Digital assistant for seizure or depression status monitoring and therapy

Abstract

A patient management system can include an implantable neurostimulation device configured to deliver vagus nerve stimulation (VNS) therapy to a patient and a non-invasive, external monitoring device comprising at least one sensor for detecting indicators of seizure or depression in the patient. In an example, the external monitoring device is configured to use information from the at least one sensor to detect a seizure event or a seizure precursor event, audibly ask questions of the patient and / or provide behavioral modification commands to the patient, and / or control the neurostimulation device to deliver the VNS therapy to the patient in response to a detected seizure event, seizure precursor event, or progression of depression symptoms.

Description

DIGITAL ASSISTANT FOR SEIZURE OR DEPRESSION STATUS MONITORING AND THERAPYCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application is related to and claims priority to United States Provisional Application No. 63 / 615,117, filed on December 27, 2023, and entitled “DIGITAL ASSISTANT FOR SEIZURE OR DEPRESSION STATUS MONITORING AND THERAPY,” 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.

[0003] Clinical depression is a mood disorder characterized by persistent feelings of sadness, hopelessness, and loss of interest that can disrupt daily functioning. Depression is typically treated first with antidepressant medications and psychotherapy. If medications are not effective, then other options may be explored such as nerve or brain stimulation therapy. Stimulation therapies for depression include electroconvulsive therapy (ECT), vagus nerve stimulation (VNS), transcranial magnetic stimulation (TMS), and deep brain stimulation (DBS). ECT involves sending electric currents through the brain to trigger a brief seizure, under anesthesia. It can be effective for severe depression not responsive to medications. VNS uses an implanted device to send electric signals to the brain via the vagus nerve. TMS is a noninvasive procedure that uses magnetic fields to stimulate nerve cells in the brain. DBS uses surgically implanted electrodes that provideelectric currents to target areas involved in mood regulation. When antidepressant medications and psychotherapy do not sufficiently improve depressive symptoms, neurostimulation therapies may be considered.BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0004] 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.

[0005] FIG. 1 illustrates generally an example of a neurostimulation system.

[0006] FIG. 2 illustrates generally an example of a tripolar lead assembly.

[0007] FIG. 3 illustrates generally a pictorial representation of physiological parameters and therapy parameters.

[0008] FIG. 4 illustrates generally a graphical representation of an external monitoring device for a patient.

[0009] FIG. 5 illustrates generally an example of a method that includes using an external monitoring device to monitor a patient.

[0010] FIG. 6 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.DETAILED DESCRIPTION

[0011] A vagus nerve stimulation (VNS) system can include an implantable pulse generator (IPG), a lead that attaches to the vagus nerve and the IPG, and a programmer used to program or assess the status of IPG. The implantable device can be configured to detect heart rate increase as a surrogate for a possible seizure event, and in response to detecting a specific heart rate increase, the device can provide VNS therapy to the patient. Other seizure detection techniques can similarly be used.

[0012] The present inventors have recognized that a problem to be solved includes improving seizure detection accuracy. The problem can further include providing an effective seizure therapy in response to detected seizure events, such as without unnecessarily increasing a magnitude or duration of VNS therapy. The present inventors have recognized that a solution to these and other problems can include or use a system that includes an implantable neurostimulation device and an external monitoring device. The implantable neurostimulation device and the external monitoring device can be used together to provide closed-loop therapy for seizure intervention.

[0013] In clinical seizure monitoring, patients can be observed for seizure onset, and upon commencement of a seizure, caregivers interact with patients to reduce seizure duration and intensity. This interaction often involves asking the patient questions and instructing them to perform simple tasks. The present inventors have recognized that there exists an opportunity to integrate the functions of vagus nerve stimulation and caregiver interaction into a comprehensive and automated system.

[0014] In an example, a solution according to the present disclosure includes a system that combines an IPG configured for VNS with a software application on an external monitoring device, such as a mobile phone or tablet. The external monitoring device can be equipped with a camera, microphone, or one or more other sensors, such as an accelerometer, to monitor the patient. For instance, the device can use high-resolution imagebased analysis to monitor patient behavior, or other patient characteristics such as respiration or heart rate.

[0015] The patient can position the external monitoring device to allow its sensor(s) to monitor the patient. The external monitoring device can optionally be configured to communicate with the patient's IPG or implanted neurostimulation device. Upon detecting a seizure event, or seizure precursor event, using sensor data (e.g., using video or image recognition, sound recognition, or movement recognition), the system initiates closed- loop therapy. The therapy can involve the external monitoring device asking the patient questions, similar to a clinician’s intervention, or can includecommunicating instructions to the IPG to initiate, update, or adjust a neurostimulation therapy (e.g., a vagus nerve therapy). For example, the external monitoring device can initiate the therapy, or the IPG can determine whether to initiate therapy based on information from the external monitoring device. The therapy can be delivered at physician-directed settings that define the therapy intensity, duration, or other characteristic of the therapy.

[0016] In response to detection of a seizure event or detection of a seizure precursor event, the external monitoring device can suggest or provide behavioral modifications to soothe the patient or alleviate seizure effects. These suggestions can be delivered through the external monitoring device, the IPG, or another device. For example, the patient may be instructed to sit or lay down or to seek caregiver assistance. In an example, a haptic feedback device or haptic actuator may be configured to deliver a stimulus to the patient in response to seizure detection by the external monitoring device. The haptic feedback can include vibration patterns or other tactile signals designed to help interrupt the seizure progression or redirect the patient's attention. In an example, the external monitoring device can communicate with another interface device, such as a mobile phone or tablet of the patient, an external speaker or graphical display, etc., and such other device can be configured to deliver the instructions or feedback to the patient.

[0017] In an example, information about the seizure event or seizure precursor event (e.g., information such as sound, image, video, or physiologic status information about the patient, etc.), can be recorded in a seizure log by the external monitoring device, and these records can be transmitted to a clinician or caregiver. The data can be useful in diagnosing and understanding the patient's condition, disease progression, or individual seizure events, thereby improving future therapy decisions. The patient can also input information about the seizure event into the external monitoring device (or other patient device), which is then added to the seizure log and sent to the clinician.

[0018] Patients suffering from epilepsy may also suffer from clinical depression. The two conditions may co-occur because the chronic nature of epilepsy and dealing with uncontrolled seizures can lead to feelings of hopelessness, frustration, and mood changes that may trigger depression. Additionally, some of the same neurotransmitters and neural circuits are implicated in both epilepsy and depression. Abnormalities in serotonin, GABA, and glutamate function may contribute to both conditions.Furthermore, structural changes in the brain due to recurrent seizures may play a role in depression risk. Some patients dealing with epilepsy and clinical depression report symptoms of fatigue, insomnia, and cognitive dysfunction, which may have a compounding effect.

[0019] The present inventors have recognized that a neurostimulation device may be configured to treat epilepsy and depression in a coordinated manner. For example, the present inventors have recognized that effective therapies for epilepsy and depression can include or use vagus nerve stimulation, such as can be provided by an implantable device.

[0020] In an example, the external monitoring device can monitor the patient for behavior or mood changes, such as symptoms of depression. The IPG may be configured to provide therapy for epilepsy and depression, and the external monitoring system can be configured to recognize depression symptoms, such as excessive sleeping or lethargy. The external monitoring device can be configured to communicate mood disorder information to the patient, clinician, or caregiver. In some examples, the external monitoring device can communicate with the IPG, which can then update or adjust neurostimulation therapy parameters for a depression therapy based on information received from the external monitoring device.

[0021] The external monitoring device can be configured to detect seizure events and to provide real-time behavioral suggestions to the patient. This feature extends the therapeutic capabilities of the system beyond traditional neurostimulation. The device leverages its sensor data to generate personalized instructions that can assist the patient in mitigating the severity of an ongoing seizure or managing their mood in the case of depression.

[0022] An illustrative (but non-restrictive) example, as shown in FIG. 1, includes a system for providing neurostimulation to a vagus nerve 102, or vagus nerve stimulation (VNS). In an example, the system can be configured to sense nerve activity or other electrical activity or motion. In an example, the system includes an implantable device 116 such as can comprise a processor circuit 118 and a signal generator 120. The processor circuit 118, or control circuit, can control operation of the signal generator 120 according to various therapy delivery algorithms or therapy signal-defining parameters. The signal generator 120 can be configured to generate neurostimulation signals or pulses according to parameters or instructions from the control circuit. In an example, the signal generator 120 includes independent current sources and controllers to enable independent and simultaneous output of multiple respective therapy signals. In an example, the implantable device 116 and the processor circuit 118 are configured to execute a multiple-therapy optimization algorithm that modulates stimulation intensity for a higher priority therapy while holding a lower priority therapy at a constant reduced intensity. The algorithm helps titrate overall dosage below specified limits while maintaining some therapeutic benefit for multiple conditions.

