Optimizing VNS therapy using therapy intensity and effectiveness metrics

Abstract

A display device can be configured to show neurostimulation parameter information about a neurostimulation therapy provided to a patient during a first time interval, wherein the neurostimulation parameter information includes an indication of an intensity of the neurostimulation therapy. The display device can be configured to show neurostimulation therapy effectiveness information about a patient response to the neurostimulation therapy. The therapy intensity and therapy effectiveness information can be displayed together to allow patients and clinicians to more readily identify trends and effects of therapy and therapy changes.

Description

Docket No. 8508.003W01OPTIMIZING VNS THERAPY USING THERAPY INTENSITY AND EFFECTIVENESSMETRICSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application is related to and claims priority to United States Provisional Application No. 63 / 584,709, filed on September 22, 2023, and entitled “OPTIMIZING PATIENT THERAPY USING THERAPY INTENSITY AND EFFECTIVENESS METRICS,” the entirety of which is incorporated herein by reference.BACKGROUND

[0002] Epilepsy is a disorder in which nerve cell activity in the brain is disturbed, causing seizures. During a seizure, a person can experience abnormal behavior, symptoms, and sensations, sometimes including loss of consciousness. Epilepsy is usually treated by medications and in some cases by surgery, devices, or dietary changes. Though some seizures can be controlled with medication, if medication becomes ineffective, other forms of treatment may be considered, including neurostimulation therapy.SUMMARY

[0003] The present inventors have recognized that a problem to be solved includes optimizing neurostimulation therapy for seizure disorders. Prior methods for adjusting and titrating neurostimulation parameters often rely on trial and error, leading to suboptimal treatment outcomes and prolonged adjustment periods. The present inventors have recognized there is a need for more efficient and data-driven approaches to therapy optimization that can account for the complex relationships between stimulation parameters, physiological responses, and therapeutic outcomes. Additionally, the vast amount of data generated during neurostimulation therapy can be overwhelming for clinicians to interpret and use effectively in real-time decision-making. The lack of intuitive visualization tools for this complex data hinders the ability of healthcare providers to make rapid, informed adjustments to therapy parameters. Furthermore, there is a challenge in predicting and preventing seizure events proactively, rather than merely responding to them after they occur. These issues collectively contribute to suboptimal patient outcomes, increased healthcare costs, and reduced quality of life for individuals with seizure disorders.Docket No. 8508.003W01I methods described herein provide several benefits through improved data visualization for neurostimulation therapy. The dynamic, interactive visual representations of therapy intensity and effectiveness enable patients and clinicians to rapidly identify optimal treatment parameters, leading to more efficient therapy titration and improved patient outcomes. By presenting complex neurostimulation data in an intuitive, easily interpretable format, the system reduces cognitive load on healthcare providers and facilitates more informed decision-making.

[0005] In an example, the data visualization discussed herein includes real-time data visualization that allows for immediate feedback on therapy adjustments, enabling fine- tuning of parameters. Additionally, the integration of machine learning algorithms for data analysis and prediction enhances the system's ability to identify subtle patterns and correlations that may not be apparent through traditional methods. This analytical capability, combined with the interactive visual interface, empowers clinicians and patients to proactively adjust therapy in anticipation of seizure events, potentially preventing adverse outcomes before they occur. Overall, the improved data visualization scheme represents a significant advancement in neurostimulation therapy management, offering the potential for more personalized, effective, and efficient treatment for epilepsy or other seizure disorders, among other disorders.

[0006] In an example, a system is provided that includes an interface device with a processor circuit and a display. The processor circuit is configured to determine a seizure therapy intensity index for a first neurostimulation therapy provided to a patient, where the seizure therapy intensity index is based on one or more parameters that define the first neurostimulation therapy. The processor circuit can determine a therapy effectiveness index that corresponds to the seizure therapy provided according to the determined therapy intensity index. The display is configured to provide a dynamic pictorial representation of the seizure therapy intensity index together with the therapy effectiveness index over time. This system enables real-time visualization and analysis of neurostimulation therapy parameters and their effectiveness, facilitating optimized treatment for patients with seizure disorders.

[0007] In an example, a method includes providing a first seizure therapy to a patient at a first therapy intensity, where the first therapy intensity is a function of parameters of an electrostimulation signal, such as can include an electrostimulation magnitude, pulse width, duty cycle, or frequency, among other parameters. Following the onset of providing the first seizure therapy, the method can include determining a therapy effectiveness for the firstDocket No. 8508.003W01 display device, the method can include providing a pictorial representation of the first therapy intensity over a first time duration in visual correspondence with a pictorial representation of the determined therapy effectiveness over the same first time duration. In an example, the method allows for visualization of the relationship between therapy intensity and effectiveness, enabling more informed decisionmaking in seizure treatment.

[0008] In an example, an interface device includes a display that presents physiological parameter information for a patient corresponding to a first time interval. The display also provides at least one seizure indication in visual correspondence with at least a portion of the physiological parameter information. In an example, the display shows neurostimulation parameter information about a neurostimulation therapy provided to the patient during the first time interval, including an indication of the intensity of the neurostimulation therapy over the first time interval. The interface device can display at least one neurostimulation parameter change indication in correspondence with at least a portion of the neurostimulation parameter information. This comprehensive visual representation allows for detailed analysis of the relationship between physiological parameters, seizure events, and neurostimulation therapy adjustments.

[0009] This Summary is not intended to provide an exclusive or exhaustive explanation of the innovations discussed herein.BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0010] To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced. The drawings are not drawn to scale.

[0011] FIG. 1 illustrates generally an example of a neurostimulation system.

[0012] FIG. 2 illustrates generally an example of a display showing a therapy intensity index, an efficacy index, and a side effect index.

[0013] FIG. 3 illustrates generally an example of a display showing therapy effectiveness and side effect indices.

[0014] FIG. 4 illustrates generally an example of a display showing neurostimulation parameters and physiological parameters.

[0015] FIG. 5 illustrates a method in accordance with one embodiment.

[0016] FIG. 6 illustrates an aspect of the subject matter in accordance with one embodiment.Docket No. 8508.003W01 s generally an example of a machine in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein, according to an example embodiment.DETAILED DESCRIPTION

[0018] The present disclosure describes several technical improvements that enable optimization of neurostimulation therapy parameters in a practical and meaningful way, leading to improved therapeutic outcomes for patients. In an example, an implantable neurostimulation device can deliver electrical stimulation to a patient in a closed-loop manner, and can be configured to automatically adjust therapy parameters based on an analysis of patient response data. In an example, therapy parameters can be adjusted manually by a clinician, such as after clinician review of therapy parameters and patient responses to prior therapy.

[0019] Systems and methods discussed herein integrate data from sensors and an implanted neurostimulation device to monitor patient status and the effects of therapy delivery. Processor-implemented algorithms can be used to analyze this integrated data and determine optimal adjustments to therapy parameters that will improve patient outcomes. For example, the techniques and algorithms discussed herein can include analyzing patient metrics related to seizure frequency, duration, or severity and automatically adjusting stimulation parameters like amplitude, pulse width, or duty cycle to improve seizure control. This provides the practical application of optimizing therapy in a personalized, data-driven manner, among other benefits.

[0020] In an example, systems and methods discussed herein improve visualization of therapy data through a graphical user interface that displays (e.g., concurrently displays) therapy intensity and effectiveness information. By displaying this information together, patients and clinicians can better understand the impact of therapy adjustments and use the visualization to further improve and titrate therapy parameters. This provides the real-world benefit of empowering patients or clinicians to make better informed therapy decisions, improving patient quality of life, among other benefits.

[0021] A vagus nerve stimulation (VNS) device, or neurostimulator, can be used for the treatment of epilepsy or other seizure disorder, such as continuously, predictively, or in response to detecting that a seizure (or other adverse event) is occurring. An aspect of the present subject matter can include or can be used to perform therapy titration, such asDocket No. 8508.003W01 i VNS therapy delivery approach. Such an approach can include use of personalized seizure (or other adverse event) detection or therapy selection (or both). In an example, one or more of detection criteria or therapy can be varied over time.

[0022] A system for providing seizure detection and VNS can include one or two vagus nerve sensing electrodes (e.g., “recording cuff’ or helical electrodes), such as located in different longitudinal positions along the cervical vagus region, relative a stimulation site. The same or other, additional stimulating electrodes (e.g., an anode and a cathode) can be positioned to provide VNS.

[0023] Such a count and position of sensing and stimulation electrodes is merely illustrative. For example, an implantable device can include circuitry for sensing (e.g., recording) neural activity (e.g., an action potential or compound action potential), and circuitry for generating VNS. In an example, the sensing electrodes and related circuitry can be separate from the stimulating electrodes and the sensing electrodes could be monitored by a separate unit (e.g., an external assembly) that could be used in an acute or temporary manner, such as supporting an implant procedure or implantable device configuration. For example, in the case that the sensing and stimulating cuffs are separate, the sensing cuff may be explanted acutely as a portion of a first procedure or soon after the first procedure. In yet another example, there could be three or more electrodes that could each be configurable as either a stimulating electrode or a sensing electrode at any time. For example, two electrodes closest to a brain of a patient could be assigned as an anode and a cathode, respectively, and another electrode that is located more distally could be assigned as a sensing electrode to detect efferent nerve activation. As another illustration, two electrodes most distal to the brain could be assigned as an anode and a cathode, respectively, and an electrode more or most proximal to the brain could be assigned as a sensing electrode to detect afferent activity.

[0024] Neurostimulation therapy for treatment of drug-resistant epilepsy can be provided by a VNS device. The therapy may become less effective over time, for example due to patient habituation to therapy. Habituation can manifest as a patient adapting or acclimating to ongoing treatment, in some cases reducing an effectiveness of a therapy or requiring a more intense therapy to achieve the same therapeutic result that was previously achieved using a lower intensity therapy. Methods and devices for displaying data from, or for programming, an epilepsy treatment system can be provided to assist in improving therapy effectiveness. The same or other methods and devices can be used to titrate an amount of neurostimulation therapy, or therapy intensity, received by a patient. In an example, patientDocket No. 8508.003W01 neurostimulation therapy intensity changes over time, allowing for therapy intensity to be progressively increased to a therapeutic level. The systems and methods discussed herein facilitate rapid titration and help provide a patient or clinician feedback on the patient response to therapy, such as to help maximize therapeutic benefit to the patient while minimizing adverse effects experienced by the patient and maximizing device longevity.

[0025] In an example, a method can include providing a first seizure therapy using an implanted neurostimulation device and at a first therapy intensity. The first therapy intensity at a particular time, or over a particular time duration, can be a function of one or more electrostimulation parameters, such as an electrostimulation magnitude, pulse width, duty cycle, and / or frequency, among others. Following an onset of providing the first seizure therapy, a therapy effectiveness for the first seizure therapy can be determined. In an example, the method can include providing a pictorial representation of the first therapy intensity and the therapy effectiveness. The pictorial representation can be provided using a display device for a patient or clinician.

