WO2023087053A1

WO2023087053A1 - Circuits and methods for detecting biosignals in an implantable device

Info

Publication number: WO2023087053A1
Application number: PCT/AU2022/051364
Authority: WO
Inventors: Peter Scott Vallack SINGLE
Original assignee: Saluda Medical Pty Ltd
Priority date: 2021-11-16
Filing date: 2022-11-15
Publication date: 2023-05-25

Abstract

Measuring a biosignal having a lower frequency than a sampling rate comprises measuring at least a portion of the biosignal using a stateless amplifier, storing the measured biosignal, wherein the stored measured biosignal includes at least a portion of the biosignal; and repeating the measuring and storing based on a frequency of the biosignal in relation with the sampling rate, and reconstructing the measured biosignal based on one or more stored portions of the biosignal.

Description

CIRCUITS AND METHODS FOR DETECTING BIOSIGNALS IN AN IMPLANTABLE

DEVICE

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of New Zealand Provisional Patent Application No.

782407 filed 16 November 2021, which is incorporated herein by reference

TECHNICAL FIELD

[0002] The present invention relates to measuring low frequency biosignals and in particular to amplifier configurations and methods for measuring low frequency biosignals in an implantable device.

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 system applies an electrical pulse (stimulus) to neural tissue (fibres, or neurons) in order to generate a therapeutic effect. In general, the electrical stimulus generated by a neuromodulation system evokes a neural 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 system typically comprises an implanted electrical pulse generator, and a power source such as a battery that may be transcutaneously rechargeable by wireless means, such as inductive transfer. An electrode array is connected to the pulse generator, and is positioned 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 a response known as an action potential in the fibres. Action potentials propagate along the fibres in orthodromic (towards the head, or rostral) and antidromic (towards the cauda, or caudal) directions. The fibres being stimulated in this way 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 almost all neuromodulation applications, response from a single class of fibre is desired, but the stimulus waveforms employed can evoke action potentials in other classes of fibres which cause unwanted side effects. In pain relief, is therefore necessary to apply stimuli with intensity below a comfort threshold, above which uncomfortable or painful percepts arise due to over-recruitment of A[3 fibres. When recruitment is too large, A[3 fibres produce uncomfortable sensations. Stimulation at high intensity may even recruit A6 fibres, which are sensory nerve fibres associated with acute pain, cold and pressure sensation. It is therefore desirable to maintain stimulus intensity within a therapeutic range between the recruitment threshold and the comfort threshold.

[0006] The task of maintaining appropriate neural recruitment is made more difficult by electrode migration (change in position over time) and/or postural changes of the implant recipient (patient), either of which can significantly alter the neural recruitment 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-fibre 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 and/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 systems 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 systems, increased power consumption results in more frequent charging and, given that batteries only permit a limited number of charging cycles, ultimately this 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. WO 2012/155188 by the present applicant. Feedback control seeks to compensate for relative nerve / electrode movement by controlling the intensity of the delivered stimuli so as to maintain a substantially constant neural recruitment. The intensity of a neural response evoked by a stimulus may be used as a feedback variable representative of the amount of neural recruitment. A signal representative of the neural response may be generated by a measurement electrode in electrical communication with the recruited neural fibres, and processed to obtain the feedback variable. Based on the response intensity, the intensity of the applied stimulus may be adjusted to maintain the response within a therapeutic range.

[0009] It is therefore desirable to accurately detect and record 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 measurement are described by the present applicant in International Patent Publication No. WO 2012/155183, the content of which is incorporated herein by reference.

[0011] However, neural response measurement can be a difficult task as an observed CAP signal component in the measured response will typically have a maximum amplitude in the range of microvolts. In contrast, a stimulus applied to evoke the CAP is typically several volts, and manifests in the measured response as crosstalk of that magnitude. Moreover, stimulus generally results in electrode artefact, which manifests in the measured response as a decaying output of the order of several millivolts after the end of the stimulus. As the CAP signal can be contemporaneous with the stimulus crosstalk and/or the stimulus artefact, CAP measurements present a difficult challenge of measurement amplifier design. For example, to resolve a 10 pV CAP with 1 pV resolution in the presence of stimulus crosstalk of 5 V requires an amplifier with a dynamic range of 134 dB, which is impractical in implantable devices. In practice, many non-ideal aspects of a circuit lead to artefact, and as these aspects mostly result a time-decaying artefact waveform of positive or negative polarity, their identification and elimination can be laborious.

[0012] Evoked neural responses are less difficult to detect when they appear later in time than the artefact, or when the signal-to-noise ratio is sufficiently high. The artefact is often restricted to a time of 1 - 2 ms after the stimulus and so, provided the neural response is detected after this time window, a neural response measurement can be more easily obtained. This is the case in surgical monitoring where there are large distances (e.g. more than 12 cm for nerves conducting at 60 ms'¹) between the stimulating and measurement electrodes so that the propagation time from the stimulus site to the measurement electrodes exceeds 2 ms.

[0013] However, to characterize the responses from the dorsal column, high stimulation currents are required. Similarly, any implanted neuromodulation device will necessarily be of compact size, so that for such devices to monitor the effect of applied stimuli, the stimulus electrode(s) and measurement electrode(s) will necessarily be in close proximity. In such situations the measurement process must overcome artefact directly.

[0014] The difficulty of this problem is further exacerbated when attempting to implement CAP detection in an implanted device. Typical implanted devices have a power budget that permits a limited number, for example in the hundreds or low thousands, of processor instructions per stimulus, in order to maintain a desired battery lifetime. Accordingly, if a CAP detector for an implanted device is to be used regularly (e.g. once a second), then care must be taken that the detector should consume only a small fraction of the power budget.

[0015] A functional feedback loop can also produce useful data for live operation and/or postanalysis, such as observed neural response amplitude and applied stimulus intensity. However, device operation at tens of Hz over the course of hours or days quickly produces large volumes of such data which far exceed an implanted device’s data storage capacities.

[0016] Local field potentials (LFPs) are produced by the brain, including the spinal cord, when many neurons fire in approximate synchrony. These may be detected in the epidural space during walking, or as a result of stimulus on the limbs, for example, scratching. Measuring LFPs is useful for, say, varying stimulation strength with gait, to make stimulation stronger when it is less likely to be felt. Also measurable in the epidural space are voltages from EMGs, allowing a stimulator to detect the level of activity, for example, based on heartbeat, and signals from the central pattern generators. In terms of frequency, LFPs have a lower frequency range than ECAPs. These signals, as a set, will be referred to as Tow-frequency signals’ (LFSs) or low frequency biosignals to distinguish them from higher frequency biosignals such as evoked responses. An example recording is shown in Fig. 14.