[0023] In an example, the implantable device 116 comprises or is coupled to one or more physiologic status sensors that are configured to sense information about a patient. For example, the system can include a sensor 122. The sensor 122 can comprise a portion of the implantable device 116 or can be coupled to a lead that is coupled to the implantable device 116. In an example, the physiological status sensor can include an electrocardiogram sensor, a heart rate sensor, a blood pressure sensor, a respiratory rate sensor, a blood oxygen saturation sensor, a sleep sensor, a blood glucose sensor, an accelerometer, a vagal electroneurogram sensor, or a combination thereof. Other sensors can additionally or alternatively be used.

[0024] In an example, the system includes an external monitoring device 124 that can communicate with the implantable device 116. The external monitoring device 124 can include a patient device or clinician device that isconfigured to receive information from, or provide information to, the implantable device 116. For example, the external monitoring device 124 can be used to set one or more neurostimulation parameters for a neurostimulation therapy that is provided by the implantable device 116. In an example, the external monitoring device 124 can be used to report information to a patient or clinician about one or more therapies provided by the implantable device 116. In an example, the external monitoring device 124 includes one or more sensors that are configured to monitor physiologic or behavioral information about the patient.

[0025] In an example, the external monitoring device 124 can be used to monitor, display, initiate, modify, or control one or more therapies provided by the implantable device 116. The external monitoring device 124 can receive physiological sensor data or therapy delivery parameters, display real-time and historical patient status information, initiate new or modify existing therapeutic protocols, or otherwise control ongoing treatment and device settings.

[0026] The interaction between the external monitoring device 124 and the implantable device 116 is facilitated through a bidirectional communication link using a wireless coupling 126 that allows for the continuous exchange of data and commands between the two devices. The communication is established using wireless technology protocols that are specifically designed for medical devices, ensuring secure and reliable data transmission.

[0027] The external monitoring device 124 can include or can be coupled (e.g., wirelessly) with various sensors, including a high-resolution camera, a microphone, or an accelerometer, which can be configured to collect a wide array of physiological and environmental data, as described below. This data can include visual and audio records of the patient's movements, vocalizations, and surrounding environment. In an example, the collected data can include quantitative measurements such as detected motion patterns and respiration rates. The collected data is then processed (e.g., at the external monitoring device 124, at the implantable device 116, or elsewhere)using advanced algorithms to identify potential seizure events, seizure precursor events, or signs or symptoms of depression, among other things.

[0028] In an example, the external monitoring device 124 can be configured to establish baseline behavioral and physiological characteristics for a patient during an initial monitoring period. During this period, the device can collect and analyze sensor data to determine the patient's typical daily patterns, including normal movement patterns, speech patterns, sleepwake cycles, and activity levels. The camera and microphone can be used, for example, to establish baseline facial expressions, vocal characteristics, and typical behavioral patterns when the patient is not experiencing seizures or depression symptoms. This baseline data can then be stored and used as a reference point against which subsequent monitoring data can be compared to detect deviations that may indicate seizure activity or changes in mood state. For example, the system can identify unusual movements or vocalizations that differ from the patient's established baseline patterns, potentially indicating seizure activity. Similarly, for depression monitoring, the system can detect gradual changes in activity levels, sleep patterns, or speech characteristics that deviate from the patient's baseline, which may signal progression of depressive symptoms. This personalized baseline approach enables the system to account for individual patient variations and provide more accurate detection of clinically significant changes in patient status.

[0029] In an example, the external monitoring device 124 implements an adaptive multi-modal detection system that dynamically adjusts the weighting and importance of different sensor inputs based on their demonstrated accuracy for each specific patient. The system maintains a historical record of detection accuracy for each sensor modality, tracking both confirmed seizure events and false positive detections. For example, if camera-based detection consistently provides more reliable seizure detection for a particular patient compared to acoustic monitoring or accelerometerbased monitoring, then the system automatically increases the weighting of visual inputs in its detection algorithms. The system can be configured to usemachine learning techniques to continuously optimize this approach. In an example, machine learning models can be configured to analyze patterns in the multi-sensor data streams, identifying which combinations of sensor inputs most accurately predict and detect seizure events for an individual patient. This approach allows the system to accommodate patient-specific variations seizure manifestation. For example, some patients may exhibit more pronounced or specific physical movements while others may demonstrate more distinctive acoustic patterns during seizure events.

[0030] In an example, an optimization process includes tracking the correlation between sensor inputs and confirmed seizure events, allowing the system to progressively refine its detection parameters. When a seizure event is confirmed (either through patient input, caregiver verification, or physiological data from the implanted device), the system analyzes the sensor data leading up to and during the event to identify the most reliable early indicators. This learning process enables the system to continuously improve its detection accuracy while reducing false positives, ultimately providing more personalized and reliable seizure monitoring for each patient.

[0031] In an example, after a potential seizure event or a depression indicator is detected, the external monitoring device 124 transmits a signal to the implantable device 116. This signal can include detailed information about the nature of the detected event, such as the time of occurrence, the duration, the detected physiological changes, and any behavioral modifications suggested to the patient. The implantable device 116, which is implanted within the patient's body and connected to the vagus nerve, receives this signal and processes it using the processor circuit 118.

[0032] In an example, the processor circuit 118 is programmed with a set of parameters that define thresholds for initiating or adjusting VNS therapy. Upon receiving the signal from the external monitoring device 124, the processor circuit 118 can be configured to analyze the data against these predefined parameters. If the data indicates that a seizure is occurring or imminent, then the implantable device 116 can automatically adjust theneurostimulation therapy parameters accordingly. This adjustment may involve changing the intensity, frequency, or duration of the electrical impulses delivered to the vagus nerve to provide an appropriate therapeutic response.

[0033] In the case of depression monitoring, the implantable device 116 may adjust the parameters of neurostimulation therapy to address the longterm treatment of depression. This can include modifying the stimulation schedule or the electrical characteristics of the therapy to better manage the patient’s mood and behavioral health.

[0034] Overall, the sophisticated interaction between the external monitoring device and the IPG allows for a dynamic and responsive patient health management system that adapts to the patient's immediate and evolving therapeutic needs, providing a personalized and coordinated approach to seizure intervention and depression treatment.

[0035] In an example, seizure detection and VNS can include or use 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 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 includes a first electrode 108, a separate second electrode 110, a separate third electrode 112, and a separate nth electrode 114 positioned at or near the vagus nerve 102. The various electrodes can be used in various combinations to provide an epilepsy therapy, a depression therapy, or both epilepsy and depression therapies.

[0036] 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 “AI”-based technique) can be instantiated by the implant circuitry or the processor circuit 118. Such a machine-learning-based model can be used for detection of a seizure, or for therapy control in response thereto, or both.

[0037] In an example, the sensing electrodes and related circuitry can be separate from the stimulating electrodes and the sensing electrodes can be monitored by a separate unit (e.g., an external assembly) that can be used in an acute or temporary manner, such as supporting the implant procedure or implantable device configuration. For example, in the case that the sensing and stimulating electrodes are separate, the sensing electrode 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 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.

[0038] In an example, a lead can comprise one or more electrodes and can optionally comprise a retention or affixation feature. The affixation feature can be provided at a proximal or distal end of the lead, or can be provided at an intermediate location along the length of the lead. The affixation feature can be electrically functional (e.g., comprising one or more electrodes for sensing or delivery of electrical neurostimulation) or electrically nonfunctional (e.g., without conductive materials or without electrodes). In some examples, an electrode can be coupled to, or integrated with, a retention feature. In some examples, the electrodes can be made of biocompatible conductive materials, such as, platinum, platinum-iridium alloy, medical-grade stainless steel, gold, silver, titanium, titanium alloys, or a combination thereof. In some examples, insulating or coating materials of the leads can be made of biocompatible materials, such as, silicone rubber,polyurethane, ploy tetrafluoroethylene (PTFE), medical-grade silicone, biocompatible polymers, or combinations thereof.

[0039] FIG. 2 illustrates generally an example of a first tripolar lead assembly 200 with a first retention feature 218. The first tripolar lead assembly 200 can be coupled to a stimulator circuit (e.g., in an implantable housing) and can be configured for implantation at a neural target, such as at the vagus nerve 202. The first tripolar lead assembly 200 can comprise a lead body 204 and one or more distal electrodes, anchors, or affixation features. In an example, the first tripolar lead assembly 200 comprises the first electrode 108, the second electrode 110, and the third electrode 112 from the example of FIG. 1.