[0026] In an example, in response to the determined therapy effectiveness being less than a specified threshold therapy effectiveness amount, a second seizure therapy using a greater therapy intensity can be provided. Stated differently, if a specified minimum amount of therapy effectiveness is not attained using the first seizure therapy, then the second seizure therapy, using the greater therapy intensity, can be provided.

[0027] In an example, in response to the determined therapy effectiveness meeting or exceeding the threshold therapy effectiveness amount, a third seizure therapy can be provided using the same or a lesser therapy intensity. The same or lesser therapy intensity can be selected to help maintain a particular therapeutic benefit, maximize a device longevity, or for another reason. In another example, if a specified maximum amount of therapy effectiveness is not attained using the first seizure therapy, then the third seizure therapy can be provided using a lesser therapy intensity. That is, the therapy intensity can be reduced if there is no discernible therapeutic benefit from the greater intensity therapy.

[0028] In an example, multiple different effectiveness thresholds can be used. In response to a determined therapy effectiveness being at or below a lower effectiveness threshold, a therapy intensity can be increased (e.g., automatically by the device). In response to the determined therapy effectiveness being at or above an upper effectiveness threshold, a therapy intensity can be maintained or decreased (e.g., automatically by the device). In some examples, it can be disadvantageous to exceed an upper effectiveness thresholdDocket No. 8508.003W01 icreases in therapy intensity can trigger undesirable side effects or adversely affect device longevity. In another example, and in response to the determined therapy effectiveness being at or above an upper effectiveness threshold, different neurostimulation parameters can be used, such as corresponding to the same or lesser therapy intensity. In this case, an attempt can be made to improve the therapy effectiveness (e.g., and exceed the upper effectiveness threshold) without triggering undesirable side effects or adversely affecting device longevity.

[0029] In some examples, an effectiveness threshold can be determined in relation to a side effect index, such as to help maximize therapeutic benefit while minimizing side effects experienced by the patient in response to the therapy. For example, the upper effectiveness threshold can be based on a desired difference from a side effect index value, and the side effect index value can be determined concurrently with therapy delivery (e.g., at a particular therapy intensity) or other recent therapy delivery.

[0030] The second and / or third seizure therapies can be initiated automatically by the device or manually by a patient or clinician. Providing lower intensity therapy while maintaining therapeutic effectiveness can help prolong device longevity by consuming less power and can help minimize patient habituation to ongoing therapy.

[0031] In an example, the interface devices discussed herein can include or use a display comprising neurostimulation parameter information about a neurostimulation therapy provided to a patient during a first time interval. The neurostimulation parameter information can include an indication of an intensity of the neurostimulation therapy. The display can comprise neurostimulation therapy effectiveness information about a patient response to the neurostimulation therapy. In an example, the display can be configured to display information about a single patient or about a population of multiple patients.

[0032] In an example, the interface device includes a patient device that is configured to be used by or with the patient. The patient device can optionally include one or more sensors or inputs configured to receive information about a patient health status or a patient response to therapy. The received patient information can be used to determine the therapy effectiveness, according to various examples.

[0033] In an example, physiologic information about a patient can be analyzed automatically using a processor-implemented algorithm such as can include a machine learning-based algorithm, or can be analyzed manually by a clinician, to identify one or more patient patterns relative to seizure events. The seizure events can be automatically detected or can be reported by a patient. Patient patterns can include patient rhythms (e.g.,Docket No. 8508.003W01 er cyclic processes or behaviors, or other information or correlations between two or more metrics of patient physiologic status (e.g., posture, activity level, heart rate, autonomic tone, ECG activity, respiration rate, heart rate variability, among others). The pattern information can be analyzed in coordination with seizure events to help improve seizure prediction and titrate more effective therapies, with a goal of reducing severity or frequency of seizure events. Showing therapy details in terms of a therapy intensity or other therapy attribute, and corresponding effectiveness in terms of seizure type, severity, or frequency, can enable patients and clinicians to better understand or identify the effects of particular therapies and can help patients and clinicians make better informed decisions about therapy changes.

[0034] In an example, the systems discussed herein can be configured for use with multiple patients, or a population of patients. The systems can include or use a common database of patient attributes and therapy dosing information correlated with patient results including information about therapy efficacy or incidence of side effects. In an example, therapy optimization can be achieved through understanding how therapy was programmed across many patients, correlated with patient attributes and health status or outcomes. The system can have information about therapy dosing from a population of patients, including all patients or subgroups of patients sharing similar characteristics or attributes.

[0035] In an example, the systems can be configured to display or visualize data about the patient population therapies and patient outcomes, as well as display or visualize information about a particular patient relative to the population data. This visualization can indicate whether a particular patient is an outlier compared to the population data. For example, the system may generate a scatter plot or heat map showing the distribution of therapy intensities and efficacy outcomes across the patient population, with the current patient's data point highlighted for comparison. The system aggregates therapy intensity, efficacy, and side effect information from a population of patients to help inform therapy decisions and improve outcomes for particular patients.

[0036] The output or visualization can take many forms, including heat maps, Poincare plots, or other diagrams showing data clusters or trends. For instance, a Poincare plot could be used to visualize the relationship between consecutive therapy intensity adjustments and corresponding changes in efficacy, helping to identify optimal adjustment strategies. These visualizations allow clinicians to quickly identify patterns and relationships between therapy parameters, patient characteristics, and outcomes across the patient population, facilitating more informed decision-making for individual patient care. The system can use machineDocket No. 8508.003W01 lalyze the population data and suggest personalized therapy adjustments based on patterns observed in patients with similar profiles (e.g., profiles that respectively indicate a patient response to or tolerance for particular therapy intensities).

[0037] An illustrative example of a vagus nerve stimulation (VNS) system, as shown in FIG. 1, includes a system 100 for providing neurostimulation (or neuromodulation) to a vagus nerve 102. In an example, the system 100 can be configured to sense nerve activity or other electrical activity or motion. The example of the system 100 includes an implantable device 112 such as can comprise a processor circuit 114 and a signal generator 116. The processor circuit 114, or control circuit, can control operation of the signal generator 116 according to various therapy delivery algorithms or therapy signal-defining parameters. The signal generator 116 can be configured to generate stimulation signals or pulses according to parameters or instructions from the control circuit. In an example, the signal generator 116 includes independent current sources and controllers to enable independent and simultaneous output of multiple respective therapy signals.

[0038] In an example, the implantable device 112 comprises or is coupled to one or more physiologic status sensors configured to sense information about a patient. For example, the system can include a sensor 118. The implantable device 112 can further include a recording unit 120 configured to record information from the sensor 118. The sensor 118 can comprise a portion of the implantable device 112 or can be coupled to a lead that is coupled to the implantable device 112. In other examples, the sensor 118 can be an external sensor that is coupled to, or otherwise configured to receive information from, the patient. In an example, the sensor 118 comprises an accelerometer configured to sense motion information about the patient. In an example, the sensor 118 comprises one or more electrodes configured to sense electrical signals from the patient. In an example, the one or more electrodes can be implanted at or near a vagus nerve of the patient and can be configured to deliver electrical signals to, or receive electrical signals from, the vagus nerve (or other neural target). For example, the one or more electrodes can be configured to sense vagal activity information from the patient.

[0039] In an example, the system 100 includes an external device 122 that can communicate with the implantable device 112. The external device 122 can include a patient device or clinician device that is configured to receive information from, or provide information to, the implantable device 112. In an example, the external device 122 can include a display configured to receive and display data from the implantable device 112, including individual sensor data and seizure detection annotations. The system 100 or theDocket No. 8508.003W01 calculate and display seizure burden, and / or display an event log.In an example, the external device 122 includes an interface that allows patients to confirm events and / or add comments or annotations to detected events.

[0040] For example, the external device 122 can be used to set one or more parameters for a stimulation therapy that is provided by the implantable device 112. In an example, the external device 122 can be used to report information to a patient or clinician about one or more therapies provided by the implantable device 112. In an example, the external device 122 includes one or more sensors that are configured to monitor physiologic or behavioral information about the patient.

[0041] The external device 122 and the implantable device 112 are coupled using a bidirectional communication link, such as using a wireless coupling 124, that allows for the continuous exchange of data and commands between the two devices. The communication can be established using wireless technology protocols that are specifically designed for medical devices, ensuring secure and reliable data transmission.

[0042] The external device 122 is equipped with various sensors, including a high- resolution camera, a microphone, and an accelerometer, which collect a wide array of physiological and environmental data. This data includes visual and audio records of the patient's movements, vocalizations, and surrounding environment, as well as quantitative measurements such as detected motion patterns, heart rate, temperature, and / or respiration rate. The collected data is then processed (e.g., at the external device 122, at the implantable device 112, or elsewhere) using various algorithms, for example, to identify potential seizure events or assess a therapy effectiveness, among other things.

[0043] In an example, the processor circuit 114 is programmed with a set of parameters that define thresholds for initiating or adjusting VNS therapy. Upon receiving a therapyindicating signal from the external device 122 or based on information from the sensor 118, the processor circuit 114 can be configured to analyze the sensor data against the predefined parameters. If the data indicates that a seizure is occurring or imminent, the implantable device 112 adjusts the therapy parameters accordingly. In an example, the processor circuit 114 is configured to provide an alert or notification to the patient or clinician (e.g., using the external device 122) before adjusting a therapy parameter. This adjustment may involve changing the intensity, frequency, duration, or other characteristic of the electrical impulses delivered to the vagus nerve to provide an appropriate therapeutic response.

[0044] In an example, seizure detection and VNS can include or use one, two, three, or more vagus nerve sensing electrodes (e.g., “recording cuff’ or helical electrodes), such asDocket No. 8508.003W01 tudinal positions along the cervical vagus region, relative to a stimulation site. Separate stimulating electrodes (e.g., an anode and a cathode) can be positioned to provide VNS. In the example of FIG. 1, the system 100 includes a first electrode 104, a separate second electrode 106, a separate third electrode 108, and a separate nth electrode 110 positioned at or near the vagus nerve 102. The various electrodes can be used in various combinations to provide an epilepsy therapy or to sense activity from the vagus nerve (or other nerve). In an example, the multiple electrodes comprise respective portions of a single lead, or multiple leads can be used, with each lead comprising one or more electrode.