[0017] Therefore, a need exists for a solution to detect low frequency biosignals such as local field potentials using the measurement circuitry in the implantable device.

[0018] Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is solely for the purpose of providing a context for the present invention. 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.

[0019] 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.

[0020] In this specification, a statement that an element may be “at least one 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.

SUMMARY OF THE INVENTION

[0021] Disclosed herein is an implantable device and methods for measuring low frequency biosignals using a measurement circuitry with higher sampling rates.

[0022] According to a first aspect of the present technology, there is provided an implantable device for measuring a biosignal having a fundamental frequency below that of a sampling rate. The implantable device comprises: a control unit configured to control a measurement circuitry, a memory unit and a reconstruction unit; wherein the measurement circuitry measures at least a portion of the biosignal, wherein the measurement circuitry comprises an amplifier; wherein the memory unit stores the portion of the measured biosignal, wherein the measured biosignal includes at least a portion of the biosignal; repeating the step of measuring and storing based on the frequency of the signal in relation with the sampling rate; and wherein the reconstruction unit configured to reconstruct the measured biosignal based on one or more portions of the biosignal.

[0023] In an embodiment, the measurement circuitry is configured to measure high frequency biosignals and has a higher sampling rate than the frequency of the biosignal. In some cases, there measurement circuitry may include separate circuits to measure high frequency biosignals and low frequency biosignals.

[0024] In another embodiment, the reconstruction unit includes an integrator. In some cases, the reconstruction unit may include an approximator configured to reconstruct the low frequency biosignals based on at least one of the portions of the biosignal that is measured.

[0025] In a further embodiment, the reconstruction unit includes an integrating circuit coupled to an Analog-to-Digital converter. In this case, the measured low frequency biosignal is digitized for further processing. Further processing may include extracting temporal and spectral features of the biosignal.

[0026] In yet another embodiment, the reconstruction unit includes a software code for reconstructing the stored portions of the biosignal. The software code may be a part of the control unit or a separate module. In some embodiments, the software code may be a part of an external device, where in the implantable device relays the measured signal to the external device.

[0027] In a second aspect, a system for reconstructing a biosignal from one or more samples is disclosed. The system is configured to: receive one or more portions of a measured low frequency biosignal; reconstruct the biosignal by integrating the one or more portions of the low frequency biosignal.

[0028] In an embodiment, the reconstruction is performed using one or more portions of the biosignal by applying an approximation algorithm.

[0029] In another embodiment, the reconstruction of the biosignal is performed using software or an electrical circuit. In an example, the measurement circuit may include an integrator coupled to an Analog to Digital converter. [0030] In a third aspect, a computer-readable storage medium comprising instructions that, when executed, cause one or more processors to: measure the biosignal, storing the measured signal, wherein the measured signal indicates at least a portion of the biosignal; store the measured portion of the biosignal; measuring the biosignal during a subsequent period, wherein the measured biosignal includes a subsequent portion of the signal; repeating the step of measuring and storing based on the frequency of the biosignal in relation with the sampling rate; and reconstructing the biosignal based on one or more portions of the biosignals.

[0031] In a fourth aspect, a measurement circuit configured to measure a biosignal is disclosed. The measurement circuit comprises: a differential amplifier; a source of drift current that causes the differential amplifier to become unstable; a switching unit to switch the polarity of the amplifier to counter the drift current.

[0032] In an embodiment, the switching unit is a chopper mechanism that eliminates the drift current by inducing noise.

[0033] In a fourth aspect, a method of measuring a biosignal having a fundamental frequency below that of a sampling rate, the method comprising: measuring a first portion of the biosignal, wherein the first portion indicates a portion of the signal; storing the first portion of the biosignal; measuring a second portion of the biosignal during a subsequent period, wherein the second portion of the biosignal includes a subsequent portion of the biosignal; repeating the step of measuring and storing based on the frequency of the biosignal in relation with a sampling rate; and reconstructing the biosignal based on one or more portions of the biosignal.

[0034] In an embodiment, the signal is a low frequency signal (LFS) include at least one of a heartbeat, a breathing, a movement, and a local field potential. The implantable device may be configured to effect one or more changes to the therapy based on the measured and/or reconstructed LFSs. In an example, the implantable device may detect motion related LFSs and adjust the stimulation based on the movement of the patient. Further, the based on the characteristic of LFSs the measurement circuitry may be able to classify the type of movement and adjust the stimulation pattern accordingly.

[0035] In another embodiment, reconstructing the signal includes at least one of integrating and approximating the portions of the biosignal. [0036] 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 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 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

[0037] One or more implementations of the invention will now be described with reference to the accompanying drawings, in which:

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

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

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

Fig. 4a illustrates an idealised activation plot for one posture of a patient undergoing neurostimulation;

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

Fig. 5 is a schematic illustrating elements and inputs of a closed loop neurostimulation system, according to one implementation of the present technology;

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

Fig. 7 is a block diagram of a neuromodulation therapy system including the implanted stimulator of Fig. 1 according to one implementation of the present technology;

Fig. 8 is a block diagram illustrating the data flow of a neuromodulation therapy system such as the system of Fig. 7; Fig. 9 is a schematic of a stage of an amplifier with zeroing switches used for measuring neural responses;

Fig. 10 illustrates a stage of an amplifier stage with blanking switches used for measuring neural responses;

Fig. 11 illustrates an amplifier output while measuring the neural response ;

Figs. 12A - 12E illustrates the phases of measuring a neural response;

Fig. 13 illustrates an exemplary circuit for measuring neural responses;

Fig. 14 illustrates traces of various types of local field potentials measured in a patient;

Fig. 15 illustrates a segment of the recording by an amplifier configured to measure local field potentials as well as high frequency neural responses;

Fig. 16 illustrates low frequency signal as measured by a zeroing amplifier;

Fig. 17 illustrates an exemplary reconstruction method according to one implementation of the present technology;

Fig. 18 illustrates the reconstruction method of Fig. 17 implemented in a measurement circuit;

Fig. 19 illustrates the reconstruction method of Fig. 17 implemented using software;

Fig. 20 illustrates a measurement circuit for detecting low frequency biosignals;

Fig. 21 illustrates drift current in amplifiers while measuring biosignals;

Fig. 22 illustrates method steps for reconstructing biosignals having a frequency lower than the sampling rate of the measurement circuit; and

Fig. 23 illustrates method steps for measuring high frequency biosignal and a low frequency biosignal.

