recording electrode from the stimulus site. Further, the component observed in the timeframe around 4 to 10 ms is non-propagating, as evidenced by simultaneous appearance of this component on all three electrodes. While the fast conduction velocity of the A|3 ECAP means that this component is obscured by artefact in the very early part of the signals (e.g. in the period < 1 ms), the existence of an Aa (A- alpha) efferent response component may be inferred because of the existence of the non-propagating signal component (labelled “EMG”) observed in the timeframe around 4 to 10 ms. The EMG is an electrical field resulting from muscle activation and which therefore must arise due to Aa efferent activation (either from direct activation or via a reflex arc by means of, for example, la afferent fibre activation) as muscle activation is the role of Aa efferent fibres.

[0096] Nerve fibres are classified by their function as well as their physical properties (mostly myelination state and fibre diameter). It is important to note that this separation of physical properties and function is not absolute, and neural signals of a variety of functions are carried via fibres of similar properties (there are more functions than there are classes of fibres). As a rule of thumb, the diameter of myelinated fibres (in pm) is correlated to the conduction velocity (in m / s) by a factor of about 6. Thanks to this property, it is possible to use ECAPs measured from SNS electrodes to determine conduction velocity, and in turn to determine which fibre types are being recruited by the stimulation. This can improve the therapy on two levels: enabling aetiology-specific targeting of the appropriate nerves, as well as enabling closed-loop feedback control to maintain a stable therapy and improve effectiveness.

[0097] As the sacral nerves are non-homogeneous mixed nerves, as is illustrated in Fig. 9, the location of the lead with respect to the nerve fibres plays a large role in determining which fibre types are activated by SNS. Without intending to be being limited by theory, the signals representing the electrophysiological response captured using SNS electrodes can differentiate whether muscle efferents, sensory afferents, somatic afferents and efferents, parasympathetic fibres, or C fibres are being activated. The propagating response properties as well as the non-propagating response properties may be used to determine which fibre types are activated by the stimulation. For example, in Fig. 10, the presence of a non-propagating component indicates activation of Aa efferents (either directly or indirectly), and the presence of an A|3 component indicates sensory fibres are activated.

[0098] Fig. 11 is a flow chart illustrating a method 1100 of obtaining an ensemble of signals representing a complex electrophysiological response of neural tissue containing a mixed nerve to neural stimulation captured under different capture conditions, according to one aspect of the present technology. The method 1100 may be carried out by an external device, such as the clinical interface 740, in communication with a neuromodulation device such as the device 710. The clinical interface 740 may be configured to implement the method 1100 by a suitable Clinical Programming Application (CPA) stored in an instruction memory of the clinical interface. Alternatively, the method 1100 may be carried out by a controller such as the controller 116 of the electronics module 110 of the stimulator 100. The controller 116 may be configured to implement the method 1100 by firmware 122 stored in the memory 118 of the electronics module.

[0099] The method 1100 starts at step 1110, which chooses new signal capture conditions. The signal capture conditions for each iteration of the method 1100 comprise the stimulus parameters and the measurement parameters. In one example, step 1110 chooses a new stimulus electrode configuration while keeping the same measurement parameters. In another example, step 1110 chooses a new measurement electrode configuration while keeping the same stimulus parameters.

[0100] The next step 1120 instructs the pulse generator 124 to deliver a stimulus to the neural tissue according to the stimulus parameters of the chosen capture conditions. Step 1130 then instructs the measurement circuitry 128 to capture a signal window subsequent to the delivered stimulus, and representing the electrophysiological response of the neural tissue thereto, according to the measurement parameters of the chosen capture conditions. Step 1140 then adds the captured signal window to an ensemble of signal windows (also referred to herein as signals) representing the electrophysiological response.

[0101] Step 1150 then checks whether there are any more conditions under which a signal window is to be captured. If so (“Y”), the method 1100 returns to step 1110 to choose new capture conditions. Otherwise (“N”), the method 1100 ends at step 1160.

[0102] In some implementations, the method 1100 may not be implemented as a sequential loop over repeated stimuli delivered according to different stimulus parameters, but may instead capture multiple signal windows near-simultaneously according to different capture conditions subsequent to a single delivered stimulus. Such implementations are suitable for the case where the different capture conditions comprise different measurement parameters and the same stimulus parameters, as illustrated for example in Fig. 10.

