Guided programming of adaptive deep brain stimulation

Abstract

In general, devices, systems, and techniques are described for automating programming of an adaptive stimulation therapy, e.g., adaptive deep brain stimulation (aDBS), which may include monitoring brain signals, stimulation parameter values, patient events, or other aspects related to the patient and the aDBS therapy. In one example, a system includes processing circuitry configured to control stimulation circuitry of an implantable medical device (IMD) to generate electrical stimulation at a plurality of different values of a stimulation parameter that at least partially defines the electrical stimulation during a period of time, receive information representative of bioelectrical signals sensed during at least a portion of the period of time, and select an adaptive stimulation mode from a plurality of adaptive stimulation modes, wherein each of the plurality of adaptive stimulation modes define different respective algorithms for adjusting electrical stimulation in response to subsequent sensed bioelectrical signals.

Description

GUIDED PROGRAMMING OF ADAPTIVE DEEP BRAIN STIMULATION

[0001] This application is a PCT application that claims priority to and the benefit of U.S. Provisional Patent Application No. 63 / 625,628, filed January 26, 2024, the entire contents of which is incorporated herein by reference.TECHNICAL FIELD

[0002] This disclosure generally relates to electrical stimulation therapy.BACKGROUND

[0003] Medical devices may be external or implanted and may be used to deliver electrical stimulation therapy to various tissue sites of a patient to treat a variety of symptoms or conditions such as chronic pain, tremor, Parkinson’s disease, other movement disorders, epilepsy, urinary or fecal incontinence, sexual dysfunction, obesity, or gastroparesis. A medical device may deliver electrical stimulation therapy via one or more leads that include electrodes located proximate to target locations associated with the brain, the spinal cord, pelvic nerves, peripheral nerves, or the gastrointestinal tract of a patient. Hence, electrical stimulation may be used in different therapeutic applications, such as deep brain stimulation (DBS), spinal cord stimulation (SCS), pelvic stimulation, gastric stimulation, or peripheral nerve field stimulation (PNFS).

[0004] A clinician may select values for a number of programmable parameters in order to define the electrical stimulation therapy to be delivered by the implantable stimulator to a patient. For example, the clinician may select one or more electrodes for delivery of the stimulation, a polarity of each selected electrode, a voltage or current amplitude, a pulse width, and a pulse frequency as stimulation parameters. A set of parameters, such as a set including electrode combination, electrode polarity, voltage or current amplitude, pulse width and pulse rate, may be referred to as a program in the sense that they define the electrical stimulation therapy to be delivered to the patient.SUMMARY

[0005] In general, the disclosure describes devices, systems, and techniques for automating programming of an adaptive stimulation therapy, e.g., adaptive deep brain stimulation (aDBS), which may include monitoring brain signals, stimulation parameter values, patient events, or other aspects related to the patient and the aDBS therapy. Forexample, a programming device may be configured to automate one or more aspects of the aDBS programming therapy to reduce or even eliminate user input required to select parameters that define adaptive stimulation (e.g., closed-loop stimulation). In some examples, the system may be configured to present a user interface that presents information related to aDBS therapy and / or brain signal monitoring. The system may include an external programming device that communicates with a medical device and / or the medical device (e.g., an implantable medical device) configured to sense physiological signals such as electrical signals originating in the patient’s brain. The system may employ aspects of these signals for presenting information to the user and / or partially or fully automating selection of various parameters for sensing and / or delivering stimulation. Although aDBS is one nonlimiting example therapy, the techniques of this disclosure may be applied to many forms of adaptive stimulation therapy that may be configured to treat other conditions and / or other anatomical structures of the patient.

[0006] In one example, an external device (e.g., an external programmer) may be configured to automatically select various parameters that define sensing and / or delivering stimulation based on sensed physiological signals. The external device may select these parameters or present these selections to the user for approval or confirmation via a user interface. In some examples, the user interface of the external programmer may be configured to receive user input that impacts the selection of parameters (e.g., the user input is an input in the determination of the parameter). In some examples, the external programmer may be configured to receive user input selecting one or more suggested parameters via a user interface. In some examples, the external programming device may control a medical device to perform one or more signal tests for one or more electrode combinations (e.g., one or more signal pathways) of an implanted lead. In some examples, the system may determine the type of adaptive therapy algorithm that will be used to adjust one or more parameters that define stimulation therapy. The programming devices, user interfaces, and techniques described herein may thus automate one or more aspects of aDBS therapy.

[0007] In one example, a method of programming an implantable medical device (IMD) includes controlling, by processing circuitry, stimulation circuitry to generate electrical stimulation at a plurality of different values of a stimulation parameter that at least partially defines the electrical stimulation during a period of time; receiving, by the processing circuitry and from sensing circuitry, information representative of bioelectrical signals sensed during at least a portion of the period of time; and selecting, by the processing circuitry and based on the information representative of the bioelectrical signals, an adaptive stimulationmode from a plurality of adaptive stimulation modes, wherein each of the plurality of adaptive stimulation modes define different respective algorithms for adjusting electrical stimulation in response to subsequent sensed bioelectrical signals.

[0008] In another example, an external programmer comprises: processing circuitry configured to: control stimulation circuitry of an implantable medical device (IMD) to generate electrical stimulation at a plurality of different values of a stimulation parameter that at least partially defines the electrical stimulation during a period of time; receive information representative of bioelectrical signals sensed during at least a portion of the period of time; and select an adaptive stimulation mode from a plurality of adaptive stimulation modes, wherein each of the plurality of adaptive stimulation modes define different respective algorithms for adjusting electrical stimulation in response to subsequent sensed bioelectrical signals.

[0009] In another example, a non-transitory computer-readable medium comprises instructions that, when executed, cause processing circuitry to: control stimulation circuitry of an implantable medical device (IMD) to generate electrical stimulation at a plurality of different values of a stimulation parameter that at least partially defines the electrical stimulation during a period of time; receive information representative of bioelectrical signals sensed during at least a portion of the period of time; and select an adaptive stimulation mode from a plurality of adaptive stimulation modes, wherein each of the plurality of adaptive stimulation modes define different respective algorithms for adjusting electrical stimulation in response to subsequent sensed bioelectrical signals.

[0010] The details of one or more examples of the techniques of this disclosure are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the techniques will be apparent from the description and drawings, and from the claims.BRIEF DESCRIPTION OF DRAWINGS

[0011] FIG. l is a conceptual diagram illustrating an example system that includes an implantable medical device (IMD) configured to deliver DBS to a patient according to an example of the techniques of the disclosure.

[0012] FIG. 2 is a block diagram of the example IMD of FIG. 1 for delivering DBS therapy according to an example of the techniques of the disclosure.

[0013] FIG. 3 is a block diagram of the external programmer of FIG. 1 for controlling delivery of DBS therapy according to an example of the techniques of the disclosure.

[0014] FIG. 4 is a conceptual diagram illustrating an example home screen for navigating within a user interface.

[0015] FIG. 5 is a conceptual diagram illustrating an example screen for displaying a selected sensing electrode combination of a lead.

[0016] FIG. 6 is a flowchart illustrating an example technique for selecting a sensing electrode combination of a lead.

[0017] FIG. 7 is a conceptual diagram illustrating an example screen for selecting a frequency for monitoring in a clinic setting.

[0018] FIG. 8 is a flowchart illustrating an example technique for selecting a frequency for monitoring.

[0019] FIG. 9 is a conceptual diagram illustrating an example screen for selecting a frequency for monitoring based on bioelectrical signal snapshots during patient events.

[0020] FIG. 10 is a conceptual diagram illustrating an example screen for selecting an adaptive therapy mode.

[0021] FIG. 11 is a flowchart illustrating an example technique for selecting an adaptive therapy mode.

[0022] FIG. 12 is a flowchart illustrating an example technique for selecting an adaptive therapy mode specific for aDBS therapy.

[0023] FIG. 13 is a conceptual diagram illustrating an example screen for setting one or more thresholds associated with an aDBS therapy.

[0024] FIG. 14 is a flowchart illustrating an example technique for setting one or more thresholds associated with an aDBS therapy.

[0025] FIG. 15 is a flowchart illustrating an example technique for adjusting a stimulation parameter.

[0026] FIG. 16 is a conceptual diagram illustrating an example screen for setting a ramping rate associated with an aDBS therapy.

[0027] FIG. 17 is a flowchart illustrating an example technique for adjusting a stimulation parameter.

[0028] FIG. 18 is a conceptual diagram illustrating an example screen summarizing parameters selected via automated aDBS therapy setup.DETAILED DESCRIPTION

[0029] This disclosure describes example devices, systems, and techniques for programming adaptive stimulation therapy in which a system can adjust one or moreparameters of stimulation therapy based on signals sent from the patient. A patient may suffer from one or more symptoms treatable by electrical stimulation therapy. For example, a patient may suffer from brain disorder such as Parkinson’s disease, Alzheimer’s disease, or another type of movement disorder. Deep brain stimulation (DBS) may be an effective treatment to reduce the symptoms associated with such disorders. However, it may be time consuming for a clinician to manually determine appropriate stimulation parameters that define effective electrical stimulation therapy. Typically, a clinician may need to manually identify each parameter that defines electrical stimulation therapy. Moreover, DBS is typically delivered continuously in an open loop fashion for the patient. Not only does this open loop delivery consume more battery power due to stimulation being delivered when not needed by the patient, but a system cannot adjust stimulation parameters to provide more targeted therapy as the condition of the patient changes over time or under certain conditions. In addition, it can be challenging for clinicians to identify patient events, e.g., falls, and patient conditions and what types of adjustments could be made to improve therapy over time. Even if a system could detect an indication of these patient changes, that introduces another parameter that the clinician would need to identify as part of initial set-up of therapy and / or over the life of therapy delivery for that patient.

[0030] As described herein, various devices, systems, and techniques enable partial or fully automatic programming and management of DBS therapy and / or brain sensing for a patient. For example, systems described herein may be configured to sense and record brain signals (e.g., electroencephalogram (EEG signals), local field potentials (LFP signals), or other brain signals) associated with brain disorders. In some examples, a system may select a sensing electrode combination and appropriate frequencies for monitoring based on information corresponding to the recorded brain signals. The system can also display the selections and / or information corresponding to the recorded brain signals for review, selection, and / or confirmation by a user, e.g., a clinician. In some examples, the system can be configured to select an aDBS mode in which the system adjusts the value of one or more stimulation parameters in order to maintain the brain signals above or below (or exceeding or satisfying) one or more respective thresholds. In some examples, the system may automatically select the one or more respective thresholds based on one or more characteristics of the recorded brain signals. In some examples, the system may receive user input specifying or adjusting the selected adaptive mode and the one or more respective thresholds.

[0031] In some examples, the system may identify one or more appropriate frequencies or frequency bands for different electrode combinations and / or suggest an electrode combination for sensing. The system may also display the recorded brain signals or aspects thereof for review by the clinician. In some examples, the system may operate in an aDBS mode in which the system adjusts the value of one or more stimulation parameters in order to maintain the brain signals above or below one or more respective thresholds. In some examples, the system may automatically select an aDBS mode and corresponding one or more thresholds. The system may receive user input specifying or adjusting any of these one or more thresholds. In addition, the system may employ a setup mode to capture brain signal thresholds that correspond to respective stimulation parameter values.

[0032] These various features of the systems and techniques described herein may provide advantages over other systems and improve system functionality and patient outcomes. For example, a system may capture brain signals from multiple different electrode combinations and select or suggest (for a user to confirm) an electrode combination to use for brain signal sensing, which reduces clinician trial and error during patient setup. The system may automatically select an adaptive stimulation mode and / or one or more respective thresholds based on the sensed signals from the patient. For example the system may determine which adaptive mode may be effective to adjust stimulation based on amplitudes of signals, variation of signals over time, or even the direction in which sensed signal characteristics move in response to stimulation changes. These automatic selections associated with aDBS may reduce expended clinician time, improve consistency of parameter selection, and eventually improve therapeutic results for the patient by increasing therapeutic stimulation efficacy and reducing side effects.

[0033] FIG. 1 is a conceptual diagram illustrating an example system 100 that includes an implantable medical device (IMD) 106 configured to deliver adaptive deep brain stimulation to a patient 112. DBS may be adaptive (aDBS) in the sense that IMD 106 may adjust, increase, or decrease the value of one or more stimulation parameters that define the DBS in response to changes in patient activity or movement, a severity of one or more symptoms of a disease of the patient, a presence of one or more side effects due to the DBS, or one or more sensed signals of the patient, etc. For example, system 100 may use one or more sensed signals of the patient as a control signal such that the IMD 106 adjusts the magnitude of the one or more parameters of the electrical stimulation in response to the magnitude or change in magnitude of the one or more sensed signals. This process enables system 100 toautomatically adjust stimulation therapy in response to changes to the patient condition, such as changes to brain activity indicative of a level of therapy efficacy.

[0034] Example therapy system 100 includes medical device programmer 104, implantable medical device (IMD) 106, lead extension 110, and leads 114A and 114B with respective sets of electrodes 116, 118. In the example shown in FIG. 1, electrodes 116, 118 of leads 114A, 114B are positioned to deliver electrical stimulation to a tissue site within brain 120, such as a deep brain site under the dura mater of brain 120 of patient 112. In some examples, delivery of stimulation to one or more regions of brain 120, such as the subthalamic nucleus, globus pallidus or thalamus, may be an effective treatment to manage movement disorders, such as Parkinson’s disease. Some or all of electrodes 116, 118 also may be positioned to sense bioelectrical brain signals within brain 120 of patient 112. In some examples, some of electrodes 116, 118 may be configured to sense bioelectrical brain signals and others of electrodes 116, 118 may be configured to deliver adaptive electrical stimulation to brain 120. In other examples, all of electrodes 116, 118 are configured to both sense bioelectrical brain signals and deliver adaptive electrical stimulation to brain 120.

[0035] IMD 106 includes a therapy module (e.g., which may include processing circuitry, signal generation circuitry or other electrical circuitry configured to perform the functions attributed to IMD 106) that includes a stimulation generator configured to generate and deliver electrical stimulation therapy to patient 112 via a subset of electrodes 116, 118 of leads 114A and 114B, respectively. The subset of electrodes 116, 118 that are used to deliver electrical stimulation to patient 112, and, in some cases, the polarity of the subset of electrodes 116, 118, may be referred to as a stimulation electrode combination. As described in further detail below, the stimulation electrode combination can be selected for a particular patient 112 and target tissue site (e.g., selected based on bioelectrical signal information and the patient condition). The group of electrodes 116, 118 includes at least one electrode and can include a plurality of electrodes. In some examples, the plurality of electrodes 116 and / or 118 may have a complex electrode geometry such that two or more electrodes are located at different positions around the perimeter of the respective lead.

[0036] According to some techniques of the disclosure, system 100, via IMD 106, delivers electrical stimulation therapy defined by one or more parameters, such as voltage or current amplitude within a therapeutic window (e.g., a window defined by one or more limits for the voltage or current amplitude), adjusted in response to a signal deviating from a range defined by a homeostatic window (e.g., a window defined by one or more thresholds for a brain signal, such as a lower threshold and upper threshold). The homeostatic window maybe used as part of an adaptive mode for adjusting stimulation therapy over time. In some examples, system 100 may change other parameters in response to sensed signals such as stimulation pulse frequency, pulse burst duration, pulse burst frequency, duty cycle, or electrode combination. Throughout this disclosure, the terms “limit,” “threshold,” and “bound” can refer to similar concepts, e.g., each defines a window or range of acceptable values associated with adaptive therapy.