[0045] The count and position of electrodes in the example of FIG. 1 is merely illustrative. For example, an implantable device can include circuitry for sensing (e.g., recording) neural activity (e.g., an action potential or compound action potential), along with circuitry for generating VNS. In such an example, a machine learning approach, such as an instance of a machine learning-based model (e.g., such as can be referred to as an artificial intelligence or “Al” -based technique) can be instantiated by the implant circuitry or the processor circuit 114. Such a machine learning-based model can be used for detection of a seizure, or for therapy control (e.g., titration) in response thereto, or both. In an example, machine learning-based models or techniques can be applied to help identify correlations between therapy parameters (e.g., including therapy intensity) and effectiveness.

[0046] FIG. 2 illustrates generally a first example interface 202 that can be provided using a display device. In an example, the display device is an example of the external device 122 of the system 100. The first example interface 202 can include substantially real-time pictorial representations of therapy intensity, therapy efficacy, and side effect burden over time. Non-pictorial representations can similarly be used.

[0047] The first example interface 202 includes a therapy intensity index 204, an efficacy index 206, a side effect index 208, and time 210. The therapy intensity index 204 represents a metric that describes a relative or absolute magnitude of an intensity of a neurostimulation therapy provided by a neurostimulation device to a patient, such as at a particular time or over a particular time duration. For example, the therapy intensity index 204 can represent an intensity of a VNS therapy provided to the vagus nerve 102 using the implantable device 112 in real-time. The therapy intensity index 204 can be a numerical value that is determined (e.g., calculated) as a function of one or more neurostimulation parameters such as neurostimulation magnitude, pulse width, duty cycle, frequency, or other parameter. In other words, various algorithms can be used to receive one or more therapy-defining inputsDocket No. 8508.003W01: a therapy intensity index value. The numerical value of the therapy intensity index 204 can be determined at a particular time, or over a particular time duration, or can be updated substantially continuously.

[0048] The efficacy index 206 represents a metric that describes an effectiveness of a therapy provided to a patient, or a patient response to the neurostimulation therapy. The efficacy index 206 can be a numerical value that is determined (e.g., substantially in realtime) as a function of one or more patient response indicators, such as can include information about a seizure type, seizure frequency, seizure intensity, or presence, absence, or severity of a side effect. The therapy intensity index 204 and the efficacy index 206 can be represented over time 210 such that therapy information (e.g., from the therapy intensity index 204) is shown in visual or pictorial correspondence with information about an effect of the therapy (e.g., using the efficacy index 206). In an example, the efficacy index 206 can be expressed on a fixed or sliding scale. In an example, the efficacy index 206 can be expressed on a scale (e.g., 0 to 100, with 0 representing an ineffective therapy or no detectable response to the therapy, and 100 representing a highly effective therapy with positive therapeutic response by the patient). In some examples, the efficacy index 206 can be expressed as a percentage or in other ways.

[0049] The side effect index 208 represents a metric that describes a side effect burden or load experienced by a patient in response to the neurostimulation therapy. The side effect index 208 can be numerical value that is determined (e.g., substantially in real-time) as a function of one or more patient response indicators. The side effect index 208 is represented as a percentage from 0 (e.g., no side effect present or experienced by the patient) to 100 (e.g., significant side effects experienced by the patient and / or high device load, such as corresponding or leading to minimum device longevity). The quantification of the side effects that contribute to the side effect index 208 can be determined automatically using sensor data, or can be determined subjectively, such as using information reported by the patient or a clinician, for example, using the external device 122.

[0050] In an example, the respective values that represent the therapy intensity index 204, the efficacy index 206, and / or the side effect index 208 can be determined at a particular time, or over a particular time duration, or can be updated substantially continuously.

[0051] In the example of FIG. 2, the therapy intensity index 204 can be a function of one or more stimulation parameters, and the efficacy index 206 can exhibit a sigmoidal relationship to increasing therapy intensity. For example, before a first time T1 and below a first threshold intensity index value 212 of II, an efficacy of the therapy can be low or zeroDocket No. 8508.003W01Dan be approximately 0%, indicating an ineffective therapy). At or above a second threshold intensity index value 214 of 12, following time T2, an efficacy of the therapy can be maximized (e.g., efficacy index = 100%). In the example of FIG. 2, no additional therapeutic benefit is realized for therapy intensities that exceed the second threshold intensity index value 214. For example, at later time T4, the therapy intensity exceeding 12 correlates to essentially the same efficacy index value at time T2.

[0052] In a particular non-limiting example, the therapy intensity index 204 can be a function of a magnitude of a neurostimulation pulse signal (e.g., expressed in mA), a frequency of the pulse signal (e.g., expressed in Hertz), a pulse width of the pulse signal (e.g., expressed in microseconds), and a duty cycle of the pulse signal (e.g., expressed as a percentage). For example,Intensity index = 100 * (magnitude) * (frequency) * (pulse width) * (duty cycle) / (magnitude max ) * (frequency max ) * (pulse width max ) * (duty cycle max) where magnitudemax is a maximum available magnitude of the pulse signal, frequencymax is a maximum available frequency of the pulse signal, pulse widthmax is a maximum available pulse width of the pulse signal, and duty cyclemax is a maximum available duty cycle of the pulse signal. This is but one example of a particular algorithm or technique for determining a numerical intensity value. Any other algorithm or technique can similarly be used to assign or determine a relative intensity value to one or more parameters that define a neurostimulation signal. In an example, a look-up table or other means for mapping parameters to intensity can similarly be used. In an example, different techniques for determining intensity can be used for different patients or different patient populations.

[0053] In a particular non-limiting example, the efficacy index 206 can be a function of one or more parameters that indicate an effect of the therapy on a patient, such as a therapy provided at a particular therapy intensity level. In practice, and particularly for new patients, the efficacy index can be expected to show a low-efficacy phase in which there is little or no therapeutic efficacy, a mid-efficacy phase, in which therapeutic efficacy increases in coordination with increases in therapy intensity, and a maximum-efficacy phase in which further increases in therapy intensity lead to adverse conditions (e.g., side effects experienced by the patient, or device battery depletion) without a corresponding increase in therapeutic efficacy.

[0054] In an example, the efficacy index 206 can represent a value that is a function of one or more of a reduction in number of seizures over time, a reduction in seizure duration (e.g.,Docket No. 8508.003W01 ent duration, etc.) over time, a reduction in average severity of seizures (e.g., as determined objectively by sensor data or as reported by a patient), or a reduction in average complexity of seizures, among other things. The components of the efficacy index 206 can be differently weighted.

[0055] In a particular example, the efficacy index can be calculated as,100Efficacy indexwhere I is a therapy intensity index, d is a scaling index (e.g., 3 maximum therapy intensity), ti is a therapy efficacy onset threshold value, and t2 is a therapy efficacy plateau threshold value. Other functions can similarly be used to determine a numerical efficacy index.

[0056] In another example, the efficacy index 206 can be a function of seizure occurrences or seizure characteristics. For example, the efficacy index 206 can be calculated as the sum of (1) a number of seizure events for a particular time, multiplied by the duration of the seizure events, multiplied by a weighting factor, and (2) a number of seizure events for a particular time multiplied by a seizure recovery duration, and (3) a side effect burden value that is based on an intensity or frequency of side effects experienced by the patient. Other inputs can similarly be used to weight or influence the efficacy index 206 value, such as seizure type, or other objective or subjective measures of patient status.

[0057] In an example, an efficacy index threshold THEF can be provided. Therapies that do not meet the efficacy index threshold can be deemed to be insufficient and, accordingly, one or more therapy parameters can be updated or adjusted to try to improve the therapy effectiveness. On the other hand, therapies that meet or exceed the efficacy index threshold can be permitted and used.

[0058] In an example, multiple efficacy index thresholds can be used, such as upper and lower efficacy index thresholds. In response to a determined therapy effectiveness being less than the lower efficacy index threshold, one or more therapy parameters can be updated or adjusted to try to improve the therapy effectiveness. For example, an intensity of the therapy can be increased. In response to a determined therapy effectiveness being greater than the upper efficacy index threshold, one or more therapy parameters can be updated or adjusted to try to maintain the therapy effectiveness while minimizing a side effect burden or enhancing a device longevity. For example, an intensity of the therapy can be reduced.Docket No. 8508.003W01 es, a patient can determine a baseline value for the efficacy index 206 such as without neurostimulation therapy. The patient can use the implantable neurostimulation device (e.g., the implantable device 112) to increase their efficacy index 206 from the baseline value through neurostimulation therapy. Increases in the efficacy index 206 can correspond to, for example, a decrease in a number of seizure events per unit time, or a decrease in a total seizure recovery time, or a decreasing side effect burden value, among other things.

[0060] In an example, the side effect index 208 can represent a value that is a function of one or more side effects experienced by, detected from or by, or reported by the patient. The side effect index can represent a value that is a function of one or more of an incidence of side effects over time, an average severity of side effects, or a type of side effect, among other things. The components of the side effect index can be differently weighted.

[0061] In an example, a side effect tolerance threshold THSE can be provided. Therapy corresponding to side effects that are at or below the side effect tolerance threshold can be well-tolerated by the patient and therefore allowed at any time. In an example, therapy corresponding to side effects that exceed the threshold can be intolerable and can be disallowed. In an example, some therapies may be permitted to exceed the side effect tolerance threshold if the severity of an adverse event requires significant intervention, such as a higher-intensity therapy. A tolerance for exceeding the side effect tolerance threshold can be patient-specific and may be adjusted by the patient or clinician.

[0062] In the example of FIG. 2, the side effect index 208 exceeds the side effect tolerance threshold THSE at about time T3, which coincides in time with a change (increase) in the therapy intensity index 204. Following time T3, there is no apparent increase in the efficacy index 206. Accordingly, at around time T4, the therapy intensity can be reduced to attempt to reduce the side effect index 208 without compromising the effectiveness of the therapy.

[0063] In an example, the first example interface 202 of FIG. 2 can be updated to include information about a population of patients and, optionally, information about how a particular patient relates to the population. For example, the therapy intensity index (204), efficacy index (206), and side effect index (208) graphs can be enhanced with shaded areas representing the range of values observed across the patient population. This would create a visual envelope or bounds that show a distribution of values for each metric for the particular patient population. In an example, percentile markers or lines can be added to one or more of the graphs to show population percentiles (e.g., 25th, 50th, 75th). In an example, a similarity or percentile score can be included for each metric, indicating how closely theDocket No. 8508.003W01 h the population distribution. These and other techniques can be used by clinicians or patients to quickly assess where an individual patient falls within the broader population distribution for each metric.

[0064] In an example, the first example interface 202 can include or use one or more additional sections to display a summary of statistics for the population, such as mean therapy intensity, median efficacy, and common side effects. Such additional sections can provide context for interpreting an individual patient's data.

[0065] In an example, the first example interface 202 can color-code or highlight an individual patient's data to visually distinguish it from the population data. For example, the patient's specific therapy intensity, efficacy, and side effect curves could be shown in a bold, contrasting color overlaid on the population data. In an example, the first example interface 202 can include an option to toggle between individual patient and patient population views, or to filter the population data based on relevant patient characteristics (e.g., age, seizure type, comorbidities).

