Sensed electrical signal trend detection

Abstract

In general, devices, systems, and techniques are described for determining periodicity metrics for electrical signals sensed from a patient. In one example, a system includes processing circuitry configured to receive, from sensing circuitry, a plurality of electric signals from a patient sensed over a time duration, determine characteristic values for the plurality of bioelectric signals, and determine, based on the characteristic values, a periodicity metric indicative of one or more repeating periods for the plurality of bioelectric signals. The processing circuitry can then control delivery of electrical stimulation therapy based on the periodicity metric.

Description

Docket No. : A0013147 WOO 1 / 1123-859WOO 1SENSED ELECTRICAL SIGNAL TREND DETECTION

[0001] This application is a PCT application claiming priority to, and the benefit of, U.S. Provisional Patent Application No. 63 / 726,084, filed November 27, 2024, the entire contents of which is incorporated herein by reference.TECHNICAL FIELD

[0002] This disclosure generally relates to medical devices, and, more particularly, sensing of electrical signals.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 determining trends, such as periodicity metrics, from electrical signals sensed from a patient. The system can be configured to analyze sensed electric signals from a patient over time (e.g., local field potential (LFP) signals) and determine periodicity metrics which may represent trends, occurrence frequency, or recurring periods within the sensed electrical signals that may beDocket No. : A0013147 WOO 1 / 1123-859WOO 1 representative of one or more events. For example, the periodicity metrics may indicate the strongest recurring period and / or when such a period occurs, such as a circadian rhythm, night and / or day periods (e.g., sleep and wake periods), and / or any other recurring trends or periods that are present within the electric signals.

[0006] The system may perform one or more analyses on the sensed electrical signals that can identify one or more periodicity metrics representing a period, trend, occurrence frequency, or other reoccurrence within the data. For example, the system may generate spectral powers for different period lengths to identify strongest recurring periods within the sensed signal. The system may analyze different period lengths over a time duration in order to determine periodicity metrics indicative of stability of the sensed signal over the time duration. In some examples, the system may generate histogram data and identify one or more thresholds as a periodicity metric that can be applied to sensed data in order to identify different periods within the sensed data (e.g., separate day vs. night activity for the patient). The system may then display the periodicity metric and / or control the delivery of electrical stimulation therapy (e.g., DBS therapy or other type of therapy) to the patient based on the periodicity metric.

[0007] In one example, a system includes processing circuitry configured to receive, from sensing circuitry, a plurality of electrical signals from a patient sensed over a time duration; determine characteristic values for the plurality of electrical signals; determine, based on the characteristic values, a periodicity metric indicative of one or more repeating periods for the plurality of electrical signals; and control delivery of electrical stimulation therapy based on the periodicity metric.

[0008] In another example, a method includes receiving, from sensing circuitry, a plurality of electrical signals from a patient sensed over a time duration; determining, by processing circuitry, characteristic values for the plurality of electrical signals; determining, by the processing circuitry and based on the characteristic values, a periodicity metric indicative of one or more repeating periods for the plurality of electrical signals; and controlling, by the processing circuitry, delivery of electrical stimulation therapy based on the periodicity metric.

[0009] In another example, a non-transitory computer-readable medium includes instructions that, when executed, control processing circuitry to receive, from sensing circuitry, a plurality of electrical signals from a patient sensed over a time duration; determine characteristic values for the plurality of electrical signals; determine, based on the characteristic values, a periodicity metric indicative of one or more repeating periods for the plurality of electrical signals; and control delivery of electrical stimulation therapy based on the periodicity metric.Docket No. : A0013147 WOO 1 / 1123-859WOO 1

[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 sense electrical signals and / or 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 sensing electrical signals and / or 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 block diagram illustrating an example system that includes an external device, such as a server, and one or more computing devices that are coupled to an implantable medical device and external programmer shown in FIG. 1 via a network.

[0015] FIG. 5 is a flow chart of an example technique for determining a periodicity metric for a patient.

[0016] FIG. 6 is a graph of example LFP signals sensed over a time duration.

[0017] FIG. 7A is a spectrogram of example LFP power for different periods over time.

[0018] FIG. 7B is a graph of example aggregated power of LFP signals for different periods.

[0019] FIG. 8A is a graph of example detrended aggregate values for the different time values.

[0020] FIG. 8B is a graph of normalized aggregate values and identified peaks from respective periods.

[0021] FIG. 9 is a flow chart of an example technique for determining a periodicity metric for a patient.

[0022] FIG. 10 is a spectrogram of normalized LFP power for different periods over time.

[0023] FIG. 11 is a graph of example normalized LFP power for selected periods over time.

[0024] FIG. 12 is a flow chart of an example technique for determining a periodicity metric and stability of the periodicity for a patient.

[0025] FIG. 13 is an example graph of LFP signals sensed for a patient during different time durations.

[0026] FIG. 14A includes example graphs of histogram characteristics calculated from an example histogram of one time duration in FIG. 14B.Docket No. : A0013147 WOO 1 / 1123-859WOO 1

[0027] FIG. 15A includes example graphs of histogram characteristics calculated from an example histogram of a second time duration of FIG. 15B.

[0028] FIG. 16 is a graph of example periodicity metrics for respective time durations and respective LFP signals.

[0029] FIG. 17 is a flow chart of an example technique for determining a periodicity metric and identifying different repeating periods.

[0030] FIGS. 18A and 18B are graphs of example LFP power over time and medication events for different hemispheres of the brain of a patient.DETAILED DESCRIPTION

[0031] This disclosure describes example devices, systems, and techniques for determining trends, such as periodicity metrics, from electrical signals sensed from a patient. A system may present the periodicity metric via a user interface and / or use the periodicity metric that represents a trend or recurring period to control subsequent delivery of therapy. A patient may suffer from one or more symptoms that can be reflected in one or more different types of physiological signals (e.g., electric signals, movement signals, chemical signals, temperature signals, etc.) that can be sensed by one or more sensors. For example, a patient may suffer from brain disorder such as Parkinson’s disease, or another type of movement disorder that can be monitored using a sensed electrical signal such as an LFP signal, evoked resonant neural activity (ERNA) signal, or other such signal. In some examples, the techniques described herein may be applied to any other disorder, condition, or even to identify periodic rhythms of an otherwise healthy person.

[0032] A clinician may attempt to review an LFP signal, for example, to attempt to identify issues with the patient’s condition or other attributes contributing to the patient condition. In some examples, the clinician may desire to try and use the LFP signal to develop a therapy that can reduce the severity and / or frequency of the patient’s condition. However, LFP signals are complicated and inherently noisy, and it can be very difficult to identify any trends or repeating or reoccurring periods that may be occurring within the LFP signal. For example, the patient’s circadian rhythm or night / day activities may be helpful to know when developing the patient’s therapy, but these periods or activities may not be easily identifiable from the sensed LFP data.

[0033] One example therapy for a movement disorder may include deep brain stimulation (DBS) which can 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. This manual process may include testing various combinations of settings and observing patientsDocket No. : A0013147 WOO 1 / 1123-859WOO 1 symptoms and side effects in order to determine the most optimal settings for the patient. For example, without knowing how the patient condition changes during the day or otherwise over time, it may be difficult to adjust the therapy as needed by the patent. Although the system may be able to provide closed-loop stimulation that uses sensed electric signals (e.g., LFP signals) as a feedback value for automatic adjustment, the clinician may still need to manually identify one or more thresholds from the sensed LFP signals or other information to use in the automated control of one or more stimulation parameters. Without being able to identify important trends or other recurring periods within the sensed signals, the closed-loop stimulation may not be as effective as it could be for the particular patient. Moreover, any sensed data in the clinic, may not be reflective of real-world activities and symptoms for the patient.