[0103] Fig. 12 is a graph showing an ensemble 1200 of three captured signals 1210, 1220, and 1230 representing an electrophysiological response to a single stimulus. The signals 1210, 1220, and 1230 are captured at three different measurement electrode configurations. The signal 1210 is captured ata measurement electrode configuration that is closest to the stimulus electrode configuration through which the stimulus was delivered. The signal 1230 is captured at a measurement electrode configuration that is furthest from the stimulus electrode configuration. The signal 1220 is captured at a measurement electrode configuration that is at an intermediate distance to the stimulus electrode configuration. It may be seen that the peaks of the signals 1210, 1220, and 1230 are somewhat separated in time, indicating the presence of a propagating component in the electrophysiological response represented by the ensemble 1200.Pairwise analysis

[0104] Fig. 13 is a flow chart illustrating a method 1300 of analysing an ensemble S of signals, such as the ensemble 1200 of Fig. 12, representing an electrophysiological response captured under different capture conditions, for example using the method 1100, according to one aspect of the present technology. The method 1300 aims to identify one or more components of the response. The method 1300 may be carried out by the same device as the method 1100. Alternatively, if the method 1100 is carried out by the controller 116 of the electronics module 110 of the stimulator 100, the method 1300 may be carried out by an external device in communication with the stimulator 100, such as a suitably configured clinical interface 740.

[0105] The method 1300 starts at step 1310, which chooses a pair of signals (st, sj) from the ensemble S of signals. Step 1320 then determines a location of a feature template pkt within the signal st. In some implementations, the feature template pki represents an expected response component of a particular fibre type within the mixed response. In other implementations, the feature template pki is a portion of the signal st. The portion pki is some sequential subset of the samples of the signal st of a certain length, such as 100 samples at a sampling frequency of 16 kHz, and starting index. In such an implementation, the location of the feature template pki is the starting index of the portion pki within the signal st.

[0106] In other implementations of step 1320, the feature template pki is a predetermined sample sequence matching an expected response component of a particular fibre type. For example, the expected response component may be an ECAP, and the feature template pki may be an “ideal ECAP” such as the ECAP 600 illustrated in Fig. 6. In another such implementation, the predetermined sample sequence is the four-lobe ECAP-detecting filter disclosed in the above-mentioned International Patent Publication no. W02015 / 074121. In such implementations, the location of the feature template pki within the signal st is the location of a peak of the correlation of the feature template pki with the signalSi.

[0107] Step 1330 detects the recurrence of the feature template pki within the signal sj. In one implementation, step 1330 correlates the feature template pki with the signal j to produce a correlation function Cijk. The correlation function Cijk indicates the most probable location within the signal Sj of a recurrence of the feature template pki. Step 1330 then finds the location of the largest peak of the correlation function Cijk.

[0108] Step 1340 then converts the recurrence of the feature template pki within the signal j to a value tijk of a characteristic of a fibre type corresponding to a component of the response represented by the feature template pki. Step 1340 uses the capture conditions under which the signals i and j were captured in order to determine the characteristic estimate tijk. In one implementation, the characteristic estimate tijk is the delay in time between the location of the feature template pki within the signal i and its recurrence within the signal j, normalised by the distance between the recording electrode that was used to capture the signal j and the recording electrode that was used to capture the signal Sj. The normalised time delay tijk (in units of time per unit of length) is the inverse of the conduction velocity vijk of the fibre type corresponding to the component of the response represented by the feature template pki. In an alternative, the characteristic estimate tijk may be an estimate vijk of the conduction velocity. However, if the normalised time delay is near zero, as for a non-propagating component, such an estimate of conduction velocity may become dominated by measurement error and therefore effectively meaningless, so the normalised time delay is a more useful characteristic to estimate. Step 1340 then adds the characteristic estimate tijk to a set C of estimates of a characteristic from the ensemble S.

[0109] Step 1350 then checks whether there are more feature templates pki to be detected. If so (“Y”), the method 1300 returns to step 1320 to choose a new feature template pk’i of the signal i. In the implementation in which the feature template is a portion of the signal i, the new feature template pk’i may be a portion of a different length to the previous portion but the same starting index, or a portion of the same length as the previous portion but a different starting index, or a portion of a different length and a different starting index from the previous portion. In some implementations, the choice of the portions at step 1320 may be exhaustive in that all combinations of length and starting index are tried. In other implementations, the choice at step 1320 is more selective, for example favouring portions at or near to a starting index that is reasonable for a component corresponding to a certain conduction velocity.