[0066] FIG. 3 illustrates generally a second example interface 302. The second example interface 302 includes a graphical representation of the efficacy index 206 and a side effect index 304 as respective functions of therapy intensity.

[0067] In the example of FIG. 3, the therapy intensity is represented as a scalar value. The efficacy index 206 is represented as a percentage from 0 (e.g., indicating a therapy is not effective, or no therapy is provided) to 100 (e.g., indicating maximum therapeutic benefit is realized).

[0068] In the example of FIG. 3, the side effect index 304 is represented as a percentage from 0 (e.g., indicating that no side effect is present or experienced by the patient) to 100 (e.g., indicating that significant side effects are experienced by the patient and / or there is high device load, such as corresponding or leading to minimum device longevity). Although the side effect index 304 is represented notionally in the example of FIG. 3 as a smooth sigmoid function, the side effect index 304 can have different forms. In some examples, the side effect index 304 can be a stepwise function.

[0069] In the example of FIG. 3, the second example interface 302 can show an optimum zone 306 for therapy for a particular patient. In the optimum zone 306, a range of therapy intensities can be used (e.g., the therapy intensity can be about 1.2 to about 3.0). In the optimum zone 306, at least a minimum therapy efficacy is realized by the patient (e.g., the efficacy index 206 is between about 10% and about 80%) and side effects are minimized or tolerable (e.g., the side effect index 304 is at or below about 30%). Other ranges or boundsDocket No. 8508.003W016 can be set by the patient or clinician. In an example, the optimum zone 306 can be determined at least in part by a patient tolerance to side effects, or at least in part based on projected device longevity. For example, if a patient has a greater tolerance for side effects, then the optimum zone 306 can be enlarged and higher-intensity therapies can be provided to the patient, which in turn can potentially improve therapy efficacy.

[0070] Side effects can include, but are not limited to, pain or discomfort, tingling sensations, voice changes, coughing, difficulty swallowing, mood changes, sleep disturbances, headaches, among other things. The side effects can be experienced by the patient during or following a neurostimulation therapy. In an example, the magnitude or severity of the side effects can be reported by the patient or clinician, or can be monitored or measured using the sensors 118, such as can comprise implanted or external sensors. In an example, side effects can be identified using the external device 122 to monitor the patient. For example, a microphone and / or camera of the external device 122 (or other device) can receive audio and / or visual information about the patient which can be automatically analyzed to identify changes in patient behavior that are indicative of side effects, particularly when correlated with therapy delivery.

[0071] FIG. 4 illustrates generally a third example interface 402. The third example interface 402 includes a graphical representation of time-varying physiologic signal information sensed from a patient, stimulation parameters for a seizure therapy delivered to the patient using an implanted neurostimulation device, and information about a composite index, such as a therapy intensity index, for the therapy delivered to the patient. FIG. 4 can represent an example of a display of a clinician or patient device, such as the external device 122, with information for monitoring therapy effectiveness relative to particular physiologic parameters.

[0072] The display or graphical representation in the example of FIG. 4 can include a first display portion showing physiological parameters 404 and a second display portion showing stimulation parameters 414. In the example of FIG. 4, the first display portion can include electrocardiogram (ECG) based parameters such as heart rate 406 and heart rate variability 408, and can include information about seizure detection 410. The second display portion can include information about an intensity 416 of a therapy provided to a patient over time. The intensity 416 can comprise an example of the therapy intensity index 204 from the example of FIG. 2. The second display portion can include other information about components of the provided therapy, such as therapy amplitude 422, pulse width 420, and frequency 418 information. In an example, the displayed components can be the sameDocket No. 8508.003W01 rmine the intensity 416; other or additional components can be similarly displayed, such as components that are not used to determine the intensity 416.

[0073] In an example, events derived from the physiological parameter information, or events that are reported by the patient or clinician, can be represented on the third example interface 402. In an example, changes in programming of the neurostimulation device can be represented on the third example interface 402, such as to help identify a patient physiologic response to changes in seizure therapy.

[0074] In various examples, the third example interface 402 can include or display physiological parameter information for a patient corresponding to a first time interval. The third example interface 402 can include or display at least one seizure indication provided in visual correspondence with at least a portion of the physiological parameter information. The third example interface 402 can include neurostimulation parameter information about a neurostimulation therapy provided to the patient during the first time interval. In an example, the neurostimulation parameter information includes the indication of the intensity 416 of the neurostimulation therapy. In an example, the display can include at least one neurostimulation parameter change indication that is provided or displayed in visual correspondence with one or more of the neurostimulation parameter information (e.g., the stimulation parameters 414) and the physiological parameter information (e.g., the physiological parameters 404). For example, therapy parameter change events can be indicated together with the seizure detection 410 information.

[0075] In an example, the physiological parameter information includes information about one or more of an ECG, heart rate, heart rate variability, patient activity level, patient posture, and about one or more detected seizure events (e.g., including information about a seizure type, seizure frequency, or seizure severity, among other seizure metrics). For example, the seizure detection 410 portion of the display can include a seizure eventindicating signal 412. The seizure event-indicating signal 412 can represent a determined likelihood of a seizure event based on a detected or reported physiologic status of the patient. The likelihood determination can be based on, among other things, physiologic information received from the sensor 118 or other information reported by the patient (e.g., using the external device 122). In an example, the seizure event-indicating signal 412 can be determined using a machine learning approach that receives various physiologic status information about the patient, time of day information, environmental information, or other inputs, and identifies correlations with actual seizure events.Docket No. 8508.003W01:ction 410 portion of the display can include information about confirmed seizure events, such as at a first seizure event 424a, a second seizure event 424b, a third seizure event 424c, and so on. The confirmed seizure events can be indicated by the patient or clinician at particular times. For example, the system 100 can record a time of a received magnet swipe from the patient to indicate occurrence of a seizure event, such as the first seizure event 424a at a first time. The third example interface 402 can then display the physiological parameters 404 and stimulation parameters 414 in visual correspondence with an indication of the first seizure event 424a, to help enable better patient or clinician analysis of the patient's physiologic status leading up to, during, or following the first seizure event 424a. In an example, the third example interface 402 provides the indication of the first seizure event 424a in visual correspondence with information about the therapy used to address the first seizure event 424a.

[0077] The third example interface 402 can include neurostimulation parameter information about a neurostimulation therapy provided to a patient during a first time interval. The neurostimulation parameter information can include an indication of an intensity of the neurostimulation therapy. The interface device can include, or can be communicatively coupled to, a processor circuit (e.g., the processor circuit 114) configured to determine the indication of the intensity of the neurostimulation therapy based on two or more of an amplitude, frequency, pulse width, duty cycle, and duration of the neurostimulation therapy, among other factors. The processor circuit can be configured to determine the neurostimulation therapy effectiveness based on a seizure event rate at or following the first time interval. The processor circuit can be configured to quantify or determine the neurostimulation therapy effectiveness based on a seizure event type or seizure event severity at or following the first time interval.

[0078] In FIG. 4, therapy parameter changes are visually indicated in coordination with the seizure detection 410 information and the seizure event-indicating signal 412. A first therapy parameter change event 428a is shown in correspondence with an increasing risk of seizure as-indicated by the rising trend of the seizure event-indicating signal 412 preceding the first therapy parameter change event 428a. The example of FIG. 4 includes a second therapy parameter change event 428b in coordination with a later peak of the seizure eventindicating signal 412. Other therapy parameter change event indicators are also shown. The therapy parameter change event indicators can help a patient or clinician to readily identify correlations between therapy adjustments and their physiologic effects (e.g., including seizure events experienced by the patient).Docket No. 8508.003W01 iterface and / or the information displayed therein can be augmented in various ways. For example, the interface can be configured to allow customization or configuration of the visualization (e.g., of therapy effectiveness, side effects, therapy intensity, or other patient or therapy-related parameters) by clinicians, such as selecting specific parameters to display, adjusting or scaling of axes, or overlaying information about patient events (e.g., patient-reported or automatically detected or sensed events).

[0080] In another example, the interface can include or use predictive analytics or machine learning to forecast future therapy effectiveness (e.g., based on tried or projected therapy intensity) or can make automated recommendations for neurostimulation parameter changes.

[0081] In another example, the interface can be configured to allow interaction with the visualization to simulate potential therapy changes and view projected outcomes. This interactive component can enable more informed decisions about therapy parameter changes.

[0082] In another example, the interface can be configured to incorporate multi-modal patient data like audio, video (e.g., such as can be sensed using the external device 122 or one or more other sensors configured to receive information about the patient), or textual patient reports. Integrating and displaying correlations with this data can further improve understanding of therapy efficacy, incidence of side effects, and more.

[0083] In another example, the user interface can be configured to provide additional statistical views or analyses, such as correlating effectiveness with patient demographics or other conditions or events. In another example, the user interface can be configured to enable access to the visualization remotely from multiple devices, or to share visualizations across clinicians.

[0084] FIG. 5 illustrates an example of a first method 500 for updating or adjusting a seizure therapy intensity. Although the example of the first method 500 depicts a particular sequence of operations, the sequence may be altered without departing from the scope of the present disclosure. For example, some of the operations depicted may be performed in parallel or in a different sequence that does not materially affect the function of the first method 500. In other examples, different components of an example device or system that implements the first method 500 may perform functions at substantially the same time or in a specific sequence.

[0085] At operation 502, the first method 500 includes providing a first neurostimulation seizure therapy to a patient. The first neurostimulation seizure therapy can have a first therapy intensity, and can be delivered to the patient using an implanted neurostimulationDocket No. 8508.003W01 to deliver the therapy to one or more neural targets, such as can include the vagus nerve of a patient. In an example, operation 502 can include or use the system 100. The first therapy intensity can be a numerical index that is a function of, for example, an electrostimulation magnitude, pulse width, duty cycle, and / or frequency. For example, the first therapy intensity can be a function of the electrostimulation magnitude and pulse width, or a function of the electrostimulation magnitude and frequency. In other examples, the first therapy intensity can be a function of the electrostimulation magnitude, pulse width, duty cycle, and frequency, among other parameters.

[0086] At operation 504, the first method 500 can include, following an onset or beginning of providing the first seizure therapy, determining a therapy effectiveness for the first seizure therapy. The therapy effectiveness can be based on information about a patient response to the first seizure therapy provided at operation 502. The information about the patient response can be sensed or received automatically, such as from one or more sensors. In an example, the information about the patient response can be reported or recorded by the patient, by a clinician, or by a caregiver for the patient. In an example, operation 504 includes receiving therapy effectiveness information using the sensor 118 or using the external device 122.