DETAILED DESCRIPTION OF THE PRESENT TECHNOLOGY

[0038] 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 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 a flank or sub-clavicular. Stimulator 100 further comprises 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 the 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.

[0039] 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.

[0040] 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 communication 190, such as infrared (IR), radiofrequency (RF), capacitive and inductive transfer, may be used by telemetry module 114 to transfer power and/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, patient settings 121, control programs 122, and the like. Controller 116 controls a pulse generator 124 to generate stimuli, such as in the form of pulses, in accordance with the patient settings 121 and control programs 122. Electrode selection module 126 switches the generated pulses to the selected electrode(s) of electrode array 150, for delivery of the pulses to the tissue surrounding the selected electrode(s). Measurement circuitry 128, which may comprise an amplifier and / or an analog-to-digital converter (ADC), is configured to process measurements of neural responses sensed at measurement electrode(s) of the electrode array 150 as selected by electrode selection module 126.

[0041] Fig. 3 is a schematic illustrating interaction of the implanted stimulator 100 with a nerve 180 in the patient 108. In the implementation illustrated in Fig. 3 the nerve 180 may be located in the spinal cord, however in alternative implementations the stimulator 100 may be positioned adjacent any desired neural tissue including a peripheral nerve, visceral 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. A pulse may comprise one or more phases, e.g. a biphasic stimulus pulse 160 comprises two phases. The electrode selection module 126 selects a stimulus electrode 2 to deliver the pulse to surrounding tissue including nerve 180. Electrode selection module 126 also selects a return electrode 4 of the electrode array 150 for stimulus charge recovery in each phase, to maintain a zero net charge transfer. The use of two electrodes in this manner for delivering and recovering current in each stimulus phase is referred to as bipolar stimulation. Alternative embodiments may apply other forms of bipolar stimulation, or may use a greater number of stimulus electrodes. Electrode selection module 126 is illustrated as connecting to a ground 130 of the pulse generator 124 to enable stimulus charge recovery via the return electrode 4. However, other connections for charge recovery may be used in other implementations.

[0042] Delivery of an appropriate stimulus from stimulus electrodes 2 and 4 to the nerve 180 evokes a neural response comprising an evoked compound action potential 170 (ECAP) which will propagate along the nerve 180 as illustrated, 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 stimulus 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 “fit” 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 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, the clinician nominates that configuration for ongoing use.

[0043] Fig. 6 illustrates the typical form 600 of an ECAP of a healthy subject, as recorded at a single measurement electrode referenced to the system ground 130. The shape and duration of the ECAP 600 shown in Fig. 6 is predictable because it is a result of the ion currents produced by the ensemble of fibres depolarising and generating action potentials (APs) in response to stimulation. The evoked action potentials (EAPs) generated synchronously among a large number of fibres sum to form the ECAP 600. The propagation velocity of the AP on each fibre is determined largely by the diameter of that fibre. 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. [0044] The ECAP may be recorded differentially using two measurement electrodes, as illustrated in Fig. 3. Depending on the polarity of recording, a differential ECAP may take an inverse form to that shown in Fig. 6, i.e. a form having two negative peaks 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 form 600, or more generally the difference between the form 600 and a time-delayed copy thereof.

[0045] The ECAP 600 may be parametrised by any suitable parameter(s) of which some are indicated in Fig. 6. The amplitude of the positive peak Pl is Ap\ 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.

[0046] The stimulator 100 is further configured to sense the existence and intensity of ECAPs 170 propagating along nerve 180, whether such ECAPs are evoked by the stimulus from electrodes 2 and 4, or otherwise evoked. To this end, any electrodes of the array 150 may be selected by the electrode selection module 126 to serve as measurement electrode 6 and measurement reference electrode 8, whereby the electrode selection module 126 selectively connects the chosen electrodes to the inputs of the measurement circuitry 128. Thus, signals sensed by the measurement electrodes 6 and 8 are passed to the measurement circuitry 128, which may comprise an amplifier and an analog-to-digital converter (ADC). The measurement circuitry 128 for example may operate in accordance with the teachings of International Patent Application Publication No. WO2012155183 by the present applicant, the content of which is incorporated herein by reference.

[0047] Neural responses obtained from the measurement electrodes 6, 8 via measurement circuitry 128 are processed by controller 116 to obtain information regarding the effect of the applied stimulus upon the nerve 180. In some implementations, neural responses are processed by controller 116 in a manner which extracts and stores one or more parameters from each response or group of responses. In one such implementation, the parameter comprises a peak-to-peak ECAP amplitude in microvolts (pV). For example, the neural responses may be processed to determine the peak-to-peak ECAP amplitude in accordance with the teachings of International Patent Publication No. WO 2015/074121, the contents of which are incorporated herein by reference. Alternative implementations may extract and store an alternative parameter from the response to be stored, or may extract and store two or more parameters from the response. [0048] Stimulator 100 applies stimuli over a potentially long period such as days, weeks, or months and during this time may store parameters of neural responses, stimulation settings, paraesthesia target level, 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 parameters such as a measure of the amplitude 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 which may be stored in the clinical data store 120 of 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.

[0049] An activation plot, or growth curve, is an approximation to the relationship between stimulus intensity (e.g. an amplitude of the current pulse 160) and intensity of neural response 170 resulting from the stimulus (e.g. an ECAP 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 amplitude 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 amplitude 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 amplitude and the ECAP amplitude. Such a relationship may be modelled as:

where 5 is the stimulus amplitude, 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). The slope S and the ECAP threshold T are the key parameters of the activation plot 402. [0050] Fig. 4a also illustrates a comfort threshold 408, which is an ECAP amplitude above which the patient 108 experiences uncomfortable or painful stimulation. Fig. 4 also illustrates a perception threshold 410. The perception threshold 410 corresponds to an ECAP amplitude that is perceivable 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 amplitude 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 amplitude 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.