[0110] If there are no more feature templates to be detected (“N” at step 1350), the method 1300 proceeds to step 1360, which checks whether there are any more signal pairs (si, j) to analyse. If so (“Y”), the method 1300 returns to step 1310 to choose a new signal pair ( i, j) to analyse. If not (“N”), step 1370 applies a clustering algorithm to the set C of characteristic estimates tijk. The clusteringalgorithm returns N cluster centres tnfor n = 1, N. In one implementation, the clustering algorithm is a K-means clustering algorithm. In another implementation, the clustering algorithm is a dBScan clustering algorithm. The N cluster centres tnin the set C correspond to distinct components of the electrophysiological response and consequently to the activation of distinct fibre types by the delivered stimuli. The relative size of each cluster found by the clustering algorithm at step 1370 may be used as an indicator of the confidence in the presence of the corresponding component in the response.

[0111] In the case where the different capture conditions comprise different measurement electrode configurations and the same stimulus parameters, and the characteristic being estimated is normalised time delay, a cluster centre near zero corresponds to a non-propagating component of the electrophysiological response, such as a component resulting from the contraction of muscular tissue.

[0112] In the case where the different capture conditions comprise different stimulus electrode configurations and the same measurement parameters, a non-propagating component may correspond to a cluster centre with a finite conduction velocity or a non-zero normalised time delay. This is because as the stimulus electrode moves with respect to the muscular tissue whose contraction is recorded as the non-propagating component, the ECAP evoked by the stimulus (e.g. in the Aa efferent fibre) that subsequently activates the muscular tissue contraction has a different distance to propagate to the location of activation, and therefore activates the muscular tissue at a different normalised time delay relative to the stimulus. Thus, the component corresponding to the muscular tissue contraction appears to propagate at a finite conduction velocity that will be greater than or equal to the conduction velocity of the Aa efferent fibres, depending on where the recording electrode is located relative to the contracting muscular tissue.

[0113] Fig. 14a is a graph 1400 illustrating estimates of a characteristic (conduction velocity) of components of the electrophysiological response represented by the captured signal ensemble 1200 of Fig. 12, as obtained using the method 1300 of Fig. 13 with portion lengths of 80, 200, 250, 300, 350, 400, 450, and 500 samples at a sampling frequency of 16 kHz. Each point on the graph, such as the point 1405, represents a conduction velocity estimate vyk from the set C, plotted against the length of the portion pkt that yielded that estimate (80 samples in the case of the point 1405). Also shown are horizontal lines 1410, 1420, 1430, and 1440, each of which represents a cluster centre tnfound by a clustering algorithm on the set C of conduction velocity estimates vyk. The solid lines 1410 and 1420 represent the cluster centres ti and ti found by a K-means clustering algorithm on the set S. The dashed lines 1430 and 1440 represent the cluster centres ti and ti found by a dBScan clustering algorithm on the set C. There is good agreement between the cluster centres ti, at around 15 m / s, and ti, at around11 m / s, found by the two clustering algorithms, indicating that the response represented by the ensemble 1200 comprises two propagating components corresponding to different fibre types.

[0114] The size of a cluster may be used to determine an indicator of confidence in the presence of the corresponding component in the response. For example, Fig. 14b is a histogram 1450 illustrating estimates of a characteristic (conduction velocity) of components of the electrophysiological response represented by the ensemble 1200 of captured signals of Fig. 12, as obtained using the method 1300 of Fig. 13. The histogram 1450 contains two peaks, a higher peak 1460 representing the cluster centre t2 at around 11 m / s, and a lower peak 1470 representing the cluster centre ti at around 15 m / s. The vertical axis (the number of estimates in each bin divided by the total number of estimates) may be used as an indicator of the confidence in the presence of the corresponding component in the response. It may be seen in Fig. 14b that the peak 1460 at around 11 m / s is of higher confidence than the peak 1470 at around 15 m / s.

[0115] Fig. 15 is a graph or “heat map” 1500 showing multiple stacked histograms of estimates of characteristics, as obtained by repeatedly using the method of Fig. 13 on respective ensembles captured at different stimulus intensities. Each row of the heat map 1500 represents one histogram such as the histogram 1450 of Fig. 14b, such that the lightness of the colour at any position along the row corresponds to the height of the histogram for the corresponding conduction velocity, for a given stimulus intensity, given in units of stimulus current. A heatmap such as the heat map 1500 shows how the activation of various fibre types varies with stimulus intensity. The heat map 1500 shows that a fibre type with a conduction velocity of 5 m / s is activated at a stimulus current of around 1 mA (the threshold current for that fibre type). If another fibre type were activated, the heat map 1500 would contain another vertical line of light colour at another position along the conduction velocity axis and possibly at a different threshold current.