[0037] In some examples, the medication taken by patient 112 is a medication for controlling one or more symptoms of Parkinson’s disease, such as tremor or rigidity due to Parkinson’s disease. Such medications include extended release forms of dopamine agonists, regular forms of dopamine agonists, controlled release forms of carbidopa / levodopa (CD / LD), regular forms of CD / LD, entacapone, rasagiline, selegiline, and amantadine. Typically, to set the upper threshold and lower threshold of the homeostatic window, the patient has been off medication, i.e., the upper and lower thresholds are set when the patient is not taking medication that is intended to reduce the symptoms. The patient may be considered to be not taking the medication when the patient, prior to the time the upper threshold is set, has not taken the medication for at least approximately 72 hours for extended release forms of dopamine agonists, the patient has not taken the medication for at least approximately 24 hours for regular forms of dopamine agonists and controlled release forms of CD / LD, and the patient has not taken the medication for at least approximately 12 hours for regular forms of CD / LD, entacapone, rasagiline, selegiline, and amantadine. If only stimulation is suppressing brain signals (e.g., LFP signals), then system 100 can measure these brain signals for various values of stimulation parameters without outside inputs. Once the upper threshold and lower threshold is established, system 100 can identify when medication wears off because the brain signals will cross the lower or upper threshold. In response to identifying the brain signal crossing a threshold, system 100 may turn on, or adjust the amplitude or intensity of, electrical stimulation to bring back brain signal amplitudes back between the lower threshold and the upper threshold to reduce symptoms once again. Programmer 104 or IMD 106 may initially set the lower threshold and the upper threshold and make adjustments to one or both thresholds over time. Programmer 104 or IMD 106 may also determine and display information regarding the amount of time stimulation amplitude is above, below, or between the thresholds.

[0038] As described herein, “reducing” or “suppressing” the symptoms of the patient refer to alleviating, in whole or in part, the severity of one or more symptoms of the patient. In one example, the clinician makes a determination of the severity of one or more symptomsof Parkinson’s disease of patient 112 with reference to the Unified Parkinson's Disease Rating Scale (UPDRS) or the Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). A discussion of the application of the MDS-UPDRS is provided by Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS): Scale Presentation and Clinimetric Testing Results, C. Goetz et al, Movement Disorders, Vol. 23, No. 15, pp. 2129-2170 (2008), the content of which is incorporated herein in its entirety.

[0039] As described herein, system 100 may be configured to determine the upper limit of a therapeutic window while the patient is not taking medication, and while, via IMD 106, electrical stimulation therapy is delivered to the brain 120 of patient 112. In one example, system 100 determines the point at which increasing the magnitude of one or more parameters defining the electrical stimulation therapy, such as voltage amplitude or current amplitude, begins to cause one or more side effects for the patient 112. For example, system 100 may gradually increase the magnitude of one or more parameters, such as amplitude, defining the electrical stimulation therapy and determine the point at which further increase to the magnitude of one or more parameters defining the electrical stimulation therapy causes a perceptible side effect for patient 112. As described herein, IMD 106 may sense bioelectrical signals, e.g., LFPs during this process and display the LFP signal and / or LFP signal magnitude that may correspond to the respective limits. The LFP signal magnitude may define an upper threshold or a lower threshold of a homeostatic window, e.g., upper threshold for Beta signals and lower threshold for Gamma signals.

[0040] As also described above, system 100 can also determine the lower limit of the therapeutic window while the patient is off medication and while, via IMD 106, electrical stimulation therapy is delivered to the brain 120 of patient 112. In one example, system 100 determines the point at which decreasing the magnitude of one or more parameters, such as amplitude, defining the electrical stimulation therapy causes break-through of one or more symptoms of the patient 112. This break-through of symptoms may refer to re-emergence of at least some symptoms that were substantially suppressed up to the point of re-emergence due to the decrease in magnitude of the one or more electrical stimulation therapy parameters. For example, system 100 may gradually decrease the magnitude of one or more parameters defining the electrical stimulation therapy and determine the point at which the symptoms of Parkinson’s disease in patient 112 emerge, as measured by sudden increase with respect to tremor or rigidity, in the score of patient 112 under the UPDRS or MDS-UPDRS. In another example, system 100 measures a physiological parameter of patient 112 correlated to one ormore symptoms of the disease of patient 112 (e.g., wrist flexion of patient 112) and determines the point at which further decrease to the magnitude of one or more parameters defining the electrical stimulation therapy causes a sudden increase in the one or more symptoms of the disease of patient 112 (e.g., onset of lack of wrist flexion of patient 112). IMD 106 may sense LFPs during this process and display the LFP signal and / or LFP signal magnitude that may correspond to the respective limits. The LFP signal magnitude may define a threshold, e.g., an upper threshold or a lower threshold, of a homeostatic window.

[0041] At the magnitude of one or more parameters defining the electrical stimulation therapy at which further decrease to the magnitude of one or more parameters defining the electrical stimulation therapy causes a sudden increase in the one or more symptoms of the disease of patient 112, system 100 can measure the magnitude of the signal of the patient 112 and set this magnitude as the upper threshold of the homeostatic window for Beta signals, and the lower threshold of the homeostatic window for Gamma signals. In some examples, for Beta signals, system 100 may select an upper threshold of the homeostatic window to be a predetermined amount, e.g., 5% or 10%, lower than the magnitude at which the symptoms of the patient 112 first emerge during decrease in the magnitude of one or more electrical stimulation parameters to prevent emergence of the symptoms of the patient 112 during subsequent use.

[0042] In another example, system 100 can set a lower threshold, e.g., for a Beta signal, by first ensuring that the patient is off medication for the one or more symptoms. In this example, system 100 delivers electrical stimulation having a value for the one or more parameters approximately equal to the upper limit of the therapeutic window. In some examples, system 100 delivers electrical stimulation having a value for the one or more parameters slightly below the magnitude which induces side effects in the patient 112. Typically, this causes greater reduction of the one or more symptoms of the disease of the patient 112, and therefore greater reduction of the signal. At this magnitude of the one or more parameters, system 100 measures the magnitude of the signal of the patient 112 and sets, via external programmer 104, this magnitude as the lower threshold of the homeostatic window. In some examples, system 100 may select a value for the lower threshold of the homeostatic window to be a predetermined amount, e.g., 5% or 10%, higher than the magnitude at which the symptoms of the patient 112 emerge to prevent emergence of the symptoms of the patient 112 during subsequent use.

[0043] As described herein, system 100 can monitor one or more signals of the patient for selecting one or more parameters defining stimulation and / or adjusting stimulation in aclosed-loop manner. In one example, the signal is a bioelectrical signal of a patient, such as a brain signal (e.g., LFP) with a frequency within a Beta frequency band and / or a Gamma frequency band of the brain of the patient. For example, the monitored signal may be a power of the respective Beta frequency band and / or Gamma frequency band. In yet a further example, the signal can be a signal indicative of a physiological parameter of the patient, such as a severity of a symptom of the patient, a movement of the patient, a posture of the patient, a respiratory function of the patient, a heart rate, or an activity level of the patient. System 100 may use a single signal or combination of different signals for initially selecting and / or adjusting one or more parameters that define subsequent stimulation therapy. System 100, via IMD 106, can be configured to deliver electrical stimulation to the patient, wherein one or more parameters defining the electrical stimulation are related by a transfer function (which may or may not be proportional to the magnitude of the monitored signal or adjusted in response to a magnitude of the monitored signal exceeding one or more thresholds).

[0044] System 100 may be configured to treat one or more patient conditions, such as a movement disorder, neurodegenerative impairment, a mood disorder, or a seizure disorder of patient 112. Patient 112 ordinarily is a human patient. In some cases, however, therapy system 100 may be applied to other mammalian or non-mammalian, non-human patients. While movement disorders and neurodegenerative impairment are primarily referred to herein, in other examples, therapy system 100 may provide therapy to manage symptoms of other patient conditions, such as, but not limited to, seizure disorders (e.g., epilepsy) or mood (or psychological) disorders (e.g., major depressive disorder (MDD), bipolar disorder, anxiety disorders, post-traumatic stress disorder, dysthymic disorder, and obsessive- compulsive disorder (OCD)). At least some of these disorders may be manifested in one or more patient movement behaviors. As described herein, a movement disorder or other neurodegenerative impairment may include symptoms such as, for example, muscle control impairment, motion impairment or other movement problems, such as rigidity, spasticity, bradykinesia, rhythmic hyperkinesia, nonrhythmic hyperkinesia, and akinesia. In some cases, the movement disorder may be a symptom of Parkinson’s disease. However, the movement disorder may be attributable to other patient conditions.

[0045] In some examples, the bioelectrical signals sensed within brain 120 may reflect changes in electrical current produced by the sum of electrical potential differences across brain tissue, or any voltage potentials between electrodes. Examples of bioelectrical brain signals include, but are not limited to, electrical signals generated from local field potentials (LFP) sensed within one or more regions of brain 120, such as an electroencephalogram(EEG) signal, an electrocorticogram (ECoG) signal, or an action potential sensed by microelectrodes. LFPs, however, may include a broader genus of electrical signals within brain 120 of patient 112.

[0046] In some examples, the bioelectrical brain signals that are used to select a stimulation electrode combination may be sensed within the same region of brain 120 as the target tissue site for the electrical stimulation. As previously indicated, these tissue sites may include tissue sites within anatomical structures such as the thalamus, subthalamic nucleus or globus pallidus of brain 120, as well as other target tissue sites. The specific target tissue sites and / or regions within brain 120 may be selected based on the patient condition. Thus, in some examples, the electrodes used for delivering electrical stimulation may be different than the electrodes used for sensing bioelectrical brain signals. In other examples, the same electrodes may be used to deliver electrical stimulation and sense brain signals. However, this configuration may require system 100 to switch between stimulation generation and sensing circuitry and may reduce the time system 100 can sense brain signals.

[0047] Electrical stimulation generated by IMD 106 may be configured to manage a variety of disorders and conditions. In some examples, the stimulation generator of IMD 106 is configured to generate and deliver electrical stimulation pulses to patient 112 via electrodes of a selected stimulation electrode combination. However, in other examples, the stimulation generator of IMD 106 may be configured to generate and deliver a continuous wave signal, e.g., a sine wave or triangle wave. In either case, a stimulation generator within IMD 106 may generate the electrical stimulation therapy for DBS according to a therapy program that is selected at that given time in therapy. In examples in which IMD 106 delivers electrical stimulation in the form of stimulation pulses, a therapy program may include a set of therapy parameter values (e.g., stimulation parameters), such as a stimulation electrode combination for delivering stimulation to patient 112, pulse frequency, pulse width, and a current or voltage amplitude of the pulses. As previously indicated, the electrode combination may indicate the specific electrodes 116, 118 that are selected to deliver stimulation signals to tissue of patient 112 and the respective polarities of the selected electrodes.

[0048] IMD 106 may be implanted within a subcutaneous pocket above the clavicle, or, alternatively, on or within cranium 122 or at any other suitable site within patient 112. Generally, IMD 106 is constructed of a biocompatible material that resists corrosion and degradation from bodily fluids. IMD 106 may comprise a hermetic housing to substantially enclose components, such as a processor, therapy module, and memory.

[0049] As shown in FIG. 1, implanted lead extension 110 is coupled to IMD 106 via connector 108 (also referred to as a connector block or a header of IMD 106). In the example of FIG. 1, lead extension 110 traverses from the implant site of IMD 106 and along the neck of patient 112 to cranium 122 of patient 112 to access brain 120. In the example shown in FIG. 1, leads 114A and 114B (collectively “leads 114”) are implanted within the right and left hemispheres, respectively, of patient 112 in order deliver electrical stimulation to one or more regions of brain 120, which may be selected based on the patient condition or disorder controlled by therapy system 100. In some examples, more than two leads may be used in total or in each hemisphere, e.g., two leads per hemisphere (4 leads total), or an uneven number of leads between the hemispheres. The specific target tissue site and the stimulation electrodes used to deliver stimulation to the target tissue site, however, may be selected, e.g., according to the identified patient behaviors and / or other sensed patient parameters. Other lead 114 and IMD 106 implant sites are contemplated. For example, IMD 106 may be implanted on or within cranium 122, in some examples. Or leads 114 may be implanted within the same hemisphere or IMD 106 may be coupled to a single lead implanted in a single hemisphere.

[0050] Existing lead sets include axial leads carrying ring electrodes disposed at different axial positions and so-called “paddle” leads carrying planar arrays of electrodes. Selection of electrode combinations within an axial lead, a paddle lead, or among two or more different leads presents a challenge to the clinician. In some examples, more complex lead array geometries may be used.

[0051] Although leads 114 are shown in FIG. 1 as being coupled to a common lead extension 110, in other examples, leads 114 may be coupled to IMD 106 via separate lead extensions or directly to connector 108. Leads 114 may be positioned to deliver electrical stimulation to one or more target tissue sites within brain 120 to manage patient symptoms associated with a movement disorder of patient 112. Leads 114 may be implanted to position electrodes 116, 118 at desired locations of brain 120 through respective holes in cranium 122. Leads 114 may be placed at any location within brain 120 such that electrodes 116, 118 are capable of providing electrical stimulation to target tissue sites within brain 120 during treatment. For example, electrodes 116, 118 may be surgically implanted under the dura mater of brain 120 or within the cerebral cortex of brain 120 via a burr hole in cranium 122 of patient 112, and electrically coupled to IMD 106 via one or more leads 114.

[0052] In the example shown in FIG. 1, electrodes 116, 118 of leads 114 are shown as ring electrodes. Ring electrodes may be used in aDBS applications because they are relativelysimple to program and are capable of delivering an electrical field to any tissue adjacent to electrodes 116, 118. In other examples, electrodes 116, 118 may have different configurations. For example, in some examples, at least some of the electrodes 116, 118 of leads 114 may have a complex electrode array geometry that is capable of producing shaped electrical fields. The complex electrode array geometry may include multiple electrodes (e.g., partial ring or segmented electrodes) around the outer perimeter of each lead 114, rather than one ring electrode. In this manner, electrical stimulation may be directed in a specific direction from leads 114 to enhance therapy efficacy and reduce possible adverse side effects from stimulating a large volume of tissue. In some examples, a housing of IMD 106 may include one or more stimulation and / or sensing electrodes. In alternative examples, leads 114 may have shapes other than elongated cylinders as shown in FIG. 1. For example, leads 114 may be paddle leads, spherical leads, bendable leads, or any other type of shape effective in treating patient 112 and / or minimizing invasiveness of leads 114.

[0053] In the example shown in FIG. 1, IMD 106 includes a memory to store a plurality of therapy programs that each define a set of therapy parameter values. In some examples, IMD 106 may select a therapy program from the memory based on various parameters, such as sensed patient parameters and the identified patient behaviors. IMD 106 may generate electrical stimulation based on the selected therapy program to manage the patient symptoms associated with a movement disorder.

[0054] External programmer 104 wirelessly communicates with IMD 106 as needed to provide or retrieve therapy information. Programmer 104 is an external computing device that the user, e.g., a clinician and / or patient 112, may use to communicate with IMD 106. For example, programmer 104 may be a clinician programmer that the clinician uses to communicate with IMD 106 and program one or more therapy programs for IMD 106. Alternatively, programmer 104 may be a patient programmer that allows patient 112 to select programs and / or view and modify therapy parameters. The clinician programmer may include more programming features than the patient programmer. In other words, more complex or sensitive tasks may only be allowed by the clinician programmer to prevent an untrained patient from making undesirable changes to IMD 106.

[0055] When programmer 104 is configured for use by the clinician, programmer 104 may be used to transmit initial programming information to IMD 106. This initial information may include hardware information, such as the type of leads 114 and the electrode arrangement, the position of leads 114 within brain 120, the configuration of electrode array 116, 118, initial programs defining therapy parameter values, and any other information theclinician desires to program into IMD 106. Programmer 104 may also be capable of completing functional tests (e.g., measuring the impedance of electrodes 116, 118 of leads 114). In addition, or as an alternative, to programmer 104, a different external computing device may perform any of the functionality of programmer 104. The external computing device may be a networked device and in communication with IMD 106 directly or via programmer 104.