[0051] For effective and comfortable operation of an implantable neuromodulation device such as the stimulator 100, it is desirable to maintain stimulus amplitude within a therapeutic range. A stimulus amplitude within a therapeutic range is above the ECAP threshold 404 and evokes an ECAP amplitude that is below the comfort threshold 408. In principle, it would be straightforward to measure these limits and ensure that stimulus amplitude, 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.

[0052] 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 neurons. While the activation plots for only three postures, 702, 704 and 706, 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 708, 709 and 712 for the respective activation plots 702, 704, and 706. Additionally, as the patient’s posture changes, the slope of the activation plot also changes, as indicated by the varying slopes of activation plots 702, 704 and 706. In general, as the distance between the stimulus electrodes and the spinal cord increases, the ECAP threshold increases and the slope of the activation plot decreases. The activation plots 702, 704, and 706 therefore correspond to increasing distance between stimulus electrodes and spinal cord, and decreasing patient sensitivity.

[0053] 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 amplitude based on a feedback variable that is determined from one or more extracted ECAP parameters. In one implementation, the device may adjust the stimulus amplitude to maintain the extracted ECAP amplitude at a target response intensity. For example, the device may calculate an error between a target ECAP value 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 an extracted ECAP parameter is said to be operating in closed loop mode and will also be referred to as a closed loop neural stimulus (CLNS) device. By adjusting the applied stimulus intensity to maintain the extracted ECAP amplitude at an appropriate target response intensity, such as an ECAP target 720 illustrated in Fig. 4b, a CLNS device will generally keep the stimulus intensity within the therapeutic range as patient posture varies.

[0054] 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 characterised by multiple parameters including stimulus intensity (amplitude), pulse width, number of phases, order of phases, number of stimulus electrode poles (two for bipolar, three for tripolar etc.), and stimulus rate or frequency. At least one of the stimulus parameters, usually the stimulus intensity, is controlled by the feedback loop.

[0055] In an example CLNS system, a user (e.g. the patient or a clinician) sets a target neural response value, and the CLNS performs proportional-integral-differential (PID) control. In some implementations, the differential contribution is disregarded and the CLNS system 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 evoked neural response (e.g. an ECAP) is detected and its amplitude measured by the CLNS and compared to the target neural response value.

[0056] The measured neural response amplitude, and its deviation from the target neural response value, is used by the feedback loop to determine possible adjustments to the stimulus intensity parameter to maintain the neural response at the target value. If the target value is properly chosen, the patient receives consistently comfortable and therapeutic stimulation through posture changes and other perturbations to the stimulus / response behaviour.

[0057] Fig. 5 is a schematic illustrating elements and inputs of a closed loop neurostimulation 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 value) s, in accordance 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 number of stimulus electrode poles, the pulse width, and the stimulus rate or frequency.

[0058] The generated stimulus crosses from the electrodes to the spinal cord, which is represented in Fig. 5 by the dashed box 308. The box 309 represents the evocation of a neural response y by the stimulus as described above. The box 311 represents the evocation of an artefact signal a, which is dependent on stimulus intensity and other stimulus parameters, as well as the electrical environment of the measurement electrode. Various sources of noise n may add to the evoked response y at the summing element 313 before the evoked response is measured, including electrical noise from external sources such as 50 Hz mains power; electrical disturbances produced by the body such as neural responses evoked not by the device but by other causes such as peripheral sensory input, EGG, EMG; and electrical noise from amplifier 318.

[0059] 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 applied stimulus to evoke the response is typically several volts.

[0060] The total response signal r (including evoked neural response, artefact, and noise) is amplified by the signal amplifier 318 and then measured by the detector 320. The detector 320 outputs a measured response intensity d. In one implementation, the neural response intensity comprises an ECAP value. The measured response intensity d is then compared to a target ECAP value (set by the target ECAP controller 304) by the comparator 324 to produce an error value e. The error value e is input into the feedback controller 310.

[0061] The comparator 324 compares the ECAP value of the total response signal r to the target ECAP value as set by the target ECAP controller 304 and provides an indication of the difference between the ECAP value of the total response signal r and the target ECAP value to the feedback controller 310. This difference is the error value, e.

[0062] 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 value. Accordingly, the feedback controller 310 adjusts the stimulus intensity parameter 5 to minimise the error value, e. In one implementation, the controller 310 utilises a first order integrating function, using a gain element 336 and an integrator 338, in order to provide suitable adjustment to the stimulus intensity parameter 5. According to such an implementation, an adjustment 85 to the current stimulus intensity parameter 5 may be computed by the feedback controller 310 as

3s = J Kedt (2)

[0063] A target ECAP value is input to the comparator 324 via the target ECAP controller 304. In one embodiment, the target ECAP controller 304 provides an indication of a specific target ECAP value. In another embodiment, the target ECAP controller 304 provides an indication to increase or to decrease the present target ECAP value. The target ECAP controller 304 may comprise an input into the neural stimulus device, via which the patient or clinician can input a target ECAP value, or indication thereof. The target ECAP controller 304 may comprise memory in which the target ECAP value is stored, and provided to the comparator 324.

[0064] A clinical settings controller 302 provides clinical parameters to the system, including the gain K for the gain controller 336 and the stimulation parameters for the stimulator 312. The clinical settings controller 302 may be configured to adjust the gain value, K, of the gain controller 336 to adapt the feedback loop to patient sensitivity. The clinical settings controller 302 may comprise an input into the neural stimulus device, via which the patient or clinician can adjust the 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.

[0065] In some implementations, two clocks (not shown) are used, being a stimulus clock operating at the stimulus frequency (e.g. 60 Hz) and a sample clock for sampling the measured response r (for example, operating at 10 kHz). As the detector 320 is linear, only the stimulus clock affects the dynamics of the CLNS 300. On the next stimulus clock cycle, the stimulator 312 outputs a stimulus in accordance with the adjusted stimulus intensity 5. Accordingly, there is a delay of one stimulus clock cycle before the stimulus is updated in light of the error value e. [0066] Fig. 7 is a block diagram of a neuromodulation system 700. The neuromodulation system 700 is centred on a neuromodulation device 710. In one example, the neuromodulation device 709 may be implemented as the stimulator 100 of Fig. 1, implanted within a patient (not shown). The neuromodulation device 709 is connected wirelessly to a remote controller (RC) 711. The remote controller 711 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 709, including one or more of the following functions: enabling or disabling stimulation; adjustment of stimulation intensity; and selection of a stimulation control program from the control programs stored on the neuromodulation device 709.