[0034] As described herein, devices, systems, and techniques for determining trends, such as periodicity and / or frequency metrics, from electrical signals sensed from a patient. Periodicity, and periodicity metrics are generally described herein, but this term can cover how long a period is, or the frequency of events (e.g., how often a cycle or event occurs). Periodicity metrics can also be representative of the fluctuation or oscillatory patterns within data. In this manner, the periodicity metric described herein can be indicative of any of these repeating patterns (or changes in cycles) of events or signal characteristics. The periodicity metric may be indicative of patient condition or events, and may also be used to identify external impacts on the patient, such as medication, activity, or other environmental induced fluctuations in the sensed signal over time. The system can be configured to analyze sensed electric signals from a patient over time (e.g., local field potential (LFP) signals) and determine periodicity metrics which may represent trends or recurring periods within the sensed electrical signals. For example, the periodicity metrics may indicate the strongest recurring period and / or when such a period occurs, such as a circadian rhythm, night and / or day periods (e.g., sleep and wake periods), and / or any other recurring trends or periods that are present within the electric signals. In this manner, the disclosure describes techniques that can detect, quantify, and / or classify various characteristics of sensed data, which can include trends and / or periods that occur or repeat for the patient.

[0035] The system may perform one or more analyses on the sensed electrical signals that can identify one or more periodicity metrics representing a period, trend, frequency of occurrence, or other reoccurrence within the data. For example, the system may calculate spectral power for different period lengths to identify strongest recurring periods within the sensed signal. The system may analyze different period lengths over a time duration in order to determine periodicity metrics indicative of stability of the sensed signal over the time duration. In some examples, the system may generate histogram data and identify one or more thresholds as a periodicity metric that can be applied to sensed data in order to identify different periodsDocket No. : A0013147 WOO 1 / 1123-859WOO 1 within the sensed data (e.g., separate day vs. night activity for the patient). These analyses may be performed individually or in combination in order to identify any trend or reoccurring period within the sensed data.

[0036] In some examples, the system may then display the periodicity metric via a user interface for review by a patient, clinician, or other user. The periodicity metric may be presented to indicate characteristics such as when the signals repeat, when the patient is likely sleeping or awake, when the signals become unstable, or stable, over time, or any other information. The user interface may accept user input accepting the periodicity metric, suggested sensing thresholds, suggested values for a threshold or other feedback variables for closed-loop stimulation, etc. In some examples, the system may control the delivery of electrical stimulation therapy (e.g., DBS therapy or other type of therapy) to the patient based on the periodicity metric. For example, the system may update one or more thresholds based on the periodicity metric or related information, use different feedback variables or values for different expected periods of time based on the periodicity metric, or other changes to stimulation therapy delivery. Example feedback variables may include a lower limit of stimulation amplitude based on what stimulation amplitudes suppress LFP signals in a beta band (e.g., where symptoms are suppressed) according to the periodicity metric and / or an upper limit of stimulation amplitude based on what amplitudes begin to elicit detectable LFP signals in the gamma band (e.g., over stimulation symptoms) according to the periodicity metric.

[0037] 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, the techniques herein enable the system to determine various calculations and / or transformations of chronic LFP data that can represent intuitive and actionable insights based on the LFP ’’periodicity” (e.g., the characteristics of the periodicity at one or more intervals). In some examples, the periodicity metric may represent or provide automatic detection / separation of the LFP data into daytime & nighttime intervals. In some examples, the periodicity metrics may provide a prominence (e.g., absolute, relative, or both) or periodic responses at various periods or frequencies (e.g., a general 24-hour circadian rhythm strength), one or more states or clusters of LFP magnitudes and degree of separability of the one or more states relative to the time of occurrence (e.g., different periods). In some examples, one or more periodicity metrics may indicate whether a 24-hour circadian rhythm is present (and to what degree), any significance of non-24 hour cycles or periods, sleep and awake time (and potentially degree of difference between each one), thresholds for automatic adjustment of DBS therapy, quality of sleep, or any other such information. The automated process described herein may be able to uncover these trends or periodicities in the sensed data that are not otherwise identifiable from the sensed data.Docket No. : A0013147 WOO 1 / 1123-859WOO 1In addition, the system may improve electrical stimulation therapy, or other therapy, by tailoring the therapy to specific repeating periods of time for the patient and / or other trends that may be occurring in the patient. Results of such techniques may reduce clinician time, improve therapy efficacy, and increase the number of patients that may receive efficacious therapy.

[0038] LFP signals are generally described herein as an example electrical signal sensed from the patient, but any other physiological signals may be used in other examples. Example physiological signals include evoked resonant neural activity (ERNA) signals, evoked compound action potential (ECAP) signals, compound action potentials, electroencephalogram (EEG) signals, electromyograph (EMG) signals, cardiac signals, nerve signals, temperature signals, movement signals, and the like. In addition, DBS therapy is described as one example therapy. However, other therapies may be adjusted based on a periodicity metric such as spinal cord stimulation (SCS), pelvic stimulation, gastric stimulation, peripheral nerve field stimulation (PNFS), medication schedule, etc. Any of these signals, therapies, or combinations thereof, can be applicable to one or more periodicity metric as described herein.

[0039] FIG. 1 is a conceptual diagram illustrating an example system 100 that includes an implantable medical device (IMD) 106 configured to deliver deep brain stimulation to a patient 112. DBS may be open loop or 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. ' 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 and / or based on one or more periodicity metrics. This process enables system 100 to automatically adjust stimulation therapy in response to changes to the patient condition, such as changes to brain activity indicative of a level of therapy efficacy.

[0040] 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 ofDocket No. : A0013147 WOO 1 / 1123-859WOO 1 electrodes 116, 118 are configured to both sense bioelectrical brain signals and deliver adaptive electrical stimulation to brain 120.

[0041] 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.

[0042] 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, 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 to which a brain signal is compared, such as a lower threshold and upper threshold). The homeostatic window may be used as part of an adaptive stimulation 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. Furthermore, the adjustable stimulation parameters may have parameter limits (lower and / or upper limits) to reduce the possibility of undesired stimulation being delivered during automated closed-loop control. These one or more thresholds and / or limits may be adjusted based on one or more periodicity metrics.

[0043] 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 symptoms of 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,Docket No. : A0013147 WOO 1 / 1123-859WOO 1Movement Disorders, Vol. 23, No. 15, pp. 2129-2170 (2008), the content of which is incorporated herein in its entirety.

[0044] In some examples, system 100 may be configured to determine the upper threshold of a homeostatic 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 LFPs during this process and display the LFP signal and / or LFP signal magnitude that may correspond to the respective thresholds. In this manner, system 100 may automatically determine these thresholds.

[0045] As also described herein, system 100 can also determine the lower threshold of the homeostatic 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 or more 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). Although initial thresholds of the homeostatic window may be determined when medication is off for patient 112, threshold determination when patient 112 is taking medication may be more effective in some examples. My monitoring patient events and sensed brain signals over time that therapy is delivered, the thresholds may beDocket No. : A0013147 WOO 1 / 1123-859WOO 1 selected to more appropriately account for patient consumption of medication that may not be otherwise possible via manual identification during a clinic visit.

[0046] 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 lower threshold of the homeostatic window. In some examples, system 100 may select a 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 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.

[0047] In another example, system 100 can set a lower threshold 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 threshold 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.

[0048] System 100 can monitor one or more signals of the patient for selecting one or more parameters defining stimulation and / or adjusting stimulation in a closed-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 (determined based on which frequency varies during stimulation delivery and / or under the influence of medication). 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 parametersDocket No. : A0013147 WOO 1 / 1123-859WOO 1 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 proportional to the magnitude of the monitored signal or adjusted in response to a magnitude of the monitored signal exceeding one or more thresholds.

[0049] 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. Any therapy can be determined based on one or more periodicity metrics in order to improve the patient-specific details of the therapy for potentially improved efficacy.

[0050] 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. 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, or an electrocorticogram (ECoG) signal. Local field potentials, however, may include a broader genus of electrical signals within brain 120 of patient 112.

[0051] 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 sensingDocket No. : A0013147 WOO 1 / 1123-859WOO 1 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.

[0052] 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.

[0053] 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.

[0054] 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. 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 craniumDocket No. : A0013147 WOO 1 / 1123-859WOO 1122, 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.

[0055] 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.

[0056] 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.

[0057] In the example shown in FIG. 1, electrodes 116, 118 of leads 114 are shown as ring electrodes. Ring electrodes may be used in DBS or aDBS applications because they are relatively simple 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.

[0058] 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 106Docket No. : A0013147 WOO 1 / 1123-859WOO 1 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.

[0059] 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. Programmer 104 may be any type of device that is configured to communicate with IMD 106, such as a smart watch, hand-held computer, mobile device, recharger, or any other device.

[0060] 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 the clinician 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.