[0116] In some implementations, the captured signals st making up the ensemble S may be filtered before applying the method 1300. In one such implementation, the captured signals st are filtered by complementary low-pass and high-pass filters to form a low-pass sub-signal and a high-pass subsignal Sj. The method 1300 is then carried out separately on the ensemble 5 of low-pass sub-signals Si and on the ensemble S of high-pass sub-signalsand all characteristic estimates are assembled into a single set C. The lengths of the portions pki may be dependent on whether the ensemble S of low-pass sub-signals or the ensemble S of high-pass sub-signals is being analysed, such that the portions pki are longer for the ensemble 5 of low-pass sub-signals than for the ensemble S of high- pass sub-signals. This is beneficial for the estimation of characteristics by correlation because the low-pass filtering de-emphasises higher-frequency features that are generally of shorter duration andemphasises lower-frequency features that are generally of longer duration, while the reverse is true for the high-pass filtering.

[0117] In some implementations, such complementary low-pass / high-pass filtering may be applied repeatedly to the low-pass filter output. The output of the low-pass filter at each iteration may be decimated by 2 before the next iteration. The result is a dyadic filter bank 1600 as illustrated in Fig. 16. The dyadic filter bank 1600 is of depth K = 3 in that there are 3 iterations of the complementary low-pass / high-pass filter pair labelled as 1620 and 1610 respectively, but other depths K may be contemplated (to an upper limit of log2( V) where N is the length of the signals si). The output of the dyadic filter bank 1600 when applied to the signal si is a set of K high-pass sub-signals {si ,Si ,s , and a residual low-pass sub-signal s . The method 1300 may then be applied to one or more of the ensembles Sk, where each ensemble Skis made up of all the high-pass sub-signals {s-C } at level k of the dyadic filter bank. The method 1300 may then also be applied to the ensemble SK, made up of the residual low-pass sub-signals {s } at level K of the dyadic filter bank. All the characteristic estimates from all the sub-signal pairs at all levels k may be stored in the same set C before the clustering step 1370. The decimation by 2 before each iteration in the dyadic filter bank 1600 (implemented by the decimator 1630) means that the length of the feature template may be kept constant when detecting its occurrence in a pair {s-Sof high-pass sub-signals in the ensemble Skor a pair {s , of residual low-pass sub-signals in the ensemble SK.

[0118] In a variant of such a filter-bank implementation, the complementary high-pass and low-pass filters 1610 and 1620 of the dyadic filter bank 1600 may be implemented respectively as a wavelet filter and its complementary scaling filter according to a conventional discrete wavelet decomposition. In such an implementation, the wavelet filter may be used as the feature template corresponding to a response component in the analysis method 1300. In such an implementation, the wavelet filter may resemble an ideal ECAP. The high-pass filtering stages therefore convolve the feature template (the wavelet filter) with the residual low-pass signal from the previous stage (or the original signal at the first stage) to form the high-pass sub-signals sk. This means that the location of the feature template within the high-pass sub-signals skand skat level k may be detected (as at steps 1320 and 1330 respectively of the method 1300) as the location of the highest peaks of the sub-signal s^and skrespectively. This is an efficient implementation as the wavelet filter acts as both a decomposition filter and as a feature template for detecting components of the response.INTERPRETATION

[0119] References herein to estimation, determination, comparison and the like are to be understood as referring to an automated process carried out on data by a processor operating to execute a predefined procedure suitable to effect the described estimation, determination or comparison step(s). 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-readable code 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.Wireless

[0120] The invention may be embodied using devices conforming to other network standards and for other applications, including, for example other WLAN standards and other wireless standards such as MICS. Applications that can be accommodated include IEEE 802.11 wireless LANs and links, and wireless Ethernet.