[0067] The charger 750 is configured to recharge a rechargeable power source of the neuromodulation device 709. The recharging is illustrated as wireless in Fig. 7 but may be wired in alternative implementations.

[0068] The neuromodulation device 709 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.

[0069] The clinical interface 740 may be implemented as the external computing device 192 of Fig. 1. The CI 740 is configured to program the neuromodulation device 709 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 clinical interface 740.

[0070] Fig. 8 is a block diagram illustrating the data flow 800 of a neuromodulation 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, stimulation settings, paraesthesia target level, and other operational parameters, discussed further below. Neuromodulation device 804 may comprise a Closed Loop Stimulator (CLS), in that the recorded neural responses are used in a feedback arrangement to control stimulation 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 neural response recordings following every stimulus, or at least obtaining such recordings regularly. Each recording 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 the stimulation parameters 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.

[0071] 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.

[0072] 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 and/or before storage into the Clinical Data storage 120 to enable storage by neuromodulation device 804 of higher resolution data. This higher resolution allows neuromodulation device 804 to provide more data for post-analysis and more detailed data mining for events during use. Alternatively, compression enables faster transmission of standard-resolution clinical data.

[0073] 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.

[0074] 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.

[0075] 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 across patient populations for later analysis, with a view to diagnosing aetiologies and predicting patient outcomes.

Amplifiers for detecting biosignals

[0076] Fig. 9 is a schematic of a stage of an amplifier with zeroing switches, also referred to as a zeroed amplifier, used for measuring biosignals. The measurement may be obtained by passing a signal from a single sense electrode to a single-ended amplifier. Alternatively, as in Fig. 9, the measurement may be a differential measurement obtained by passing signals from two sense electrodes to a differential amplifier. Such an amplifier might have a gain of 10, with multiple stages being connected in series to obtain a higher gain. When the switches 920a and 920b are closed, the amplifier may have a gain of < 1 and its integrating capacitors 910a and 910b are discharged. The amplifier output is reset to zero, due to the discharging of the capacitors, when the switches 920a and 920b are closed. This allows for measurement amplifier to sense the neural response afresh after a first measurement, without retaining the values of the first measurement. Further, an amplifier is said to be ‘zeroed’ if its output is set to zero regardless of its input. Further, a person skilled in the art will note that a zeroed amplifier is blanked by definition, but a blanked amplifier need not be zeroed.

[0077] US Patent No. 9,386,934 (‘934), assigned to Saluda Medical, describes the method of using a zeroed amplifier that resets itself after each measurement interval, for measuring action potentials, and is incorporated herein by reference. Fig. 10 illustrates a stage of an amplifier stage with blanking switches used for measuring neural responses. The blanking switches 1002 are primarily used to eliminate the effect of stimulus artefacts. At the time when stimulus is applied to the tissue, the blanking switches 1002 are opened to disconnect the measurement electrodes, thereby preventing the measurement of the relatively high voltage of the stimulus as compared with the neural response. Stimuli can be in the volts as compared to a biosignal of interest in the tens of microvolts. [0078] An amplifier is said to be ‘blanked’ if, for a period, its output does not depend on its input. Amplifiers are often blanked during stimulus as their output is often meaningless during this time and will disturb subsequent circuitry. Fig. 11 illustrates an amplifier output 1010 while measuring the neural response. The amplifier output is null when the amplifier is blanked to avoid the stimulus transients. In the subsequent phase, the amplifier output indicates the neural response or the biosignal that is measured at the tissue. Thereafter, in the third phase, the amplifier output 1010 is reset to zero (zeroed) to prepare for the next neural measurement. Therefore, Fig. 11 indicates the output of an amplifier that is blanked and zeroed. The output 1010 of the amplifier illustrates the blanking interval and the subsequent measurement of the biosignal.

[0079] A biosignal such as an Evoked Compound Action Potential (ECAP) is generated after the application of stimulus to the tissue. The stimulus may be applied using an electrode configured to apply the stimulus pulse to the tissue. Further, a sense electrode connected to the measurement circuitry is configured to measure the biosignal evoked by the stimulus. In practical implementation a measurement amplifier used to measure the evoked response does not have infinite bandwidth, and will normally have infinite impulse response filter poles, and so the stimulus will produce an output during the evoked response, this output being referred to as electrical artefact.

[0080] The measurement amplifier output will therefore contain the sum of these various contributions. Separating the evoked response of interest from the artefacts is a major technical challenge. For example, to resolve a 10 pV SCP with 1 pV resolution, and have at the input a 5 V stimulus, requires an amplifier with a dynamic range of 134 dB. As the response can overlap the stimulus this represents a difficult challenge of amplifier design.

[0081] Further, US patent 9386934 (‘934), assigned to Saluda Medical, describes the method of using a blanked and zeroed amplifier for measuring action potentials, and is incorporated herein by reference. An amplifier such as the amplifier 900 may be referred to as ‘stateless’, as it does not maintain state across recording intervals. Fig. 12 illustrates an amplifier stage that may be used measuring the neural responses or biosignals. Figs. 12A - 12E, reproduced from ‘934, illustrates the phases of measuring an evoked potential using the measurement circuitry. The measurement circuitry may include a sample and hold circuit to record a parameter of the neural response. The first phase shown in FIG. 12A open circuits the stimulus electrodes 1208 and connects the measurement electrodes 1202 to the measurement amplifier 1210 by closing the switches 1206. The first phase shown in FIG. 12A allows the amplifier to settle, with no disturbance from the stimulating electrodes. [0082] In the second phase shown in FIG. 12B, the stimulus electrodes are short circuited to each other by closing the switches. This allows the stimulating electrodes to recover charge, so as to avoid DC injection to the tissue as is required for electrical implants.

[0083] In the third phase shown in FIG. 12C, the stimulation is applied by closing the switches 1208. The stimulus electrodes are switched to the current source, and the sample-and-hold remains in “hold” so that the large stimulus crosstalk seen on electrode 1102 is not presented to the measurement amplifier 1210. At the same time, switches 1206 are opened disconnecting the measurement electrodes from the applied stimulus voltage. This is referred to as ‘blanking’ as the amplifier 1210 is isolated from the transients induced by the stimulus.