[0061] 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 combinations 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,Docket No. : A0013147 WOO 1 / 1123-859WOO 1 etc.). The periodicity metrics may also be stored and / or used to identify the different states or changes in the patient over time.

[0062] 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 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] In some examples, system 100 can define a homeostatic window (e.g., one or more thresholds of an adaptive stimulation mode) and / or a therapeutic window for delivering aDBS to patient 112. System 100 may adaptively deliver electrical stimulation and adjust one or moreDocket No. : A0013147 WOO 1 / 1123-859WOO 1 parameters defining the electrical stimulation within a parameter range defined by upper and lower parameter 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. As described herein, system 100 may automatically suggest one or more parameter limits based on sensed bioelectrical signals. 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 (e.g., from the sensed bioelectric signals) 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.

[0067] 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 (e.g., amplitude or pulse width) 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 pulse width. 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. In addition, stimulation may be withheld, or delivered, during periods of time based on the one or more periodicity metrics.

[0068] 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 theDocket No. : A0013147 WOO 1 / 1123-859WOO 1 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.

[0069] 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.

[0070] 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.

[0071] 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 a bioelectrical 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 stimulationDocket No. : A0013147 WOO 1 / 1123-859WOO 1 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. In some examples, one or more frequencies in the Gamma band may be responsive to stimulation for some patients. This Gamma band responsiveness may come with or without Beta suppression from stimulation therapy.

[0072] 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. Any of these sensed signals may be used to identify a patient event that may be a symptom and / or side effect of the patient.

[0073] 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.

[0074] 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. When accelerometers,Docket No. : A0013147 WOO 1 / 1123-859WOO 1 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.

[0075] 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. These signals may be used as a part of determining the one or more periodicity metrics and / or to confirm or correlate the one or more periodicity metrics.

[0076] 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 or confirm patient sleep, as described herein.

[0077] 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 sensorsDocket No. : A0013147 WOO 1 / 1123-859WOO 1 coupled to IMDs 106 wirelessly or via one of leads 114. CSF pressure changes associated 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.

[0078] 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 bound 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 bound 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.

[0079] 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.

[0080] 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 these occurrences, system 100 may determine a value for the at least one electrical stimulation parameter as defined by the homeostatic window, as described above. Further, the IMD 106 of system 100 may increase the value for the at leastDocket No. : A0013147 WOO 1 / 1123-859WOO 1 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. The system may adjust any of these thresholds, limits, or periods when certain thresholds or limits are used, based on one or more periodicity metrics.

[0081] 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.

[0082] FIG. 2 is a block diagram of the example IMD 106 of FIG. 1 configured for delivering 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.

[0083] 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, individual therapy programs may be stored as a therapy group, which defines a set of therapy programs with which stimulation mayDocket No. : A0013147 WOO 1 / 1123-859WOO 1 be generated. The stimulation signals defined by the therapy programs of the therapy group may be delivered together on an overlapping or non-overlapping (e.g., time-interleaved) basis. Therapy programs 214 may also store adaptive stimulation parameters that define adaptive stimulation, such as one or more thresholds for a homeostatic window and / or one or more limits for a therapeutic window (e.g., parameter limits). Processing circuitry 210 may directly change and / or update any of these parameter values based on commands from programmer 104, for example.

[0084] 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.

[0085] 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:

[0086] 1. Pulse Rate, i.e., Frequency: between approximately 1 Hertz and approximately 500Hertz, such as between approximately 40 to 185 Hertz or such as approximately 140 Hertz.

[0087] 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.Docket No. : A0013147 WOO 1 / 1123-859WOO 1

[0088] 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.

[0089] 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.

[0090] 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 threshold 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.

[0091] 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.

[0092] 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.Docket No. : A0013147 WOO 1 / 1123-859WOO 1

[0093] 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.

[0094] 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.

[0095] 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 with processing 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.

[0096] 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,Docket No. : A0013147 WOO 1 / 1123-859WOO 1 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). Processing circuitry 210 may identify and / or store patient events according to signals from sensor 212.

[0097] 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.

[0098] 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 trickle charge a rechargeable battery. In other examples, traditional batteries may be used for a limited period of time.

[0099] 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 DBS therapy is defined by one or more therapy programs 214 having one or more parameters stored within memory 211 (and may specify the adaptive mode and corresponding one or more thresholds and / or parameter limits). For example, the one or more parameters may include a current amplitude (for a current-controlled system) or a voltage amplitude (for a voltage-Docket No. : A0013147 WOO 1 / 1123-859WOO 1 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. In some examples, processing circuitry 210 can adjust any parameters of stimulation or parameters defining closed-loop stimulation based on one or more periodicity metrics.

[0100] 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 thresholds of a therapeutic window based on the activity of the sensed signal within the homeostatic window.

[0101] 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 detect an increase in the magnitude of bioelectrical signals within the Beta frequency band of patient 112.

[0102] 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 frequencyDocket No. : A0013147 WOO 1 / 1123-859WOO 1 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 a decrease 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 an increase in the magnitude of the signal within the Gamma frequency band of patient 112.

[0103] 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 bound 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 bound 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 that the 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.

[0104] 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.

[0105] 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,Docket No. : A0013147 WOO 1 / 1123-859WOO 1 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.

[0106] 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.

[0107] 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).

[0108] 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.Docket No. : A0013147 WOO 1 / 1123-859WOO 1

[0109] 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.

[0110] 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.[oni] 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,Docket No. : A0013147 WOO 1 / 1123-859WOO 1 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, a 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.

[0112] 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, memory 311 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. In some examples, memory 311 may store sensed physiological signals (or representative information) one or more periodicity metrics and / or instructions for determining the one or more periodicity metrics.

[0113] 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 user input may be, for example, in the form of pressing a button on a keypad or selecting an icon from a touch screen. The user input may indicate that a patient event has occurred, and may, in some examples, indicate the type of patient event (e.g., a symptom occurred, a side effect occurred, or any other type of patient event). In some examples, processing circuitry 310 may time stamp this patient event to correlate with sensed signals later retrieved from IMD 106. In some examples, processing circuitry 310 may transmit the indication of the patient event to IMD 106 to associate the patient event and storeDocket No. : A0013147 WOO 1 / 1123-859WOO 1 sensed signals and / or immediately request the sensed signals from IMD 106 for association with the patient event and storage in memory 311 for further processing. 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.

[0114] 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.

[0115] 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 wireless connection. 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.

[0116] In some examples, processing circuitry 310 of external programmer 104 defines the parameters of a homeostatic 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. Programmer 104 may also determine one or more periodicity metrics based on sensed signals from the patient that are received from IMD 106 and / or other sensors.

[0117] The following examples illustrate various user interfaces and techniques for managing the sensing of physiological signals, such as brain signals, determining one or more periodicity metrics, programming adaptive stimulation therapy, and managing electrical stimulation as described herein. Programmer 104, or another external computing device, may output the userDocket No. : A0013147 WOO 1 / 1123-859WOO 1 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 one or more automated programming processes to assist the user through the programming process for setting up or adjusting adaptive stimulation therapy. 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. In other examples, IMD 106 may perform some or all of the techniques described for determining the one or more periodicity metrics.

[0118] FIG. 4 is a block diagram illustrating an example system 1400 that includes an external device, such as a server 482, and one or more computing devices 484A-484N, that are coupled to IMD 106 and external programmer 104 shown in FIG. 1 via a network 486. In this example, IMD 106 may use its telemetry circuit 88 to communicate with external programmer 104 via a first wireless connection, and to communicate with an access point 488 via a second wireless connection. In some examples, system 400 may include additional devices. For example, system 400 may also include communication device 402 which may be an intermediary device that is configured to communicate with IMD 106 via one communication protocol and communicate with programmer 104 and / or network 486 with a different communication protocol. Communication device 402 may provide other functionality, such as recharging functionality for a rechargeable battery of IMD 106. Communication device 402 may be referred to as an “intermediate device” that can act to transfer data between IMD 106 and network 486 and other servers or computing devices.