[0040] The first tripolar lead assembly 200 includes multiple helical anchors, and each of the anchors comprises a separately addressable electrode. For example, the first tripolar lead assembly 200 includes a first helical anchor 206 with a first electrode 208 (e.g., comprising an example of the first electrode 108), a second helical anchor 210 with a second electrode 212 (e.g., comprising an example of the second electrode 110), and a third helical anchor 214 with a third electrode 216 (e.g., comprising an example of the third electrode 112). Any one or more of the anchors can optionally comprise an array of multiple, separately-addressable electrodes. Each of the helical anchors can be configured to receive a respective portion of the vagus nerve 202 (or other nerve) and can be adjustable in size to accommodate variations in width of the vagus nerve 202 and other tissue. For ease of reference herein, the first electrode 208 can be referred to as “electrode A” or “A,” the second electrode 212 can be referred to as “electrode B” or “B,” and the third electrode 216 can be referred to as “electrode C” or “C.” Combinations or pairs of the electrodes used for electrostimulation can be referred to by letters, for example, electrode pair A-B can refer to one of the first electrode 208 and the second electrode 212 configured as an anode and the other of the electrodes configured as a cathode for use in an electrostimulation vector. In other examples, two or more of the electrodes can be electrically coupled to provide an anode or cathode for anotherelectrostimulation vector. For example, the first electrode 208 and the second electrode 212 can be electrically coupled to provide an anode and the third electrode 216 can be used as a cathode. Other combinations can similarly be used to provide other electrostimulation vectors for neurostimulation therapy delivery or sensing. The various combinations can be used for respective different therapies or can be used together for one or multiple therapies.

[0041] In the example of FIG. 2, the electrodes are illustrated schematically as having discrete locations, however, other locations in, on, or around the helical anchors can be used. In an example, one or more of the electrodes can comprise a ring electrode or conductive ribbon that extends partially or entirely around a revolution of its respective helical anchor, such as to encircle the target tissue (e.g., the vagus nerve 202). Other configurations can similarly be used.

[0042] In an example, the first retention feature 218 comprises a mesh or other structure. In the example of FIG. 2, the mesh structure can be coupled to a distal portion of the lead body 204 and configured to grow into tissue at, near, adjacent to, or around the vagus nerve 202 or other nerve tissue. In an example, additionally or alternatively to providing the first retention feature 218 at the distal portion of the lead body 204, one or more other instances of the first retention feature 218 can be coupled to a proximal or intermediate portion of the lead body 204.

[0043] FIG. 3 illustrates generally an example of an interface device 302, such as can comprise an example of the external monitoring device 124. The interface device 302 can be configured to provide, among other things, pictorial representations of therapy intensity and therapy efficacy over time. An intensity index 324 displayed on the interface device 302 represents a relative or absolute magnitude of an intensity of a neurostimulation therapy provided by a neurostimulation device to a patient. The intensity index can be a numerical value that is determined as a function of one or more neurostimulation parameters such as neurostimulation magnitude, frequency, pulse width, pulse morphology, duty cycle, therapy duration, or otherparameter. For example, the intensity index can be a number between 0 and 100. In the example of FIG. 3, the intensity index 324 is displayed as “97.” Representations of therapy intensity other than numerical values can be similarly used, for example, colors or other icons can be used.

[0044] An efficacy index 326 displayed on the interface device 302 represents a metric that describes a patient response to the neurostimulation therapy. The efficacy index 326 can be a numerical value that is determined as a function of one or more patient response indicators, such as can include information about a seizure type, seizure frequency, or seizure intensity, or a presence or absence of side effect. In an example, the efficacy index 326 can be based in part on information about the patient that is sensed using the external monitoring device 124. In the example of FIG. 3, the efficacy index 326 is displayed as “84.” Representations of therapy efficacy other than numerical values can be similarly used, for example, colors or other icons can be used.

[0045] The intensity index 324 and the efficacy index 326 can be represented over time such that therapy information (e.g., the intensity index) is shown in visual or pictorial correspondence with information about an effect of therapy (e.g., the efficacy index). In a particular non-limiting example, the intensity index 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,

[0046] Intensity index = 100 * (magnitude) * (frequency) * (pulse width) * (duty cycle) / (magnitudemax) * (frequencymax) * (pulse widthmax) * (duty cyclemax)

[0047] 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 cyclemaxis a maximum available duty cycle of the pulse signal.

[0048] In a particular non-limiting example, the efficacy index can be a function of one or more parameters that indicate an effect of the therapy (e.g., at a particular therapy intensity) on a patient.

[0049] In the example of FIG. 3, the interface device 302 shows an example interface 304. The example interface 304 includes a graphical representation of time-varying physiological signal information sensed from a patient, stimulation parameters for a therapy (or combination of therapies) delivered to the patient using an implanted neurostimulation device, and information about a composite index or therapy intensity for the therapy delivered to the patient. FIG. 3 can represent an example of a clinician or patient device display with information for monitoring therapy effectiveness relative to therapy parameters.

[0050] The display or graphical representation in the example of FIG. 3 can include a first display portion showing physiological parameters 312 over time and a second display portion showing intensity and effectiveness metrics 314 over time. The first display portion can include parameters such as heart rate 306 and heart rate variability 308, and can include information about seizure detection 310 or patient-reported depression episodes or severity. In an example, the first display portion can include information about physiological parameters, such as heart rate, blood pressure, respiratory rate, blood oxygen saturation, blood glucose, sleep quality, or a combination thereof. The first display portion can thus show graphically relationships between seizure activity and one or more physiological status indicators for the patient. The second display portion can include information about an intensity index 316 of a therapy (or therapies) provided to a patient over time.

[0051] In an example, the second display portion can include a therapy effectiveness index 322, a depression index 320, and information about one or more therapy parameters, such as therapy pulse frequency 318 information. The second display portion can thus show graphically relationships between therapy intensity, therapy effectiveness, and one or more therapy parameters. The time scales for the first and second displayportions can be the same or different. In some examples, the interface device can include information about a current therapy intensity index 324 and current therapy efficacy index 326.

[0052] In an example, events derived from the physiological parameter information (or reported by the patient or clinician, or sensed using the external monitoring device 124) can be represented on the example interface 304. In an example, changes in programming of the neurostimulation device can be represented on the example interface 304, such as to help identify a patient physiological response to changes in therapy parameters.

[0053] 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 intensity and effectiveness metrics 314 and the physiological parameter information (e.g., physiological parameters 312). For example, therapy parameter change events can be indicated together with the seizure detection 310 information.

[0054] In an example, the first display portion can include seizure detection 310 information. For example, the seizure detection 310 portion of the display can include a seizure event-indicating signal 328. The seizure eventindicating signal 328 can represent a determined likelihood of a seizure event based on a detected or reported physiological status of the patient. The likelihood determination can be based on, among other things, physiological information received from a physiological status sensor or other information reported by the patient. In an example, the seizure event-indicating signal 328 can be determined using a machine learning approach that receives various physiological status information about the patient, time of day information, environmental information, or other inputs, and identifies correlations with actual seizure events.

[0055] The seizure detection 310 portion of the display can include information about confirmed seizure events, such as at a first seizure event 330, a second seizure event 332, a third seizure event 334, and so on. The confirmed seizure events can be indicated by the patient or clinician at particular times. For example, the system can record a time of a receivedmagnet swipe from the patient to indicate occurrence of a seizure event, such as the first seizure event 330 at a first time. The first display portion can then display the physiological parameters 312 and intensity and effectiveness metrics 314 in visual correspondence with an indication of the first seizure event 330, to help enable better patient or clinician analysis of the patient’s physiological status leading up to, during, or following the first seizure event 330. In an example, the interface device 302 provides the indication of the first seizure event 330 in visual correspondence with information about the therapy used to address the first seizure event 330.

[0056] In an example, the interface device 302 can show or include neurostimulation parameter information about a neurostimulation therapy provided to a patient during a first time interval. The neurostimulation parameter information can include information about a particular parameter (e.g., magnitude, frequency, pulse width, etc.) or 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 118) configured to determine the indication of the intensity of the neurostimulation therapy based on, for example, two or more neurostimulation parameters such as 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 118 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.

[0057] In FIG. 3, therapy parameter changes can be visually indicated in coordination with the seizure detection 310 information and the seizure event-indicating signal 328. A first therapy parameter change event 336 is shown in correspondence with an increasing risk of seizure as-indicated by the rising trend of the seizure event-indicating signal 328 preceding the first therapy parameter change event 336. The example of FIG. 3 includes asecond therapy parameter change event 338 in coordination with a later peak of the seizure event-indicating signal 328. 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 physiological effects (e.g., including seizure events experienced by the patient, or changes in a mood or depression status of the patient). In an example, upon seizure detection, the processor circuit 118 can be configured to automatically initiate or adjust a neurostimulation therapy to treat the seizure.

[0058] In an example, the system can detect depression using information reported from the patient or using one or more sensed physiological parameters 312. For example, depression in a patient can be deduced from reduced heart rate variability patterns, irregular heart rhythm variations, elevated resting heart rate, disrupted sleep patterns, changes in rapid eye movement (REM) sleep cycles, altered sleep-wake rhythms, total sleep duration, blood pressure variations, breathing pattern irregularities, shallow breathing, respiratory changes linked to anxiety symptoms, oxygen level fluctuations, metabolic changes associated with depression, or the like.These physiological parameters, among others, can be sensed and tracked to determine the depression index 320. For example, one or more sensors such as an electrocardiogram sensor, a heart rate sensor, a blood pressure sensor, a respiratory rate sensor, a blood oxygen saturation sensor, a sleep sensor, a blood glucose sensor, or a combination thereof can be used. In an example, depression experienced by the patient can be manually logged into a depression log in the system through manual patient input, manual clinician input, or a combination thereof. Upon detection of depression either through physiological parameters 312, patient input, clinician input, or a combination thereof, the processor circuit can be configured to automatically initiate or adjust a neurostimulation therapy to treat the depression event.