[0087] At operation 506, the first method 500 can optionally include providing a pictorial representation of the first therapy intensity and a pictorial representation of the determined therapy effectiveness for the first seizure therapy. For example, the external device 122 can use the first example interface 202 or the third example interface 402, among others, to provide the pictorial representations. In an example, the representations can be displayed relative to a common or shared time axis. That is, the display can show time-aligned information about the therapy intensity and the therapy effectiveness.

[0088] At operation 508, the first method 500 can include comparing the therapy effectiveness determined at operation 504 to a specified threshold therapy effectiveness value. The specified threshold therapy effectiveness value can be a value that is set by the device (e.g., the implantable device 112), or a value that is set by a patient, clinician, or other caregiver.

[0089] At operation 508, in response to the determined therapy effectiveness being less than the specified threshold therapy effectiveness value, the first method 500 can include providing a second seizure therapy to the patient. The second seizure therapy can have a greater therapy intensity than the first seizure therapy. In an example, providing the second seizure therapy to the patient at operation 508 can be initiated automatically by theDocket No. 8508.003W01 vhen the determined therapy effectiveness for the first seizure therapy is low, such as below the specified threshold therapy effectiveness value.

[0090] At operation 510, in response to the determined therapy effectiveness meeting or exceeding the specified threshold therapy effectiveness value, the first method 500 can include providing a third seizure therapy to the patient. The third seizure therapy can have an equal or lesser therapy intensity than the first seizure therapy. In an example, providing the third seizure therapy to the patient at operation 510 is initiated automatically by the implantable device 112 when the determined therapy effectiveness for the first seizure therapy is high, such as above the specified threshold therapy effectiveness value. The example of the first method 500 is not limited to three seizure therapies; other therapies and / or effectiveness thresholds can be used.

[0091] In an example, the various interface examples and displays discussed herein can be used to help optimize neurostimulation therapy by showing relationships between two or more of therapy intensity, therapy effectiveness, presence or severity of side effects, physiologic parameters, patient-reported information, or other therapy or device-related parameters. In an example, systems discussed herein can be configured to provide an interactive scatter plot or heat map that displays the therapy intensity index on one axis and the therapy effectiveness index on the other. This allows clinicians to visually identify optimal regions where high effectiveness correlates with lower intensity, guiding them towards more efficient therapy settings. In an example, a graphical interface can extend beyond a two-dimensional representation to include additional parameters such as side effect indices. This multi-dimensional visualization can use techniques like parallel coordinates or radar charts to guide clinicians towards optimal therapy settings that balance intensity, effectiveness, and minimized side effects.

[0092] In an example, systems discussed herein can use trend analysis to help optimize therapy. For example, a display can show trend lines or curves that illustrate how changes in therapy intensity over time correspond to changes in effectiveness. This visual representation can help identify patterns and guide decisions on when and how to adjust therapy parameters.

[0093] In an example, systems discussed herein can include or use other visual cues such as color-coded regions or dynamic markers that highlight when the therapy intensity reaches certain thresholds relative to effectiveness. For example, it might use green to indicate optimal ranges where effectiveness is high and intensity is moderate, yellow for suboptimal ranges, and red for ineffective or potentially harmful intensity levels.Docket No. 8508.003W01;ystems discussed herein can be used for or with predictive modeling. In this example, machine learning algorithms can be used to analyze the relationship between intensity and effectiveness data, and generate predictive models that are visually represented. This could include projected effectiveness curves for different intensity adjustments, helping clinicians and patients understand and anticipate the potential impact of therapy changes.

[0095] In an example, systems discussed herein can provide a time-based view that shows how the relationship between intensity, effectiveness, and / or side effects evolves over different time scales (e.g., hours, days, weeks). This temporal mapping can guide long-term optimization strategies and help identify cyclical patterns in therapy response.

[0096] FIG. 6 illustrates an example second method 600 for graphically representing information about a seizure therapy and a patient response to the seizure therapy, and optionally optimizing the therapy. Although the example of the second method 600 depicts a particular sequence of operations, the sequence may be altered without departing from the scope of the present disclosure. For example, some of the operations depicted may be performed in parallel or in a different sequence that does not materially affect the function of the second method 600. In other examples, different components of an example device or system that implements the second method 600 may perform functions at substantially the same time or in a specific sequence.

[0097] At operation 602, the second method 600 includes determining a seizure therapy intensity index. Operation 602 can include calculating a numerical value that represents the relative or absolute magnitude of the intensity of a neurostimulation therapy provided by a neurostimulation device to a patient. The seizure therapy intensity index can be determined as a function of one or more neurostimulation parameters, such as neurostimulation magnitude, pulse width, duty cycle, frequency, or other parameters.

[0098] At operation 604, the second method 600 includes determining a therapy effectiveness index. Operation 604 can include calculating a numerical value that describes the effectiveness of a therapy provided to the patient, or the patient's response to a neurostimulation therapy. The therapy effectiveness index can be determined as a function of one or more patient response indicators, such as information about seizure type, seizure frequency, seizure intensity, or the presence, absence, or severity of side effects.

[0099] At operation 606, the second method 600 includes determining a side effect index. Operation 606 can include calculating a numerical value that describes the side effect burden or load experienced by the patient in response to the neurostimulation therapy. TheDocket No. 8508.003W01 mined as a function of one or more patient response indicators or device function (e.g., battery depletion) metrics. The quantification of the side effects that contribute to the side effect index can be determined automatically using sensor data, or can be determined subjectively using information reported by the patient or a clinician. Side effects may include pain or discomfort, tingling sensations, voice changes, coughing, difficulty swallowing, mood changes, sleep disturbances, headaches, among other things.

[0100] At operation 608, the second method 600 includes providing a pictorial representation of two or more of the seizure therapy intensity index, the therapy effectiveness index, and the side effect index. Operation 608 can include or use a display device to present a visual representation of the calculated indices, allowing patients and clinicians to easily interpret the relationship between therapy intensity, effectiveness, and side effects over time. The pictorial representation can take various forms, such as line graphs, bar charts, or other visual formats that effectively communicate the relationship between the indices over time. For example, the display can show the therapy intensity index and the efficacy index on the same graph, with the x-axis representing time and the y- axis representing the respective index values. This allows for easy identification of trends and correlations between therapy intensity and effectiveness.

[0101] At operation 610, the second method 600 includes providing indications of actual or predicted seizure events together with the pictorial representation that is provided at operation 608. Operation 608 can include overlaying information about seizure events on the visual representation of the one or more indices. Actual seizure events can be indicated based on patient reports or automatic detection by the neurostimulation device or associated sensors. Predicted seizure events can be determined using algorithms (e.g., optionally including machine learning algorithms) that analyze the physiological parameter information and neurostimulation parameter information to identify patterns and correlations associated with seizure onset. In some examples, operation 610 includes displaying both actual and predicted seizure events alongside the therapy indices.

[0102] At operation 612, the second method 600 includes determining an optimized therapy protocol for a subsequent seizure therapy. Operation 612 can include using the information gathered and displayed in the previous operations to make data-driven decisions about adjusting the neurostimulation therapy parameters. The optimization process can involve several steps, including analyzing the relationship between the therapy intensity index and the therapy effectiveness index to identify the optimal intensity range where effectiveness is maximized; considering the side effect index to ensure that the therapyDocket No. 8508.003W01 a tolerable range for the patient; evaluating the timing and frequency of actual seizure events in relation to therapy adjustments; incorporating predictions of future seizure events to proactively adjust therapy parameters; and optionally using machine learning algorithms to identify subtle patterns and correlations in the data that may not be apparent through visual inspection alone. In an example, the optimized therapy protocol may involve adjustments to one or more neurostimulation parameters, such as the electrostimulation magnitude, pulse width, duty cycle, or frequency. These adjustments aim to increase the therapy effectiveness while minimizing side effects and maximizing device longevity.

[0103] FIG. 7 illustrates generally an example of a machine in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein, according to an example embodiment. FIG. 7 is a diagrammatic representation of a machine 700 within which instructions 708 (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine 700 to perform any one or more of the methodologies discussed herein may be executed.

[0104] In an example, the instructions 708 may cause the machine 700 to execute any one or more of the methods, controls, therapy algorithms, signal generation routines, or other processes described herein. The instructions 708 transform the general, non-programmed machine 700 into a particular machine 700 programmed to carry out the described and illustrated functions in the manner described, such as can include but are not limited to automated therapy adjustments. The machine 700 may operate as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine 700 may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine 700 can comprise, but is not limited to, various systems or devices that can include components of, or can communicate with components of, the system 100, such as can include a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a PDA, an entertainment media system, a cellular telephone, a smart phone, a mobile device, a wearable device (e.g., a smart watch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions 708, sequentially or otherwise, that specify actions to be taken by the machine 700. Further, while only a single machine 700 isDocket No. 8508.003W01 chine” shall also be taken to include a collection of machines that individually or jointly execute the instructions 708 to perform any one or more of the methodologies discussed herein.

[0105] The machine 700 may include processors 702, memory 704, and I / O components 742, which may be configured to communicate with each other via a bus 744. In an example embodiment, the processors 702 (e.g., a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) processor, a Complex Instruction Set Computing (CISC) processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an ASIC, a Radio-Frequency Integrated Circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor 706 and a processor 710 that execute the instructions 708. The term “processor” is intended to optionally include multi-core processors that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions contemporaneously. Although FIG. 7 shows multiple processors 702, the machine 700 may include a single processor with a single core, a single processor with multiple cores (e.g., a multi-core processor), multiple processors with a single core, multiple processors with multiples cores, or any combination thereof.

[0106] The memory 704 includes a main memory 712, a static memory 714, and a storage unit 716, both accessible to the processors 702 via the bus 744. The main memory 704, the static memory 714, and storage unit 716 store the instructions 708 embodying any one or more of the methodologies or functions described herein. The instructions 708 may also reside, completely or partially, within the main memory 712, within the static memory 714, within a machine-readable medium 718 within the storage unit 716, within at least one of the processors 702 (e.g., within the processor’s cache memory), or any suitable combination thereof, during execution thereof by the machine 700.

[0107] The I / O components 742 may include a variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I / O components 742 that are included in a particular machine will depend on the type of machine. For example, portable machines such as device programmers or mobile phones may include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I / O components 742 may include other components that are not shown in FIG. 7. In various example embodiments, the I / O components 742 may include output components 728 and input components 730. The output components 728 may include pictorial, graphical, or visual components (e.g., a display such as a plasmaDocket No. 8508.003W01 ght emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)) such as can be used to provide the first example interface 202, the third example interface 402, or other interfaces that can be configured to display therapy intensity and therapy effectiveness metrics. The output components 728 can include acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth. The input components 730 may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point-based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or another pointing instrument), tactile input components (e.g., a physical button, a touch screen that provides location and / or force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), physiologic sensor components, and the like.