[0119] System 400 may be a part of a digital health platform that can share information between devices and enable certain functionality, such as sharing information sensed or otherwise obtained by IMD 106 and / or programmer 104 to other remote computing devices in addition to transferring updated sensing and / or programming instructions from remote computing devices such as server 482 and / or computing devices 484A-484N back to programmer 104 and / or IMD 106. In some examples, the techniques described herein may be commanded to be performed remotely via system 400. For example, any of the devices of system 400, such as programmer 104, server 482, or computing device 484N, may be configured to perform at least some or all of the techniques described herein for determining one or more periodicity metrics.

[0120] In the example of FIG. 4, access point 488, external programmer 104, server 482, and computing devices 484A-484N are interconnected, and able to communicate with each other,Docket No. : A0013147 WOO 1 / 1123-859WOO 1 through network 486. In some cases, one or more of access point 488, external programmer 104, server 482, and computing devices 484A-484N may be coupled to network 486 through one or more wireless connections. IMD 106, external programmer 104, server 482, and computing devices 484A-484N may each comprise one or more processors, such as one or more microprocessors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), programmable logic circuitry, or the like, that may perform various functions and operations, such as those described in this disclosure.

[0121] Access point 488 may comprise a device, such as a home monitoring device, that connects to network 486 via any of a variety of connections, such as telephone dial-up, digital subscriber line (DSL), cable modem connections, fiber optic communications, etc. In other examples, access point 488 may be coupled to network 486 through different forms of connections, including wired or wireless connections.

[0122] During operation, IMD 106 may collect and store various forms of data. For example, IMD 106 may collect sensed electrical signals and / or event data during therapy delivery that indicate therapy efficacy and / or disease state of patient 12. In some cases, IMD 106 may directly analyze the collected data to evaluate the patient 12, such as identifying events and / or sensed data. In other cases, however, IMD 106 may send stored data relating to events and / or other sensed data to external programmer 104 and / or server 482, either wirelessly or via access point 488 and network 1486, for remote processing and analysis. For example, IMD 106 may transmit sensed information, such as event data, to computing device 484A, and computing device 484A can evaluate the sensed information and generated updated stimulation parameters, event data, or other information pertaining to the patient.

[0123] In some cases, server 482 may be configured to provide a secure storage site for archival of information that has been collected from IMD 106, external programmer 104, or any other devices of system 400. Network 486 may comprise a local area network, wide area network, or global network, such as the Internet. In some cases, external programmer 104 or server 482 may assemble sensed data, event data, or other therapy information in web pages or other documents for viewing by trained professionals, such as clinicians, via viewing terminals associated with computing devices 484A-484N. In this manner, system 400 functioning as a digital health platform may enable cloud-based functionality for any of the processes and / or user interactions described herein.

[0124] For example, a user may be able to log into the digital health platform of system 400 via one or more devices such as any of computing devices 484A-484N. In one example, a user may log into the digital health platform using computing device 484A. The user may interact with a user interface that is displayed via an internet browser or specific application. In someDocket No. : A0013147 WOO 1 / 1123-859WOO 1 examples, the user interface may present information of suggested parameter limits, suggested thresholds, periodicity metrics, or other recommended information for view and / or confirmation by a clinician. The user interface can receive the confirmation input, and then system 400 may then transmit the updated limits, thresholds, periodicity metrics, etc. back to programmer 104 and / or IMD 106 via network 486.

[0125] FIG. 5 is a flow chart of an example technique for determining a periodicity metric for a patient. The example of FIG. 5 (and other techniques regarding periodicity metrics) will be described with respect to programmer 104 and processing circuitry 310. However, some or all of these techniques may alternatively be performed by other devices or systems, such as IMD 106, server 482, or another external device. In some examples, multiple devices may perform these techniques in a distributed manner toward completion.

[0126] As shown in the example of FIG. 5, processing circuitry 310 receives electrical signals from sensing circuitry of IMD 106, such as a plurality of electrical signals sensed from a patient over a time duration (500). Processing circuitry 310 can then determine characteristic values for the plurality of electrical signals (502). Characteristic values may include LFP magnitudes, spectral powers of LFP signals, frequency bands, or any other information that is representative of some characteristic of the sensed electrical signals.

[0127] Using the characteristic values of the electrical signals, processing circuitry 310 can then determine a periodicity metric indicative of one or more repeating periods for the plurality of electrical signals (504). Processing circuitry 310 may determine the periodicity metric using one or more different techniques, such that the periodicity metrics may include different aspects of any trend or recurring period(s) that are present within the sensed signals. In some examples, the one or more algorithms configured to determine one or more periodicity metrics may be one or more multi-use algorithm(s) with variations on characteristics such as execution timing, periodicity interval(s), the detection and classification method, and / or the quantification method.

[0128] For the execution timing, processing circuitry 310 may execute the algorithm on retrospective data or on chronic data analyzed in clinic (e.g., on a clinician tablet or via webbased portal). In some examples, the algorithm may be stored and deployed in medical device (e.g., IMD 106) to run near-real time detection and / or flagging of periodicity metrics. In some examples, processing circuitry 310 may execute the algorithm(s) in a distributed manner with IMD 106 or other device. In some examples, the periods of time that are investigated (e.g., the periodicity intervals) may be flexible for different patients, conditions, and / or sensed-data specific. The frequency and / or periodicity of the LFP characteristics is highly variable, so the system may thus adjust periods, or use flexible intervals, when determining the periodicity metrics. In addition, processing circuitry 310 may be configured to tailor the specific algorithmDocket No. : A0013147 WOO 1 / 1123-859WOO 1 that includes the detection, classification, and / or quantification concepts desired based on the specific characteristics of interest of the electrical signal (e.g., LFP spectral power, LFP frequency, LFP amplitude, etc.).

[0129] Generally, examples described herein are directed to determining periodicity metrics for LFP signals, but similar techniques may be used for other physiological signals. As described in examples, herein, the algorithm(s) for detecting, classifying, and quantifying LFP trend frequency and / or periodicity characteristics (e.g., types of periodicity metrics) are primarily based on chronic brain sensing data. In some examples, the algorithm can be configured to be applied to LFP trend timeseries data (e.g., LFP values and date / timestamps for the LFP values) and generate one or more periodicity metrics (e.g., types of quantification metrics) for the LFP characteristics that were provided based on the sensed LFP signals from the patient. Each of these algorithms can be used alone or in combination with other algorithms for periodicity metrics, and can provide insight into how the LFP signals vary during different periods of time, which periods have stronger repetition, and / or the stability of the LFP signals with respect to which periods are strong from the sensed signals.

[0130] Example algorithms may include some of the following detection methods for determining a periodicity metric. In some examples, the algorithm may include fixed period (interval) division or assignment (e.g., wherein the periods include midnight-to-midnight for circadian rhythm, 10 PM - 6 AM for nighttime, etc.). In some examples, the algorithm may include determination of the LFP value frequency, modality, and kernel density estimate (KDE) of the signal data. In some examples, the algorithm may include determination of peak(s), trough(s), fluctuation(s), and / or inflection(s) within the LFP signal. In some examples, the algorithm may include the transformation of the LFP trend to frequency domain (e.g., via Fast Fourier Transform (FFT)) such as in a spectrogram. In some examples, the algorithm may include an LFP trend model fit (e.g., sinusoidal or other wave fit, binarization, etc.). In some examples, the algorithm may include a clustering analysis (e.g., 2 primary clusters could be day / night) of the LFP data. In some examples, the algorithm may include machine learning classifier-based classification (e.g., deep learning network) of periods from the LFP data in order to output the one or more periodicity metrics.

[0131] Once processing circuitry 310 determines the periodicity metric, processing circuitry 310 may be configured to control delivery of electrical stimulation therapy based on the periodicity metric (506). For example, processing circuitry 310 may control IMD 106 to deliver subsequent stimulation within the parameter limits and according to comparison of sensed LFP values to one or more thresholds of the feedback variable. Processing circuitry 310 may adjust one or more of the thresholds based on the periodicity metric, for example. In one example,Docket No. : A0013147 WOO 1 / 1123-859WOO 1 processing circuitry 310 is configured to control the delivery of the electrical stimulation therapy by at least determining, based on the periodicity metric, one or more feedback values (e.g., one or more threshold values) for adjusting one or more stimulation parameters that at least partially defines the electrical stimulation therapy. Processing circuity 310 may then be configured to receive subsequent electrical signals sensed from the patient during a second time duration and compare the subsequent electrical signals (or periodicity metric derived therefrom) to the one or more feedback values that were determined based on the periodicity metric. In some examples, processing circuitry 310 may compare a new determined periodicity metric to one or more periodicity metrics from different time periods (e.g., previous time periods), different spectral locations (e.g., different frequencies of sensed signals), different recording locations (e.g., different sensing electrode configurations), or any other comparison that may be indicative of a patient condition, or change in condition. Processing circuity 310 can then adjust, based on the comparison, the one or more stimulation parameters that define stimulation therapy. In addition, or alternative, to controlling stimulation delivery, processing circuitry 310 may control user interface 302 to present a representation of the one or more periodicity metrics instead of, or in addition to, the LFP values. The user can then use the periodicity metric(s) to adjust therapy or make any other changes to the treatment or monitoring of the patient.