[0084] The fourth phase shown in FIG. 12D provides for a post-stimulus delay. In this phase the stimulus electrodes are open circuited, and the sample-and-hold circuit which is a part of the measurement circuitry remains in the “hold” position, to allow the electrodes settle towards equilibrium, as defined by bio-electrical conditions. Further, zeroing switches 1204 are opened that completes the resetting of the measurement amplifier, effectively erasing any previous values in the amplifier. Therefore, the amplifier 1210 in the measurement circuitry 1200 is blanked and reset at various instances during measurement of the neural response.

[0085] Finally, in the fifth phase shown in FIG. 12E, the SCP present at sense electrode 1202 is measured by switching the sample-and-hold circuit to “sample”.

[0086] When performing repeated measurement cycles in this fashion, it is noted that the switch positions are the same in the phase 1 “settling” and the phase 5 “measuring” states. Thus, the state of phase 5 is maintained, by virtue of a subsequent phase 1, until the electrodes and circuitry are in equilibrium, even after the time that useful SCP data is no longer present or being captured. Such embodiments thus provide a greater length of the “settle” state.

[0087] FIG. 13 illustrates an exemplary circuit 1300 for measuring evoked potentials. The measurement circuit 1300 has a fast first stage (not shown) that could follow the stimulus pulse, while being disconnected from a slower second stage. When the stimulus phase is complete and transients settled, the second stage is enabled. The average value of the tissue voltage is stored on C1302 and used as the reference voltage for the following stimulus cycle. The second stage amplifies the difference between its input and the reference voltage. [0088] The time constant of the resistor R1304 and capacitor C1302 is 120 milliseconds. In circuits where the sampling rate is 50Hz (20ms interval) several stimulation cycles are required for the tissue voltage to stabilize. This voltage stored on C1302 is referred to in the ‘934 patent as the ‘bioelectrically defined steady state’ voltage.

Local Field Potentials

[0089] Local field potentials (LFPs) are produced by the brain, including the spinal cord, when many neurons fire in approximate synchrony. LFSs or LFPs have a much lower frequency (about 0.1 Hz to a few Hz) than ECAPs. Therefore, the LFSs may be detected in the epidural space during walking, or as a result of external stimulus on the limbs, for example, scratching. Measurement of LFSs is useful for, say, varying stimulation strength with gait, to make stimulation stronger when it is less likely to be felt. Further, in the epidural space one may measure LFSs from heartbeat and breathing, allowing a stimulator to detect the level of activity (movement) of the subject. Throughout the present disclosure, the term Tow-frequency signals’ (LFSs) is used interchangeably with Tow-frequency potentials’ (LFPs) or low frequency biosignals. The LFSs have a frequency lower than the high frequency biosignals such as evoked responses. LFSs may include non-evoked neural responses that may be generated based on various physiological activities such as movement, breathing, heartbeat, swallowing, bladder function and the like.

[0090] In an exemplary implementation, the amplifier used for measuring ECAPs (see Fig. 13) may be modified for measuring LFPs. In an instance, the low frequency comer of the measurement circuit may be lowered to detect LFPs. The higher RC time constant allows for detection of lower frequency signals such as heartbeats and breathing. FIG. 14 illustrates traces 1400 of movement related local field potentials measured in a patient. FIG. 14 shows movement related LFSs such as rubbing leg 1404, lifting leg 1406, and walking 1408 measured using a measurement circuitry configured to measure LFSs. Further FIG. 14 also shows the periods where there is no movement, such as 1402, which aids in the measurement circuitry to discern whether the patient is moving or stationary. Further, the based on the characteristic of LFSs the measurement circuitry may be able to classify the type of movement and adjust the stimulation pattern accordingly. Based on the capability of measuring the LFSs the implantable device will be able to adjust the stimulation solely based on the measured movement. The trace in FIG. 14 also includes artefacts from the recording process (labelled as ‘blanking’ 1502) and 50Hz mains noise. The graph in FIG. 15 represents a raw signal measured in a patient showing various LFSs along with sources of perturbances such as wireless communications 1506 and AC mains 1508. The LFS detected in Fig 15 is the heartbeat 1504, when post processing is not applied to the raw signal for extracting ECAPs. In some examples, the implantable device may be configured to take action based on the characteristics of the raw signal.

LFP Reconstruction

[0091] Now, a zeroed amplifier 900 as explained in conjunction with FIG. 9 maintains no state between one ECAP recording interval and the next, and therefore, may be incapable of making a measurement such as FIG. 15. The amplifier as shown in FIG. 9 resets itself to zero for each measuring each portion of the biosignal. If this mechanism is applied to a low frequency signal, for example, a sine wave, the signal is distorted as in FIG. 16, in which one can observe the effect of zeroing on a low frequency signal 1600. Each measuring interval of the stateless amplifier is, for example, 1602. Each portion of a low frequency signal is detected independently at every measurement interval of the zeroed amplifier. Therefore, the low frequency signal is not recorded accurately in a zeroed amplifier.

[0092] Embodiments in the present disclosure represent a measurement system that records a signal whose fundamental frequency is below that of the sampling rate, while using an amplifier that resets after recording high frequency biosignals, such as the amplifier of Fig. 9. In other words, LFSs and ECAPs could be recorded using an amplifier that resets itself after each measuring interval as disclosed herein.

[0093] In an exemplary embodiment, one method of reconstruction is to add the value of the last sample of each portion of the output waveform to the entire next portion. Fig. 17 illustrates an exemplary reconstruction method. In the graph 1700, a current sample of the low frequency signal is integrated with the previous samples of the signal to reconstruct the complete signal. The amplifier output 1702, which is at least a portion of the sensed low frequency signal, is added to the previously sensed portions 1704 of the low frequency signal. The reconstructed signal 1704 is obtained after all the portions of LFS are integrated consecutively. In case some portions are corrupted due to noise, it may be approximated based on the rest of the portions.

[0094] Fig. 18 illustrates a measurement circuit 1800 that measures portions of the biosignal, according to the present technology. In Fig. 18, the measurement circuit 1800 includes a stateless amplifier 1801 coupled to an Analog to Digital converter 1802. The Z-A ADC 1802 outputs the digitized version of the measure portion biosignal to aid further processing, such as feature extraction or spectral analysis. A person skilled in the art may acknowledge that there could be other types of ADC modules that can be used to digitise the measured biosignal.