[0056] The clinician may also store therapy programs within IMD 106 with the aid of programmer 104. During a programming session, system 100 may determine one or more therapy programs that may provide efficacious therapy to patient 112 to address symptoms associated with the patient condition, and, in some cases, specific to one or more different patient states, such as a sleep state, movement state or rest state. For example, system 100 may select one or more stimulation electrode combination with which stimulation is delivered to brain 120. During the programming session, system 100 may evaluate the efficacy of the specific program being evaluated based on feedback provided by the clinician, patient 112, or based on one or more physiological parameters of patient 112 (e.g., muscle activity, muscle tone, rigidity, tremor, etc.). Alternatively, identified patient behavior from video information may be used as feedback during the initial and subsequent programming sessions.

[0057] Programmer 104 may also be configured for use by patient 112. When configured as a patient programmer, programmer 104 may have limited functionality (compared to a clinician programmer) in order to prevent patient 112 from altering critical functions of IMD 106 or applications that may be detrimental to patient 112. In this manner, programmer 104 may only allow patient 112 to mark events and to adjust values for certain therapy parameters or set an available range of values for a particular therapy parameter. When programmer 104 is configured for use by patient 112 (e.g., a patient programmer), programmer 104 may have a limited set of adjustments and / or data available to the user compared with a clinician programmer. In this manner, the patient programmer version may prevent the patient from causing detrimental changes to therapy, but allow the patient to make some adjustments to therapy as desired.

[0058] Programmer 104 may also provide an indication to patient 112 when therapy is being delivered, when patient input has triggered a change in therapy or when the power source within programmer 104 or IMD 106 needs to be replaced or recharged. For example, programmer 112 may include an alert LED, may flash a message to patient 112 via a programmer display, generate an audible sound or somatosensory cue to confirm patient input was received, e.g., to indicate a patient state or to manually modify a therapy parameter.

[0059] Therapy system 100 may be implemented to provide chronic stimulation therapy to patient 112 over the course of several months or years. However, system 100 may also be employed on a trial basis to evaluate therapy before committing to full implantation. If implemented temporarily, some components of system 100 may not be implanted within patient 112. For example, patient 112 may be fitted with an external medical device, such as a trial stimulator, rather than IMD 106. The external medical device may be coupled to percutaneous leads or to implanted leads via a percutaneous extension. If the trial stimulator indicates DBS system 100 provides effective treatment to patient 112, the clinician may implant a chronic stimulator within patient 112 for relatively long-term treatment.

[0060] Although IMD 104 is described as delivering electrical stimulation therapy to brain 120, IMD 106 may be configured to direct electrical stimulation to other anatomical regions of patient 112 in other examples. In other examples, system 100 may include an implantable drug pump in addition to, or in place of, IMD 106. Further, an IMD may provide other electrical stimulation such as spinal cord stimulation to treat a movement disorder.

[0061] According to the techniques of the disclosure, system 100 can define a homeostatic window and / or a therapeutic window for delivering aDBS to patient 112. System 100 may adaptively deliver electrical stimulation and adjust one or more parameters defining the electrical stimulation within a parameter range defined by upper and lower limits of the therapeutic window based on the activity of the sensed bioelectrical signal, e.g., LFP signal, evoked resonant neural activity (ERNA), and EEG, within the homeostatic window. For example, system 100 may adjust the one or more parameters defining the electrical stimulation in response to the sensed signal falling below the lower threshold or exceeding the upper threshold of the homeostatic window but may not adjust the one or more parameters defining the electrical stimulation such that they fall below the lower limit or exceed the upper limit of the therapeutic window.

[0062] In one example, external programmer 104 issues commands to IMD 106, via instructions transmitted from external programmer 104 to IMD 106, causing IMD 106 to deliver electrical stimulation therapy via electrodes 116, 118 via leads 114. As described above, in one example, the therapeutic window can define an upper bound and / or a lower bound for one or more parameters defining the delivery of electrical stimulation therapy to patient 112. In other words, the one or more bounds for the therapeutic window may refer to the limits of values that the parameter defining stimulation can be adjusted. For example, the one or more parameters include a current amplitude (for a current-controlled system) or a voltage amplitude (for a voltage-controlled system), a pulse rate or frequency, and a pulsewidth. In examples where the electrical stimulation is delivered according to a “burst” of pulses, or a series of electrical pulses defined by an “on-time” and an “off-time,” the one or more parameters may further define one or more of a number of pulses per burst, an on-time, and an off-time. In one example, the therapeutic window defines an upper bound and a lower bound for one or more parameters, such as upper and lower threshold for a current amplitude of the electrical stimulation therapy (in current-controlled systems) or upper and lower threshold of a voltage amplitude of the electrical stimulation therapy (in voltage-controlled systems). While the examples herein are typically given with respect to adjusting a voltage amplitude or a current amplitude, the techniques herein may equally be applied to a homeostatic window and a therapeutic window using other parameters, such as, e.g., pulse rate or pulse width. Example implementations of the therapeutic window are provided in further detail below.

[0063] Typically, a patient programmer 104 may not have access to adjustments to any thresholds or limits for sensing or stimulation related to aDBS. For example, patient programmer 104 may only enable a patient to adjust a stimulation parameter value between limits set by the clinician programmer. However, in other examples, system 100 may provide aDBS by permitting a patient 112, e.g., via a patient programmer 104, to indirectly adjust the activation, deactivation, and magnitude of the electrical stimulation by adjusting the lower and upper threshold of the homeostatic window. In one example, the patient programmer 104 may only be enabled to adjust an upper or lower threshold of the homeostatic window a small magnitude or percentage of the clinician-set value. In another example, by adjusting one or both thresholds of the homeostatic window, patient 112 may adjust the point at which the sensed signal deviates from the homeostatic window, triggering system 100 to adjust one or more parameters of the electrical stimulation within a parameter range defined by the lower and upper threshold of the therapeutic window.

[0064] In some examples, a patient may provide feedback, e.g., via programmer 104, to adjust one or both thresholds of the homeostatic window. For example, programmer 104 may provide an input mechanism where the patient can provide an input indicating when therapy is no longer effective or a side effect is felt. Programmer 104 may then adjust a threshold of the homeostatic window and / or a bound of the therapeutic window in order to reduce the issue associated with the patient feedback. In another example, programmer 104 and / or IMD 106 may automatically adjust one or both threshold of the homeostatic window, as well as one or more parameters of the electrical stimulation within the parameter range defined by the lower and upper limit of the therapeutic window. For example, programmer 104 and / orIMD 106 may automatically adjust one or more thresholds of the homeostatic window based on one or more physiological or bioelectrical signals of patient 112 sensed by IMD 106 or an external sensor, e.g., a wearable senor (not shown). For example, in response to deviations in the signal of the patient outside of the homeostatic window, system 100 (e.g., IMD 106 or programmer 104) may automatically adjust one or more parameters defining the electrical stimulation therapy delivered to the patient in a manner that is proportional to the magnitude of the sensed signal and within the therapeutic window defining lower and upper limits for the one or more parameters. The adjustment to the one or more stimulation therapy parameters based on the deviation of the sensed signal may be proportional or inversely proportional to the magnitude of the signal.

[0065] Hence, in some examples, system 100, via programmer 104 or IMD 106, may adjust one or more parameters of the electrical stimulation, such as voltage or current amplitude, within the therapeutic window based on patient input that adjusts the homeostatic window, or based on one or more signals, such as sensed physiological parameters or sensed bioelectrical signals, or a combination of two or more of the above. In particular, system 100 may adjust a parameter of the electrical stimulation, automatically in response to the sensed signal satisfying the one or more thresholds of the homeostatic window and / or in response to patient input that adjusts the homeostatic window, provided the value of the electrical stimulation parameter is constrained to remain within a range specified by the upper and lower bound of the therapeutic window. This range may be considered to include the upper and lower bound themselves.

[0066] In some examples where system 100 adjusts multiple parameters of the electrical stimulation, system 100 may adjust at least one of a voltage amplitude or current amplitude, a stimulation frequency, a pulse width, or a selection of electrodes, and the like. In such an example, system 100 may set an order or sequence for adjustment of the parameters (e.g., adjust voltage amplitude or current amplitude, then adjust stimulation frequency, and then adjust the selection of electrodes). In other examples, system 100 may randomly select a sequence of adjustments to the multiple parameters. In either example, system 100 may adjust a value of a first parameter of the parameters of the electrical stimulation. If the signal does not exhibit a response to the adjustment of the first parameter, system 100 may adjust a value of a second parameter of the parameters of the electrical stimulation, and so on until the signal returns to within the homeostatic window.

[0067] To adaptively adjust a parameter that defines DBS based on a bioelectrical signal, for example, two or more electrodes 116, 118 of IMD 106 may be configured to monitor abioelectrical signal (e.g., an LFP signal) of patient 112. In some examples, at least one of electrodes 116, 118 may be provided on a housing of IMD 106, providing a unipolar stimulation and / or sensing configuration. In one example, the bioelectrical signal may be selected to be a signal within a Beta frequency band of brain 120 of patient 112. For example, bioelectrical signals within the Beta frequency band of patient 112 may correlate to one or more symptoms of Parkinson’s disease in patient 112. Generally, bioelectrical signals within the Beta frequency of patient 112 may be approximately proportional to the severity of the symptoms of patient 112. For example, as tremor induced by Parkinson’s disease increases, bioelectrical signals within the Beta frequency of patient 112 increase (e.g., magnitude of the signal and / or spectral power). Moreover, bioelectrical signals within the Beta frequency are considered proportional because system 100 may be configured such that an increase in signal magnitude may trigger system 100 to increase delivered stimulation therapy magnitude according to disclosed techniques. Similarly, as tremor induced by Parkinson’s disease decreases, bioelectrical signals within the Beta frequency of patient 112 decrease (e.g., magnitude of the signal and / or spectral power), and the decrease may trigger system 100 to decrease the magnitude of delivered stimulation. However, in some examples, these relationships between signal changes and symptom changes may be inversed for some patients which require the system to react in an inverse manner.

[0068] In some examples, each of a sensor within IMD 106 is an accelerometer, a bonded piezoelectric crystal, a mercury switch, or a gyro. In some examples, these sensors may provide a signal that indicates a physiological parameter of the patient, which in turn varies as a function of patient activity. For example, the device may monitor a signal that indicates the heart rate, electrocardiogram (ECG) morphology, electroencephalogram (EEG) morphology, respiration rate, respiratory volume, core temperature, subcutaneous temperature, or muscular activity of the patient.

[0069] In some examples, the sensors generate a signal both as a function of patient activity and patient posture. For example, accelerometers, gyros, or magnetometers may generate signals that indicate both the activity and the posture of a patient 112. External programmer 104 may use such information regarding posture to determine whether external programmer 104 should perform adjustments to the therapeutic window.

[0070] For example, in order to identify posture, the sensors such as accelerometers may be oriented substantially orthogonally with respect to each other. In addition to being oriented orthogonally with respect to each other, each of the sensors used to detect the posture of a patient 112 may be substantially aligned with an axis of the body of a patient 112. Whenaccelerometers, for example, are aligned in this manner, the magnitude and polarity of DC components of the signals generate by the accelerometers indicate the orientation of the patient relative to the Earth’s gravity, e.g., the posture of a patient 112. Further information regarding use of orthogonally aligned accelerometers to determine patient posture may be found in a commonly assigned U.S. Patent No. 5,593,431, which issued to Todd J. Sheldon, the entire content of which is incorporated by reference herein.

[0071] Other sensors that may generate a signal that indicates the posture of a patient 112 include electrodes that generate a signal as a function of electrical activity within muscles of a patient 112, e.g., an electromyogram (EMG) signal, or a bonded piezoelectric crystal that generates a signal as a function of contraction of muscles. Electrodes or bonded piezoelectric crystals may be implanted in the legs, buttocks, chest, abdomen, or back of a patient 112, and coupled to one or more of external programmer 104 and IMD 106 wirelessly or via one or more leads. Alternatively, electrodes may be integrated in a housing of the IMD 106, or piezoelectric crystals may be bonded to the housing when IMD 106 is implanted in the buttocks, chest, abdomen, or back of a patient 112. The signals generated by such sensors when implanted in these locations may vary based on the posture of a patient 112, e.g., may vary based on whether the patient is standing, sitting, or lying down.

[0072] Further, the posture of a patient 112 may affect the thoracic impedance of the patient. Consequently, sensors may include an electrode pair, including one electrode integrated with the housing of IMDs 106 and one of electrodes 116, 118, that generate a signal as a function of the thoracic impedance of a patient 112, and IMD 106 may detect the posture or posture changes of a patient 112 based on the signal. In one example (not depicted), the electrodes of the pair may be located on opposite sides of the patient’s thorax. For example, the electrode pair may include electrodes located proximate to the spine of a patient for delivery of SCS therapy, and IMD 106 with an electrode integrated in its housing may be implanted in the abdomen or chest of patient 112. As another example, IMD 106 may include electrodes implanted to detect thoracic impedance in addition to leads 114 implanted within the brain of patient 112. The posture or posture changes may affect the delivery of DBS or SCS therapy to patient 112 for the treatment of any type of bioelectrical disorder, and may also be used to detect patient sleep, as described herein.

[0073] Additionally, changes of the posture of a patient 112 may cause pressure changes with the cerebrospinal fluid (CSF) of the patient. Consequently, sensors may include pressure sensors coupled to one or more intrathecal or intracerebroventricular catheters, or pressure sensors coupled to HMDs 106 wirelessly or via one of leads 114. CSF pressure changesassociated with posture changes may be particularly evident within the brain of the patient, e.g., may be particularly apparent in an intracranial pressure (ICP) waveform.

[0074] Accordingly, in some examples, instead of, or in addition to, monitoring a bioelectrical signal of the patient, system 100 monitors one or more signals from sensors indicative of a magnitude of a physiological parameter of patient 112. Upon detecting that one or more signals from sensors exceed the upper threshold of a homeostatic window, system 100 increases stimulation at a maximum ramp rate determined by system 100 until one or more signals from sensors return to within the homeostatic window, or until the magnitude of the electrical stimulation reaches an upper limit of a therapeutic window determined by system 100. Similarly, upon detecting that one or more signals from sensors falls below the lower threshold of the homeostatic window, system 100 decreases stimulation at a maximum ramp rate determined by system 100 until one or more signals from sensors return to within the homeostatic window, or until the magnitude of the electrical stimulation reaches a lower limit of a therapeutic window determined by system 100. Upon detecting that one or more signals from sensors are within the threshold of the homeostatic window, system 100 holds the magnitude of the electrical stimulation constant.

[0075] Such a system 100 for delivering aDBS to the patient by monitoring a physiological parameter may provide advantages over other techniques that use a bioelectrical signal as a threshold in that the techniques of the disclosure allow an IMD to control delivery of therapy using hysteresis. In other words, such a system 100 can be configured to use the physiological parameter (alone or in addition to a sensed bioelectric signal) of the patient to create a closed loop feedback algorithm for not only controlling the delivery of therapy, but also controlling the magnitude of the delivered therapy. Such a system may be less intrusive on the activity of a patient because system 100 adapts the stimulation to the current needs of the patient, and thus may reduce the side effects that the patient experiences.

[0076] In some circumstances, system 100, as described herein, may deliver, based on the upper and lower threshold of the homeostatic window, a lower magnitude of electrical stimulation than patient 112 requires to prevent breakthrough of his or her symptoms. For example, a patient receiving therapy from an IMD 106 that controls delivery of electrical stimulation therapy using the homeostatic window may, in certain circumstances, experience results that are less optimal than if the patient received continuous electrical stimulation therapy at a maximum therapy magnitude. To prevent this occurrence, system 100 may determine a value for the at least one electrical stimulation parameter as defined by thehomeostatic window, as described above. Further, the IMD 106 of system 100 may increase the value for the at least one electrical stimulation parameter by a bias amount greater than the determined magnitude defined by the homeostatic window so as to further prevent breakthrough of the symptoms of patient 112. Thus, system 100 may avoid delivering electrical stimulation therapy that is of a magnitude that may be insufficient for prevention of symptom breakthrough.