[0132] FIGS. 6-17 describe various algorithms or techniques for determining one or more periodicity metrics. Programmer 104 and processing circuitry 310 will be described as one example, but other devices and / or circuitry may perform at least some or all of these algorithms. In the example of FIGS. 6-9, the system can determine one or more periodicity metrics that identify the strength of different possible periods over time, such as a circadian rhythm. In this example, the periodicity metric may be indicative of the periodicity of the normalized power of the LFP signal over a certain number of days, such as 3 days.

[0133] FIG. 6 is a graph of example LFP signals sensed over a time duration. As shown in the example of FIG. 6, graph 600 includes LFP signal 602 that is displayed as the LFP magnitude within a frequency band over time. Processing circuitry 310 may identify a time duration of interest, such as a three day window which is identified as including three days 604. Graph 606 shows that in some examples a window function, such as a Hamming window, may be applied to the number of days of the window for sampling the data. Fewer or greater number of days, or any other amount of time duration, may be used in other examples. Although FIG. 6 shows the LFP data for the right hemisphere, this process may also include LFP data sensed from the left hemisphere to determine periodicity metrics for both hemispheres as they may be different.

[0134] FIG. 7A is a spectrogram 700 of example LFP power for different periods over time. As shown in the example of FIG. 7 A, processing circuitry 310 may generate the spectrogramDocket No. : A0013147 WOO 1 / 1123-859WOO 1 may generating power of the LFP values for different periods of time and normalize the power across the different periods. This may include determining spectral powers for the plurality of different periods within a window of time. As shown, example periods may include period of different hours, such as a range of periods from 2.06 hours to 72 hours (i.e., the total length of the windowing function). The color coded spaces for each period and day correlate to the magnitude of the normalized LFP values for each period. From spectrogram 700, higher LFP values appear to occur for periods around 2.88 hours, 6 hours, and 24 hours, but it is not very clear from this data alone.

[0135] FIG. 7B is a graph of example aggregated power of LFP signals for different periods. As shown in the example of FIG. 7B, graph 710 includes traces 712 of normalized power for different periods within the window of time (the duration). Processing circuitry 310 can determine aggregate values from the spectral powers for the plurality of different periods within the window of time. The aggregate values may include an average, weighted average, median, or some other calculation. The resulting aggregate values are shown as aggregate trace 714.

[0136] FIG. 8A is a graph 800 of example detrended aggregate values for the different time values. As shown in the example of FIG. 8 A, graph 800 includes aggregate trace 802 over time, which includes a curve, trend, or drift in the data. Trend 804 may represent this curve that is not representative of actual values. Processing circuitry 310 can then detrend the aggregate values by compensating for low frequency drift within the aggregate values across the plurality of different periods. In other words, processing circuitry 310 can adjust the aggregate values in trace 802 by flattening trend 804 into detrended aggregate values 806.

[0137] FIG. 8B is a graph 820 of normalized aggregate values 822 and identified peaks from respective periods. As shown in the example of FIG. 8B, normalized aggregate values 822 are plotted over time to identify one or more peaks within the values 822. Line 824 may correspond to the 24-hour period as a reference point. In this manner, processing circuitry 310 may be configured to normalize the aggregate values across the plurality of different periods and then identify one or more peaks of the aggregate values 822 for respective periods of the plurality of different periods. In some examples, this may be the highest predetermined number of peaks, each peak above a certain threshold or percentage of the maximum peak, or some other determination. These peaks correspond to strong periods within the time duration and indicate the periodicity of the signal. In this manner, processing circuitry 310 may be configured to determine the periodicity metric based on the respective periods of the one or more peaks. The periodicity metric may indicate the highest period, which would be a period of 6 hours in this example. In some examples, the periodicity metric may include the three highest peaks, which indicate periods of 6 hours, 2.88 hours, and 24 hours. In some examples, the periodicity metricDocket No. : A0013147 WOO 1 / 1123-859WOO 1 may indicate the lowest period, or a ratio of one or more pairs of period peaks. The system or user may use this periodicity metric to adjust how to delivery therapy, such as using different thresholds for each period, withholding changing of thresholds until after a period is complete, or other types of actions.

[0138] FIG. 9 is a flow chart of an example technique for determining a periodicity metric for a patient. The example of FIG. 9 will be described with respect to programmer 104 and processing circuitry 310. However, some or all of these techniques may alternatively be performed by other devices or systems, such as IMD 106, server 482, or another external device. In some examples, multiple devices may perform these techniques in a distributed manner toward completion.

[0139] As shown in the example of FIG. 9, processing circuitry 310 receives electrical signals from sensing circuitry of IMD 106, such as a plurality of electrical signals sensed from a patient over a time duration (900). Processing circuitry 310 can then determine characteristic values for the plurality of electrical signals (902). Processing circuitry 310 then determines spectral powers for a plurality of different periods within a window of time (904) and then determines aggregate values from the spectral powers for the plurality of different periods within the window of time (906). The aggregate values may be averages of the spectral powers for each period.

[0140] Processing circuitry 310 may then detrend the aggregate values by compensating for low frequency drift within the aggregate values across the plurality of different periods (908) and then normalize the aggregate values across the plurality of different periods (910). This normalization may equalize the sensed LFP values from their detection at different times which may include noise or other signal issues. Processing circuitry 310 can then identify one or more peaks of the aggregate values for respective periods of the plurality of different periods (910) and then determine the periodicity metric based on the respective periods of the one or more peaks (912). Processing circuitry 310 can then take action based on the periodicity metric, such as adjusting closed-loop therapy, adjusting stimulation delivery, and / or presenting the periodicity metric to a user via a user interface.

[0141] In the example of FIGS. 10-12, the system can determine one or more periodicity metrics that identify the prominence of the circadian rhythm, or other period, over time. FIG. 10 is a spectrogram 1000 of normalized LFP power for different periods over time. As shown in the example of FIG. 10, this may include determining spectral powers for the plurality of different periods within a window of time (the time duration). As shown, example periods may include period of different hours, such as a range of periods from 2.06 hours to 72 hours (i.e., the total length of the windowing function). The color coded spaces for each period and day correlate toDocket No. : A0013147 WOO 1 / 1123-859WOO 1 the magnitude of the normalized LFP values for each period. From spectrogram 700, one or more periods of interest may be selected of the plurality of different periods. These “periods of interest” may be specific periods for which activity can be tracked over time. Arrows 1002 indicate the selected periods, which include periods of 6 hours, 12 hours, and 24 hours in this example. Other periods may also be included in other examples. Although 72 hours seems to show strong correlation, that may be due to that period being the length of the entire window analyzed.

[0142] FIG. 11 is a graph 1100 of example normalized LFP power for selected periods over time, and relates to spectrogram 1000 of FIG. 10. As shown in the example of FIG. 11, processing circuitry 310 has plotted normalized power over time for each period of the two or more periods, which are selected from more periods as shown in FIG. 10. These plots include trace 1102 for the 6 hour period, trace 1104 for the 12 hour period, and trace 1106 for the 24 hour period. Processing circuitry 310 can determine, based on the normalized power, respective periodicity metrics for different windows of time within the time duration, wherein the respective periodicity metrics comprises the periodicity metric.