[0059] The graphical interface of the interface device 302 and / or the information displayed therein can be augmented in various ways. For example, the interface device 302 can be configured to allow customizationor 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).

[0060] In an example, the interface device 302 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.

[0061] In another example, the interface device 302 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.

[0062] In another example, the interface device 302 can be configured to incorporate multi-modal patient data like audio, video (e.g., such as can be sensed using the external monitoring device 124 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.

[0063] In another example, the user interface or interface device 302 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.

[0064] FIG. 4 illustrates generally an example of an external monitoring device 402, such as can comprise an example of the external monitoring device 124. The external monitoring device 402 can comprise one or more sensors configured to monitor physiological or behavioral characteristics of a patient 404. In an example, the patient 404 can have the implantable device 116 implanted in their body and the implantable device 116 can beconfigured to provide VNS therapy. In an example, sensor data from the implantable device 116, or from one or more other implanted or implantable sources, can be communicated to the external monitoring device 402.

[0065] In an example, the external monitoring device 402 can be a mobile device carried or worn by the patient 404. In an example, the external monitoring device 402 can be a device that is configured to be mounted or installed in areas or environments frequented by the patient 404. For example, the external monitoring device 402 can be configured to be installed in a home 416, such as in a bedroom of the patient 404, or in a vehicle 418 used by the patient 404. When installed, the sensors of the external monitoring device 402 can be configured to repeatably monitor the patient from a fixed vantage point.

[0066] In an example, the external monitoring device 402 includes one or more sensors, a display 414, or a speaker 410. The sensors can include, among other things, a microphone 406, a first camera 408, an accelerometer or gyroscope, a temperature sensor, a GPS or other position sensor, a conductivity sensor, or one or more other environmental sensors. In an example, any one or more of the sensors can be provided separately from the external monitoring device 402 and configured for data communication with the external monitoring device 402 or with the implantable device 116. In an example, one or more of the sensors is wearable by the patient 404. In an example, a second camera 420 can be mounted inside the home 416, such as on a wall or headboard of a bed in a bedroom of the patient 404. Image information from the second camera 420 can be communicated to the external monitoring device 402 or to another device with a data processor.

[0067] In an example, the external monitoring device 402 comprises a processor circuit and a memory circuit, and can be configured to receive the information from the one or more sensors. The external monitoring device 402 can process the information from the one or more sensors to identify the physiological or behavioral characteristics of the patient. In an example, the external monitoring device 402 comprises a centralized processor thatanalyzes the patient status information from the one or more sensors, and can include or use information received from the implantable device 116.

[0068] In an example, the external monitoring device 402 can include or use a processor to identify a seizure event, a seizure precursor event, or a depression status of the patient based on the information from the one or more sensors. In an example, at least a portion of the sensor information processing can be performed elsewhere, such as at or using processors coupled to the respective sensors, or at another device or cloud-based server.

[0069] In response to identification of the seizure event, seizure precursor event, and / or depression status of the patient, the external monitoring device 402 can provide information to the patient 404 or can provide commands or instructions to the implantable device 116 of the patient 404. For example, the external monitoring device 402 can use the speaker 410 to communicate with the patient, such as to deliver commands to the patient to change their behavior, posture, or perform particular actions or gestures. In an example, the external monitoring device 402 can be configured to establish a voice or data connection with a clinician or caregiver, who can then administer commands directly to the patient through the interface of the external monitoring device 402. In an example, the external monitoring device 402 can use the display 414 of the external monitoring device 402 to show the patient one or more images or commands.

[0070] In an example, the external monitoring device 402 is configured to implement or use image-based analysis. This technology employs image processing algorithms to analyze visual data captured by a camera (e.g., the first camera 408, or the second camera 420, etc.), optionally in real-time. In an example, the camera can be configured to operate under various lighting conditions, ensuring reliable monitoring during both day and night. The image-based analysis is capable of detecting subtle or significant changes in the patient's appearance and movements that may indicate the onset of a seizure event, or a mood or depressive state of the patient 404.

[0071] The external monitoring device 402 can be configured to use or apply machine learning techniques to information received by the sensors(e.g., using the first camera 408, the microphone 406, etc.) to differentiate between normal patient behaviors and those characteristic of seizure activity. For example, in processing of multiple images, the system can identify specific patterns, such as the rhythmic jerking movements associated with convulsive seizures or the more subtle signs of non-convulsive seizures. This level of detail allows for a more accurate and timely detection of seizures compared to traditional methods, which may rely on less sensitive metrics such as changes in heart rate or gross motor activity.

[0072] In an example, the image-based analysis can use facial recognition capabilities, enabling it to monitor changes in the patient's facial expression that can be indicative of seizure events. This feature is particularly useful in detecting complex partial seizures, which may not involve significant body movement but can still be identified through facial cues.

[0073] The external monitoring device 402 can be configured to monitor and analyze the patient's respiration rate using the same high-resolution camera. By tracking the rise and fall of the patient's chest or abdominal area, the system can detect anomalies in breathing patterns that often accompany seizures, such as hyperventilation or apnea.

[0074] In addition to seizure detection, the image-based analysis can be used to monitor depression symptoms. For example, the image information can be used to assess the patient's daily activities and behaviors, identifying prolonged periods of inactivity or changes in sleep patterns that may signal depression. This dual-functionality not only aids in the comprehensive care of patients with epilepsy but also addresses the common comorbidity of depression, providing a holistic approach to patient well-being.

[0075] In an example, the external monitoring device 402 is configured to sample acoustic information from the microphone 406 and analyze the acoustic information to detect acoustic signals associated with a seizure event or seizure precursor event or depression. The external monitoring device 402 can continuously or intermittently sample ambient acoustic information in the patient’s environment. The sampled acoustic informationcan then be processed using algorithms designed to identify specific acoustic patterns that may be indicative of a seizure event or seizure precursor event.

[0076] For example, during a seizure, a patient can exhibit particular vocalizations or breathing patterns, such as gasping, crying out, or unusual breathing noises, which can be distinct from normal sounds. The external monitoring device 402 can be configured to execute algorithms or recognition models that are configured or trained to recognize these seizure- related acoustic signals. Upon detection of such signals, the external monitoring device 402 can initiate a series of responses, including alerting the patient or a caregiver, activating the implantable neurostimulation device to deliver appropriate therapy, or logging the event for clinical review.

[0077] In an example, the external monitoring device 402 can perform acoustic analysis to monitor for signs of depression, which may manifest as changes in the patient's vocal tone, speech patterns, or frequency of verbal communication. For example, a decrease in the patient's speech activity or a consistent pattern of monotone speech may suggest depressive episodes. By analyzing these changes over time, the external monitoring device 402 can provide valuable insights into the patient's mental health status.

[0078] The acoustic analysis may be combined with other sensor data, such as physiological or movement information, to enhance the accuracy of seizure and depression detection. This multimodal approach allows for a more comprehensive understanding of the patient's condition, enabling timely and tailored therapeutic interventions.

[0079] In an example, the external monitoring device 402 can implement multi-variable pattern recognition by combining and analyzing data from multiple sensors simultaneously to improve detection accuracy. For instance, the device can correlate visual data from a camera with acoustic information from a microphone to provide more reliable seizure detection.

[0080] The camera can detect physical manifestations of a seizure such as rhythmic jerking movements or facial expression changes, while the microphone can identify seizure-related vocalizations or breathing patterns.The system can use machine learning techniques to analyze these multipledata streams together, looking for temporal correlations and patterns that indicate seizure activity with greater confidence than single-sensor detection alone. For depression monitoring, the system can similarly combine visual detection of reduced activity levels or changes in sleep patterns with acoustic analysis of speech patterns and vocal tone to assess mood state progression. The external monitoring device 402 can be configured to weight and combine these different sensor inputs using classification models that are trained to recognize the complex patterns associated with seizure events and depression symptoms. This multi-modal approach enables more nuanced detection capabilities. For example, the system can identify a potential seizure event when subtle movement changes detected by the camera coincide with characteristic breathing sounds picked up by the microphone, even if neither signal alone would trigger detection.

[0081] In an example, the external monitoring device incorporates environmental sensors and context awareness into its detection and therapy algorithms. The environmental components can include a sensor to detect light, temperature sensors to monitor ambient temperature, humidity sensors, pressure sensors, acoustic sensors to detect background noise, proximity sensors to detect nearby objects, and other sensors. These sensors can be configured to monitor environmental factors that may be known to trigger seizures in certain patients. In an example, the system integrates this environmental data with physiological monitoring and baseline behavioral patterns to enable preemptive therapy adjustments.