[0077] The architecture of system 100 illustrated in FIG. 1 is shown as an example. The techniques as set forth in this disclosure may be implemented in the example system 100 of FIG. 1, as well as other types of systems not described specifically herein. Nothing in this disclosure should be construed so as to limit the techniques of this disclosure to the example architecture illustrated by FIG. 1.

[0078] FIG. 2 is a block diagram of the example IMD 106 of FIG. 1 configured for delivering adaptive deep brain stimulation therapy. In the example shown in FIG. 2, IMD 106 includes processing circuitry 210, memory 211, stimulation generator 202, sensing module 204, switch module 206, telemetry module 208, sensor 212, and power source 220. Each of these modules may be or include electrical circuitry configured to perform the functions attributed to each respective module. For example, processing circuitry 210 may include one or more processors part of the processing circuitry, switch module 206 may include switch circuitry, sensing module 204 may include sensing circuitry, stimulation generator 202 may include stimulation generation circuitry, and telemetry module 208 may include telemetry circuitry. Switch module 204 may not be necessary for multiple current source and sink configurations in which each current source and sink are directly connected to each electrode but may be connected or disconnected via a respective switch. Memory 211 may include any volatile or non-volatile media, such as a random-access memory (RAM), read only memory (ROM), non-volatile RAM (NVRAM), electrically erasable programmable ROM (EEPROM), flash memory, and the like. Memory 211 may store computer-readable instructions that, when executed by processing circuitry 210, cause IMD 106 to perform various functions. Memory 211 may be a storage device or other non-transitory medium.

[0079] In the example shown in FIG. 2, memory 211 stores therapy programs 214 and sense electrode combinations and associated stimulation electrode combinations 218 in separate memories within memory 211 or separate areas within memory 211. Each stored therapy program 214 defines a particular set of electrical stimulation parameters (e.g., a therapy parameter set), such as a stimulation electrode combination, electrode polarity, current or voltage amplitude, pulse width, and pulse rate. In some examples, individualtherapy programs may be stored as a therapy group, which defines a set of therapy programs with which stimulation may be generated. The stimulation signals defined by the therapy programs of the therapy group may be delivered together on an overlapping or nonoverlapping (e.g., time-interleaved) basis.

[0080] Sense and stimulation electrode combinations 218 stores sense electrode combinations and associated stimulation electrode combinations. As described above, in some examples, the sense and stimulation electrode combinations may include the same subset of electrodes 116, 118, a housing of IMD 106 functioning as an electrode, or may include different subsets or combinations of such electrodes. Thus, memory 211 can store a plurality of sense electrode combinations and, for each sense electrode combination, store information identifying the stimulation electrode combination that is associated with the respective sense electrode combination. The associations between sense and stimulation electrode combinations can be determined, e.g., automatically by processing circuitry 210. In some examples, corresponding sense and stimulation electrode combinations may comprise some or all of the same electrodes. In other examples, however, some or all of the electrodes in corresponding sense and stimulation electrode combinations may be different. For example, a stimulation electrode combination may include more electrodes than the corresponding sense electrode combination in order to increase the efficacy of the stimulation therapy. In some examples, as discussed above, stimulation may be delivered via a stimulation electrode combination to a tissue site that is different than the tissue site closest to the corresponding sense electrode combination but is within the same region, e.g., the thalamus, of brain 120 in order to mitigate any irregular oscillations or other irregular brain activity within the tissue site associated with the sense electrode combination. Alternatively, stimulation may be delivered via one of leads 114, and sensing may be performed via the other lead of leads 114.

[0081] Stimulation generator 202, under the control of processing circuitry 210, generates stimulation signals for delivery to patient 112 via selected combinations of electrodes 116, 118. An example range of electrical stimulation parameters believed to be effective in DBS to manage a movement disorder of patient include:

[0082] 1. Pulse Rate, i.e., Frequency: between approximately 40 Hertz and approximately500 Hertz, such as between approximately 40 to 185 Hertz or such as approximately 140 Hertz.

[0083] 2. In the case of a voltage controlled system, Voltage Amplitude: between approximately 0.1 volts and approximately 50 volts, such as between approximately 2 volts and approximately 3 volts.

[0084] 3. In the alternative case of a current controlled system, Current Amplitude: between approximately 0.2 milliamps to approximately 100 milliamps, such as between approximately 1.3 milliamps and approximately 2.0 milliamps.

[0085] 4. Pulse Width: between approximately 10 microseconds and approximately 5000 microseconds, such as between approximately 100 microseconds and approximately 1000 microseconds, or between approximately 180 microseconds and approximately 450 microseconds.

[0086] Accordingly, in some examples, stimulation generator 202 generates electrical stimulation signals in accordance with the electrical stimulation parameters noted above, subject to application of the upper and lower limit of a therapeutic window to one or more of the parameters, such that an applicable parameter resides within the range prescribed by the window. Other ranges of therapy parameter values may also be useful and may depend on the target stimulation site within patient 112. While stimulation pulses are described, stimulation signals may be of any form, such as continuous-time signals (e.g., sine waves) or the like.

[0087] Processing circuitry 210 may include fixed function processing circuitry and / or programmable processing circuitry, and may comprise, for example, any one or more of a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), discrete logic circuitry, or any other processing circuitry configured to provide the functions attributed to processing circuitry 210 herein may be embodied as firmware, hardware, software or any combination thereof. Processing circuitry 210 may control stimulation generator 202 according to therapy programs 214 stored in memory 211 to apply particular stimulation parameter values specified by one or more of programs, such as voltage amplitude or current amplitude, pulse width, or pulse rate.

[0088] In the example shown in FIG. 2, the set of electrodes 116 includes electrodes 116A, 116B, 116C, and 116D, and the set of electrodes 118 includes electrodes 118A, 118B, 118C, and 118D. Processing circuitry 210 also controls switch module 206 to apply the stimulation signals generated by stimulation generator 202 to selected combinations of electrodes 116, 118. In particular, switch module 204 may couple stimulation signals to selected conductors within leads 114, which, in turn, deliver the stimulation signals across selected electrodes 116, 118. Switch module 206 may be a switch array, switch matrix,multiplexer, or any other type of switching module configured to selectively couple stimulation energy to selected electrodes 116, 118 and to selectively sense bioelectrical brain signals with selected electrodes 116, 118. Hence, stimulation generator 202 is coupled to electrodes 116, 118 via switch module 206 and conductors within leads 114. In some examples, however, IMD 106 does not include switch module 206.

[0089] Stimulation generator 202 may be a single channel or multi-channel stimulation generator. In particular, stimulation generator 202 may be capable of delivering a single stimulation pulse, multiple stimulation pulses, or a continuous signal at a given time via a single electrode combination or multiple stimulation pulses at a given time via multiple electrode combinations. In some examples, however, stimulation generator 202 and switch module 206 may be configured to deliver multiple channels on a time-interleaved basis (e.g., pulses from one channel are at least partially alternating with at least some pulses from another channel). For example, switch module 206 may serve to time divide the output of stimulation generator 202 across different electrode combinations at different times to deliver multiple programs or channels of stimulation energy to patient 112. Alternatively, stimulation generator 202 may comprise multiple voltage or current sources and sinks that are coupled to respective electrodes to drive the electrodes as cathodes or anodes. In this example, IMD 106 may not require the functionality of switch module 206 for time-interleaved multiplexing of stimulation via different electrodes.

[0090] Electrodes 116, 118 on respective leads 114 may be constructed of a variety of different designs. For example, one or both of leads 114 may include two or more electrodes at each longitudinal location along the length of the lead, such as multiple electrodes at different perimeter locations around the perimeter of the lead at each of the locations A, B, C, and D. On one example, the electrodes may be electrically coupled to switch module 206 via respective wires that are straight or coiled within the housing the lead and run to a connector at the proximal end of the lead. In another example, each of the electrodes of the lead may be electrodes deposited on a thin film. The thin film may include an electrically conductive trace for each electrode that runs the length of the thin film to a proximal end connector. The thin film may then be wrapped (e.g., a helical wrap) around an internal member to form the lead 114. These and other constructions may be used to create a lead with a complex electrode geometry.

[0091] Although sensing module 204 is incorporated into a common housing with stimulation generator 202 and processing circuitry 210 in FIG. 2, in other examples, sensing module 204 may be in a separate housing from IMD 106 and may communicate withprocessing circuitry 210 via wired or wireless communication techniques. Example bioelectrical brain signals include, but are not limited to, a signal generated from local field potentials (LFPs) within one or more regions of brain 28. EEG and ECoG signals are other examples of electrical signals that may be measured within brain 120 or by electrodes placed in other locations with respect to brain 120.

[0092] Sensor 212 may include one or more sensing elements that sense values of a respective patient parameter. For example, sensor 212 may include one or more accelerometers, optical sensors, chemical sensors, temperature sensors, pressure sensors, or any other types of sensors. Sensor 212 may output patient parameter values that may be used as feedback to control delivery of therapy. IMD 106 may include additional sensors within the housing of IMD 106 and / or coupled via one of leads 114 or other leads. In addition, IMD 106 may receive sensor signals wirelessly from remote sensors via telemetry module 208, for example. In some examples, one or more of these remote sensors may be external to patient (e.g., carried on the external surface of the skin, attached to clothing, or otherwise positioned external to the patient).

[0093] Telemetry module 208 supports wireless communication between IMD 106 and an external programmer 104 or another computing device under the control of processing circuitry 210. Processing circuitry 210 of IMD 106 may receive, as updates to programs, values for various stimulation parameters such as magnitude and electrode combination, from programmer 104 via telemetry module 208. The updates to the therapy programs may be stored within therapy programs 214 portion of memory 211. Telemetry module 208 in IMD 106, as well as telemetry modules in other devices and systems described herein, such as programmer 104, may accomplish communication by radiofrequency (RF) communication techniques. In addition, telemetry module 208 may communicate with external medical device programmer 104 via proximal inductive interaction of IMD 106 with programmer 104. Accordingly, telemetry module 208 may send information to external programmer 104 on a continuous basis, at periodic intervals, or upon request from IMD 106 or programmer 104.

[0094] Power source 220 delivers operating power to various components of IMD 106. Power source 220 may include a small rechargeable or non-rechargeable battery and a power generation circuit to produce the operating power. Recharging may be accomplished through proximal inductive interaction between an external charger and an inductive charging coil within IMD 220. In some examples, power requirements may be small enough to allow IMD 220 to utilize patient motion and implement a kinetic energy-scavenging device to tricklecharge a rechargeable battery. In other examples, traditional batteries may be used for a limited period of time.

[0095] According to the techniques of the disclosure, processing circuitry 210 of IMD 106 delivers, electrodes 116, 118 interposed along leads 114 (and optionally switch module 206), electrical stimulation therapy to patient 112. The aDBS therapy is defined by one or more therapy programs 214 having one or more parameters stored within memory 211. For example, the one or more parameters may include a current amplitude (for a current- controlled system) or a voltage amplitude (for a voltage-controlled system), a pulse rate or frequency, and a pulse width, or quantity of pulses per cycle. The collection of one or more of these parameter values may define a parameter set that defines each therapy program. In examples where the electrical stimulation is delivered according to a “burst” of pulses, or a series of electrical pulses defined by an “on-time” and an “off-time,” the one or more parameters may further define one or more of a number of pulses per burst, an on-time, and an off-time. In one example, the therapeutic window defines an upper limit and / or a lower limit for a voltage amplitude of the electrical stimulation therapy. In another example, the therapeutic window defines an upper limit and / or a lower limit for a current amplitude of the electrical stimulation therapy. In particular, a parameter of the electrical stimulation therapy, such as voltage or current amplitude, is constrained to a therapeutic window having an upper limit and a lower limit, such that the voltage or current amplitude may be adjusted provided the amplitude remains greater than or equal to the lower limit and less than or equal to the upper limit. It is noted that a single limit may be used in some examples.

[0096] In one example, processing circuitry 210, via electrodes 116, 118 of IMD 106, monitors the behavior of a signal of patient 112 that correlates to one or more symptoms of a disease of patient 112 within a homeostatic window. Processing circuitry 210, via electrodes 116, 118, delivers to patient 112 aDBS and may adjust one or more parameters defining the electrical stimulation within a parameter range defined by lower and upper limits of a therapeutic window based on the activity of the sensed signal within the homeostatic window.

[0097] In one example, the signal is a bioelectrical signal (e.g., a LFP signal) within the Beta frequency band of brain 120 of patient 112. The signal within the Beta frequency band of patient 112 may correlate to one or more symptoms of Parkinson’s disease in patient 112. Generally speaking, bioelectrical signals within the Beta frequency band of patient 112 may be approximately proportional to the severity of the symptoms of patient 112. For example, as tremor induced by Parkinson’s disease increases, one or more of electrodes 116, 118 detectan increase in the magnitude of bioelectrical signals within the Beta frequency band of patient 112.

[0098] Similarly, as tremor induced by Parkinson’s disease decreases, processing circuitry 210, via the one or more of electrodes 116, 118, detects a decrease in the magnitude of the bioelectrical signals within the Beta frequency band of patient 112. In another example, the signal is a bioelectrical signal within the Gamma frequency band of brain 120 of patient 112. The signal within the Gamma frequency band of patient 112 may also correlate to one or more side effects of the electrical stimulation therapy. However, in contrast to bioelectrical signals within the Beta frequency band, generally speaking, bioelectrical signals within the Gamma frequency band of patient 112 may be approximately inversely proportional to the severity of the side effects of the electrical stimulation therapy. For example, as side effects due to electrical stimulation therapy increase, processing circuitry 210, via the one or more of electrodes 116, 118, detects an increase in the magnitude of the signal within the Gamma frequency band of patient 112. Similarly, as side effects due to electrical stimulation therapy decrease, processing circuitry 210, via the one or more of electrodes 116, 118, detects a decrease in the magnitude of the signal within the Gamma frequency band of patient 112.

[0099] In response to detecting that the signal of the patient, e.g., a sensed bioelectrical signal, has deviated from the homeostatic window, processing circuitry 210 dynamically adjusts the magnitude of the one or more parameters of the electrical stimulation therapy such as, e.g., pulse current amplitude or pulse voltage amplitude, to drive the signal of the patient back into the homeostatic window. For example, wherein the signal is a bioelectrical signal within the Beta frequency band of brain 120 of patient 112, processing circuitry 210, via the one or more of electrodes 116, 118, monitors the Beta magnitude of patient 112. Upon detecting that the Beta magnitude of patient 112 exceeds the upper threshold of the homeostatic window, processing circuitry 210 increases a magnitude of the electrical stimulation delivered via electrodes 116, 118 at a maximum ramp rate, e.g., determined automatically or by the clinician until the magnitude of the bioelectrical signal within the Beta band falls back to within the homeostatic window, or until the magnitude of the electrical stimulation reaches an upper limit of a therapeutic window determined by system 100 (FIG. 1). Similarly, upon detecting that the Beta magnitude of patient 112 falls below the lower threshold of the homeostatic window, processing circuitry 210 decreases stimulation magnitude at a maximum ramp rate determined by system 100 until the Beta magnitude rises back to within the homeostatic window, or until the magnitude of the electrical stimulation reaches a lower limit of a therapeutic window determined by system 100. Upon detecting thatthe Beta magnitude is presently within the threshold of the homeostatic window or has returned to within the threshold of the homeostatic window, processing circuitry 210 holds the magnitude of the electrical stimulation constant. In other examples, processing 210 may automatically determine the ramp rate at which stimulation parameters are adjusted to cause the brain signal to fall back within the target range. The ramp rate may be selected based on prior data indicating general patient comfort or comfort or preferences of the specific patient.

[0100] In some examples, processing circuitry 210 continuously measures the signal in real time. In other examples, processing circuitry 210 periodically samples the signal according to a predetermined frequency or after a predetermined amount of time. In some examples, processing circuitry 210 periodically samples the signal at a frequency of approximately 150 Hertz.