[0121] In the context of this document, 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 this document, 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 electromagnetic radiation through a solid medium. The term does not imply that the associated devices are coupled by electrically conductive wires.Processes

[0122] Unless specifically stated otherwise, as apparent from the following discussions, it is appreciated that throughout the specification discussions utilizing terms such as “processing”, “computing”, “calculating”, “determining”, “analysing” or the like, refer to the action and / orprocesses of a computer or computing system, or similar electronic computing device, that manipulate and / or transform data represented as physical, such as electronic, quantities into other data similarly represented as physical quantities.Processor

[0123] 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 and / or memory to transform that electronic data into other electronic data that, e.g., may be stored in registers and / or memory. A “computer” or a “computing device” or a “computing machine” or a “computing platform” may include one or more processors.

[0124] The methodologies 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 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. 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 and / or a static RAM, and / or ROM.Computer-Readable Medium

[0125] Furthermore, a computer-readable carrier medium may form, or be included in a computer program product. A computer program product can be stored on a computer usable carrier medium, the computer program product comprising a computer readable program means for causing a processor to perform a method as described herein.Networked or Multiple Processors

[0126] In alternative embodiments, the one or more processors operate as a standalone device 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 server-client 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.

[0127] 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 theinventive aspect. For example, while only a single machine is 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 methodologies discussed herein.Additional Embodiments

[0128] Thus, one embodiment of each of the methods described herein is in the form of a computer- readable carrier 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, embodiments of the present invention may be embodied as a method, an apparatus such as a special purpose apparatus, an apparatus such as a data processing system, or a computer-readable carrier medium. The computer-readable carrier medium carries computer readable code including a set of instructions that when executed on one or more processors cause a processor or processors to implement a method. Accordingly, aspects of the present invention may take the form of a method, an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of carrier medium (e.g., a computer program product on a computer-readable storage medium) carrying computer-readable program code embodied in the medium.Carrier Medium

[0129] 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, and / or associated caches and servers) that store the one or more sets of instructions. The term “carrier medium” shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by one or more of the processors and that cause the one or more processors to perform any one or more of the methodologies of the present invention. A carrier medium may take many forms, including but not limited to, nonvolatile media, volatile media, and transmission media.Implementation

[0130] It will be understood that the steps of methods discussed are performed in one embodiment by an appropriate processor (or processors) of a processing (i.e., computer) system executing instructions (computer-readable code) stored in storage. It will also be understood that the invention is not limited to any particular implementation or programming technique and that the invention maybe implemented using any appropriate techniques for implementing the functionality described herein. The invention is not limited to any particular programming language or operating system.Means For Carrying out a Method or Function

[0131] Furthermore, some of the embodiments 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 embodiment is an example of a means for carrying out the function performed by the element for the purpose of carrying out the invention.Implementations

[0132] Reference throughout this specification 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 invention. Thus, appearances of the phrases “in one implementation” or “in an implementation” in various places throughout this specification 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.

[0133] Similarly, it should be appreciated that in the above description of example implementations of the invention, various features of the invention 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 this invention.

[0134] 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 invention, and form different implementations, as would beunderstood by those in the art. For example, in the following claims, any of the claimed implementations can be used in any combination.

[0135] 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 and / or position to indicate that the value and / or position described is within a reasonable expected range of values and / 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 when a value is disclosed that "less than or equal to" the value, "greater than or equal to the value" and possible ranges between values are also disclosed, as appropriately understood by the skilled artisan. For example, if the value "X" is disclosed the "less than or equal to X" as well as "greater than or equal to X" (e.g., where X is a numerical value) is also disclosed. It is also understood that each unit between two particular units are also disclosed. For example, if 10 and 15 are disclosed, then 11, 12, 13, and 14 are also disclosed.Different Instances of Objects

[0136] As used herein, unless otherwise specified the use of the ordinal adjectives “first”, “second”, “third”, etc., to describe a common object, merely indicate that different instances of like objects are being referred to, and are 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

[0137] In the description provided herein, numerous specific details are set forth. However, it is understood that implementations of the invention 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 this description.Terminology

[0138] Throughout this specification, the terms "a" and "an" mean "one or more", unless expressly specified otherwise.

[0139] Throughout this specification, 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.

[0140] In this specification, 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.

[0141] In this specification the word “or” is to be read inclusively rather than exclusively, except where otherwise indicated.

[0142] Neither the title nor any abstract of the present application should be taken as limiting in any way the scope of the claimed invention.

[0143] Where the preamble of a claim recites a purpose, benefit or possible use of the claimed invention, it does not limit the claimed invention to having only that purpose, benefit or possible use.

[0144] 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.

[0145] 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 not be 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 that two or more elements are either 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.