[0082] For example, when high-risk environmental conditions are detected in combination with subtle changes in patient behavior or physiology, the system can initiate early therapeutic intervention before a full seizure event occurs. In an example, the external monitoring device can be configured to automatically adjust its monitoring sensitivity and detection algorithms based on the patient's location context. For example, different sensitivity profiles can be used at home, in a vehicle, or in other environments where different types of sensor data may be more or less reliable.

[0083] The integration of sensor data analysis, such as including imagebased analysis or acoustic sample-based analysis, into the external monitoring device 402 helps improve seizure detection and patient monitoring technology. By providing a more nuanced and comprehensive understanding of the patient's condition, the system enables a proactive approach to seizure management and the treatment of depression, ultimately leading to improved patient outcomes and quality of life.

[0084] FIG. 5 illustrates an example of a first method 500 for patient intervention to address a seizure event or depression. Although the example 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] According to some examples, the method includes receiving sensor information from an external monitoring device at operation 502. The information may be received from one or a combination of sensors, including but not limited to a camera, a microphone, and an accelerometer. The sensors can be configured to monitor physiological and behavioral characteristics of a patient, such as respiration rate, heart rate, tremors, movements, and acoustic signals that may indicate a seizure event, a seizure precursor event, or a depression status. In an example, operation 502 can include receiving patient status information from the implantable device 116 or from one or more implanted or implantable sensors. For example, the patient status information can include vagal electroneurogram information, or information sensed from an implanted accelerometer.

[0086] According to some examples, the first method 500 includes detecting one or more of a seizure event, a seizure precursor event, or a depression status in a patient at operation 504. The detection can be based on analysis of the sensor information received at operation 502. The externalmonitoring device employs algorithms for video or image recognition, sound recognition, and movement recognition to accurately identify the occurrence of a seizure, a seizure precursor, or the presence of depression indicators. In an example, operation 504 can include using information from multiple different sensors to detect the seizure-related event or depression-related status. For example, the respective sensor information can be used as respective inputs to a classification model that provides, as an output, the seizure-related event information or depression-related status information. In an example, respective different classification models can be used for the respective different types of sensor information.

[0087] According to some examples, following the detection at operation 504, the first method 500 includes communicating instructions and / or verbal (i.e., acoustic) questions to the patient using a speaker, a display, a haptic actuator, or other device at operation 506. The instructions can include behavioral modification commands, such as can include asking the patient to perform specific tasks or movements, or providing suggestions to sit or lay down, which can be aimed at reducing the duration and intensity of the seizure, minimizing a likelihood of injury to the patient, or alleviating effects of depression.

[0088] At operation 506, the first method 500 can include posing one or more questions to the patient. The particular question asked of a patient can be designed to be simple and to help assess the patient's level of consciousness, orientation, and ability to respond. The question can be selected to help cognitively engage the patient, which can help to mitigate a seizure intensity or duration. Examples of questions can include orientation- related questions (e.g., “Can you tell me your name’-), or simple command- related questions (e.g., “Can you blink your eyes?”), or cognitive engagement-related questions (e.g., “What color is the sky?”), or yes / no questions (e.g., “Can you hear me?”), or other simple recognition-related questions (e.g., “Can you identify the object displayed on the screen?”). Other questions can similarly be used. The questions are intentionally straightforward to allow a patient who is experiencing a seizure event orseizure precursor event to respond with minimal cognitive effort. The responses, or lack thereof, such as can be detected by or using the external monitoring device 402, can provide caregivers and medical professionals with information about a patient's current status and can be used to guide intervention.

[0089] Various behavioral suggestions can be communicated to patients at operation 506. For example, to assist in mitigating the onset of a seizure, the external monitoring device can suggest relaxation techniques such as deep breathing exercises or guided imagery to help the patient remain calm and reduce stress levels. In an example, the device can instruct the patient to move to a safer location, such as sitting down or lying on the ground, to prevent injury during a seizure. It may also suggest clearing the area of hard or sharp objects.

[0090] In an example, if a seizure is detected while the patient is in bed, then the device can suggest positional changes to ensure an open airway, such as turning to the side, which can help prevent aspiration and facilitate easier breathing. In an example, for patients with prescribed rescue medications, the device can remind or instruct the patient on the appropriate usage of these medications when a seizure event is detected. In an example, the system can advise the patient to alert a caregiver or activate a pre-set emergency response system if they feel a seizure is imminent or if they require immediate assistance.

[0091] After a seizure event, the device can provide suggestions for patient recovery, such as staying hydrated, resting in a comfortable position, or contacting a healthcare provider for follow-up. In an example, the device can recommend mindfulness exercises or suggest focusing on a specific object or sound to help the patient distract from the seizure and regain control over their senses.

[0092] In an example, after communicating the instructions to the patient at operation 506, the first method 500 can include receiving subsequent information from the one or more sensors of the external monitoring device at operation 507. At operation 508, the external monitoring device can usethe received subsequent information to determine whether the patient complied with the instructions communicated at operation 506. For example, if the instruction communicated at operation 506 included an instruction for the patient to perform a particular movement (e.g., “raise your left hand”), then operation 508 can include using image information from a camera (e.g., the first camera 408 of the external monitoring device 402) to determine whether the patient performed the movement. In an example, the determination of compliance can include different levels or grades to indicate partial compliance or full compliance. If the patient adequately performed the movement, then the first method 500 can proceed with delivering other instructions to the patient. If the patient did not perform the movement, then the first method 500 can proceed with another intervention, such as at operation 509, by commanding the implanted neurostimulation device to deliver a neurostimulation therapy, or to update or adjust a neurostimulation therapy parameter.

[0093] According to some examples, following the detection at operation 504, the first method 500 includes communicating one or more commands to an implanted neurostimulation device at operation 509. The commands can be based on the detected seizure event, seizure precursor event, or depression status and can be configured to control the delivery of neurostimulation therapy to the patient. According to some examples, the first method 500 includes updating or adjusting a parameter of the neurostimulation therapy at operation 510, and then providing neurostimulation therapy to the patient at operation 511, such as using the updated or adjusted parameters from operation 510. The therapy may involve vagus nerve stimulation (VNS) and can be directed to treat a seizure disorder, depression, or both, depending on the patient's condition.

[0094] In an example, in response to a detected seizure event, the device can be configured to command the neurostimulation device to deliver a relatively higher intensity therapy to interrupt the seizure event. In an example, in response to a detected seizure precursor event, the device can be configured to command the neurostimulation device to deliver a lowerintensity therapy, or a therapy with an intensity that increases over time, to attempt to prevent an oncoming seizure event. If the external monitoring device does not detect a seizure (e.g., within a specified heightened monitoring duration), then the device can be configured to control the neurostimulation device to stop or ramp-down the therapy, unless or until the patient’s physiologic or behavioral status changes. In an example, in response to detecting a change in depression symptoms, the device can be configured to change an intensity or duration of a neurostimulation depression therapy. For example, the therapy can be reduced in intensity or duration when depression symptoms improve, or the therapy can be increased in intensity or duration when depression symptoms persist.

[0095] According to some examples, the method includes providing recorded sensor information (e.g., the sensor information received at operation 502) to a clinician or caregiver at operation 520. The information provided can include the sensor data received at operation 502, as well as any additional patient-entered information or logs. This comprehensive data set is used by clinicians to diagnose and understand the patient’s condition, monitor disease progression, and make informed decisions regarding future therapy adjustments.

[0096] In an example, the system can be configured to implement personalized therapeutic response optimizations for patients by continuously learning and adapting behavioral modification commands and / or neurostimulation parameters based on measured patient outcomes. For example, the external monitoring device can maintain a therapy response record that tracks the effectiveness of different therapeutic interventions for each specific type of detected event. For seizure events, the system records which combinations of verbal instructions, haptic feedback signals, and VNS parameter adjustments most effectively reduced seizure duration or intensity for the individual patient. The system can use machine learning algorithms to analyze these response patterns and develop optimized therapy progression pathways.

[0097] For example, if a particular sequence of behavioral commands (such as “sit down” followed by specific breathing instructions) consistently helps reduce seizure intensity when combined with a gradual increase in VNS amplitude, the system will prioritize this intervention pattern for similar future events. The optimization process takes into account both immediate patient responses (such as compliance with behavioral instructions) and longer-term outcomes (such as reduced seizure frequency or severity).

[0098] In an example, for depression therapy, the system can be configured to track an effectiveness of different therapeutic approaches across varying depression states. The external monitoring device can identify patterns in which specific combinations of behavioral modifications and VNS parameter adjustments lead to improved mood outcomes. This may include correlating successful therapy responses with time of day, activity levels, or environmental factors to further refine the therapeutic approach. The system continuously updates these therapy progression pathways based on ongoing monitoring of patient compliance and physiological responses, enabling increasingly personalized and effective treatment strategies over time.