[0143] In this example, different periods dominated during different times for the patient. Bracket 1110 indicates that the 12 hour period periodicity dominated during the first week of sensing, but bracket 1112 indicates that the 6 hour period periodicity dominated for a few days later. However, the last few weeks of graph 1100 show that the 24 hour period periodicity emerged and dominated. In this manner, processing circuitry 310 can identify the timing and duration of the relative rank or strength of various periodicities. The periodicity metric can thus change over time, or include multiple metrics indicative of the respective times of the shift. Using this changing information of the periodicity, in some examples, processing circuitry 310 can be configured to determine the stability of the plurality of electric signals for at least a portion of the time duration based on the respective periodicity metrics. The stability may be used for various reasons, such as determining when the patient has recovered from implantation, determining when thresholds or other parameters should be reviewed or changed, or even determining when the patient can be transitioned from continuous electrical stimulation to aDBS based on sensed LFP signals.

[0144] FIG. 12 is a flow chart of an example technique for determining a periodicity metric and stability of the periodicity for a patient. The example of FIG. 12 will be described with respect to programmer 104 and processing circuitry 310. However, some or all of these techniques may alternatively be performed by other devices or systems, such as IMD 106, server 482, or another external device. In some examples, multiple devices may perform these techniques in a distributed manner toward completion.Docket No. : A0013147 WOO 1 / 1123-859WOO 1

[0145] As shown in the example of FIG. 12, processing circuitry 310 receives electrical signals from sensing circuitry of IMD 106, such as a plurality of electrical signals sensed from a patient over a time duration (1200). Processing circuitry 310 can then generate a spectrogram of normalized power from the LFP values for different periods by day (1202). Based on the spectrogram that was generated, processing circuitry 310 can select two or more periods of interest for comparison (1204). For example, the periods of interest may be the periods that have the highest values at different times during the time duration.

[0146] Processing circuitry 310 can then plot the normalized power over time for each period of interest (1206). From this plot, processing circuitry 310 can determine the periodicity metrics for different times (1208). For example, the periodicity, or periods that dominate the LFP data, may change over time. Processing circuitry 310 can also determine the stability of the periodicity of the LFP data based on the periodicity metrics (1210). In some examples, the periodicity may change due to anatomical changes, disease progression, changes to therapy efficacy, etc. Processing circuitry 310 may suggest or automatically make one or more changes to therapy to address any changes to the periodicity or in response to detecting that the periodicity has become unstable, or become stable.

[0147] In the example of FIGS. 13-17, the system can determine one or more periodicity metrics that can distinguish between two different periods, such as night and day. The periodicity metric may represent an occurrence frequency. In some examples, the system may determine a periodicity for an event based on the occurrence frequency. FIG. 13 is an example graph 1300 of LFP signals sensed for a patient during different time durations. As shown in graph 1300, LFP signal 1302 is plotted as amplitude vs. time. Duration 1304 is distinguished from duration 1306 because the LFP signal changed due to a patient event. Therefore, the periodicity metric can be determined for each of durations 1304 and 1306 (e.g., the night and day periodicity may be different between these two durations of time.

[0148] FIG. 14A includes example graphs of histogram characteristics calculated from an example histogram of one time duration in FIG. 14B. As shown in the example of FIGS. 14A and 14B, the frequency of occurrence is calculated for duration 1304 of FIG. 13. Processing circuitry 310 may be configured to generate, based on the characteristic values of LFP signals 1302 of FIG. 13, histogram 1410 of the characteristic values over the time duration. In some examples, processing circuitry 310 may first remove outlier data from the LFP signals 1302, apply smoothing to the LFP signals 1302, or both. The histogram of LFP values does not include any time information, but includes the kernel density estimate (KDE) on a regular or log scale.

[0149] Processing circuitry 310 may determine one or more histogram characteristics of histogram 1410. Example histogram characteristics may be identified from one or more ofDocket No. : A0013147 WOO 1 / 1123-859WOO 1 normalized LFP KDE in graph 1400 (e.g., peak detection), the KDE derivate values of graph 1402, and / or other characteristics of the KDE such as the KDE derivative zero crossing or a statistical summary (e.g., percentile) shown in graph 1404. The normalized LFP KDE graph indicates the average values of the LFP values from histogram 1410. In some examples, the peak of graph 1400 may be used. Instead, in this example, the maximum derivative of the KDE values is calculated because histogram has a unimodal distribution. This maximum derivative may be reflective of an inflection point in the LFP signal that corresponds to a change in the frequency of occurrence of the LFP values. The maximum derivative is shown as threshold 1412 in histogram 1412. In this specific example, the threshold corresponds to the maximum inflection which is a threshold value of 1630 for the LFP values.

[0150] In other examples, possible histogram characteristics that may be used for the periodicity metric (e.g., an occurrence frequency in this case or a periodicity in other cases) may include a percentage of the data, or locations at which the LFP signals flatten out, which correspond to the zero crossings of the KDE derivative. In this manner, processing circuitry 310 can be configured to determine, based on the one or more histogram characteristics, a threshold value for different periods as an occurrence frequency (e.g., one type of periodicity metric). This threshold value may separate day and night periods within the LFP data. Processing circuitry 310 can then identify, based on the periodicity metric, the one or more repeating periods (e.g., day or night or some other different repeating periods).

[0151] As described, possible day and night thresholding methods may be obtained from a unimodal distribution. These thresholding methods may identify the first derivative maximum, the zero crossing with the maximum slope, a certain percentile of the data, or any other characteristics. In some examples, the thresholding methods may be obtained from a multimodal distribution to identify one or more peaks (e.g., highest peak), local minima (e.g., troughs) between modal peaks, or any other characteristic.

[0152] FIG. 15A includes example graphs of histogram characteristics calculated from an example histogram of a second time duration of FIG. 15B. As shown in the example of FIGS. 15A and 15B, the frequency of occurrence is calculated for duration 1306 of FIG. 13, in a very similar manner as described with respect to FIGS. 14A and 14B.

[0153] Processing circuitry 310 may be configured to generate, based on the characteristic values of LFP signals 1302 of FIG. 13, histogram 1510 of the characteristic values over the time duration. In some examples, processing circuitry 310 may first remove outlier data from the LFP signals 1302, apply smoothing to the LFP signals 1302, or both. The histogram of LFP values does not include any time information, but includes the kernel density estimate (KDE) on a regular or log scale.Docket No. : A0013147 WOO 1 / 1123-859WOO 1

[0154] Processing circuitry 310 may determine one or more histogram characteristics of histogram 1510. Example histogram characteristics may be identified from one or more of normalized LFP KDE in graph 1500 (e.g., peak detection), the KDE derivate values of graph 1502, and / or other characteristics of the KDE such as the KDE derivative zero crossing or a statistical summary (e.g., percentile) shown in graph 1504. The normalized LFP KDE graph indicates the average values of the LFP values from histogram 1510. In some examples, the peak or trough of graph 1500 may be used. In this example, due to histogram 1510 having a multimodal distribution, the trough between peaks in graph 1500 is calculated. This local minima of the trough corresponds to threshold 1512 in histogram 1512. In this specific example, the threshold corresponds to a threshold value of 820 for the LFP values.

[0155] FIG. 16 is a graph of an example of the utility of using periodicity metrics 1602 and 1622 determining different periods from respective time durations and respective LFP signals. As shown in FIG. 16, graph 1600 corresponds to data from duration 1304 of FIG. 13 and graph 1620 corresponds to data from duration 1306 of FIG. 13. Graph 1600 provides LFP signals over time, where threshold 1602 (e.g., threshold 1412 from FIG. 14) separates the LFP signals into periods 1604 (night) and 1606 (day). In this manner, LFP signals below threshold 1602 typically fall during period 1604 while LFP signals above threshold 1602 fall during period 1606.

[0156] Graph 1620 provides LFP signals over time, where threshold 1622 (e.g., threshold 1512 from FIG. 15) is the periodicity metric (indicating an occurrence frequency of changes to the LFP signal) that separates the LFP signals into periods 1624 (night) and 1626 (day). In this manner, LFP signals below threshold 1622 typically fall during period 1624 while LFP signals above threshold 1622 fall during period 1626.

[0157] A single threshold approach was used in these examples of FIGS. 13-16. In some examples, the single threshold may correspond to a timing, number, location, slope, interval, or any other characteristic of the LFP data. In other examples, multiple thresholds may be used to distinguish between different periods. For example, different periods may correspond to LFP values above, below, and / or between different thresholds that correspond to different characteristics of the LFP signals such as the timing, number location, slope, or interval.