[0146] It will be appreciated by persons skilled in the art that numerous variations or modifications may be made to the invention 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 fromthe 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 invention. The disclosed implementations are, therefore, to be considered in all respects as illustrative and not limiting or restrictive.

[0147] Any discussion of documents, acts, materials, devices, articles or the like which has been included in this specification is solely for the purpose of providing a context for the present invention. 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 invention as it existed before the priority date of each claim of this application.

[0148] 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 this specification, including in the claims, are to be understood as falling within the scope of the disclosure of this specification.INDUSTRIAL APPLICABILITY

[0149] It is apparent from the above that the arrangements described are applicable to the health care industries.LABEL LIST stimulator 100 external device 192 patient 108 system 300 electronics module 110 clinical settings controller 302 battery 112 target ECAP controller 304 telemetry module 114 box 308 controller 116 box 309 memory 118 feedback controller 310 clinical data 120 box 311 clinical settings 121 stimulator 312 control programs 122 element 313 pulse generator 124 measurement circuitry 318 electrode selection module 126 ECAP detector 320 measurement circuitry 128 comparator 324 system ground 130 gain element 336 electrode array 150 integrator 338 current pulse 160 activation plot 402 neural response 170 ECAP threshold 404 nerve 180 discomfort threshold 408 transcutaneous perception threshold 410 communications channel 190 therapeutic range 412activation plot 502 signal 1230 activation plot 504 method 1300 activation plot 506 step 1310ECAP threshold 508 step 1320ECAP threshol d 510 step 1330ECAP threshol d 512 step 1340 target ECAP amplitude 520 step 1350ECAP 600 step 1360 neural stimulation system 700 step 1370 neuromodulation device 710 graph 1400 remote controller 720 point 1405CST 730 solid line 1410CI 740 solid line 1420 charger 750 line 1430 method 1100 line 1440 step 1110 histogram 1450 step 1120 peak 1460 step 1130 peak 1470 step 1140 heat map 1500 step 1150 dyadic filter bank 1600 step 1160 low - pass filter 1610 ensemble 1200 low - pass filter 1620 signal 1210 decimator 1630 signal 1220

Claims

CLAIMS:

1. A method of identifying one or more components of a mixed electrophysiological response to neural stimulation, the method comprising: delivering one or more neural stimuli to neural tissue of a patient; capturing an ensemble comprising a plurality of signals representing the mixed electrophysiological response of the neural tissue to the one or more neural stimuli, each signal being captured under different capture conditions; estimating a value of a characteristic of a component of the mixed electrophysiological response by estimating a time delay between an occurrence of a feature template in a first signal from the ensemble and a recurrence of the feature template in a second signal from the ensemble, wherein the estimating a value comprises using the respective capture conditions of the first signal and the second signal; repeating the estimating on at least a third signal and a fourth signal from the ensemble to form a set of estimated characteristic values; and clustering the estimated characteristic values within the set to obtain at least one cluster centre, wherein each cluster centre corresponds to a component of the mixed electrophysiological response.2 The method of claim 1, wherein said feature template is a first portion of the first signal.3 The method of claim 2, further comprising repeating the estimating for at least one further portion of the first signal, wherein said further portion differs from the first portion in at least one of starting position and length.4 The method of claim 1, wherein said feature template is a predetermined sample sequence.5 The method of any one of claims 1 to 4, wherein the characteristic is one of: conduction velocity; and normalised time delay.6 The method of any one of claims 1 to 5, wherein detecting the recurrence of the feature template in the second signal comprises: correlating the feature template with the second signal to obtain a correlation function; and finding a location of a peak of the correlation function.

7. The method of any one of claims 1 to 6, further comprising filtering the signals of the ensemble into sub-signals before the estimating.