[0099] The method 500, as illustrated in FIG. 5 and described herein, provides a detailed and systematic approach to managing seizure events and depression in patients using a combination of monitoring, detection, communication, and therapy delivery. The integration of the external monitoring device with the implantable neurostimulation device enables a closed-loop system that enhances patient care and treatment outcomes.

[0100] FIG. 6 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. 6 is a diagrammatic representation of a machine 600 within which instructions 608 (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine 600 to perform any one or more of the methodologies discussed herein may be executed. In an example, one or more of the implantable device 116, the external monitoring device 124, theinterface device 302, or the external monitoring device 402 comprise all or a portion of the machine 600.

[0101] In an example, the instructions 608 may cause the machine 600 to execute any one or more of the methods, controls, therapy algorithms, signal generation routines, or other processes described herein. The instructions 608 transform the general, non-programmed machine 600 into a particular machine 600 programmed to carry out the described and illustrated functions in the manner described. The machine 600 may operate as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine 600 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 600 can comprise, but is not limited to, various systems or devices that can communicate with the interface device 302, the external monitoring device 402, or the implantable device 116, or the external monitoring device 124, 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 608, sequentially or otherwise, that specify actions to be taken by the machine 600. Further, while only a single machine 600 is illustrated, the term “machine” shall also be taken to include a collection of machines that individually or jointly execute the instructions 608 to perform any one or more of the methodologies discussed herein.

[0102] The machine 600 may include processors 602, memory 604, and I / O components 642, which may be configured to communicate with each other via a bus 644. In an example embodiment, the processors 602 (e.g., a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) processor, a Complex Instruction Set Computing (CISC) processor, aGraphics 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 606 and a processor 610 that execute the instructions 608. 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. 6 shows multiple processors 602, the machine 600 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.

[0103] The memory 604 includes a main memory 612, a static memory 614, and a storage unit 616, both accessible to the processors 602 via the bus 644. The main memory 604, the static memory 614, and storage unit 616 store the instructions 608 embodying any one or more of the methodologies or functions described herein. The instructions 608 may also reside, completely or partially, within the main memory 612, within the static memory 614, within a machine-readable medium 618 within the storage unit 616, within at least one of the processors 602 (e.g., within the processor’s cache memory), or any suitable combination thereof, during execution thereof by the machine 600.

[0104] The I / O components 642 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 642 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 642 may include other components that are not shown in FIG. 6. In various example embodiments, the I / O components 642 may include output components 628 and input components 630. The output components628 may include pictorial, graphical, or visual components (e.g., a display such as a plasma display panel (PDP), a light 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 various display components discussed herein, the external monitoring device 402, or other interfaces that can be configured to display therapy parameter, intensity or effectiveness metrics, among other information. The output components 628 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 630 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.

[0105] In further example embodiments, the I / O components 642 may include biometric components 632, motion components 634, environmental components 636, or position components 638, among others. For example, the biometric components 632 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 634 can include an acceleration sensor (e.g., an accelerometer), gravitation sensor components, rotation sensor components (e.g., a gyroscope), or similar. The environmental components 636 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 sensorcomponents (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 638 can include location sensor components (e.g., a GPS receiver component), altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor components (e.g., magnetometers), and the like.

[0106] Communication may be implemented using a wide variety of technologies. The I / O components 642 further include communication components 640 operable to couple the machine 600 to a network 620 or other devices 622 via a coupling 624 and a coupling 626, respectively. For example, the communication components 640 may include a network interface component or another suitable device to interface with the network 620. In further examples, the communication components 640 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 622 may be another machine or any of a wide variety of peripheral devices such as can include other implantable or external devices.

[0107] The various memories (e.g., memory 604, main memory 612, static memory 614, and / or memory of the processors 602) and / or storage unit 616 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 608), when executed by processors 602, cause various operations to implement thedisclosed embodiments, including various neuromodulation or neurostimulation therapies or functions supportive thereof.

[0108] The following Examples provide a non-limiting overview of, among other things, the therapy coordination, therapy parameter visualization, and therapy optimization techniques discussed herein.

[0109] Example 1 is a seizure management system comprising: an implantable neurostimulation device configured to deliver vagus nerve stimulation (VNS) therapy to a patient; a non-invasive external monitoring device comprising at least one sensor for detecting indicators of a seizure in the patient; wherein the external monitoring device is configured to: use information from the at least one sensor to detect a seizure event or a seizure precursor event; provide one or more signals to the patient, the one or more signals including behavioral modification commands for the patient or acoustic information that includes, a question directed to the patient; and control the neurostimulation device to deliver the VNS therapy to the patient in response to the detected seizure event or seizure precursor event.

[0110] In Example 2, the subject matter of Example 1 optionally includes the at least one sensor is configured to detect a physiological status indicator of a seizure in the patient. In an example, Example 2 can include the external monitoring device configured to use information from multiple sensors to detect the seizure event or the seizure precursor event, and the external monitoring device is configured to adjust a weighting of the information from the sensors based on a historical event detection accuracy for the patient for each sensor.

[0111] In Example 3, the subject matter of any one or more of Examples 1-2 optionally includes the at least one sensor is configured to detect a behavioral indicator of a seizure in the patient.

[0112] In Example 4, the subject matter of any one or more of Examples 1-3 optionally includes the at least one sensor is configured to detect indicators of depression in the patient.

[0113] In Example 5, the subject matter of any one or more of Examples 1-4 optionally includes the external monitoring device is configured to useinformation from the at least one sensor and vagal electroneurogram information from the implantable neurostimulation device to detect the seizure event or the seizure precursor event.

[0114] In Example 6, the subject matter of any one or more of Examples 1- 5 optionally includes the at least one sensor includes a camera configured to receive image information about the patient.

[0115] In Example 7, the subject matter of Example 6 optionally includes the external monitoring device is configured to receive the image information about the patient and analyze the image information about the patient to identify one or more of a respiration rate, a heart rate, a tremor, or other movement of the patient that indicates the seizure event or the seizure precursor event.

[0116] In Example 8, the subject matter of any one or more of Examples 6- 7 optionally includes the external monitoring device is configured to receive the image information about the patient and analyze the image information about the patient to identify behavioral characteristics associated with depression in the patient.

[0117] In Example 9, the subject matter of Example 8 optionally includes the external monitoring device is configured to control the neurostimulation device to update or adjust a depression therapy provided to the patient by the neurostimulation device.

[0118] In Example 10, the subject matter of any one or more of Examples 1-9 optionally includes the at least one sensor includes a microphone configured to receive acoustic information from the patient.

[0119] In Example 11, the subject matter of Example 10 optionally includes the external monitoring device is configured to receive the acoustic information from the patient and analyze the acoustic information to identify vocalizations or other sounds associated with the seizure event, the seizure precursor event, or a depressive status of the patient.

[0120] In Example 12, the subject matter of any one or more of Examples 1-11 optionally includes the at least one sensor includes a camera configured to receive image information about the patient and a microphoneconfigured to receive acoustic information about the patient. In Example 12, the external monitoring device can be configured to use the image information and the acoustic information together to detect the seizure event or the seizure precursor event.

[0121] In Example 13, the subject matter of any one or more of Examples 1-12 optionally includes the at least one sensor includes a wearable accelerometer configured to receive movement information about the patient. In Example 13, the external monitoring device can be configured to use the movement information to detect the seizure event or the seizure precursor event.

[0122] In Example 14, the subject matter of any one or more of Examples 1-13 optionally includes the external monitoring device is configured to control a haptic stimulator device (e.g., a haptic actuator) to deliver a haptic feedback signal (e.g., a haptic command) to the patient in response to the detected seizure event or seizure precursor event.

[0123] In Example 15, the subject matter of any one or more of Examples 1-14 optionally includes the external monitoring device is configured to be mounted on a structure in the patient's sleeping area.

[0124] In Example 16, the subject matter of any one or more of Examples 1-15 optionally includes the external monitoring device comprises a display screen, a speaker, and / or a haptic feedback device configured to deliver the behavioral modification commands to the patient.

[0125] In Example 17, the subject matter of any one or more of Examples 1-16 optionally includes the external monitoring device is configured to communicate information about the detected seizure event or seizure precursor event to a clinician or caregiver of the patient.

[0126] Example 18 is a method for managing seizure events for a patient, the method comprising: detecting, by an external monitoring device, a seizure event or a seizure precursor event in the patient; in response to detecting the seizure event or the seizure precursor event, at least one of: communicating one or more commands from the external monitoring device to the patient; communicating one or more commands from the externalmonitoring device to an implanted neurostimulation device in the patient; or communicating one or more verbal questions from the external monitoring device to the patient.

[0127] In Example 19, the subject matter of Example 18 optionally includes communicating the one or more commands to the implanted neurostimulation device, and based on the one or more commands, updating or adjusting a parameter of a vagus nerve stimulation (VNS) therapy provided by the neurostimulation device to a vagus nerve of the patient.