[0101] Furthermore, processing circuitry 210 delivers electrical stimulation therapy that is constrained by an upper limit and a lower limit of a therapeutic window. In some examples, values defining the therapeutic window are stored within memory 211 of IMD 106. For example, in response to detecting that the brain signal has deviated from the homeostatic window, processing circuitry 210 of IMD 106 may adjust one or more parameters of the electrical stimulation therapy to provide responsive treatment to patient 112. For example, in response to detecting that the signal has exceeded an upper threshold of the homeostatic window and prior to delivering the electrical stimulation therapy, processing circuitry 210 increases an amplitude of stimulation (e.g., but not above the upper limit) in order to bring the signal back down below the upper threshold. For example, in a voltage-controlled system wherein the clinician has set the upper limit of the therapeutic window to be 3 Volts, processing circuitry 210 can increase the voltage amplitude to values no greater than 3 Volts in an attempt to decrease the brain signal below the upper threshold.

[0102] In another example, in response to detecting that the signal has fallen below a lower threshold of the homeostatic window and prior to delivering the electrical stimulation therapy, processing circuitry 210 decreases the voltage amplitude, for example, but not lower than the magnitude of the lower limit. For example, in the above voltage-controlled system wherein the clinician has set the lower bound of the therapeutic window to be 1.2 Volts, processing circuitry 210 can decrease the voltage amplitude down to no lower than 1.2 Volts in an attempt to raise the brain signal back above the lower threshold and into the homeostatic window. Thus, processing circuitry 210 of IMD 106 may deliver aDBS to patient 112 wherein the one or more parameters defining the aDBS is within the therapeutic window defined by a lower and upper limit for the parameter.

[0103] In the foregoing example, the limit of the therapeutic window is inclusive (i.e., the upper and lower limit are valid values for the one or more parameters). However, in other examples, the limit of the therapeutic window is exclusive (i.e., the upper and lower limits are not valid values for the one or more parameters). In such an example of an exclusive therapeutic window, processing circuitry 210 instead sets the adjustment to the one or more parameters to be the next highest valid value (in the case of an adjustment potentially exceeding the upper limit) or the next lowest valid value (in the case of an adjustment potentially exceeding the lower limit).

[0104] In another example, values defining the therapeutic window are stored within a memory 311 of external programmer 104. In this example, in response to detecting that the signal has deviated from the homeostatic window, processing circuitry 210 of IMD 106 transmits, via telemetry module 208, data representing the measurement of the signal to external programmer 104. In one example, in response to detecting that the signal has exceeded an upper threshold of the homeostatic window, processing circuitry 210 of IMD 106 transmits, via telemetry module 208, data representing the measurement of the signal to external programmer 104. External programmer 104 may determine to adjust a parameter value to reduce the signal below the upper threshold as long as the parameter value remains within the one or more limits to the parameter.

[0105] In another example, processing circuitry 210, via telemetry module 208 and from external programmer 104, receives instructions to adjust one or more limits of the therapeutic window. For example, such instructions may be in response to patient feedback on the efficacy of the electrical stimulation therapy, or in response to one or more sensors that have detected a signal of the patient. Such signals from sensors may include bioelectrical signals, such as a signal within the Beta frequency band or signal within the Gamma frequency band of brain 120 of patient 112, or physiological parameters and measurements, such as a signal indicating one or more of a patient activity level, posture, and respiratory function. Further, such signals from sensors may indicate a lack of reduction of one or more symptoms of the patient 112, such as tremor or rigidity or the presence of side effects due to electrical stimulation therapy, such as paresthesia. In response to these instructions, processing circuitry 210 may adjust one or more thresholds of the homeostatic window. For example, processing circuitry 210 may adjust the magnitude of the upper threshold, the lower threshold, or shift the overall position of the homeostatic window such that the threshold, defined by the homeostatic window, for adjustment of the one or more parameters of electrical stimulation,is itself adjusted. Thereafter, processing circuitry 210, via electrodes 116 and 118, delivers the adjusted electrical stimulation to patient 112.

[0106] FIG. 3 is a block diagram of the external programmer 104 of FIG. 1. Although programmer 104 may generally be described as a hand-held device, programmer 104 may be a larger portable device or a more stationary device. In some examples, programmer 104 may be referred to as a tablet computing device. In addition, in other examples, programmer 104 may be included as part of an external charging device or include the functionality of an external charging device. As illustrated in FIG. 3, programmer 104 may include a processing circuitry 310, memory 311, user interface 302, telemetry module 308, and power source 320. Memory 311 may store instructions that, when executed by processing circuitry 310, cause processing circuitry 310 and external programmer 104 to provide the functionality ascribed to external programmer 104 throughout this disclosure. Each of these components, or modules, may include electrical circuitry that is configured to perform some or all of the functionality described herein. For example, processing circuitry 310 may include processing circuitry configured to perform the processes discussed with respect to processing circuitry 310.

[0107] In general, programmer 104 comprises any suitable arrangement of hardware, alone or in combination with software and / or firmware, to perform the techniques attributed to programmer 104, and processing circuitry 310, user interface 302, and telemetry module 308 of programmer 104. In various examples, programmer 104 may include one or more processors, which may include fixed function processing circuitry and / or programmable processing circuitry, as formed by, for example, one or more microprocessors, DSPs, ASICs, FPGAs, or any other equivalent integrated or discrete logic circuitry, as well as any combinations of such components. Programmer 104 also, in various examples, may include a memory 311, such as RAM, ROM, PROM, EPROM, EEPROM, flash memory, a hard disk, or an optical media (e.g., DVD or CD-ROM), comprising executable instructions for causing the one or more processors to perform the actions attributed to them. Moreover, although processing circuitry 310 and telemetry module 308 are described as separate modules, in some examples, processing circuitry 310 and telemetry module 308 may be functionally integrated with one another. In some examples, processing circuitry 310 and telemetry module 308 correspond to individual hardware units, such as ASICs, DSPs, FPGAs, or other hardware units.

[0108] Memory 311 (e.g., a storage device) may store instructions that, when executed by processing circuitry 310, cause processing circuitry 310 and programmer 104 to provide the functionality ascribed to programmer 104 throughout this disclosure. For example, memory311 may include instructions that cause processing circuitry 310 to obtain a parameter set from memory, select one or more parameters for electrical stimulation or adaptive stimulation according to sensed signals, or receive user input and send a corresponding command to IMD 104, or instructions for any other functionality. In addition, memory 311 may include a plurality of programs, where each program includes a parameter set that defines stimulation therapy.

[0109] User interface 302 may include a button or keypad, lights, a speaker for voice commands, a display, such as a liquid crystal (LCD), light-emitting diode (LED), or organic light-emitting diode (OLED). In some examples the display may be a touch screen. User interface 302 may be configured to display any information related to the delivery of stimulation therapy, identified patient behaviors, sensed patient parameter values, automatically selected parameters, prompts for user input regarding stimulation parameters or adaptive stimulation parameters, patient behavior criteria, or any other such information. User interface 302 may also receive user input via user interface 302. The input may be, for example, in the form of pressing a button on a keypad or selecting an icon from a touch screen. User interface 302 may refer to hardware configured to present information to the user and / or receive input from the user. In some examples, processing circuitry 310 directly controls this hardware. In other examples, processing circuitry 310 may communicate with drive hardware that controls hardware of user interface 302. In some examples, user interface 302 may include display and / or interactive display configurations as described herein.

[0110] Telemetry module 308 may support wireless communication between IMD 106 and programmer 104 under the control of processing circuitry 310. Telemetry module 308 may also be configured to communicate with another computing device via wireless communication techniques, or direct communication through a wired connection. In some examples, telemetry module 308 provides wireless communication via an RF or proximal inductive medium. In some examples, telemetry module 308 includes an antenna, which may take on a variety of forms, such as an internal or external antenna. In some examples, telemetry modules 308 may support communications with intermediate devices between programmer 104 and IMD 106 or other external devices.

[0111] Examples of local wireless communication techniques that may be employed to facilitate communication between programmer 104 and IMD 106 include RF communication according to the 802.11 or Bluetooth specification sets or other standard, inductive telemetry, or any proprietary telemetry protocols. In this manner, other external devices may be capable of communicating with programmer 104 without needing to establish a secure wirelessconnection. As described herein, telemetry module 308 may be configured to transmit a spatial electrode movement pattern or other stimulation parameter values to IMD 106 for delivery of stimulation therapy.

[0112] According to the techniques of the disclosure, in some examples, processing circuitry 310 of external programmer 104 defines the parameters of a therapeutic window, stored in memory 311, for delivering aDBS to patient 112. In one example, processor 311 of external programmer 104, via telemetry module 308, issues commands to IMD 106 causing IMD 106 to deliver electrical stimulation therapy via electrodes 116, 118 via leads 114.

[0113] The following examples illustrate various user interfaces and techniques for managing the sensing of physiological signals, such as brain signals, programming adaptive stimulation therapy, and managing electrical stimulation as described herein. Programmer 104, or another external computing device, may output the user interfaces and screens described herein. The example user interface screens may be separately presented or selectable in any order, or programmer 106 (for example) may present each screen in order as part of a guided programming process to assist the user through the programming process. User input may be prompted at various times, either to select parameter values or to confirm automatically selected parameter values. In some examples, programmer 106 may perform each step automatically and present the user with fully automated and selected parameters at the end of the process. The user may confirm the parameter values or review one or more of the parameter values using each respective screen of the user interface as needed to customize the stimulation therapy, which may include adaptive stimulation therapy such as aDBS.

[0114] FIG. 4 is a conceptual diagram illustrating an example home screen 402 for navigating within an example user interface 400. User interface 400 may include several different screens as the user can navigate to different functions to view sensed information, view stored data, or adjust various stimulation parameter values. As shown in the example of FIG. 4, home screen 402 includes information associated with patient 122, such as patient specific information 406 such as name, patient ID, date of birth, and patient diagnosis. Other information may include device specific information such as model number, implant date, battery level, and estimated batter life remaining. Information such as impedance status for the system and event summary may also be provided in the home screen 402. Screen 402 may also include stimulation toggle switch 404 that, when selected, toggles between turning stimulation on or turning stimulation off. Stimulation toggle switch 404 may be provided in some, most, or all of the different screens within user interface 400 to enable the user to turn stimulation on or off at any time. Alert button 410 shows “no alerts” because there are noalerts to be shown. However, if there are alerts for the user, alert button 410 may indicate that there are alerts, or the number of alerts, and alert button 410 may be selectable to cause user interface 400 to show a list of the alerts for the user.

[0115] The home screen 402 in FIG. 4 may also include a menu 408 that includes several selectable buttons that enable the user to navigate to other screen and functionality supported by user interface 400. These selectable buttons include “setup,” “stimulation,” “impedance,” “MRI eligibility,” “replacement,” “events,” and “end session.” Programmer 104 may switch to the appropriate screen in response to user selection of the respective selectable button.

[0116] As shown in the example of FIG. 4, in response to user selection, the setup button takes the user to screens associated with selecting electrode combinations for sensing and / or stimulation and / or frequency for sensing. In response to user selection, the stimulation button takes the user to screens associated with managing electrical stimulation therapy for the patient, such as selecting an adaptive stimulation mode, selecting thresholds for the adaptive mode, selecting parameters that define stimulation, bounds for stimulation parameters, or any other parameters related to stimulation therapy. In response to user selection, the impedance button takes the user to screens associated with viewing impedances of one or more electrode combinations and / or leads and running impedance testing for any electrical pathways.

[0117] The MRI eligibility button takes the user to screens associated with checking MRI eligibility of any implanted device (e.g., IMD 106) and / or placing the implanted device into an MRI eligible mode. The replacement button causes user interface 400 to displace screens related to when the IMD 106 should be replaced (e.g., remaining operational life for a primary cell non-rechargeable power supply). The events button enables the user to navigate to various screens that display events and data associated with sensing and delivering electrical stimulation. The end session button enables the user to terminate the management session via user interface 400. In addition to the menu, user interface 400 may include a stimulation toggle switch that enables the user to request turning stimulation on or off. These different navigation categories in menu 408 are merely examples, and the functionality within each category may be separated into additional categories or combined into fewer categories in other examples.

[0118] Home screen 402 also includes automated aDBS setup button 412 presented for selection. In response to user selection of automated aDBS setup button 412, processing circuitry 310 may initiate fully automated selection of parameters related to adaptive stimulation. This process may include automated sensing of bioelectric signals, selection of electrode combinations for sensing, selection of electrode combinations for stimulation,selecting frequency for sensing signals, selecting an adaptive mode for stimulation control, one or more thresholds for the adaptive mode, parameter thresholds for stimulation, and / or any other selectable parameters related to closed-loop adaptive stimulation therapy. In some examples, the automated process may perform all of these processes and display recommended parameters and modes at a single final screen for user confirmation. In some examples, user interface 400 may present a screen after each parameter selection step with recommended parameter values for the user to confirm before moving to the next step. In other examples, system 100 may present automated recommendations for selectable parameters in each step via user interface 400 as the user moves through different screens of user interface 400. In this manner, the user can obtain the system recommended parameter values available to avoid manual selection. In any event, user interface 400 may provide the automated process with one or more opportunities for the user to review, confirm, and / or change the automated parameter value or other selections.

[0119] User interface 400 may be configured for a clinician programmer that enables the clinician to oversee all aspects of stimulation therapy and / or sensing, both manual and / or automated. In some examples, user interface 400 may enable the language of the clinician programmer to be different from a patient programmer configured to enable the patient to control a subset of features related to IMD 106. For example, user interface 400 may enable the clinician to set up the patient programmer language in the setup button, where the patient programmer language is different from the language of user interface 400 presented by the clinician programmer. For example, user interface 400 may enable the clinician to set up therapy group names, device names, and patient events to appear in a patient’s local language irrespective of the primary or supported clinician language of user interface 400. In this manner, user interface 400 may enable the clinician (or a translator assisting the clinician) to program group names and patient events in the desired language for the patient even if it is not the primary language of the clinician.

[0120] FIG. 5 is a conceptual diagram illustrating an example screen 500 for displaying a selected sensing electrode combination of a lead. In some examples, electrode combinations 502 may be referred to as sense channels. In some examples, system 100 may automatically select an electrode combination based on a weighted summation of multiple sensing metrics (or other criteria). In some examples, the sensing metric information is collected when system 100 controls sensing circuitry to sense respective bioelectrical signals from each of the plurality of electrode combinations 502. System 100 may perform this collection of the bioelectrical signals upon navigation to screen 500 or prior to this step of the process.System 100 may in any example analyze the sensed signals and generate one or more recommended electrode combinations for sensing and / or a frequency for sensing signals during therapy, such as during adaptive stimulation (e.g., aDBS). Message 508 indicates to the user that electrode combination 504 is the recommended electrode combination based on a weighted summation of sensing metrics. In some examples, sensing metrics include bioelectrical signal information, e.g., LFP signal information, whether the electrode combination supports already configured therapy electrodes, and the quantity of artifacts associated with each electrode combination.

[0121] Screen 500 may include information summarizing the metrics of selected electrode combination 504 (highlighted), such as LFP information summary 514, therapy electrode status 516, and artifact summary 518. Lead 512 shows the electrodes of the lead used for the selected electrode combination 504. LFP information summary 514 may include an LFP power vs. frequency graph 522. Data line 520 corresponds to the powers for the combination “0 to 2” that is selected and shows the highest magnitude of power at the frequency of 22.48 Hz. This frequency may also be recommended for this reason.Generally, the recommended electrode combinations are those with peaks present at a certain frequency as shown in the LFP power vs. frequency graph. Additionally, electrode combinations with relatively low artifact levels and already supported therapy electrodes are the best electrode combinations. Metrics LFP information summary 514, therapy electrode status 516, and artifact summary 518 may be weighted to indicate levels of importance. For example, LFP information summary 514 may have a higher weight than therapy electrode status 516. In some examples, the weighting of each metric may be patient-dependent. In other examples, the metric weights are based on aggregate patient data. If desired, the user may choose different electrode combinations and frequencies. For example, screen 500 may receive user selection of a different electrode combination of electrode combinations 504, which may cause screen 500 to update the corresponding selected data line in graph 522 and, if necessary, the frequency. The user may select a different frequency using the slider on graph 522 or another numerical input field in other examples. Selection of the close button 524 will accept the selected or recommended parameters shown in screen 500 for subsequent sensing, close screen 500, and may prompt system 100 to continue automated programming. In some examples, a different “save” or “confirm” button may be presented for the user to select when satisfied with the identified parameters in screen 500. The techniques described herein are not limited to LFP signals. Other bioelectrical signals may be used in other examples. LFP signals serve merely as a non-limiting example.