[0095] In an implementation, the digitised portions of the biosignals are then reconstructed based on the mathematical equations as given in Equation 3. If the nth portion is referred to as V_n[t], and the last sample of each portion is referred to as V_n[—1], (following the convention from the language Python), then the modified portion F_n' is given by:

[0096] This also follows the Python convention that adding a scalar to a vector implies adding that scalar to each element of the vector. This method of reconstruction may be implemented using a software application. Fig. 19 illustrates the reconstruction method implemented using a combination 1900 of hardware and software. The control unit 1902 may receive a portion of the biosignal measured by measurement circuitry 1800 and store it in a memory unit 1904 for further processing. In this case, the measured biosignal includes a portion of the low frequency biosignal as the sampling rate of the measurement circuit is higher than the fundamental frequency of the biosignal. Further, the control unit 1902 may store the subsequent measured biosignals in the memory unit 1904. When the measurement circuit completes measuring the biosignal, the control unit 1902 enables the reconstruction unit 1906 to reconstruct the biosignal. In an instance, the reconstruction unit 1906 may include software instructions that use equation 3 to reconstruct the low frequency biosignal. In some other embodiments, the reconstruction unit 1906 may use approximation methods to reconstruct the measured biosignal based on the one or more portions stored in the memory unit 1904.

[0097] In some embodiments, a computer-readable storage medium comprises instructions that, when executed, cause one or more processors to: measure the biosignal, store the measured signal, wherein the measured signal indicates at least a portion of the biosignal; store the measured portion of the biosignal; measure the biosignal during a subsequent interval, wherein the measured biosignal includes a subsequent portion of the signal; repeat the steps of measuring and storing based on the frequency of the biosignal in relation with the sampling rate; and reconstruct the biosignal based on one or more portions of the biosignals. A person skilled in the art will acknowledge that there are methods to reconstruct the portions of the biosignal using software only. [0098] This method does not recover data lost during the blanking interval. If the product of the blanking interval and the stimulus frequency is short compared to the interval of the signal being captured, this will create a distortion that may be negligible.

[0099] However, a person skilled in the art would recognise that there are advanced methods to estimate the signal that is lost during the blanking interval. For example, the system may estimate the slew rate of the signal just prior to the blanking interval and use the slew rate to estimate the most likely size of the discontinuity. Further, higher-order extrapolation methods may be used to estimate the lost portion of the signal.

[00100] A person skilled in the art will acknowledge that there may be other methods to measure the LFSs in a single recording interval rather than the summing the subsequent samples. However, the method described herein enables sensing LFSs using the amplifier that is configured to detect high frequency biosignals, such as ECAPs.

Alternative embodiments

[00101] FIG. 20 illustrates an amplifier circuit 2000 that maintains state between recording intervals, according to an embodiment. In a practical design, the resistors could be implemented as switched capacitors. The time constant of the RC networks 2004/2006 in the feedback paths is chosen to be much longer than the ECAP recording interval, so the amplifier retains state. In some embodiments, the time constant (values of R 2004 and C 2006) are chosen based on the frequency of the biosignal to be detected. In this case, there is no need to reconstruct the signals and the output of the amplifier could be used directly for the desired purpose.

[00102] In an instance, a first, high bandwidth, amplifier may be used for measuring ECAPs while a second amplifier may be used to detect LFSs. In other words, a stateless amplifier may be used for measuring ECAPs whereas an amplifier, low bandwidth, that maintains state could be used for recording LFSs. An advantage of the dual amplifier approach is that the low-bandwidth amplifier configured to measure LFSs could run at much lower power which would aid battery life.

[00103] In some other embodiments, the measurement circuitry could include chopper-stabilized amplifiers, zero-drift amplifiers, and similar amplifiers arrangement to detect ECAPs and LFSs. [00104] Further, the implantable device may be configured to effect one or more changes to the therapy based on the measured and/or reconstructed LFSs. In an example, the implantable device may detect motion related LFSs and adjust the stimulation based on the movement of the patient.

Amplifier Drift

[00105] Fig. 21 illustrates drift current in amplifiers while measuring biosignals. In medical applications, the input signals, such as neural responses or the LFSs generated in the tissue, may have a relatively low frequency spectrum, often, a corner frequency as low as ~0.1Hz to a few Hz. Therefore, Sample and Hold Amplifier (SHA) circuits, switched capacitor filters or amplifiers must be able to handle hundreds of milliseconds of hold time. In a standard sub-micron CMOS process, the leakage current of a minimum size transistor is in the order of pico Amperes. If the amplifier has a systematic drift, for example, due to parasitic leakage, then the amplifier circuit may lead to an unbounded result due to the leakage current due to the integrating effect of the capacitor. The measurement circuit 2100 includes a leakage current source 2102. In an embodiment, the amplifier drift may be addressed by flipping the amplifier polarity for each portion. Additionally, or alternatively, the drift current may be eliminated using a chopping mechanism, where the high- frequency components, like the biosignal, is amplified by the differential amplifier with its superior bandwidth, but the DC component being amplified by the chopper amplifier for which the amplifier is simply a buffer. The application of chopper stabilised amplifiers to counter the effects of drift current are known in the art. In a further embodiment, a high-pass filter may be applied to attenuate the drift current.

Method Flowcharts

[00106] Fig. 22 illustrates method steps for a method 2200 of reconstructing biosignals having a frequency lower than the sampling rate of the measurement circuit. At step 2202, a first portion of the biosignal is measured, wherein the first portion indicates at least a portion of the biosignal. In a preferred embodiment, the frequency of the biosignal is lower than the sampling rate of the measurement circuitry. Therefore, at any instance, only a portion of the biosignal is measured. At step 2204, the first portion of the biosignal is stored in a memory location. The memory location may be located in a memory unit, which includes volatile or non-volatile memory. At step 2206, a second portion of the biosignal during a subsequent interval is measured, wherein the second portion of the biosignal includes a subsequent portion of the signal. Depending on the frequency of the biosignal there may be multiple portions of the biosignal measured by the measurement circuitry. At step 2208, a decision is made whether the measurement of the biosignal is complete. If the measurement of the biosignal is not complete, then the step 2206 of measuring and storing the portions of the biosignal is repeated until the biosignal is measured completely. In some cases, an average measurement interval, based on the biosignal being sensed, may be set to eliminate step 2208. In some cases, the step 2206 of measuring and storing is repeated based on the frequency of the biosignal in relation with a sampling rate of the measurement circuitry. At step 2210, the biosignal may be reconstructed based on one or more stored portions. In some embodiments, the step of storing may be eliminated and the portions of the measured biosignal may be used to reconstruct the biosignal in real-time.