[0128] In Example 20, the subject matter of any one or more of Examples 18-19 optionally includes communicating the one or more commands and / or verbal questions to the patient using a display device and / or a speaker.

[0129] In Example 21, the subject matter of Example 20 optionally includes communicating the one or more commands and / or verbal questions to the patient including audibly instructing the patient to perform particular therapeutic movements or gestures using a speaker of the external monitoring device.

[0130] In Example 22, the subject matter of any one or more of Examples 18-21 optionally includes communicating the one or more commands to the patient using a haptic feedback device coupled to the patient.

[0131] In Example 23, the subject matter of any one or more of Examples 18-22 optionally includes detecting, by the external monitoring device, a depression status of the patient; and in response to detecting the depression status, communicating one or more commands to the implanted neurostimulation device to update or adjust a parameter of a depression neurostimulation therapy provided by the neurostimulation device to a vagus nerve of the patient.

[0132] In Example 24, the subject matter of any one or more of Examples 18-23 optionally includes receiving at least one of image information about the patient, acoustic information about the patient, or movement information about the patient, using one or more sensors coupled to the external monitoring device. In Example 24, detecting the seizure event or the seizureprecursor event can include using the received image information, acoustic information, or movement information about the patient.

[0133] In Example 25, the subject matter of any one or more of Examples 18-24 optionally includes receiving image information about the patient from a camera coupled to the external monitoring device, and analyzing the image information about the patient to identify a physiological or behavioral characteristic associated with the seizure event or the seizure precursor event or a depressive episode.

[0134] In Example 26, the subject matter of Example 25 optionally includes receiving acoustic information about the patient from a microphone coupled to the external monitoring device and analyzing the acoustic information together with the image information to identify the physiological or behavioral characteristic associated with the seizure event or the seizure precursor event. Example 26 can optionally include or use a machine learning-based model or other artificial intelligence algorithm or processing to receive the acoustic and image information as inputs and provide the event identification.

[0135] In Example 27, the subject matter of Example 26 optionally includes recording the image information and the acoustic information and providing the image information and the acoustic information to a clinician or caregiver for analysis.

[0136] In Example 28, the subject matter of any one or more of Examples 18-27 optionally includes receiving acoustic information about the patient from a microphone coupled to the external monitoring device and analyzing the acoustic information about the patient to identify a physiological or behavioral characteristic associated with the seizure event or the seizure precursor event or a depressive episode.

[0137] Example 29 is a system comprising: an external monitoring device configured to monitor physiological and / or behavioral characteristics of a patient to identify a seizure event or a seizure precursor event in the patient; and an implantable neurostimulation device configured to deliver an epilepsy therapy and a depression therapy to the patient, the implantableneurostimulation device comprising: a first electrode pair configured to provide the epilepsy therapy to a first portion of a vagus nerve; a second electrode pair configured to provide the depression therapy to a second portion of the vagus nerve; a neurostimulation signal generator circuit configured to provide first neurostimulation signals for the epilepsy therapy based on epilepsy therapy parameters, and configured to provide second neurostimulation signals for the depression therapy based on depression therapy parameters; and a control circuit configured to control the neurostimulation signal generator circuit to provide the epilepsy and depression therapies in response to a command from the external monitoring device that indicates an identified seizure event or seizure precursor event in the patient. In an example, in response to the command from the external monitoring device that indicates an identified seizure event or seizure precursor event in the patient, the control circuit is configured to command the external monitoring device to provide one or more behavioral modification commands. In a further example, a haptic actuator can be in data communication with the external monitoring device and the haptic actuator can be configured to provide the one or more behavioral modification commands to the patient.

[0138] In Example 30, the subject matter of Example 29 optionally includes the external monitoring device comprises one or more sensors configured to sense physiologic status information about the patient, and wherein the external monitoring device is configured to identify the seizure event or the seizure precursor event using the sensed physiologic status information.

[0139] In Example 31, the subject matter of any one or more of Examples 29-30 optionally includes the external monitoring device comprises a camera configured to receive images of the patient over time, and wherein the external monitoring device is configured to identify the seizure event or the seizure precursor event based on patient behavioral changes represented in the received images.

[0140] In Example 32, the subject matter of any one or more of Examples 29-31 optionally includes the external monitoring device comprises a camera configured to receive images of the patient and an acoustic sensor configured to receive acoustic information from an environment of the patient, and wherein the external monitoring device is configured to identify the seizure event or the seizure precursor event based on patient status information determined from the received images and the acoustic information.

[0141] In Example 33, the subject matter of Example 32 optionally includes the external monitoring device is configured to monitor a depression status of the patient based on information determined from the received images and the acoustic information.

[0142] Example 34 is a method for managing seizure events for a patient, the method comprising: detecting, by an external monitoring device, a seizure event, a seizure precursor event, or a depression status in the patient; in response to detecting the seizure event, the seizure precursor event, or the depression status in the patient, communicating one or more commands from the external monitoring device to an implanted neurostimulation device in the patient; and based on the one or more commands, updating or adjusting parameters of a vagus nerve stimulation (VNS) therapy provided by the neurostimulation device to a vagus nerve of the patient.

[0143] In Example 35, the subject matter of Example 34 optionally includes determining baseline behavioral or physiological characteristic information for the patient during an initial monitoring period using one or more sensors of the external monitoring device; and receiving subsequent behavioral or physiological characteristic information for the patient during a subsequent monitoring period using the same one or more sensors; wherein detecting the seizure event, the seizure precursor event, or the depression status in the patient includes comparing the baseline characteristic information with the subsequent characteristic information.

[0144] In Example 36, the subject matter of Example 35 optionally includes adjusting a weighting of inputs from the one or more sensors based on a historical event detection accuracy for the patient.

[0145] In Example 37, the subject matter of any one or more of Examples 34-36 optionally includes detecting the seizure event, the seizure precursor event, or the depression status comprises receiving and analyzing image information from a camera of the external monitoring device.

[0146] In Example 38, the subject matter of Example 37 optionally includes detecting the seizure event, the seizure precursor event, or the depression status further comprises receiving and analyzing acoustic information from a microphone of the external monitoring device together with the image information.

[0147] In Example 39, the subject matter of Example 38 optionally includes analyzing the image information comprises identifying at least one of movements, facial expression changes, or changes in sleep patterns, and wherein analyzing the acoustic information comprises identifying at least one of seizure-related vocalizations or breathing patterns.

[0148] In Example 40, the subject matter of Example 39 optionally includes establishing baseline behavioral and physiological characteristics for the patient during an initial monitoring period, and wherein the detecting comprises identifying deviations from the established baseline characteristics.

[0149] In Example 41, the subject matter of Example 40 optionally includes updating or adjusting parameters of the VNS therapy comprises: increasing therapy intensity in response to detecting the seizure event or detecting the seizure precursor event; or adjusting therapy duration in response to detecting changes in depression symptoms.

[0150] In Example 42, the subject matter of Example 41 optionally includes determining patient compliance with behavioral modification commands communicated to the patient by the external monitoring device; and providing information about the compliance determination to a clinician.

[0151] Example 43 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 1-42.

[0152] Example 44 is an apparatus comprising means to implement of any of Examples 1-42.

[0153] Example 45 is a system to implement of any of Examples 1-42.

[0154] 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 discussed herein.

[0155] 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.

[0156] 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.”

[0157] 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 ina 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.

[0158] 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 various methods. The code may 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.

[0159] 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.

[0160] 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 isessential 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 seizure management system comprising: an implantable neurostimulation device configured to deliver vagus nerve stimulation (VNS) therapy to a patient; a non-invasive external monitoring device comprising at least one sensor for detecting indicators of a seizure in the patient; wherein the external monitoring device is configured to: use information from the at least one sensor to detect a seizure event or a seizure precursor event; provide one or more signals to the patient, the one or more signals including behavioral modification commands for the patient or acoustic information that includes a question directed to the patient; and control the neurostimulation device to deliver the VNS therapy to the patient in response to the detected seizure event or seizure precursor event.

2. The seizure management system of claim 1, wherein the at least one sensor is configured to detect a physiological status indicator of a seizure in the patient.

3. The seizure management system of claim 1, wherein the external monitoring device is configured to use information from multiple sensors to detect the seizure event or the seizure precursor event, and wherein the external monitoring device is configured to adjust a weighting of the information from the sensors based on a historical event detection accuracy for the patient for each sensor.

4. The seizure management system of claim 1, wherein the at least one sensor is configured to detect a behavioral indicator of a seizure in the patient.

5. The seizure management system of claim 1, wherein the at least one sensor is configured to detect indicators of depression in the patient.

6. The seizure management system of claim 1, wherein the external monitoring device is configured to use information from the at least one sensor and vagal electroneurogram information from the implantable neurostimulation device to detect the seizure event or the seizure precursor event.

7. The seizure management system of claim 1, wherein the at least one sensor includes a camera configured to receive image information about the patient.