[0122] During the automated selection process discussed herein, programmer 104 may initiate an automatic scan of brain signals from all or most electrode combinations available to enable programmer 104 or the user to identify where signals might be located (which hemisphere, which region of a lead, which specific combinations of contacts) for the purpose of identifying such signals, the integrity or quality of the recording system, and then guiding sensing configuration and / or stimulation parameters values

[0123] In this manner, user interface 400 can provide a view of all signals in a hemisphere simultaneously and enable selection of one signal to be compared to the others. Programmer 104 may measure aspects of the signal (e.g., difference between maximum and / or minimum at a specific frequency of interest). Programmer 104 may enable IMD 106 to continuously record a subset of signals. In some examples, programmer 104 may perform statistical comparisons (e.g., an energy in a region of frequencies compared to the energy at a specific peak, the relative amplitude above 1 / frequency of the curve, the width of the peak, or simultaneous comparison or measurement of two or more peaks. In some examples, user interface 400 may provide additional views for leads having electrodes at different locations around a perimeter of a lead (e.g., segmented electrodes of directional leads). User interface 400 may also provide visualization of anatomic structures or other reference in combination with a signal location (e.g., whether or not a signal is in or out of target, or if a signal is medial or lateral from an anatomical structure).

[0124] FIG. 6 is a flowchart illustrating an example technique for selecting a sensing electrode combination of a lead. The example of FIG. 6 (and other techniques regarding guided programming herein) will be described with respect to programmer 106 and processing circuitry 310. However, some or all of these techniques may alternatively be performed by other devices or systems, such as IMD 104 or another external device. In some examples, multiple devices may perform these techniques in a distributed fashion toward completion.

[0125] As shown in the example of FIG. 6, processing circuitry 310 of system 100 may control sensing circuitry of sensing module 204 (FIG. 2) to sense a bioelectrical signal from each of a plurality of sensing electrode combinations (602). In some examples, sensing module 204 may sense one or more of an LFP, an EEG, or an evoked resonant neural activity (ERNA). Processing circuitry 310 can then determine a first metric indicative of a presence of an artifact in the respective bioelectrical signals of each sensing electrode combination of the plurality of sensing electrode combinations (604). In some examples, the first metric is a direct measurement of artifacts in the signal. In other examples, the first metric is some otherquantification of the presence of artifacts. Processing circuitry 310 additionally determines a second metric. The second metric can be indicative of whether each of the plurality of sensing electrode combinations has been previously selected for sensing signals during electrical stimulation therapy (606). The second metric may comprise a binary quantification, e.g., 1 for “yes” or 0 for “no”. Alternatively, the second metric can be indicative of an extent to which each respective sensing electrode combination is associated with a linear relationship between electrical stimulation amplitude and signal suppression of bioelectrical signals. In some examples, the sensing electrode combination and corresponding frequency band that most linearly suppresses bioelectrical signals when electrical stimulation amplitude increases may receive the highest second metric value. Processing circuitry 310 determines a third metric indicative of a magnitude of one or more sensed bioelectrical signals associated with each sensing electrode combination of the plurality of sensing electrode combinations (608). Processing circuitry 310 assigns a weight to each of the three metrics (610). In some examples, the weight of each of the three metrics is patient-dependent. In other examples, the weight of each metric is based on aggregate patient data. In some examples, this process may consider fewer criteria, such as only the magnitude of the LFP power at a frequency or frequency band or more criteria than described.

[0126] Processing circuitry 310 generates a score for each sensing electrode combination of the plurality of sensing electrode combinations based on the weighted metrics (610). In some examples, processing circuitry 310 generates the score by implementing a multiobjective cost function, wherein the inputs to the cost function comprise at least the first metric, the second metric, and the third metric of each sensing electrode combination of the plurality of sensing electrode combinations, as well as the associated weights of each of the metrics. Based on the scores, processing circuitry 310 selects one sensing electrode combination of the plurality of electrode combinations for sensing subsequent bioelectrical signals (614). In some examples, processing circuitry 310 may implement a machine learning (ML) model to select the sensing electrode combination of the plurality of electrode combinations for sensing subsequent bioelectrical signals. Processing circuitry 310 may also present the selected sensing electrode combination, and frequency for that electrode combination in some examples, via user interface 400 for confirmation and / or display.

[0127] FIG. 7 is a conceptual diagram illustrating an example screen 500 of user interface 400 for selecting a frequency for monitoring sensed signals. As shown in screen 700 of FIG.7, the brain signal 710 recorded for an selecting an electrode combination is shown in the frequency domain. Processing circuitry, e.g., processing circuitry 310 (FIG. 3), hasautomatically selected the frequency 712 as having a peak 708 in the Beta frequency band 704 as the frequency from which to monitor during brain signal sensing. Frequency indicator 702 indicates that this frequency is 22.46 Hz. This peak 708 may be selected by processing circuitry 310 because it may provide the most consistent and / or most accurate sensing for changes to brain signal information. Frequency range 714 indicates the range of frequencies from which the power of signal 710 will be used to monitor brain activity. In some examples, frequency range 714 may be a preset variance from frequency 712, such as 5 Hz on either side of frequency 712. In other examples, programmer 104 may automatically select the width of frequency range 714 based on the width of peak 708 or some other feature of signal 710. Although both Beta band 704 and Gamma band 706 are shown, other examples may only include one frequency band. In some examples, the user may want to adjust the frequency for monitoring and may do so using slider 718.

[0128] Processing circuitry 310 may automatically select frequency 712 based on peak 708. In some examples, screen 700 may receive user input selecting different frequencies and / or frequency bands for sensing subsequent signals. For example, the user may move slider 718 to lower or higher frequencies as desired to select a different frequency. The user may also adjust the width of the frequency band. In response to user selection of close button 716, user interface 400 may save the selected frequency and frequency band and close screen 700.

[0129] FIG. 8 is a flowchart illustrating an example technique for selecting a frequency for monitoring. In some examples, the processing circuitry 310 selects a frequency for monitoring after selected a sensing electrode combination. In other examples, the selections are made concurrently with the electrode combination selection and from the same sensed electrical signals.

[0130] In the example of FIG. 8, processing circuitry 310 selects a frequency for monitoring after selecting a sensing electrode combination, processing circuitry 310 controls sensing circuitry 204 to sense one or more subsequent bioelectrical signals, e.g., LFP, EEG, and ERNA, using the selected sensing electrode combination, e.g., electrode combination 504 (802). Processing circuitry 310 determines the frequency of the most common peak or peaks (804). In some examples, the most common peak is determined in the clinic setting during initial programming. In some examples, the processing circuitry updates the most common peak frequency based on the most common peak observed during a plurality of patient recoded events, e.g., patient events post-IMD implantation. Processing circuitry 310 additionally determines a degree of difference in frequency from a selected electricalstimulation frequency (806). In some examples, it may be desirable to select a frequency for monitoring that has a relatively high degree of difference in frequency from the electrical stimulation frequency, e.g., to prevent artifacts caused by stimulation from appearing in the monitoring signal. This degree of difference may be a threshold frequency magnitude different from the stimulation frequency or at some weighted combination of separation from stimulation frequency and peak magnitudes. In some examples, the peak may be selected due to variation in power between stimulation delivery and no stimulation deliver indicating the response of the patient to stimulation therapy.

[0131] Processing circuitry 310 also determines which adaptive mode of a plurality of adaptive modes is currently being used for adaptive stimulation therapy (808). In some examples, processing circuitry 310 outputs a Beta frequency or a Gamma frequency based on the selected adaptive stimulation therapy mode appropriate for the frequency band, e.g., a Beta frequency for single threshold and dual threshold modes, or a Gamma frequency for single-inverse modes. Processing circuitry 310 determines a frequency for monitoring based on one or more of the determinations (810). While the techniques are described using processing circuitry 310, other processing circuitry, such as processing circuitry 210 or a combination of processing circuitry 310 and processing circuitry 210, may also be used in some examples.

[0132] FIG. 9 is a conceptual diagram illustrating an example screen 900 of user interface 400 for selecting a frequency for monitoring based on bioelectrical signal snapshots during patient events. In some examples, processing circuitry 310 identifies that a change in the selected frequency for monitoring may be beneficial based on longitudinal (chronic) monitoring of the most common peaks observed during patient events over time in which patient 112 has received therapy. Processing circuitry 310 monitors bioelectrical signal, e.g., LFP, over time and depicts an associated power of the signal, e.g., an LFP power, as a trace 918. In some examples, trace 918 is presented in conjunction with a current amplitude 910 of stimulation pulses delivered over the same time. Hemisphere selector 906 allows the user to switch between signals sensed from different leads in respective hemispheres, and time selector allows the user to switch between different periods of time to show data corresponding to the different periods. The user may additionally select various timelines within the different periods, e.g., days of the month, using timeline 904.

[0133] LFP and stimulation amplitudes may be sampled at a specific rate, e.g., six times per hour. However, higher or lower sample rates may be used depending on data storage capabilities. During a clinic visit, e.g., a post-implantation follow-up visit, user interface 400may present screen 900 and a snapshot 912 of bioelectrical signal information, e.g., LFP peaks, stored at the same time of a patient event for clinician review. In some examples, user interface 400 may present multiple snapshots of LFP peaks. Processing circuitry may present suggestion box 916 and prompt the user to confirm the new suggested frequency for monitoring. The user may then select “Yes” of 916, “Keep Previous” of 916, or “Manual Selection” of 916. In some examples, if the user selects “Manual Selection” of 916, user interface 400 allows the user to input a selection for a different frequency for monitoring (not shown in FIG. 9). After confirming the new system-suggested frequency, keeping the previously selected frequency, or inputting a manual frequency selection by interacting with suggestion box 916, the user may close screen 900 using close button 914 to also store these settings. While the techniques are described using processing circuitry 310, other processing circuitry, such as processing circuitry 210 or a combination of processing circuitry 310 and processing circuitry 210, may also be used.

[0134] FIG. 10 is a conceptual diagram illustrating an example screen 1000 for selecting an adaptive therapy mode, e.g., an aDBS mode, from two or more different adaptive modes. As shown in the example of FIG. 10, user interface 400 may include a screen 1000 that indicates the automated system selection of dual threshold mode 1002, single threshold mode 1006, or single threshold inverse mode 1008. Dual threshold mode 1002 is shown as selected. Dual threshold mode 1002 enables the system to adjust stimulation amplitude based on upper and lower thresholds of the LFP signals. Single threshold mode 1006 enables the system to increase stimulation when LFP signals are above the threshold, and single threshold inverse mode 1008 enables the system to decrease stimulation when LFP signals are above the threshold.

[0135] This feature of user interface 400 for programming aDBS is intended to enable IMD 106 to automatically adjust, within system-defined limits and / or clinician-defined limits, one or more stimulation parameters based on changes in brain state. A patient’s brain state will be measured using a brain signal, such as LFPs, recorded concurrently from the implanted electrodes during therapy. The goal of the automatic adjustment of therapy may be to maintain the brain state (as defined by these signals) within a specified range (e.g., the range between an upper and lower threshold in the dual threshold mode example), understanding that clinical symptoms and side effects may be well correlated with these detected brain states. In this manner of managing brain states, the user may be able to manage clinical symptoms and side effects. This feature is referred to as closed loop DBS or aDBS.

[0136] In this manner, user interface 400 enables the user to monitor automated adaptive therapy configuration by confirming or modifying algorithm, thresholds, and / or stimulation settings for adaptive stimulation modes. Menu 504 allows the user to monitor the automated programming process and to switch between setup stages. Adaptive mode is the current setup stage in this example. The user may select previous button 1010 to go back to a previous setup stage, e.g., BrainSense setup, or may select next button 1012 to move on to a next setup stage, e.g., thresholds. In some examples, processing circuitry 310 controls stimulation generator 202 to generate electrical stimulation at a plurality of values of a stimulation parameter, e.g., current amplitude, that at least partially defines the electrical stimulation during a period of time. In some examples, user interface 400 presents screen 1000 to the user after selecting an adaptive mode based on information representative of bioelectrical signals sensed by sensing circuitry 204.

[0137] In some examples, in addition to the information representative of the bioelectrical signals, processing circuitry 310 determines which adaptive mode of the plurality of adaptive modes to select based on user input. For example, user input may comprise information regarding a condition of a patient, e.g., patient 112, which may be used to determine which aDBS mode to use for patient 112. Example conditions may include different symptoms or diseases, patient specific reactions to stimulation (e.g., dyskinesia at higher stimulation amplitudes), or unstable reactions to medication also consumed by the patient. For example, single threshold inverse mode may be selected for patients that have dyskinesia at higher stimulation amplitudes or if the patient responds in the Gamma band. In some examples, the system selects the single or dual threshold for Beta band sensing when the patient needs help controlling swings in symptoms from taking medication. Additionally or alternatively, user interface 400 may enable the user to select a different aDBS mode, i.e., override the aDBS selection made by processing circuitry 310, by selecting a different adaptive mode in screen 1000. While the techniques are described using processing circuitry 310, other processing circuitry, such as processing circuitry 210 or a combination of processing circuitry 310 and processing circuitry 210, may also be used. Selection of next button 1012 may save the selected adaptive mode and move to the next screen of user interface 400.

[0138] FIG. 11 is a flowchart illustrating an example technique for selecting an adaptive therapy mode. Optionally, user interface 400 may allow the user to input user indicative a patient condition, e.g., a condition of patient 112 (1102). The patient condition may comprise information related to patient symptoms and desired therapy outputs. Processing circuitry 310 controls stimulation generator 202 to generate electrical stimulation at a plurality of differentvalues of a stimulation parameter, e.g., a current amplitude, a voltage amplitude, a pulsewidth, and / or a pulse rate, that at least partially defines the electrical stimulation during a period of time (1104). The period of time may be on the order of minutes in order for the patient to respond to the delivered stimulation. In some examples, the period of time may range from 30 seconds to 10 minutes, but other ranges are also possible. In some examples, this process may be performed over the course of hours or days outside of the clinic setting.

[0139] Sensing module 204 senses bioelectrical signals, and processing circuitry 310 receives bioelectrical signal information at a selected frequency for monitoring, e.g., frequency 712 (FIG. 7) over the period of time (1106). Based on the bioelectrical signal information, processing circuitry 310 selects a first adaptive mode from a plurality of adaptive stimulation mode (1108). In the example of aDBS, the plurality of adaptive stimulation modes may comprise single threshold mode, dual threshold mode, and singleinverse threshold mode. In some examples, the automated process stops here and programmer 106 can store the selected adaptive mode.

[0140] In some examples, processing circuitry 310 may optionally cause user interface 400 to present a screen, e.g., screen 1000, to the user indicating the adaptive stimulation mode selection, and processing circuitry 310 may receive a corresponding user input related to the plurality of adaptive stimulation modes (1110). This input related to the modes may be user input identifying a patient condition, sensitivity to certain stimulation, medication issues or status, or any other information that the system may use to determine which adaptive mode to select. Processing circuitry 310 determines whether the user input is indicative of a need to change the adaptive stimulation mode selection (1112). If the user input is indicative of a need to change the adaptive stimulation mode selection (“YES” of 1112), the user may be prompted to input a selection for a second adaptive stimulation mode different from the first adaptive stimulation mode (1114). If the user input is not indicative of a need to change the adaptive stimulation mode selection (“NO” of 1112), the process ends, and processing circuitry 310 keeps the adaptive stimulation mode selection. While the techniques are described using processing circuitry 310, other processing circuitry, such as processing circuitry 210 or a combination of processing circuitry 310 and processing circuitry 210, may also be used.