[0108] In further example embodiments, the I / O components 742 may include biometric components 732, motion components 734, environmental components 736, or position components 738, among others. For example, the biometric components 732 can include components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye tracking), measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves), identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram-based identification), and the like. The motion components 734 can include an acceleration sensor (e.g., an accelerometer), gravitation sensor components, rotation sensor components (e.g., a gyroscope), or similar. The environmental components 736 can include, for example, illumination sensor components (e.g., photometer), temperature sensor components (e.g., one or more thermometers that detect ambient temperature), humidity sensor components, pressure sensor components (e.g., barometer), acoustic sensor components (e.g., one or more microphones that detect background noise), proximity sensor components (e.g., infrared sensors that detect nearby objects), gas sensors (e.g., gas detection sensors to detection concentrations of hazardous gases for safety or to measure pollutants in the atmosphere), or other components that may provide indications, measurements, or signals corresponding to a surrounding physical environment, such as may contribute to the onset of seizures. The position components 738 can include location sensor components (e.g., a GPS receiver component), altitude sensor components (e.g., altimetersDocket No. 8508.003W01 air pressure from which altitude may be derived), orientation sensor components (e.g., magnetometers), and the like.

[0109] Communication may be implemented using a wide variety of technologies. The I / O components 742 further include communication components 740 operable to couple the machine 700 to a network 720 or other devices 722 via a coupling 724 and a coupling 726, respectively. For example, the communication components 740 may include a network interface component or another suitable device to interface with the network 720. In further examples, the communication components 740 may include wired communication components, wireless communication components, cellular communication components, Near Field Communication (NFC) components, Bluetooth components, or Wi-Fi components, among others. The devices 722 may be another machine or any of a wide variety of peripheral devices such as can include other implantable or external devices.

[0110] The various memories (e.g., memory 704, main memory 712, static memory 714, and / or memory of the processors 702) and / or storage unit 716 can store one or more sets of instructions and data structures (e.g., software) embodying or used by any one or more of the methodologies or functions described herein. These instructions (e.g., the instructions 708), when executed by processors 702, cause various operations to implement the disclosed embodiments, including various neuromodulation or neurostimulation therapies or functions supportive thereof.[OHl] The following Examples provide a non-limiting overview of, among other things, the therapy, therapy response visualization, and therapy optimization techniques discussed herein.

[0112] Example 1 is a system comprising: an interface device including a processor circuit and a display; wherein the processor circuit is configured to: determine a seizure therapy intensity index for a first neurostimulation therapy provided to a patient, wherein the seizure therapy intensity index is based on one or more parameters that define the first neurostimulation therapy; and determine a therapy effectiveness index corresponding to the seizure therapy intensity index. In Example 1, the display is configured to provide a dynamic pictorial representation of the seizure therapy intensity index together with the therapy effectiveness index over time. In an example, a dynamic pictorial representation includes a substantially real-time, continuously-updating pictorial representation. The display can show the seizure therapy intensity index and therapy effectiveness index (among other metrics or indicies) as they change over time, providing a live, interactive visualization of therapy parameters and outcomes. For example, this can include or use real-Docket No. 8508.003W01 tie most current data, a time-based representation showing the evolution of indices, interactive elements allowing user engagement, showing patient responsiveness to therapy changes, and incorporation of new data as it becomes available. This dynamic representation enables clinicians and patients to observe immediate and longterm effects of therapy adjustments, facilitating informed decision-making and rapid optimization of treatment parameters.

[0113] In Example 2, the subject matter of Example 1 optionally includes a neurostimulation device configured to deliver electrical stimulation to a neural target of the patient; wherein the processor circuit is further configured to: receive real-time stimulation data from the neurostimulation device; analyze the real-time stimulation data using a machine learning algorithm to determine the seizure therapy intensity index; analyze patient response data using the machine learning algorithm to determine the therapy effectiveness index; generate an optimized therapy protocol by automatically adjusting the one or more parameters that define the first neurostimulation therapy based on the determined seizure therapy intensity index and therapy effectiveness index; and transmit the optimized therapy protocol to the neurostimulation device for implementation; wherein the display is configured to provide a dynamic, interactive pictorial representation of the seizure therapy intensity index together with the therapy effectiveness index over time to provide real-time visualization of therapy optimization and facilitate guided adjustments to the optimized therapy protocol.

[0114] In Example 3, the subject matter of any one or more of Examples 1-2 optionally includes the interface device comprises an input configured to receive a user command to update or adjust the one or more parameters that define the first neurostimulation therapy.

[0115] In Example 4, the subject matter of any one or more of Examples 1-3 optionally includes the processor circuit is configured to determine a side effect index for the patient in response to the first neurostimulation therapy being provided to the patient, and wherein the display is configured to provide a pictorial representation of the side effect index together with the pictorial representation of the seizure therapy intensity index and the therapy effectiveness index.

[0116] In Example 5, the subject matter of Example 4 optionally includes an implantable vagus nerve stimulation device configured to provide the first neurostimulation therapy to a vagus nerve of the patient.

[0117] In Example 6, the subject matter of Example 5 optionally includes the processor circuit is configured to: cause the implantable vagus nerve stimulation device to provide aDocket No. 8508.003W01 therapy to the patient in response to the determined therapy effectiveness index being less than a specified threshold therapy effectiveness amount, wherein an intensity of the second neurostimulation therapy exceeds an intensity of the first neurostimulation therapy; and cause the implantable vagus nerve stimulation device to provide a third neurostimulation therapy to the patient in response to the determined therapy effectiveness meeting or exceeding the specified threshold therapy effectiveness amount, wherein an intensity of the third neurostimulation therapy is equal to or less than an intensity of the first neurostimulation therapy.

[0118] In Example 7, the subject matter of any one or more of Examples 5-6 optionally includes the implantable vagus nerve stimulation device comprises one or more physiologic sensors (e.g., including but not limited to an accelerometer) configured to sense one or more respective physiologic status-indicating signals from the patient.

[0119] In Example 8, the subject matter of Example 7 optionally includes the processor circuit is configured to determine the side effect index using the one or more respective physiologic status-indicating signals from the patient.

[0120] In Example 9, the subject matter of any one or more of Examples 1-8 optionally includes the processor circuit is configured to receive an indication of a seizure event experienced by the patient; and wherein the display is configured to provide a visual indication of a timing of the seizure event together with the pictorial representation of the seizure therapy intensity index and the therapy effectiveness index.

[0121] In an example, the subject matter of any one or more of Examples 1-9 optionally includes the display is further configured to provide a heat map visualization showing a distribution of therapy intensities and efficacy outcomes across a patient population, and a highlighted data point representing the patient's seizure therapy intensity index and therapy effectiveness index relative to the patient population distribution.

[0122] In an example, the subject matter of any one or more of Examples 1-9 optionally includes the display is further configured to provide a visual representation of the seizure therapy intensity index and the therapy effectiveness index for a population of patients. The visual representation can include, for example, a shaded area representing a range of values for the seizure therapy intensity index and the therapy effectiveness index observed across the patient population.

[0123] Example 10 is an interface device comprising: a display comprising: physiological parameter information for a patient corresponding to a first time interval; at least one seizure indication provided in visual correspondence with at least a portion of theDocket No. 8508.003W01 information; neurostimulation parameter information about a neurostimulation therapy provided to the patient during the first time interval, wherein the neurostimulation parameter information optionally includes an indication of an intensity of the neurostimulation therapy over the first time interval; and at least one neurostimulation parameter change indication provided in correspondence with at least a portion of the neurostimulation parameter information.

[0124] In Example 11, the subject matter of Example 10 optionally includes a processor circuit configured to: analyze the physiological parameter information and neurostimulation parameter information using a machine learning algorithm to identify correlations between therapy adjustments and patient responses; generate an optimized therapy protocol by automatically adjusting neurostimulation parameters based on the identified correlations; predict future seizure events based on real-time analysis of the physiological parameter information; and based on the predicted future seizure events, provide an alert and / or a therapy adjustment recommendation to a patient or clinician using the display device.

[0125] In Example 12, the subject matter of Example 11 optionally includes the display is further configured to provide: an interactive therapy adjustment interface that enables realtime changes to the neurostimulation therapy in response to the predicted seizure events; a dynamic visualization of the correlations between therapy adjustments and patient responses; and a real-time representation of seizure risk based on the physiological parameter information and current neurostimulation parameters.

[0126] In Example 13, the subject matter of any one or more of Examples 10-12 optionally includes the physiological parameter information includes information about one or more of an ECG, heart rate, heart rate variability, patient activity level, patient posture, and detected seizure event.

[0127] In Example 14, the subject matter of any one or more of Examples 10-13 optionally includes the display includes a predictive seizure event indicator that indicates a likelihood of an actual or imminent seizure event.

[0128] In Example 15, the subject matter of Example 14 optionally includes the interface device comprises a processor circuit configured to determine the predictive seizure event indicator based on the physiologic parameter information.

[0129] In Example 16, the subject matter of Example 15 optionally includes a processor circuit configured to determine the indication of the intensity of the neurostimulation therapy based on the neurostimulation parameter information.Docket No. 8508.003W01 the subject matter of Example 16 optionally includes the neurostimulation parameter information is received from, or provided to, an implantable vagus nerve stimulation device.

[0131] In Example 18, the subject matter of any one or more of Examples 10-17 optionally includes the neurostimulation parameter information includes information about one or more of a neurostimulation signal amplitude, pulse width, duty cycle, frequency, or duration of a therapy event, or includes information about a composite value of stimulation intensity that is based on two or more of the neurostimulation signal amplitude, pulse width, duty cycle, frequency, or duration of the therapy event.

[0132] In Example 19, the subject matter of any one or more of Examples 10-18 optionally includes the physiologic parameter information and the neurostimulation parameter information is graphically represented over time.

[0133] Example 20 is an interface device comprising: a display comprising: neurostimulation parameter information about a neurostimulation therapy provided to a patient during a first time interval, wherein the neurostimulation parameter information optionally includes an indication of an intensity of the neurostimulation therapy; and neurostimulation therapy effectiveness information about a patient response to the neurostimulation therapy.

[0134] In Example 21, the subject matter of Example 20 optionally includes a processor circuit configured to determine the indication of the intensity of the neurostimulation therapy based on two or more of an amplitude, frequency, pulse width, duty cycle, or duration of the neurostimulation therapy.

[0135] In Example 22, the subject matter of Example 21 optionally includes the processor circuit is configured to determine the neurostimulation therapy effectiveness based on a seizure event rate at or following the first time interval.