8. The seizure management system of claim 7, wherein the external monitoring device is configured to receive the image information about the patient and analyze the image information about the patient to identify one or more of a respiration rate, a heart rate, a tremor, or other movement of the patient that indicates the seizure event or the seizure precursor event.

9. The seizure management system of claim 7, wherein the external monitoring device is configured to receive the image information about the patient and analyze the image information about the patient to identify behavioral characteristics associated with depression in the patient.

10. The seizure management system of claim 9, wherein the external monitoring device is configured to control the neurostimulation device to update or adjust a depression therapy provided to the patient by the neurostimulation device.

11. The seizure management system of claim 1, wherein the at least one sensor includes a microphone configured to receive acoustic information from the patient.

12. The seizure management system of claim 11, wherein the external monitoring device is configured to receive the acoustic information from the patient and analyze the acoustic information to identify vocalizations orother sounds associated with the seizure event, the seizure precursor event, or a depressive status of the patient.

13. The seizure management system of claim 1, wherein the at least one sensor includes a camera configured to receive image information about the patient and a microphone configured to receive acoustic information about the patient; wherein the external monitoring device is configured to use the image information and the acoustic information together to detect the seizure event or the seizure precursor event.

14. The seizure management system of claim 1, wherein the at least one sensor includes a wearable accelerometer configured to receive movement information about the patient; wherein the external monitoring device is configured to use the movement information to detect the seizure event or the seizure precursor event.

15. The seizure management system of claim 1, wherein the external monitoring device is configured to provide the one or more signals to the patient via a haptic actuator, and wherein the haptic actuator is configured to provide a haptic command to the patient.

16. The seizure management system of claim 1, wherein the external monitoring device is configured to control a haptic stimulator device to deliver a haptic feedback signal to the patient in response to the detected seizure event or seizure precursor event.

17. The seizure management system of claim 1, wherein the external monitoring device is configured to be mounted on a structure in the patient's sleeping area.

18. The seizure management system of claim 1, wherein the external monitoring device comprises a display screen, a speaker, and / or a hapticfeedback device configured to deliver the behavioral modification commands to the patient.

19. The seizure management system of claim 1, wherein the external monitoring device is configured to communicate information about the detected seizure event or seizure precursor event to a clinician or caregiver of the patient.

20. A method for managing seizure events for a patient, the method comprising: detecting, by an external monitoring device, a seizure event or a seizure precursor event in the patient; in response to detecting the seizure event or the seizure precursor event, at least one of: communicating one or more commands from the external monitoring device to the patient; communicating one or more commands from the external monitoring device to an implanted neurostimulation device in the patient; or communicating one or more verbal questions from the external monitoring device to the patient.

21. The method of claim 20, comprising communicating the one or more commands to the implanted neurostimulation device, and based on the one or more commands, updating or adjusting a parameter of a vagus nerve stimulation (VNS) therapy provided by the neurostimulation device to a vagus nerve of the patient.

22. The method of claim 20, comprising communicating the one or more commands and / or verbal questions to the patient using a display device and / or a speaker.

23. The method of claim 22, wherein communicating the one or more commands and / or verbal questions to the patient includes audibly instructingthe patient to perform particular therapeutic movements or gestures using a speaker of the external monitoring device.

24. The method of claim 20, comprising communicating the one or more commands to the patient using a haptic feedback device coupled to the patient.

25. The method of claim 20, comprising detecting, by the external monitoring device, a depression status of the patient; and in response to detecting the depression status, communicating one or more commands to the implanted neurostimulation device to update or adjust a parameter of a depression neurostimulation therapy provided by the neurostimulation device to a vagus nerve of the patient.

26. The method of claim 20, comprising receiving at least one of image information about the patient, acoustic information about the patient, or movement information about the patient, using one or more sensors coupled to the external monitoring device; wherein detecting the seizure event or the seizure precursor event includes using the received image information, acoustic information, or movement information about the patient.

27. The method of claim 20, comprising: receiving image information about the patient from a camera coupled to the external monitoring device; and analyzing the image information about the patient to identify a physiological or behavioral characteristic associated with the seizure event or the seizure precursor event or a depressive episode.

28. The method of claim 27, comprising: receiving acoustic information about the patient from a microphone coupled to the external monitoring device; and analyzing the acoustic information together with the image information to identify the physiological or behavioral characteristic associated with the seizure event or the seizure precursor event.

29. The method of claim 28, comprising recording the image information and the acoustic information; and providing the image information and the acoustic information to a clinician or caregiver for analysis.

30. The method of claim 20, comprising: receiving acoustic information about the patient from a microphone coupled to the external monitoring device; and analyzing the acoustic information about the patient to identify a physiological or behavioral characteristic associated with the seizure event or the seizure precursor event or a depressive episode.

31. A system comprising: an external monitoring device configured to monitor physiological and / or behavioral characteristics of a patient to identify a seizure event or a seizure precursor event in the patient; and an implantable neurostimulation device configured to deliver an epilepsy therapy and a depression therapy to the patient, the implantable neurostimulation device comprising: a first electrode pair configured to provide the epilepsy therapy to a first portion of a vagus nerve; a second electrode pair configured to provide the depression therapy to a second portion of the vagus nerve; a neurostimulation signal generator circuit configured to provide first neurostimulation signals for the epilepsy therapy based on epilepsy therapy parameters, and configured to provide second neurostimulation signals for the depression therapy based on depression therapy parameters; and a control circuit configured to control the neurostimulation signal generator circuit to provide the epilepsy and depression therapies in response to a command from the external monitoring device that indicates an identified seizure event or seizure precursor event in the patient.

32. The system of claim 31, wherein in response to the command from the external monitoring device that indicates an identified seizure event or seizure precursor event in the patient, the control circuit is configured to command the external monitoring device to provide one or more behavioral modification commands.

33. The system of claim 32, comprising a haptic actuator in communication with the external monitoring device, and wherein the haptic actuator is configured to provide the one or more behavioral modification commands to the patient.

34. The system of claim 31, wherein the external monitoring device comprises one or more sensors configured to sense physiologic status information about the patient, and wherein the external monitoring device is configured to identify the seizure event or the seizure precursor event using the sensed physiologic status information.

35. The system of claim 31, wherein the external monitoring device comprises a camera configured to receive images of the patient over time, and wherein the external monitoring device is configured to identify the seizure event or the seizure precursor event based on patient behavioral changes represented in the received images.

36. The system of claim 31, wherein the external monitoring device comprises a camera configured to receive images of the patient and an acoustic sensor configured to receive acoustic information from an environment of the patient, and wherein the external monitoring device is configured to identify the seizure event or the seizure precursor event based on patient status information determined from the received images and the acoustic information.

37. The system of claim 36, wherein the external monitoring device is configured to monitor a depression status of the patient based on information determined from the received images and the acoustic information.

38. A method for managing seizure events for a patient, the method comprising: detecting, by an external monitoring device, a seizure event, a seizure precursor event, or a depression status in the patient; in response to detecting the seizure event, the seizure precursor event, or the depression status in the patient, communicating one or more commands from the external monitoring device to an implanted neurostimulation device in the patient; and based on the one or more commands, updating or adjusting parameters of a vagus nerve stimulation (VNS) therapy provided by the neurostimulation device to a vagus nerve of the patient.

39. The method of claim 38, comprising: determining baseline behavioral or physiological characteristic information for the patient during an initial monitoring period using one or more sensors of the external monitoring device; and receiving subsequent behavioral or physiological characteristic information for the patient during a subsequent monitoring period using the same one or more sensors; wherein detecting the seizure event, the seizure precursor event, or the depression status in the patient includes comparing the baseline characteristic information with the subsequent characteristic information.

40. The method of claim 39, comprising adjusting a weighting of inputs from the one or more sensors based on a historical event detection accuracy for the patient.

41. The method of claim 38, wherein detecting the seizure event, the seizure precursor event, or the depression status comprises receiving and analyzing image information from a camera of the external monitoring device.

42. The method of claim 41, wherein detecting the seizure event, the seizure precursor event, or the depression status further comprises receiving andanalyzing acoustic information from a microphone of the external monitoring device together with the image information.

43. The method of claim 42, wherein analyzing the image information comprises identifying at least one of movements, facial expression changes, or changes in sleep patterns, and wherein analyzing the acoustic information comprises identifying at least one of seizure-related vocalizations or breathing patterns.

44. The method of claim 43, further comprising establishing baseline behavioral and physiological characteristics for the patient during an initial monitoring period, and wherein the detecting comprises identifying deviations from the established baseline characteristics.

45. The method of claim 44, wherein updating or adjusting parameters of the VNS therapy comprises: increasing therapy intensity in response to detecting the seizure event or detecting the seizure precursor event; or adjusting therapy duration in response to detecting changes in depression symptoms.

46. The method of claim 45, further comprising: determining patient compliance with behavioral modification commands communicated to the patient by the external monitoring device; and providing information about the compliance determination to a clinician.

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