[0141] FIG. 12 is a flowchart illustrating an example technique for selecting an adaptive therapy mode specific for aDBS therapy. In some examples, FIG. 12 illustrates an example technique of step 1108 (FIG. 11). Processing circuitry 310 determines whether sensed bioelectrical signal, e.g., LFP signal, decreased or increased when the stimulation parameter,e.g., a current amplitude, a voltage amplitude, a pulse-width, and / or a pulse rate, increased. If the LFP signal increased when stimulation parameter value increased (“YES” of 1204) processing circuitry 310 may recommend single-inverse threshold mode (1216). If the LFP signal decreased when the stimulation parameter value increased (“NO” of 1204), processing circuitry 310 determines an automated stimulation response time by causing stimulation generator 202 to ramp from an amplitude of zero to a therapeutic level relatively quickly, e.g., in 250 milliseconds (ms) or less. Sensing module 204 senses the LFP response to the ramping. Processing circuitry 310 receives the LFP response information, e.g., the time it took the LFP signal to response to the stimulation ramping (1208). If the response time is relatively quick, i.e., if the LFP signal responded quickly (“YES” of 1210), such as the LFP signal suppressed within 500 ms in one example, processing circuitry 310 may select the single threshold mode (1212). If the response is relatively slow (“NO” of 1210), processing circuitry 310 may select the dual threshold mode (1214). Although three adaptive modes are described in this example, selection may be made between only two adaptive modes or between four or more adaptive modes in other examples. While the techniques are described using processing circuitry 310, other processing circuitry, such as processing circuitry 210 or a combination of processing circuitry 310 and processing circuitry 210, may also be used.

[0142] FIG. 13 is a conceptual diagram illustrating an example screen 1300 for setting one or more thresholds associated with an aDBS therapy. In the example of FIG. 13, a representation of a lead and the electrodes carried thereon is displayed in screen 1300. The cathode and anode electrodes are also indicated to show the selected electrode combination as part of lead view 1304. Menu 1334 indicates the lead view 1304 is currently displayed but that annotation of the electrode combination can be shown instead. Stimulation field 1302 is shown over lead view 1304 together with the cathodes and anodes of the lead. To the right of the lead view 1304, an LFP graph 1306 and a stimulation parameter graph 1308 are displayed. On the very right of the screen 1300 is parameter view 1310 which includes inputs selectable by the user to set the lower amplitude bound (slider 1318) and upper amplitude bound (slider 1320) for the stimulation parameter after the automated threshold setting, which is current amplitude in the example of FIG. 13. Button 1320 jumps to the upper amplitude limit that has been set. Button 1322 jumps to the lower amplitude limit when set. Parameter buttons 1324 enable the user to select the desired parameter, such as amplitude, pulse width, or frequency, to adjust. On this screen of FIG. 13, the user may set parameter value limits that correspond to respective thresholds for the brain signal, such as LFPs. The user can move between different screens of user interface 400 via previous button1332 and next button 1330. Selection of next button 1330 may save the current parameter values selected and shown in FIG. 13.

[0143] Using these screens of user interface 400, the user can monitor and confirm or update the one or more thresholds correspond to the stimulation thresholds and / or adaptive mode selected by processing circuitry 310. In one example, each threshold may be set based on measuring LFPs for 25 seconds at particular amplitude levels as defined by the patient’s tolerance and symptom relief. Other durations of sensing may be used in other examples.

[0144] Typically, to set the upper threshold and lower threshold of the sensed signals for the adaptive mode of brain signal monitoring, the patient has been off medication, i.e., the upper and lower thresholds are set when the patient is not taking medication selected to reduce the symptoms. The patient may be considered to be not taking the medication when the patient, prior to the time the upper bound is set, has not taken the medication for at least approximately 72 hours for extended release forms of dopamine agonists, the patient has not taken the medication for at least approximately 24 hours for regular forms of dopamine agonists and controlled release forms of CD / LD, and the patient has not taken the medication for at least approximately 12 hours for regular forms of CD / LD, entacapone, rasagiline, selegiline, and amantadine. If only stimulation is suppressing brain signals (e.g., LFP signals), then the system can measure these brain signals for various values of stimulation parameters without outside inputs. Once the upper threshold and lower threshold is established, the system can identify when medication wears off because the brain signals will cross the lower or upper threshold. In response to identifying the brain signal crossing a threshold, the system may turn on electrical stimulation to bring back brain signal amplitudes back between the lower threshold and the upper threshold. Thresholds may be set for certain brain signals, such as signals within the Beta frequency band, when the patient is off medication. In some examples, such as when assessing signals within the Gamma frequency band, thresholds may be set when the patient is on medication. In the example of dual threshold mode, processing circuitry determines an upper threshold 1312 and lower threshold 1314 of the LFP signal based on an LFP signal response to a sweep of electrical stimulation amplitude. Lower threshold 1314 and upper threshold 1312 may comprise the homeostatic window. In other examples, i.e., single threshold mode and single-inverse threshold mode, the homeostatic window may comprise only one threshold. Electrical stimulation may have limits that the system may not exceed the bounds of. The user may move lower threshold 1314 and / or upper threshold 1312 to different values may sliding the thresholds or using an input field (not shown). Upper limit 1316 and lower limit 1318, set at 3.6 milliamps (mA)and 1.6 mA respectively, may represent upper and lower boundaries of the therapeutic window determined in the clinic that correspond to thresholds 1312 and 1314. While the techniques are described using processing circuitry 310, other processing circuitry, such as processing circuitry 210 or a combination of processing circuitry 310 and processing circuitry 210, may also be used.

[0145] FIG. 14 is a flowchart illustrating an example technique for setting one or more thresholds associated with an aDBS therapy. In the example of FIG. 14, processing circuitry 310 controls stimulation generator 202 to sweep electrical stimulation amplitude, e.g., current amplitude, over a predetermined time period (1402). Sensing module 204 senses a bioelectrical signal response, e.g., an LFP response, at the selected frequency for monitoring, e.g., frequency 712 (1404). In some examples, processing circuitry 310 may identify one or more of a maximum amplitude or a minimum amplitude of the LFP response (1406). The maximum and minimum amplitude of the LFP response may be the amplitudes at which symptoms are reduced or side effects begin to manifest. Based on the LFP response and associated maximum amplitude and / or minimum amplitude, processing circuitry 310 sets one or more thresholds based on the bioelectrical signal response (1408).

[0146] In the example of dual threshold mode, processing circuitry 310 may set a lower threshold at 25% of the full range of LFP response observed, and processing circuitry 310 may set an upper threshold at 75% of the full range of the LFP response observed. In this example, the thresholds are set to be within the full range of LFP response. In the example of single and single-inverse modes, processing circuitry may set a threshold at 75% of the full range of the LFP response observed. In some examples, the disclosed threshold settings may serve as initial thresholds, and processing circuitry 310 may implement an ML algorithm that fine tunes the threshold configuration based on learned settings across a plurality of patients. In some examples, the plurality of patient data associated with the plurality of patients is categorized into a plurality of bins, and the ML algorithm for optimizing the threshold configuration is based on one of the plurality of bins. In other examples, the ML algorithm optimizes the threshold configuration based on patient-specific data, e.g., primary Parkinsonian symptom (such as bradykinesia or tremor), type and dosing of anti-Parkinsonian medication, or patient activity level.

[0147] FIG. 15 is a flowchart illustrating an example technique for adjusting a stimulation parameter that defines stimulation therapy. In the example of FIG. 15, sensing module 204 senses bioelectrical signals, e.g., LFP signals (1502). Processing circuitry 310 determines that the LFP signal is indicative of a need to adjust at least the stimulationparameter according to the selected adaptive stimulation mode (1504). The stimulation parameter may comprise current amplitude, voltage amplitude, pulse width, and pulse rate. In some examples, processing circuitry 310 makes the determination based on the LFP signal being outside a predetermined range, e.g., outside of the lower and upper thresholds in dual threshold mode. In some examples, processing circuitry 310 monitors for patient feedback or events that may occur to identify if the LFP signals are not appropriate for maintaining effective stimulation therapy or preventing side effects. If one or more stimulation parameters need to be adjusted (“YES” of 1506), processing circuitry 310 adjusts the one or more stimulation parameters (1508) and sensing module 204 continues sensing the LFP signal. If no stimulation parameters need to be adjusted (“NO” of 1506), sensing module 204 continues sensing the LFP signal. The system may continually monitor for potential changes to parameters or perform this analysis periodically on an hourly, daily, or weekly basis, for example. Alternatively, processing circuitry 310 may monitor the signals for changes to a stimulation parameter in response to a trigger event associated with inadequate therapy.

[0148] FIG. 16 is a conceptual diagram illustrating an example screen for setting a ramping rate associated with an aDBS therapy. In some examples, processing circuitry 310 adjusts additional therapy settings, including one or more of an averaging duration, an onset duration, a stimulation transition duration, a ramping rate, and / or a blanking duration.Averaging duration may be a time between sensed bioelectrical signals being collected before generating an average bioelectrical signal value. Onset duration may be a duration between the electrical stimulation meeting a threshold of the one or more thresholds and classifying the electrical stimulation as meeting the threshold. Stimulation transition duration may be a duration over which electrical stimulation settings are changed from one configuration to another configuration. Ramping rate may be a rate at which an amplitude of stimulation is increased or decreased. Blanking duration may be a duration between the subsequent bioelectrical signal meeting a threshold before the system is allowed to classify the LFP state changing again.

[0149] In many instances, default values for each of the therapy settings are sufficient for patient needs. These default values may be established from historical data or clinician experience. In some examples, processing circuitry 310 determines the default values for each of the therapy settings using an ML algorithm based on aggregate patient data.However, in some cases, a patient, e.g., patient 112, may be a candidate for patient-specific additional therapy setting adjustments. In the example of patient 112 being a candidate for patient-specific additional therapy setting adjustments, processing circuitry 310 presentsscreen 1600 of user interface 400 to the user to adjust a ramping rate of the stimulation amplitude. User interface 400 provides screen 1600 monitor ramping rate settings determined by processing circuitry 310 and to optionally adjust how the transition occurs.

[0150] Transition curve 1602 indicates the currently set transition times. The initial transition times may be set based on the selected adaptive stimulation mode. For example, dual threshold mode may use a default rate of 5 minutes to ramp from low to high amplitude. In single threshold mode, the system may use a relatively fast ramp from low to high of 0.25 seconds. However, these rates can be changed automatically or based on user input. Parameter field 1616 provides amplitude limits, current values, and adjustment inputs. Transition up adjustment 1604 represents the current up duration of time, which can be adjusted by moving slider 1612. Transition down adjustment 1608 represents the current down duration of time, which can be adjusted by moving slider 1614. Using this transition test, the user can evaluate whether the selected ramping rate is associated with an elevated potential for side effects due to rapid transition from stimulation on to off or stimulation off to on. For example, the transition up test button 1606 or the transition down test button 1610 can be selected to initiate simulation of an adaptive therapy transition for all or part of the therapeutic window in the respective direction. Processing circuitry 310 may send the complete command to perform the entire transition up or down to IMD 106 in order to avoid communication delays on each step during the transition. In this manner, IMD 106 will perform the test transition as it would be done automatically by IMD 106 during aDBS.

[0151] Each of the ramps up and down on the top graph, and / or sliders 1612 and 1614 on the timelines below, may be selectable by the user to drag the ramp shorter or longer in time. In one example, processing circuitry 310 can initiate the ramp up or down from any starting amplitude. However, in other examples, processing circuitry 310 may require starting at the lower amplitude and ramping up fully before ramping down fully. Selection of cancel button 3220 cancels any changes to the transition durations. Selection of update button 3222 confirms any changes to the transition durations. Once the user is finished confirming the selected ramping rate or adjusting the selected ramping rate, the user may select update button 1618. If the user decides not to implement any updates to the selected ramping rate, the user may select cancel button 1620.

[0152] FIG. 17 is a flowchart illustrating an example technique for adjusting a stimulation parameter, e.g., a ramping rate. In the example of processing circuitry 310 controls stimulation generator 202 to perform a stimulation ramping test (1702). In some examples, the stimulation ramping test comprises ramping stimulation from zero amplitude toa therapeutic level at predetermined ramp rates, e.g., at 2s, 1.5s, Is, 0.5s, and 0.25s. Sensing module 204 measures a bioelectrical signal response, e.g., an LFP response (1704). During the ramping test, the user may input patient-reported side effects, e.g., paresthesia, using user interface 400 (1706). For example, programmer 106 may present one or more input fields (e.g., pull down menus, arrows, etc.) that are configured to receive user input indicative of any side effects that may be felt during this process. Based on the side effect input and the LFP response, processing circuitry 310 selects a ramping rate (1708). In some examples, processing circuitry 310 may select the ramping rate that is the fastest without causing side effects or inducing stimulation artifacts in the LFP response. In other examples, processing circuitry 310 may select a different ramping rate based on other factors.

[0153] FIG. 18 is a conceptual diagram illustrating an example summary screen 1800 summarizing parameters and modes selected via automated aDBS therapy setup. In some examples, responsive to user selection of automated aDBS setup button 412, processing circuitry 310 initiates fully automated selection of parameters and modes related to adaptive stimulation and may display the recommended parameters and modes for user confirmation, e.g., via summary screen 1800. Upon completion of fully automated parameter selection, screen 1800 summarizes a plurality of parameter and mode selections (of which some or all were automatically determined), which may include one or more of sensing electrode combination selection 1804, frequency for monitoring selection 1808, adaptive therapy selection 1810, stimulation thresholds selection 1812, transition duration selections 1814, and LFP threshold(s) selection 1816. Screen 1800 additionally comprises edit buttons 1806A, 1806B, 1806C, 1806D, 1806E, and 1806F (collectively, edit buttons 1806), which enable a user to edit the parameter corresponding to each respective edit button of edit buttons 1806.

[0154] In some examples, responsive to user selection of one of edit buttons 1806, the user moves to a different screen of user interface 400. As an example, upon user selection of edit button 1806A, the user moves to screen 500 or a substantially similar screen (FIG. 5). The user can view sensing information corresponding to each of the sensing electrode combinations. The user may select a different sensing electrode combination, as described herein.

[0155] In some examples, processing circuitry 310 may re-initiate fully automated selection of parameters in response to the user editing one or more of the parameters. For example, if the user selects edit button 1806A and chooses a different sensing electrode combination, processing circuitry 310 may automatically make a new frequency for monitoring selection 1808. As another example, if the user selects edit button 1806C andchanges the adaptive therapy mode, e.g., from dual threshold to single threshold, processing circuitry 310 may automatically make a new LFP threshold(s) selection 1816 to correspond to single threshold adaptive therapy mode. Once the user is satisfied with each of the selected parameters, the user may select save and exit button 1818, e.g., to initiate therapy.

[0156] The following examples are described herein.

[0157] Example 1. A system comprising: processing circuitry configured to: control stimulation circuitry of an implantable medical device (IMD) to generate electrical stimulation at a plurality of different values of a stimulation parameter that at least partially defines the electrical stimulation during a period of time; receive information representative of bioelectrical signals sensed during at least a portion of the period of time; and select an adaptive stimulation mode from a plurality of adaptive stimulation modes, wherein each of the plurality of adaptive stimulation modes define different respective algorithms for adjusting electrical stimulation in response to subsequent sensed bioelectrical signals.

[0158] Example 2. The system of example 1, wherein the processing circuitry is further configured to: control sensing circuitry of the IMD to sense respective bioelectrical signals from the plurality of sensing electrode combinations; determine a first metric indicative of a presence of an artifact in the respective bioelectrical signals of each sensing electrode combination of the plurality of sensing electrode combinations; determine, for each sensing electrode combination of the plurality of sensing electrode combinations, a second metric indicative of the respective sensing electrode combination being compatible with electrodes previously used during for electrical stimulation therapy or associated with a linear relationship between an electrical stimulation amplitude and a signal suppression of bioelectrical signals during an increase in the electrical stimulation amplitude; determine a third metric indicative of a magnitude of one or more sensed bioelectrical signals associated with each sensing electrode combination of the plurality of sensing electrode combinations; assign a respective weight to each of the first metric, the second metric, and the third metric to generated weighted metrics; generate, according to the weighted metrics, a score for each sensing electrode combination of the plurality of sensing electrode combinations; and select, based on the score for each sensing electrode combination of the plurality of sensing electrode combinations, one sensing electrode combination for sensing subsequent bioelectrical signals.