8. The method of claim 7, wherein the estimating a value comprises estimating a time delay between an occurrence of the feature template in a sub-signal of the first signal and a recurrence of the feature template in a sub-signal of the second signal.9 The method of claim 7, wherein the filtering comprises complementary low-pass and high- pass filtering to obtain a low-pass sub-signal and a high-pass sub-signal.10 The method of claim 9, further comprising decimating the low-pass sub-signal to form a residual low-pass sub-signal.11 The method of claim 10, further comprising repeating the complementary low-pass and high-pass filtering and the decimating on the residual low-pass sub-signal.12 The method of claim 11, wherein the high-pass filter is a wavelet filter.13 The method of claim 12, wherein detecting an occurrence of the feature template in a high- pass sub-signal of the first signal comprises: finding a location of a peak of the high-pass sub-signal of the first signal.14 The method of claim 12, wherein detecting a recurrence of the feature template in a high- pass sub-signal of the second signal comprises: finding a location of a peak of the high-pass sub-signal of the second signal.15 The method of any one of claims 1 to 14, wherein the capture conditions differ with respect to location of a recording electrode via which the signals are sensed.16 The method of claim 15, wherein the characteristic is normalised time delay.17 The method of claim 16, wherein a cluster centre corresponding to a characteristic value near zero corresponds to a non-propagating component of the mixed electrophysiological response.18 The method of any one of claims 1 to 14, wherein the capture conditions differ with respect to location of a stimulus electrode via which the neural stimuli are delivered.

19. The method of any one of claims 1 to 18, further comprising using a size of a cluster to determine an indicator of confidence in the presence of the corresponding component in the mixed electrophysiological response.

20. A neuromodulation device comprising: a stimulus source configured to provide neural stimuli to be delivered via one or more of a plurality of implanted electrodes to neural tissue of a patient, the neural tissue comprising a mixed nerve; measurement circuitry configured to capture signals sensed on the neural tissue via one or more of the plurality of implanted electrodes subsequent to respective neural stimuli; and a control unit configured to: control the stimulus source to provide one or more neural stimuli; control the measurement circuitry to capture an ensemble comprising a plurality of signals representing a mixed electrophysiological response of the neural tissue to the one or more neural stimuli, each signal being captured under different capture conditions; estimate a value of a characteristic of a component of the mixed electrophysiological response by estimating a time delay between an occurrence of a feature template in a first signal from the ensemble and a recurrence of the feature template in a second signal from the ensemble, wherein the estimating comprises using the respective capture conditions of the first signal and the second signal; repeat the estimating on at least a third signal and a fourth signal from the ensemble to form a set of estimated characteristic values; and cluster the estimated characteristic values within the set to obtain at least one cluster centre, wherein each cluster centre corresponds to a component of the mixed electrophysiological response.

21. The device of claim 20, wherein said feature template is a first portion of the first signal.

22. The device of claim 21, wherein the control unit is further configured to repeat the estimating for at least one further portion of the first signal, wherein said further portion differs from the first portion in at least one of starting position and length.

23. The device of claim 20, wherein said feature template is a predetermined sample sequence.

24. The device of any one of claims 20 to 23, wherein the characteristic is one of: conduction velocity; and normalised time delay.

25. The device of any one of claims 20 to 24, wherein the control unit is configured to detect the recurrence of the feature template in the second signal by: correlating the feature template with the second signal to obtain a correlation function; and finding a location of a peak of the correlation function.

26. The device of any one of claims 20 to 25, wherein the control unit is further configured to filter the signals of the ensemble into sub-signals before the estimating.

27. The device of claim 26, wherein the control unit is configured to estimate the value by estimating a time delay between an occurrence of the feature template in a sub-signal of the first signal and a recurrence of the feature template in a sub-signal of the second signal.

28. The device of claim 26, wherein the filtering comprises complementary low-pass and high- pass filtering to obtain a low-pass sub-signal and a high-pass sub-signal.

29. The device of claim 28, wherein the control unit is further configured to decimate the low- pass sub-signal to form a residual low-pass sub-signal.

30. The device of claim 29, wherein the control unit is further configured to repeat the complementary low-pass and high-pass filtering and the decimating on the residual low-pass subsignal.

31. The device of claim 30, wherein the high-pass filter is a wavelet filter.

32. The device of claim 31, wherein the control unit is configured to detect an occurrence of the feature template in a high-pass sub-signal of the first signal by: finding a location of a peak of the high-pass sub-signal of the first signal.

33. The device of claim 31, wherein the control unit is configured to detect a recurrence of the feature template in a high-pass sub-signal of the second signal by: finding a location of a peak of the high-pass sub-signal of the second signal.

34. The device of any one of claims 20 to 33, wherein the capture conditions differ with respect to location of a recording electrode via which the signals are sensed.

35. The device of claim 34, wherein the characteristic is normalised time delay.

36. The device of claim 35, wherein a cluster centre corresponding to a characteristic value near zero corresponds to a non-propagating component of the mixed electrophysiological response.

37. The device of any one of claims 20 to 33, wherein the capture conditions differ with respect to location of a stimulus electrode via which the neural stimuli are is delivered.