[0136] In Example 23, the subject matter of any one or more of Examples 21-22 optionally includes the processor circuit is configured to determine the neurostimulation therapy effectiveness based on a seizure event type or seizure event severity at or following the first time interval.

[0137] Example 24 is a system comprising: an implantable neurostimulation device configured to provide a seizure therapy to a patient; a user interface including a display and a user input; and a processor circuit configured to: determine an intensity of the seizure therapy provided to a patient by the implantable neurostimulation device; determine an effectiveness of the provided seizure therapy; and generate a graphical user interfaceDocket No. 8508.003W01 display of the determined intensity and the determined effectiveness; wherein the display is configured to display the generated graphical user interface; and wherein the processor circuit is further configured to receive an instruction, from the user input and in response to the displayed graphical user interface, to update or adjust a parameter of the seizure therapy and, in response to the received instruction, command the implantable neurostimulation device to provide a different second seizure therapy to the patient.

[0138] In Example 25, the subject matter of Example 24 optionally includes the processor circuit is configured to determine the intensity of the seizure therapy based on one or more parameters of the seizure therapy.

[0139] In Example 26, the subject matter of Example 25 optionally includes the processor circuit is configured to determine the effectiveness of the provided seizure therapy based on sensed physiologic status information from the patient or based on a information from the patient received using the user input.

[0140] Example 27 is a device comprising a display configured to: concurrently present a first visual representation of an intensity of neurostimulation therapy delivered to a patient and a second visual representation of an effectiveness of the delivered neurostimulation therapy; and update the first and second visual representations in response to adjusted delivery of the neurostimulation therapy to the patient.

[0141] In Example 28, the subject matter of Example 27 optionally includes a processor configured to determine the first visual representation of the intensity of the neurostimulation therapy based on one or more of an electrostimulation magnitude, pulse width, duty cycle, and frequency of the neurostimulation therapy.

[0142] In Example 29, the subject matter of Example 28 optionally includes a processor configured to determine the second visual representation of the effectiveness of the delivered neurostimulation therapy based on a sensed or patient-reported response to the neurostimulation therapy.

[0143] In Example 30, the subject matter of any one or more of Examples 28-29 optionally includes a processor configured to determine the second visual representation of the effectiveness of the delivered neurostimulation therapy based on seizure event frequency or severity.

[0144] Example 31 is a method comprising: providing, to a patient, a first seizure therapy at a first therapy intensity, wherein the first therapy intensity is a function of an electrostimulation magnitude, pulse width, duty cycle, or frequency; following an onset ofDocket No. 8508.003W01 e therapy, determining a therapy effectiveness for the first seizure therapy; and providing, using a display device, a pictorial representation of the first therapy intensity over a first time duration in visual correspondence with a pictorial representation of the determined therapy effectiveness over the same first time duration.

[0145] In Example 32, the subject matter of Example 31 optionally includes automatically adjusting, using a processor circuit, the first seizure therapy based on an analysis of the first therapy intensity and the determined therapy effectiveness, wherein the adjustment optimizes the first seizure therapy by increasing the therapy effectiveness while minimizing the therapy intensity.

[0146] In Example 33, the subject matter of any one or more of Examples 31-32 optionally includes the first therapy intensity indicates a relative amount of a neurostimulation therapy received by the patient, and wherein determining the therapy effectiveness includes determining a relative efficacy of the neurostimulation therapy in reducing a frequency or intensity of seizures experienced by the patient.

[0147] In Example 34, the subject matter of Example 33 optionally includes the first therapy intensity is a time-varying metric over the first time duration, and wherein the determined therapy effectiveness is a separate time-varying metric over the same first time duration.

[0148] In Example 35, the subject matter of any one or more of Examples 31-34 optionally includes following the onset of providing the first seizure therapy, determining a side effect index for the patient in response to the first seizure therapy; and providing, using the display device, a pictorial representation of the side effect index over the first time duration in visual correspondence with at least one of the pictorial representation of the first therapy intensity and the pictorial representation of the determined therapy effectiveness.

[0149] In Example 36, the subject matter of Example 35 optionally includes determining the side effect index includes determining a relative severity of one or more side effects experienced by the patient in response to, or during, the first seizure therapy.

[0150] In Example 37, the subject matter of Example 36 optionally includes determining the side effect index includes using information from one or more sensors configured to receive information about a physiologic status of the patient.

[0151] In Example 38, the subject matter of Example 37 optionally includes providing the first seizure therapy includes using an implanted vagus nerve stimulation system to provide a neurostimulation signal to a vagus nerve of the patient, and wherein the one or more sensors are implantable sensors coupled to the vagus nerve stimulation system.Docket No. 8508.003W01 the subject matter of any one or more of Examples 31-38 optionally includes in response to the determined therapy effectiveness being less than a specified threshold therapy effectiveness amount, providing a second seizure therapy to the patient using a second therapy intensity that is greater than the first therapy intensity; and in response to the determined therapy effectiveness meeting or exceeding the threshold therapy effectiveness amount, providing a third seizure therapy to the patient using a third therapy intensity that is equal to or less than the first therapy intensity.

[0153] In Example 40, the subject matter of any one or more of Examples 31-39 optionally includes the pictorial representation of the first therapy intensity and the pictorial representation of the determined therapy effectiveness are aligned relative to a shared time axis.

[0154] In Example 41, the subject matter of any one or more of Examples 31-40 optionally includes the first therapy intensity is a function of the electrostimulation magnitude and pulse width.

[0155] In Example 42, the subject matter of any one or more of Examples 31-41 optionally includes the first therapy intensity is a function of the electrostimulation magnitude, pulse width, duty cycle, and frequency.

[0156] In Example 43, the subject matter of any one or more of Examples 31-42 optionally includes determining the therapy effectiveness includes using information about a patient response to the first seizure therapy.

[0157] In Example 44, the subject matter of Example 43 optionally includes the patient response includes information about a seizure event type.

[0158] In Example 45, the subject matter of any one or more of Examples 43-44 optionally includes the patient response includes information about a seizure event frequency or number of seizure events during a specified time interval.

[0159] In Example 46, the subject matter of any one or more of Examples 43-45 optionally includes the patient response includes information about a seizure event severity.

[0160] In Example 47, the subject matter of Example 46 optionally includes the seizure event severity is based on at least one of a seizure event duration and a seizure recovery duration.

[0161] Example 48 is at least one machine-readable medium including instructions that, when executed by processing circuitry, cause the processing circuitry to perform operations to implement of any of Examples 1-47.Docket No. 8508.003W01 n apparatus comprising means to implement of any of Examples 1-47.

[0163] Example 50 is a system to implement of any of Examples 1-47.

[0164] Each of these non-limiting examples or embodiments can stand on its own or can be combined in various permutations or combinations with one or more of the other examples or embodiments.

[0165] This detailed description includes references to the accompanying drawings, which form a part of the detailed description. The drawings show, by way of illustration, specific embodiments in which the invention can be practiced. These embodiments are also referred to herein as “examples.” Such examples can include elements in addition to those shown or described. However, the present inventors also contemplate examples in which only those elements shown or described are provided. The present inventors contemplate examples using any combination or permutation of those elements shown or described (or one or more aspects thereof), either with respect to a particular example (or one or more aspects thereof), or with respect to other examples (or one or more aspects thereof) shown or described herein.

[0166] In this document, the terms “a” or “an” are used, as is common in patent documents, to include one or more than one, independent of any other instances or usages of “at least one” or “one or more.” In this document, the term “or” is used to refer to a nonexclusive or, such that “A or B” includes “A but not B,” “B but not A,” and “A and B,” unless otherwise indicated. In this document, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.”

[0167] In the following claims, the terms “including” and “comprising” are open-ended, that is, a system, device, article, composition, formulation, or process that includes elements in addition to those listed after such a term in a claim are still deemed to fall within the scope of that claim. Moreover, in the following claims, the terms “first,” “second,” and “third,” etc. are used merely as labels, and are not intended to impose numerical requirements on their objects.

[0168] Method examples described herein can be machine or computer-implemented at least in part. Some examples can include a computer-readable medium or machine-readable medium encoded with instructions operable to configure an electronic device to perform methods as described in the above examples. An implementation of such methods can include code, such as microcode, assembly language code, a higher-level language code, or the like. Such code can include computer readable instructions for performing variousDocket No. 8508.003W01 form portions of computer program products. Such instructions can be read and executed by one or more processors to enable performance of operations comprising a method, for example. The instructions are in any suitable form, such as but not limited to source code, compiled code, interpreted code, executable code, static code, dynamic code, and the like.

[0169] Further, in an example, the code can be tangibly stored on one or more volatile, non-transitory, or non-volatile tangible computer-readable media, such as during execution or at other times. Examples of these tangible computer-readable media can include, but are not limited to, hard disks, removable magnetic disks, removable optical disks (e.g., compact disks and digital video disks), magnetic cassettes, memory cards or sticks, random access memories (RAMs), read only memories (ROMs), and the like.

[0170] The above description is intended to be illustrative, and not restrictive. For example, the above-described examples (or one or more aspects thereof) may be used in combination with each other. Other embodiments can be used, such as by one of ordinary skill in the art upon reviewing the above description. Also, in the above Detailed Description, various features may be grouped together to streamline the disclosure. This should not be interpreted as intending that an unclaimed disclosed feature is essential to any claim. Rather, inventive subject matter may lie in less than all features of a particular disclosed embodiment. Thus, the following statements (aspects) are hereby incorporated into the Detailed Description as examples or embodiments, with each standing on its own as a separate embodiment, and it is contemplated that such embodiments can be combined with each other in various combinations or permutations.

Claims

CLAIMSWhat is claimed is:

1. A system comprising: an interface device including a processor circuit and a display; wherein the processor circuit is configured to: determine a seizure therapy intensity index for a first neurostimulation therapy provided to a patient, wherein the seizure therapy intensity index is based on one or more parameters that define the first neurostimulation therapy; and determine a therapy effectiveness index corresponding to the seizure therapy intensity index; and wherein the display is configured to provide a dynamic pictorial representation of the seizure therapy intensity index together with the therapy effectiveness index over time.

2. The system of claim 1, comprising; a neurostimulation device configured to deliver electrical stimulation to a neural target of the patient; wherein the processor circuit is further configured to: receive real-time stimulation data from the neurostimulation device; analyze the real-time stimulation data using a machine learning algorithm to determine the seizure therapy intensity index; analyze patient response data using the machine learning algorithm to determine the therapy effectiveness index; generate an optimized therapy protocol by automatically adjusting the one or more parameters that define the first neurostimulation therapy based on the determined seizure therapy intensity index and therapy effectiveness index; and transmit the optimized therapy protocol to the neurostimulation device for implementation; wherein the display is configured to provide a dynamic, interactive pictorial representation of the seizure therapy intensity index together with the therapy effectiveness index over time to provide real-time visualization of therapy optimization and facilitate guided adjustments to the optimized therapy protocol.