[0159] Example 3. The system of any of examples 1 or 2, wherein the processing circuitry is further configured to: determine a frequency for monitoring the subsequent sensed bioelectrical signals based on one or more of: a most common peak observed during aplurality of patient recorded events, a degree of difference in frequency from a selected electrical stimulation frequency, or the selected adaptive stimulation mode.

[0160] Example 4. The system of any of examples 1 through 3, the processing circuitry further configured to: receive user input indicating a patient condition; and select, based on the user input and the information representative of the bioelectrical signals, the adaptive stimulation mode of the plurality of adaptive stimulation modes.

[0161] Example 5. The system of any of examples 1 through 4, wherein the adaptive stimulation mode is a first adaptive stimulation mode, and wherein the processing circuitry is further configured to: receive user input related to the plurality of adaptive stimulation modes; determine, based on the user input, that the user input is indicative of a second adaptive stimulation mode different than the first adaptive stimulation mode; and responsive to the determination, select the second adaptive stimulation mode of the plurality of adaptive stimulation modes for subsequent electrical stimulation.

[0162] Example 6. The system of any of examples 1 through 5, the processing circuitry further configured to: sweep an electrical stimulation amplitude over a predetermined range during the predetermined time period; monitor a bioelectrical signal response, wherein the bioelectrical signal response comprises information representative of at least some of the sensed bioelectrical signals; identify one or more of a maximum amplitude or a minimum amplitude of the bioelectrical signal response; and define one or more thresholds for the selected adaptive stimulation mode based on the one or more of the maximum amplitude or the minimum amplitude.

[0163] Example 7. The system of any of examples 1 through 6, the processing circuitry further configured to: determine, based on the sensed bioelectrical signals, at least one of an averaging duration, an onset duration, a stimulation transition duration, a ramping rate, or a blanking duration for delivering subsequent electrical stimulation therapy, wherein: the averaging duration comprises a time between sensed bioelectrical signals being collected before generating an average bioelectrical signal value, the onset duration comprises a duration between the electrical stimulation meeting a threshold of one or more thresholds of the adaptive stimulation mode and classifying the electrical stimulation as meeting the threshold, the stimulation transition duration comprises a duration over which electrical stimulation settings are changed from one configuration to another configuration, the ramping rate comprises a rate at which an amplitude of electrical stimulation is increased or decreased, and the blanking duration includes a duration between the subsequent bioelectrical signalmeeting a threshold before the processing circuitry is allowed to classify another change to an LFP state .

[0164] Example 8. The system of any of examples 1 through 7, wherein the sensed bioelectrical signal comprises at least one of a local field potential (LFP), an electroencephalograph (EEG), or an evoked resonant neural activity (ERNA), and wherein the plurality of adaptive stimulation modes comprises a plurality of adaptive deep brain stimulation (DBS) modes.

[0165] Example 9. The system of example 8, wherein the plurality of aDBS modes includes one or more of: single threshold aDBS, dual threshold aDBS, or single-inverse aDBS, wherein single threshold aDBS defines a first algorithm that defines increasing electrical stimulation in response to a subsequent bioelectrical signal exceeding a single threshold, the dual threshold aDBS defines a second algorithm that defines adjusting electrical stimulation in response to the subsequent bioelectrical signal exceeding an upper threshold or a lower threshold, and the single-inverse aDBS defines a third algorithm that defines decreasing stimulation in response to the bioelectrical signal exceeding a singleinverse threshold.

[0166] Example 10. The system of any of examples 1 through 9, wherein the processing circuitry is configured to: control sensing circuitry to sense subsequent bioelectrical signals; and adjust, based on the subsequent bioelectrical signals, at least the stimulation parameter according to the selected adaptive stimulation mode.

[0167] Example 11. The system of any of examples 1 through 10, wherein the system comprises an external programmer comprising the processing circuitry.

[0168] Example 12. The system of any of examples 1 through 11, wherein the IMD is configured to adjust electrical stimulation according to the selected adaptive stimulation mode.

[0169] Example 13. A method of programming an implantable medical device (IMD), the method comprising: controlling, by processing circuitry, stimulation circuitry to generate electrical stimulation at a plurality of different values of a stimulation parameter that at least partially defines the electrical stimulation during a period of time; receiving, by the processing circuitry and from sensing circuitry, information representative of bioelectrical signals sensed during at least a portion of the period of time; and selecting, by the processing circuitry and based on the information representative of the bioelectrical signals, an adaptive stimulation mode from a plurality of adaptive stimulation modes, wherein each of theplurality of adaptive stimulation modes define different respective algorithms for adjusting electrical stimulation in response to subsequent sensed bioelectrical signals.

[0170] Example 14. The method of example 13, further comprising: controlling sensing circuitry to sense respective bioelectrical signals from the plurality of sensing electrode combinations; determining a first metric indicative of a presence of an artifact in the respective bioelectrical signals of each sensing electrode combination of the plurality of sensing electrode combinations; determining, for each sensing electrode combination of the plurality of sensing electrode combinations, a second metric indicative of the respective sensing electrode combination being compatible with electrodes previously used during for electrical stimulation therapy or associated with a linear relationship between an electrical stimulation amplitude and a signal suppression of bioelectrical signals during an increase in the electrical stimulation amplitude; determining a third metric indicative of a magnitude of one or more sensed bioelectrical signals associated with each sensing electrode combination of the plurality of sensing electrode combinations; assigning a respective weight to each of the first metric, the second metric, and the third metric to generated weighted metrics; generating, according to the weighted metrics, a score for each sensing electrode combination of the plurality of sensing electrode combinations; and selecting, based on the score for each sensing electrode combination of the plurality of sensing electrode combinations, one sensing electrode combination for sensing subsequent bioelectrical signals.

[0171] Example 15. The method of any of examples 13 or 14, further comprising determining a frequency for monitoring the subsequent sensed bioelectrical signals based on one or more of: a most common peak observed during a plurality of patient recorded events, a degree of difference in frequency from a selected electrical stimulation frequency, or the selected adaptive stimulation mode.

[0172] Example 16. The method of any of examples 13 through 15, further comprising: receiving user input indicating a patient condition; and selecting, by the processing circuitry and based on the user input and the information representative of the bioelectrical signals, the adaptive stimulation mode.

[0173] Example 17. The method of any of examples 13 through 16, wherein the adaptive stimulation mode is a first adaptive stimulation mode, and wherein the method further comprises: receiving user input related to the plurality of adaptive stimulation modes; determining, based on the user input, that the user input is indicative of a second adaptive stimulation mode different than the first adaptive mode; and responsive to the determination,selecting the second adaptive stimulation mode of the plurality of adaptive stimulation modes for subsequent electrical stimulation.

[0174] Example 18. The method of any of examples 13 through 17, further comprising: sweeping an electrical stimulation amplitude over a predetermined range during the predetermined time period; monitoring a bioelectrical signal response, wherein the bioelectrical signal response comprises information representative of at least some of the bioelectrical signals; identifying one or more of a maximum amplitude or a minimum amplitude of the bioelectrical signal response; and defining one or more thresholds for the selected adaptive stimulation mode based on the one or more of the maximum amplitude or the minimum amplitude.

[0175] Example 19. The method of any of examples 13 through 18, further comprising determining, based on the sensed bioelectrical signals, at least one of an averaging duration, an onset duration, a stimulation transition duration, a ramping rate, or a blanking duration for delivering subsequent electrical stimulation therapy, wherein: the averaging duration comprises a time between sensed bioelectrical signals being collected before generating an average bioelectrical signal value, the onset duration comprises a duration between the electrical stimulation meeting a threshold of one or more thresholds of the adaptive stimulation mode and classifying the electrical stimulation as meeting the threshold, the stimulation transition duration comprises a duration over which electrical stimulation settings are changed from one configuration to another configuration, the ramping rate comprises a rate at which an amplitude of electrical stimulation is increased or decreased, and the blanking duration comprises a duration between the subsequent bioelectrical signal meeting a threshold before the processing circuitry is allowed to classify another change to an LFP state.

[0176] Example 20. The method of any of examples 13 through 19, wherein the sensed bioelectrical signal comprises at least one of a local field potential (LFP), an electroencephalograph (EEG), or an evoked resonant neural activity (ERNA), and wherein the plurality of adaptive stimulation modes comprises a plurality of adaptive deep brain stimulation (DBS) modes.

[0177] Example 21. The method of example 20, wherein the plurality of aDBS modes includes one or more of: single threshold aDBS, dual threshold aDBS, or singleinverse aDBS, wherein single threshold aDBS defines a first algorithm that defines increasing electrical stimulation in response to a subsequent bioelectrical signal exceeding a single threshold, the dual threshold aDBS defines a second algorithm that defines adjustingelectrical stimulation in response to the subsequent bioelectrical signal exceeding an upper threshold or a lower threshold, and the single-inverse aDBS defines a third algorithm that defines decreasing stimulation in response to the bioelectrical signal exceeding a singleinverse threshold.

[0178] Example 22. The method of any of examples 13 through 21, further comprising: sensing subsequent bioelectrical signals; and adjusting, based on the subsequent bioelectrical signals, at least the stimulation parameter according to the selected adaptive stimulation mode.

[0179] Example 23. A non-transitory computer-readable medium comprising instructions that, when executed, cause processing circuitry to: control stimulation circuitry of an implantable medical device (IMD) to generate electrical stimulation at a plurality of different values of a stimulation parameter that at least partially defines the electrical stimulation during a period of time; receive information representative of bioelectrical signals sensed during at least a portion of the period of time; and select an adaptive stimulation mode from a plurality of adaptive stimulation modes, wherein each of the plurality of adaptive stimulation modes define different respective algorithms for adjusting electrical stimulation in response to subsequent sensed bioelectrical signals.

[0180] Various examples have been described. These and other examples are within the scope of the following claims.

Claims

WHAT IS CLAIMED IS:

1. A system comprising: processing circuitry configured to: control stimulation circuitry of an implantable medical device (IMD) to generate electrical stimulation at a plurality of different values of a stimulation parameter that at least partially defines the electrical stimulation during a period of time; receive information representative of bioelectrical signals sensed during at least a portion of the period of time; and select an adaptive stimulation mode from a plurality of adaptive stimulation modes, wherein each of the plurality of adaptive stimulation modes define different respective algorithms for adjusting electrical stimulation in response to subsequent sensed bioelectrical signals.

2. The system of claim 1, wherein the processing circuitry is further configured to: control sensing circuitry of the IMD to sense respective bioelectrical signals from the plurality of sensing electrode combinations; determine a first metric indicative of a presence of an artifact in the respective bioelectrical signals of each sensing electrode combination of the plurality of sensing electrode combinations; determine, for each sensing electrode combination of the plurality of sensing electrode combinations, a second metric indicative of the respective sensing electrode combination being compatible with electrodes previously used for electrical stimulation therapy or associated with a linear relationship between an electrical stimulation amplitude and a signal suppression of bioelectrical signals during an increase in the electrical stimulation amplitude; determine a third metric indicative of a magnitude of one or more sensed bioelectrical signals associated with each sensing electrode combination of the plurality of sensing electrode combinations; assign a respective weight to each of the first metric, the second metric, and the third metric to generated weighted metrics; generate, according to the weighted metrics, a score for each sensing electrode combination of the plurality of sensing electrode combinations; andselect, based on the score for each sensing electrode combination of the plurality of sensing electrode combinations, one sensing electrode combination for sensing subsequent bioelectrical signals.

3. The system of any of claims 1 or 2, wherein the processing circuitry is further configured to: determine a frequency for monitoring the subsequent sensed bioelectrical signals based on one or more of: a most common peak observed during a plurality of patient recorded events, a degree of difference in frequency from a selected electrical stimulation frequency, or the selected adaptive stimulation mode.

4. The system of any of claims 1 through 3, wherein the processing circuitry is further configured to: receive user input indicating a patient condition; and select, based on the user input and the information representative of the bioelectrical signals, the adaptive stimulation mode of the plurality of adaptive stimulation modes.

5. The system of any of claims 1 through 4, wherein the adaptive stimulation mode is a first adaptive stimulation mode, and wherein the processing circuitry is further configured to: receive user input related to the plurality of adaptive stimulation modes; determine, based on the user input, that the user input is indicative of a second adaptive stimulation mode different than the first adaptive stimulation mode; and responsive to the determination, select the second adaptive stimulation mode of the plurality of adaptive stimulation modes for subsequent electrical stimulation.

6. The system of any of claims 1 through 5, wherein the processing circuitry is further configured to: sweep an electrical stimulation amplitude over a predetermined range during the predetermined time period; monitor a bioelectrical signal response, wherein the bioelectrical signal response comprises information representative of at least some of the sensed bioelectrical signals; identify one or more of a maximum amplitude or a minimum amplitude of the bioelectrical signal response; anddefine one or more thresholds for the selected adaptive stimulation mode based on the one or more of the maximum amplitude or the minimum amplitude.

7. The system of any of claims 1 through 6, wherein the processing circuitry is further configured to: determine, based on the sensed bioelectrical signals, at least one of an averaging duration, an onset duration, a stimulation transition duration, a ramping rate, or a blanking duration for delivering subsequent electrical stimulation therapy, wherein: the averaging duration comprises a time between sensed bioelectrical signals being collected before generating an average bioelectrical signal value, the onset duration comprises a duration between the electrical stimulation meeting a threshold of one or more thresholds of the adaptive stimulation mode and classifying the electrical stimulation as meeting the threshold, the stimulation transition duration comprises a duration over which electrical stimulation settings are changed from one configuration to another configuration, the ramping rate comprises a rate at which an amplitude of electrical stimulation is increased or decreased, and the blanking duration comprises a duration between the subsequent bioelectrical signal meeting a threshold before the processing circuitry is allowed to classify another change to a local field potential (LFP) state.

8. The system of any of claims 1 through 7, wherein the sensed bioelectrical signal comprises at least one of a local field potential (LFP), an electroencephalograph (EEG), or an evoked resonant neural activity (ERNA).

9. The system of any of claims 1 through 8, wherein the plurality of adaptive stimulation modes comprises a plurality of adaptive deep brain stimulation (DBS) modes.

10. The system of claim 9, wherein the plurality of aDBS modes includes one or more of: single threshold aDBS, dual threshold aDBS, or single-inverse aDBS, wherein single threshold aDBS defines a first algorithm that defines increasing electrical stimulation in response to a subsequent bioelectrical signal exceeding a single threshold, the dual threshold aDBS defines a second algorithm that defines adjusting electrical stimulation in response to the subsequent bioelectrical signal exceeding an upper threshold or a lower threshold, and thesingle-inverse aDBS defines a third algorithm that defines decreasing stimulation in response to the bioelectrical signal exceeding a single-inverse threshold.

11. The system of any of claims 1 through 10, wherein the processing circuitry is configured to: control sensing circuitry to sense subsequent bioelectrical signals; and adjust, based on the subsequent bioelectrical signals, at least the stimulation parameter according to the selected adaptive stimulation mode.

12. The system of any of claims 1 through 11, wherein the system comprises an external programmer comprising the processing circuitry.

13. The system of any of claims 1 through 12, further comprising the IMD, and wherein the IMD is configured to adjust electrical stimulation according to the selected adaptive stimulation mode.

14. The system of any of claims 1 through 13, further comprising an implantable electrode array configured to deliver the electrical stimulation.

15. A non-transitory computer-readable medium comprising instructions that, when executed, cause the processing circuitry to perform the functions of any of claims 1 through 14.

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