[0158] FIG. 17 is a flow chart of an example technique for determining a periodicity metric (e.g., indicating an occurrence frequency of changes to a signal or indicating a periodicity of the change in signal) and identifying different repeating periods using the periodicity metric. The example of FIG. 17 will be described with respect to programmer 104 and processing circuitry 310. However, some or all of these techniques may alternatively be performed by other devices or systems, such as IMD 106, server 482, or another external device. In some examples, multiple devices may perform these techniques in a distributed manner toward completion.Docket No. : A0013147 WOO 1 / 1123-859WOO 1

[0159] As shown in the example of FIG. 17, processing circuitry 310 receives electrical signals (e.g., LFP signals) from sensing circuitry of IMD 106, such as a plurality of electrical signals sensed from a patient over a time duration (1700). Processing circuitry 310 can then determine characteristic values for the plurality of LFP signals (1702). Processing circuitry 310 then generates a histogram of values and kernel density estimate (1704). In some examples, processing circuitry 310 may pre-process the LFP signals prior to the histogram such as by detrending, removing outliers, smoothing the data, or the like.

[0160] Processing circuitry 310 can then determine one or more characteristics of the histogram of the LFP data (1706). In some examples, the characteristic determined may be dependent on the type of distribution of the histogram. A unimodal histogram may be evaluated for one characteristic, and a multimodal histogram may be evaluated using a different characteristic. Processing circuitry 310 can then determine one or more occurrence frequency value(s) as one or more threshold values that can distinguish different periods from the LFP data (1708). For example, the one or more threshold values may distinguish LFP data obtained during a night period from LFP data generally obtained during a day period. These are usually repeating periods that can enable processing circuitry 310 to apply different therapies appropriate for the respective period. Processing circuitry 310 can then identify the different periods based on the one or more threshold values (1710). This process can be retroactive on prior stored data or used to anticipate upcoming periods and, if needed, changes to therapy for the change in period.

[0161] FIGS. 18A and 18B are graphs 1800 and 1810 of example LFP power over time and medication events for different hemispheres of the brain of a patient. As shown in the example of FIG. 18 A, graph 18A shows an average LFP power 1802 over time for the left hemisphere of the brain, where the average is taken from LFP signal acquisition over several iterations of the period of time shown in FIG. 18A (a 24 hour period corresponding to one day). Variance 1804 indicate the variance of these recorded values from the calculated average LFP power 1802. Medication events 1806 are shown in the vertical bars which indicate when the patient received medication. Similar to FIG. 18A, FIG. 18B shows an average LFP power 1812 over time for the right hemisphere of the brain, where the average is taken from LFP signal acquisition over several iterations of the period of time shown in FIG. 18B (a 24 hour period corresponding to one day). Variance 1814 indicate the variance of these recorded values from the calculated average LFP power 1812. Medication events 1816 are shown in the vertical bars which indicate when the patient received medication. Medication events 1806 and 1816 are the same since the same patient received the medication at that time.

[0162] As indicated in FIGS. 18A and 18B, the system may be able to determine a periodicity metric that identifies the frequency, or periodicity, in the LFP signal that is inducedDocket No. : A0013147 WOO 1 / 1123-859WOO 1 by taking medication. This periodicity metric may be used to track medication intake and / or whether or not the patient is reacting to the medication. This periodicity metric may also be compared to other periodicity metrics, such as the circadian rhythm, to understand if the medication cycle is appropriate for the patient. By analyzing different hemispheres, the system may also be able to track efficacy of medication on each hemisphere, which could be different for different patients.

[0163] As described herein, a periodicity metric can be indicative of one or more repeating periods for the plurality of electrical signals. In some examples, the system can determine the periodicity metric by performing various calculations on the sensed electrical signal, or other physiological information, obtained from the patient. Example calculations can include counts of events within or across different time periods, duration of events within or across different time periods, statistical descriptions (e.g., mean, median, range, variance, etc.), cross-correlation with different signals, templates or models, autoregression of sense signals, template and / or pattern matching, wavelet decomposition, identification of degree of uniformity / non-uniformity within periods, identification of degree of symmetry / asymmetry within periods of time, or any other such analyses. These analyses may be performed, alone or in any combination, to determine periodicity metrics.

[0164] The following examples are described herein.

[0165] Example 1. A system comprising: processing circuitry configured to: receive, from sensing circuitry, a plurality of electrical signals from a patient sensed over a time duration; determine characteristic values for the plurality of electrical signals; determine, based on the characteristic values, a periodicity metric indicative of one or more repeating periods for the plurality of electrical signals; and control delivery of electrical stimulation therapy based on the periodicity metric.

[0166] Example 2. The system of example 1, wherein the plurality of electrical signals comprise local field potentials (LFPs), and wherein the characteristic values comprise a power of one or more frequencies of the LFPs.

[0167] Example 3. The system of any of examples 1 or 2, wherein the processing circuitry is configured to determine the periodicity metric by at least: determining spectral powers for a plurality of different periods within a window of time; determining aggregate values from the spectral powers for the plurality of different periods within the window of time; detrending the aggregate values by compensating for low frequency drift within the aggregate values across the plurality of different periods; normalizing the aggregate values across the plurality of different periods; identifying one or more peaks of the aggregate values for respective periods of theDocket No. : A0013147 WOO 1 / 1123-859WOO 1 plurality of different periods; and determining the periodicity metric based on the respective periods of the one or more peaks.

[0168] Example 4. The system of any of examples 1 through 3, wherein the processing circuitry is configured to determine the periodicity metric by at least: generating a spectrogram of normalized power for a plurality of different periods by day within the time duration; selecting, based on the spectrogram, two or more periods of the plurality of different periods; plotting normalized power over time for each period of the two or more periods; and determining, based on the normalized power, respective periodicity metrics for different windows of time within the time duration, wherein the respective periodicity metrics comprises the periodicity metric.

[0169] Example 5. The system of example 4, wherein the processing circuitry is configured to determine a stability of the plurality of electric signals for at least a portion of the time duration based on the respective periodicity metrics.

[0170] Example 6. The system of any of examples 1 through 5, wherein the processing circuitry is configured to: determine the periodicity metric by at least: generating, based on the characteristic values, a histogram of the characteristic values over the time duration; determining one or more histogram characteristics of the histogram; and determining, based on the one or more histogram characteristics, a threshold value for different periods as the periodicity metric; and identify, based on the periodicity metric, the one or more repeating periods.

[0171] Example 7. The system of any of examples 1 through 5, wherein the time duration is a first time duration, and wherein the processing circuitry is configured to control the delivery of the electrical stimulation therapy by at least; determining, based on the periodicity metric, one or more feedback values for adjusting one or more stimulation parameters that at least partially defines the electrical stimulation therapy; receiving subsequent electrical signals sensed from the patient during a second time duration; comparing the subsequent electrical signals to the one or more feedback values; and adjusting, based on the comparison, the one or more stimulation parameters.

[0172] Example 8. The system of any of examples 1 through 7, further comprising an external programmer comprising the processing circuitry.

[0173] Example 9. The system of any of examples 1 through 8, wherein the processing circuitry is configured to control a display device to present the periodicity metric.

[0174] Example 10. The system of any of examples 1 through 9, further comprising an implantable medical device comprising the sensing circuitry, the processing circuitry, and stimulation circuitry configured to deliver the electrical stimulation therapy.

[0175] Example 11. A method comprising: receiving, from sensing circuitry, a plurality of electrical signals from a patient sensed over a time duration; determining, by processing circuitry,Docket No. : A0013147 WOO 1 / 1123-859WOO 1 characteristic values for the plurality of electrical signals; determining, by the processing circuitry and based on the characteristic values, a periodicity metric indicative of one or more repeating periods for the plurality of electrical signals; and controlling, by the processing circuitry, delivery of electrical stimulation therapy based on the periodicity metric.

[0176] Example 12. The method of example 11, wherein the plurality of electrical signals comprise local field potentials (LFPs), and wherein the characteristic values comprise a power of one or more frequencies of the LFPs.

[0177] Example 13. The method of any of examples 11 or 12, wherein determining the periodicity metric comprises: determining spectral powers for a plurality of different periods within a window of time; determining aggregate values from the spectral powers for the plurality of different periods within the window of time; detrending the aggregate values by compensating for low frequency drift within the aggregate values across the plurality of different periods; normalizing the aggregate values across the plurality of different periods; identifying one or more peaks of the aggregate values for respective periods of the plurality of different periods; and determining the periodicity metric based on the respective periods of the one or more peaks.