3. The system of claim 1, wherein the interface device comprises an input configured to receive a user command to update or adjust the one or more parameters that define the first neurostimulation therapy.

4. The system of claim 1, wherein the processor circuit is configured to determine a side effect index for the patient in response to the first neurostimulation therapy being provided to the patient, and wherein the display is configured to provide a pictorial representation of the side effect index together with the pictorial representation of the seizure therapy intensity index and the therapy effectiveness index.

5. The system of claim 4, comprising an implantable vagus nerve stimulation device configured to provide the first neurostimulation therapy to a vagus nerve of the patient.

6. The system of claim 5, wherein the processor circuit is configured to: cause the implantable vagus nerve stimulation device to provide a second neurostimulation therapy to the patient in response to the determined therapy effectiveness index being less than a specified threshold therapy effectiveness amount, wherein an intensity of the second neurostimulation therapy exceeds an intensity of the first neurostimulation therapy; and cause the implantable vagus nerve stimulation device to provide a third neurostimulation therapy to the patient in response to the determined therapy effectiveness meeting or exceeding the specified threshold therapy effectiveness amount, wherein an intensity of the third neurostimulation therapy is equal to or less than an intensity of the first neurostimulation therapy.

7. The system of claim 5, wherein the implantable vagus nerve stimulation device comprises one or more physiologic sensors configured to sense one or more respective physiologic statusindicating signals from the patient.

8. The system of claim 7, wherein the processor circuit is configured to determine the side effect index using the one or more respective physiologic status-indicating signals from the patient.

9. The system of claim 1, wherein the processor circuit is configured to receive an indication of a seizure event experienced by the patient; andwherein the display is configured to provide a visual indication of a timing of the seizure event together with the pictorial representation of the seizure therapy intensity index and the therapy effectiveness index.

10. The system of claim 1, wherein the display is further configured to provide a heat map visualization showing a distribution of therapy intensities and efficacy outcomes across a patient population, and a highlighted data point representing the patient's seizure therapy intensity index and therapy effectiveness index relative to the patient population distribution.

11. The system of claim 1, wherein the display is further configured to provide a visual representation of the seizure therapy intensity index and the therapy effectiveness index for a population of patients.

12. The system of claim 11, wherein the visual representation includes a shaded area representing a range of values for the seizure therapy intensity index and the therapy effectiveness index observed across the patient population.

13. An interface device comprising: a display comprising: physiological parameter information for a patient corresponding to a first time interval; at least one seizure indication provided in visual correspondence with at least a portion of the physiological parameter information; neurostimulation parameter information about a neurostimulation therapy provided to the patient during the first time interval, wherein the neurostimulation parameter information includes an indication of an intensity of the neurostimulation therapy over the first time interval; and at least one neurostimulation parameter change indication provided in correspondence with at least a portion of the neurostimulation parameter information.

14. The interface device of claim 13, comprising: a processor circuit configured to:analyze the physiological parameter information and neurostimulation parameter information using a machine learning algorithm to identify correlations between therapy adjustments and patient responses; generate an optimized therapy protocol by automatically adjusting neurostimulation parameters based on the identified correlations; predict future seizure events based on real-time analysis of the physiological parameter information; and based on the predicted future seizure events, provide an alert and / or a therapy adjustment recommendation to a patient or clinician using the display device.

15. The interface device of claim 14, wherein the display is further configured to provide: an interactive therapy adjustment interface that enables real-time changes to the neurostimulation therapy in response to the predicted seizure events; a dynamic visualization of the correlations between therapy adjustments and patient responses; and a real-time representation of seizure risk based on the physiological parameter information and current neurostimulation parameters.

16. The interface device of claim 13, wherein the physiological parameter information includes information about one or more of an ECG, heart rate, heart rate variability, patient activity level, patient posture, and detected seizure event.

17. The interface device of claim 13, wherein the display includes a predictive seizure event indicator that indicates a likelihood of an actual or imminent seizure event.

18. The interface device of claim 17, wherein the interface device comprises a processor circuit configured to determine the predictive seizure event indicator based on the physiologic parameter information.

19. The interface device of claim 18, comprising a processor circuit configured to determine the indication of the intensity of the neurostimulation therapy based on the neurostimulation parameter information.

20. The interface device of claim 19, wherein the neurostimulation parameter information is received from, or provided to, an implantable vagus nerve stimulation device.

21. The interface device of claim 13, wherein the neurostimulation parameter information includes information about one or more of a neurostimulation signal amplitude, pulse width, duty cycle, frequency, or duration of a therapy event, or includes information about a composite value of stimulation intensity that is based on two or more of the neurostimulation signal amplitude, pulse width, duty cycle, frequency, or duration of the therapy event.

22. The interface device of claim 13, wherein the physiologic parameter information and the neurostimulation parameter information is graphically represented over time.

23. An interface device comprising: a display comprising: neurostimulation parameter information about a neurostimulation therapy provided to a patient during a first time interval, wherein the neurostimulation parameter information includes an indication of an intensity of the neurostimulation therapy; and neurostimulation therapy effectiveness information about a patient response to the neurostimulation therapy.

24. The interface device of claim 23, further comprising a processor circuit configured to determine the indication of the intensity of the neurostimulation therapy based on two or more of an amplitude, frequency, pulse width, duty cycle, or duration of the neurostimulation therapy.

25. The interface device of claim 24, wherein the processor circuit is configured to determine the neurostimulation therapy effectiveness based on a seizure event rate at or following the first time interval.

26. The interface device of claim 24, wherein the processor circuit is configured to determine the neurostimulation therapy effectiveness based on a seizure event type or seizure event severity at or following the first time interval.

27. A system comprising: an implantable neurostimulation device configured to provide a seizure therapy to a patient; a user interface including a display and a user input; and a processor circuit configured to:determine an intensity of the seizure therapy provided to a patient by the implantable neurostimulation device; determine an effectiveness of the provided seizure therapy; and generate a graphical user interface comprising a concurrent display of the determined intensity and the determined effectiveness; wherein the display is configured to display the generated graphical user interface; and wherein the processor circuit is further configured to receive an instruction, from the user input and in response to the displayed graphical user interface, to update or adjust a parameter of the seizure therapy and, in response to the received instruction, command the implantable neurostimulation device to provide a different second seizure therapy to the patient.

28. The system of claim 27, wherein the processor circuit is configured to determine the intensity of the seizure therapy based on one or more parameters of the seizure therapy.

29. The system of claim 28, wherein the processor circuit is configured to determine the effectiveness of the provided seizure therapy based on sensed physiologic status information from the patient or based on a information from the patient received using the user input.

30. A device comprising a display configured to: concurrently present a first visual representation of an intensity of neurostimulation therapy delivered to a patient and a second visual representation of an effectiveness of the delivered neurostimulation therapy; and update the first and second visual representations in response to adjusted delivery of the neurostimulation therapy to the patient.

31. The device of claim 30, comprising a processor configured to determine the first visual representation of the intensity of the neurostimulation therapy based on one or more of an electrostimulation magnitude, pulse width, duty cycle, and frequency of the neurostimulation therapy.

32. The device of claim 31, comprising a processor configured to determine the second visual representation of the effectiveness of the delivered neurostimulation therapy based on a sensed or patient-reported response to the neurostimulation therapy.

33. The device of claim 31, comprising a processor configured to determine the second visual representation of the effectiveness of the delivered neurostimulation therapy based on seizure event frequency or severity.

34. A method comprising: providing, to a patient, a first seizure therapy at a first therapy intensity, wherein the first therapy intensity is a function of an electrostimulation magnitude, pulse width, duty cycle, or frequency; following an onset of providing the first seizure therapy, determining a therapy effectiveness for the first seizure therapy; and providing, using a display device, a pictorial representation of the first therapy intensity over a first time duration in visual correspondence with a pictorial representation of the determined therapy effectiveness over the same first time duration.

35. The method of claim 34, comprising automatically adjusting, using a processor circuit, the first seizure therapy based on an analysis of the first therapy intensity and the determined therapy effectiveness, wherein the adjustment optimizes the first seizure therapy by increasing the therapy effectiveness while minimizing the therapy intensity.

36. The method of claim 34, wherein the first therapy intensity indicates a relative amount of a neurostimulation therapy received by the patient, and wherein determining the therapy effectiveness includes determining a relative efficacy of the neurostimulation therapy in reducing a frequency or intensity of seizures experienced by the patient.

37. The method of claim 36, wherein the first therapy intensity is a time-varying metric over the first time duration, and wherein the determined therapy effectiveness is a separate timevarying metric over the same first time duration.

38. The method of claim 34, comprising: following the onset of providing the first seizure therapy, determining a side effect index for the patient in response to the first seizure therapy; and providing, using the display device, a pictorial representation of the side effect index over the first time duration in visual correspondence with at least one of the pictorialrepresentation of the first therapy intensity and the pictorial representation of the determined therapy effectiveness.

39. The method of claim 38, wherein determining the side effect index includes determining a relative severity of one or more side effects experienced by the patient in response to, or during, the first seizure therapy.

40. The method of claim 39, wherein determining the side effect index includes using information from one or more sensors configured to receive information about a physiologic status of the patient.

41. The method of claim 40, wherein providing the first seizure therapy includes using an implanted vagus nerve stimulation system to provide a neurostimulation signal to a vagus nerve of the patient, and wherein the one or more sensors are implantable sensors coupled to the vagus nerve stimulation system.

42. The method of claim 34, comprising: in response to the determined therapy effectiveness being less than a specified threshold therapy effectiveness amount, providing a second seizure therapy to the patient using a second therapy intensity that is greater than the first therapy intensity; and in response to the determined therapy effectiveness meeting or exceeding the threshold therapy effectiveness amount, providing a third seizure therapy to the patient using a third therapy intensity that is equal to or less than the first therapy intensity.

43. The method of claim 34, wherein the pictorial representation of the first therapy intensity and the pictorial representation of the determined therapy effectiveness are aligned relative to a shared time axis.

44. The method of claim 34, wherein the first therapy intensity is a function of the electrostimulation magnitude and pulse width.

45. The method of claim 34, wherein the first therapy intensity is a function of the electrostimulation magnitude, pulse width, duty cycle, and frequency.

46. The method of claim 34, wherein determining the therapy effectiveness includes using information about a patient response to the first seizure therapy.

47. The method of claim 46, wherein the patient response includes information about a seizure event type.

48. The method of claim 46, wherein the patient response includes information about a seizure event frequency or number of seizure events during a specified time interval.

49. The method of claim 46, wherein the patient response includes information about a seizure event severity.

50. The method of claim 49, wherein the seizure event severity is based on at least one of a seizure event duration and a seizure recovery duration.

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