38. The device of any one of claims 20 to 37, wherein the control unit is further configured to us determine, using a size of a cluster, an indicator of confidence in the presence of the corresponding component in the mixed electrophysiological response.

39. A neurostimulation system comprising: a neuromodulation device comprising: a stimulus source configured to provide neural stimuli to be delivered via one or more of a plurality of implanted electrodes to neural tissue of a patient, the neural tissue comprising a mixed nerve; measurement circuitry configured to capture signals sensed on the neural tissue via one or more of the plurality of implanted electrodes subsequent to respective neural stimuli; and a control unit configured to: control the stimulus source to provide a neural stimulus; and control the measurement circuitry to capture a signal subsequent to the provided neural stimulus; and a processor configured to: instruct the control unit to control the stimulus source to provide one or more neural stimuli; instruct the control unit to control the measurement circuitry to capture an ensemble comprising a plurality of signals representing a mixed electrophysiological response of the neural tissue to the one or more neural stimuli, each signal being captured under different capture conditions; estimate a value of a characteristic of a component of the mixed electrophysiological response by estimating a time delay between an occurrence of afeature template in a first signal from the ensemble and a recurrence of the feature template in a second signal from the ensemble, wherein the estimating comprises using the respective capture conditions of the first signal and the second signal; repeat the estimating on at least a third signal and a fourth signal from the ensemble to form a set of estimated characteristic values; and cluster the estimated characteristic values within the set to obtain at least one cluster centre, wherein each cluster centre corresponds to a component of the mixed electrophysiological response.

40. The system of claim 39, wherein said feature template is a first portion of the first signal.

41. The system of claim 40, wherein the processor is further configured to repeat the estimating for at least one further portion of the first signal, wherein said further portion differs from the first portion in at least one of starting position and length.

42. The system of claim 39, wherein said feature template is a predetermined sample sequence.

43. The system of any one of claims 39 to 42, wherein the characteristic is one of: conduction velocity; and normalised time delay.

44. The system of any one of claims 39 to 43, wherein the processor is configured to detect the recurrence of the feature template in the second signal by: correlating the feature template with the second signal to obtain a correlation function; and finding a location of a peak of the correlation function.

45. The system of any one of claims 39 to 44, wherein the processor is further configured to filter the signals of the ensemble into sub-signals before the estimating.

46. The system of claim 45, wherein the processor is configured to estimate the value by estimating a time delay between an occurrence of the feature template in a sub-signal of the first signal and a recurrence of the feature template in a sub-signal of the second signal.

47. The system of claim 45, wherein the filtering comprises complementary low-pass and high- pass filtering to obtain a low-pass sub-signal and a high-pass sub-signal.

48. The system of claim 47, wherein the processor is further configured to decimate the low- pass sub-signal to form a residual low-pass sub-signal.

49. The system of claim 48, wherein the processor is further configured to repeat the complementary low-pass and high-pass filtering and the decimating on the residual low-pass subsignal.

50. The system of claim 49, wherein the high-pass filter is a wavelet filter.

51. The system of claim 50, wherein the processor is configured to detect an occurrence of the feature template in a high-pass sub-signal of the first signal by: finding a location of a peak of the high-pass sub-signal of the first signal.

52. The system of claim 50, wherein the processor is configured to detect a recurrence of the feature template in a high-pass sub-signal of the second signal by: finding a location of a peak of the high-pass sub-signal of the second signal.

53. The system of any one of claims 39 to 52, wherein the capture conditions differ with respect to location of a recording electrode via which the signals are sensed.

54. The system of claim 53, wherein the characteristic is normalised time delay.

55. The system of claim 54, wherein a cluster centre corresponding to a characteristic value near zero corresponds to a non-propagating component of the mixed electrophysiological response.

56. The system of any one of claims 39 to 52, wherein the capture conditions differ with respect to location of a stimulus electrode via which the neural stimuli are delivered.

57. The system of any one of claims 39 to 56, wherein the processor is further configured to us determine, using a size of a cluster, an indicator of confidence in the presence of the corresponding component in the mixed electrophysiological response.

58. The system of any one of claims 39 to 57, further comprising an external device configured to be in communication with the neuromodulation device.

59. The system of claim 58, wherein the processor forms part of the external device.

60. The system of any one of claims 39 to 57, wherein the processor forms part of the neuromodulation device.

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