[0178] Example 14. The method of any of examples 11 through 13, wherein determining the periodicity metric comprises: generating a spectrogram of normalized power for a plurality of different periods by day within the time duration; selecting, based on the spectrogram, two or more periods of the plurality of different periods; plotting normalized power over time for each period of the two or more periods; and determining, based on the normalized power, respective periodicity metrics for different windows of time within the time duration, wherein the respective periodicity metrics comprises the periodicity metric.

[0179] Example 15. The method of example 14, further comprising determining a stability of the plurality of electric signals for at least a portion of the time duration based on the respective periodicity metrics.

[0180] Example 16. The method of any of examples 11 through 15, wherein: determining the periodicity metric comprises: generating, based on the characteristic values, a histogram of the characteristic values over the time duration; determining one or more histogram characteristics of the histogram; and determining, based on the one or more histogram characteristics, a threshold value for different periods as the periodicity metric; and the method further comprising identifying, based on the periodicity metric, the one or more repeating periods.

[0181] Example 17. The method of any of examples 11 through 15, wherein the time duration is a first time duration, and wherein controlling the delivery of the electrical stimulation therapy comprises; determining, based on the periodicity metric, one or more feedback values for adjusting one or more stimulation parameters that at least partially defines the electricalDocket No. : A0013147 WOO 1 / 1123-859WOO 1 stimulation therapy; receiving subsequent electrical signals sensed from the patient during a second time duration; comparing the subsequent electrical signals to the one or more feedback values; and adjusting, based on the comparison, the one or more stimulation parameters.

[0182] Example 18. The method of any of examples 11 through 17, wherein an external programmer comprises the processing circuitry, and wherein the method further comprises displaying, by the external programmer, the periodicity metric.

[0183] Example 19. The method of any of examples 11 through 18, further comprising delivering, by stimulation circuitry of an implantable medical device, the electrical stimulation therapy.

[0184] Example 20. A non-transitory computer-readable medium comprising instructions that, when executed, control processing circuitry to: receive, from sensing circuitry, a plurality of electrical signals from a patient sensed over a time duration; determine characteristic values for the plurality of electrical signals; determine, based on the characteristic values, a periodicity metric indicative of one or more repeating periods for the plurality of electrical signals; and control delivery of electrical stimulation therapy based on the periodicity metric.

[0185] Furthermore, although the disclosure is described with respect to DBS therapy, such techniques may be applicable to IMDs that convey other therapies in which sensed data and event data information is important, such as, e.g., spinal cord stimulation (SCS), pelvic floor stimulation, gastric stimulation, occipital stimulation, functional electrical stimulation, and the like. Also, in some aspects, techniques for evaluating posture state information, as described in this disclosure, may be applied to IMDs that provide other therapy (e.g., drug pumps) or IMDs that are generally dedicated to sensing or monitoring and do not include stimulation or other therapy components.

[0186] The techniques described in this disclosure, including those attributed to IMD 106, programmer 104, or various constituent components, may be implemented, at least in part, in hardware, software, firmware or any combination thereof. For example, various aspects of the techniques may be implemented within one or more processors, including one or more microprocessors, DSPs, ASICs, FPGAs, or any other equivalent integrated or discrete logic circuitry, as well as any combinations of such components, embodied in programmers, such as clinician or patient programmers, medical devices, or other devices.

[0187] In one or more examples, the functions described in this disclosure may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored, as one or more instructions or code, on a computer- readable medium and executed by a hardware-based processing unit. Computer-readable media may include computer-readable storage media forming a tangible, non-transitory medium.Docket No. : A0013147 WOO 1 / 1123-859WOO 1Instructions may be executed by one or more processors, such as one or more DSPs, ASICs, FPGAs, general purpose microprocessors, or other equivalent integrated or discrete logic circuitry. Accordingly, the term “processor,” as used herein may refer to one or more of any of the foregoing structures or any other structure suitable for implementation of the techniques described herein.

[0188] In addition, in some respects, the functionality described herein may be provided within dedicated hardware and / or software modules. Depiction of different features as modules or units is intended to highlight different functional aspects and does not necessarily imply that such modules or units must be realized by separate hardware or software components. Rather, functionality associated with one or more modules or units may be performed by separate hardware or software components or integrated within common or separate hardware or software components. Also, the techniques may be fully implemented in one or more circuits or logic elements. The techniques of this disclosure may be implemented in a wide variety of devices or apparatuses, including an IMD, an external programmer, a combination of an IMD and external programmer, an integrated circuit (IC) or a set of ICs, and / or discrete electrical circuitry, residing in an IMD and / or external programmer.

[0189] Various examples have been described. These and other examples are within the scope of the following claims.

Claims

Docket No. : A0013147 WOO 1 / 1123-859WOO 1WHAT IS CLAIMED IS:

1. A system comprising: processing circuitry configured to: receive, from sensing circuitry, a plurality of electrical signals from a patient sensed over a time duration; determine characteristic values for the plurality of electrical signals; determine, based on the characteristic values, a periodicity metric indicative of one or more repeating periods for the plurality of electrical signals; and control delivery of electrical stimulation therapy based on the periodicity metric.

2. The system of claim 1, wherein the plurality of electrical signals comprise local field potentials (LFPs), and wherein the characteristic values comprise a power of one or more frequencies of the LFPs.

3. The system of any of claims 1 or 2, wherein the processing circuitry is configured to determine the periodicity metric by at least: determining spectral powers for a plurality of different periods within a window of time; determining aggregate values from the spectral powers for the plurality of different periods within the window of time; detrending the aggregate values by compensating for low frequency drift within the aggregate values across the plurality of different periods; normalizing the aggregate values across the plurality of different periods; identifying one or more peaks of the aggregate values for respective periods of the plurality of different periods; and determining the periodicity metric based on the respective periods of the one or more peaks.

4. The system of any of claims 1 through 3, wherein the processing circuitry is configured to determine the periodicity metric by at least: generating a spectrogram of normalized power for a plurality of different periods by day within the time duration; selecting, based on the spectrogram, two or more periods of the plurality of different periods; plotting normalized power over time for each period of the two or more periods; andDocket No. : A0013147 WOO 1 / 1123-859WOO 1 determining, based on the normalized power, respective periodicity metrics for different windows of time within the time duration, wherein the respective periodicity metrics comprises the periodicity metric.

5. The system of claim 4, wherein the processing circuitry is configured to determine a stability of the plurality of electric signals for at least a portion of the time duration based on the respective periodicity metrics.

6. The system of any of claims 1 through 5, wherein the processing circuitry is configured to determine the periodicity metric by at least: generating, based on the characteristic values, a histogram of the characteristic values over the time duration; determining one or more histogram characteristics of the histogram; and determining, based on the one or more histogram characteristics, a threshold value for different periods as the periodicity metric.

7. The system of claim 6, wherein the processing circuitry is configured to identify, based on the periodicity metric, the one or more repeating periods.

8. The system of any of claims 1 through 7, wherein the time duration is a first time duration, and wherein the processing circuitry is configured to control the delivery of the electrical stimulation therapy by at least; determining, based on the periodicity metric, one or more feedback values for adjusting one or more stimulation parameters that at least partially defines the electrical stimulation therapy; receiving subsequent electrical signals sensed from the patient during a second time duration; comparing the subsequent electrical signals to the one or more feedback values; and adjusting, based on the comparison, the one or more stimulation parameters.

9. The system of any of claims 1 through 8, further comprising an external programmer comprising the processing circuitry.

10. The system of any of claims 1 through 9, wherein the processing circuitry is configured to control a display device to present the periodicity metric.Docket No. : A0013147 WOO 1 / 1123-859WOO 111. The system of any of claims 1 through 10, further comprising an implantable medical device comprising the sensing circuitry and stimulation circuitry configured to deliver the electrical stimulation therapy.

12. The system of any of claims 1 through 11, further comprising an implantable medical device comprising the processing circuitry.

13. A non-transitory computer-readable medium comprising instructions that, when executed, control the processing circuitry to perform the functions of any of claims 1 through 12.

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