[00107] Fig. 23 illustrates method steps for a method 2300 of measuring a high frequency biosignal and a low frequency biosignal. At step 2302, a biosignal is measured, wherein the biosignal may include at least one of a high frequency signal like ECAPs and a low frequency signal like a heart rate or LFP. At 2304, a decision is made if the measured biosignal is a high frequency signal or a low frequency signal. In case the measured biosignal is a high frequency signal, the method moves to step 2306, where the high frequency signal is measured in a single interval and a reconstruction is not required. In case the measured signal is a low frequency signal, the method follows step 2308 wherein the measured portion of the low frequency biosignal is stored in a memory unit. At step 2310, the method steps 2302 through 2308 are repeated until the complete biosignal is measured. Further, at step 2312 the measured low frequency biosignal is reconstructed, for example, by integrating the stored measured biosignals as previously described.

[00108] In some embodiments, there may be separate amplifiers for detecting the high frequency biosignals and low frequency signals. In such a case, there is no reconstruction step involved as there is no need to sense a low frequency signal using an amplifier tuned to detect high frequency signals.

Advantages

[00109] The present specification discloses methods and systems for recording signals having frequencies below the stimulus rate. The system as presented herein measures evoked responses as well as LFSs. In an exemplary instance, the system teaches methods to employ an amplifier configured to detect high frequency biosignals to measure low frequency biosignals or LFPs. The ability to detect LFSs enables a host of functionality to the implantable device. In an instance, the control unit could adjust the stimulation based on LFSs such as movement or heartbeat. In certain therapies, the stimulation could start/stop based on LFSs. In some other cases, the stimulation intensity is varied based on the measured LFSs. In some further embodiments, both evoked responses and LFSs are considered in order to adjust the therapy. The control unit may be configured to record parameters such as amplitude, latency, frequency, peak to peak ratio and other spectral and morphological characteristics of LFSs.

[00110] Further, a method for countering drift enables a reliable performance of the amplifier while measuring low amplitude biosignals. The cumulative effects of amplifier drift are avoided by reversing its connection polarity.

[00111] It will be appreciated by persons skilled in the art that numerous variations and/or modifications may be made to the invention as shown in the specific embodiments without departing from the spirit or scope of the invention as broadly described. The present embodiments are, therefore, to be considered in all respects as illustrative and not limiting or restrictive.

LABEL LIST

Claims

CLAIMS:

1. A system for measuring a biosignal having a lower frequency than a sampling rate, the system comprising: a control unit configured to control a measurement circuitry, a memory unit and a reconstruction unit; wherein the measurement circuitry is configured to measure at least a portion of the biosignal, wherein the measurement circuitry comprises a stateless amplifier; wherein the memory unit is configured to store the measured biosignal, wherein the stored measured biosignal includes at least a portion of the biosignal; and the control unit configured to cause the measurement circuitry and memory unit to repeat the steps of measuring and storing based on a frequency of the biosignal in relation with the sampling rate of the measurement circuitry; and wherein the reconstruction unit is configured to reconstruct the measured biosignal based on one or more stored portions of the biosignal.

2. The system of claim 1, wherein the measurement circuitry is configured to measure high frequency biosignals.

3. The system of claim 1, wherein the measurement circuitry has a sampling rate higher than the biosignal.

4. The system of claim 1, wherein reconstruction unit includes an integrator.

5. The system of claim 1, wherein the reconstruction unit includes an integrator coupled to an £-A Analog-to-Digital converter.

6. The system of claim 1, wherein the reconstruction unit includes a software code for reconstructing the measured biosignal.

7. An implantable device for measuring a biosignal having a lower frequency below that of a sampling rate, the implantable device comprising: a control unit configured to control a measurement circuitry, a memory unit and a reconstruction unit; wherein the measurement circuitry is configured to measure at least a portion of the biosignal, wherein the measurement circuitry comprises an amplifier; wherein the memory unit is configured to store the measured biosignal, wherein the measured biosignal includes at least a portion of the biosignal; the control unit configured to cause the measurement circuitry and memory unit to repeat the step of measuring and storing based on the frequency of the biosignal in relation with the sampling rate of the measurement circuitry; and wherein the reconstruction unit is configured to reconstruct the measured biosignal based on one or more portions of the biosignal. A system for reconstructing a low frequency biosignal from one or more portions, the system being configured to: receive one or more portions of a measured biosignal; and reconstruct the biosignal by integrating the one or more portions of the biosignal. The system of claim 8, wherein reconstructing the portions of the biosignal is performed using an approximation method. The system of claim 8, wherein reconstructing the biosignal is performed using software. A computer-readable storage medium comprising instructions that, when executed, cause one or more processors to: measuring a first portion of a biosignal, wherein the first portion indicates a portion of the biosignal; storing the first portion of the biosignal; repeating the steps of measuring and storing based on a frequency of the biosignal in relation with a sampling rate; and reconstructing the biosignal based on one or more portions of the biosignal. A method of measuring a biosignal having a fundamental frequency below that of a sampling rate, the method comprising: measuring a portion of the biosignal, wherein the portion indicates a portion of the biosignal; storing the portion of the biosignal; repeating the steps of measuring and storing based on the frequency of the biosignal in relation with the sampling rate; and reconstructing the biosignal based on the portions of the biosignal. The method of claim 12, wherein the biosignal is a low frequency signal (LFS) resulting from at least one of a heartbeat, a breathing, a movement. The method of claim 12, wherein the biosignal is a local field potential. The method of claim 12, wherein reconstructing the biosignal includes at least one of integrating the portions of the biosignal. A measurement circuit configured to measure a biosignal, wherein the measurement circuit comprises: a measurement amplifier; a source of drift current that causes the measurement amplifier to become unstable; and a switching unit to switch a polarity of the measurement amplifier to counter an effect of drift current. The measurement circuit of claim 16, wherein the source of drift current includes parasitic leakage. The measurement circuit of claim 16, wherein the switching unit includes a chopping mechanism.