Detection of seizures and strokes

Abstract

Devices, systems, and techniques are described that utilize a compact system to detect stroke or seizure. For example, a system includes a memory, a plurality of electrodes, and sensing circuitry configured to sense, via at least two electrodes of the plurality of electrodes, electrical signals from a patient and generate physiological information based on the electrical signals. The system can also include processing circuitry configured to receive the physiological information from the sensing circuitry, determine, based on the physiological information, a seizure metric indicative of a seizure state of the patient and a stroke metric indicative of a stroke state of the patient, and store the seizure metric and the stroke metric in the memory. A housing can carry the plurality of electrodes and contain both the sensing circuitry and the processing circuitry.

Description

[0001] This application claims priority to U.S. Provisional Patent Application Serial No. 63 / 071,828, filed August 28, 2020, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This disclosure relates to medical devices, and more specifically, to systems and methods for detecting epileptic seizures and strokes. Background Technology

[0003] Stroke is a serious medical condition that can lead to permanent neurological damage, complications, and death. Stroke can be characterized as a rapidly developing loss of brain function due to disordered blood vessels supplying the brain. This loss of brain function may result from localized ischemia (insufficient blood supply) caused by thrombosis or embolism, which may be a result of bleeding (e.g., ruptured blood vessel). During a stroke, reduced blood supply to a brain region can lead to abnormal function of brain tissue in that region.

[0004] Stroke is the second leading cause of death and the leading cause of disability worldwide. The speed of treatment is a critical factor in stroke management, as an average of 1.9 million neurons are lost every minute during a stroke. The time between stroke diagnosis and the delivery of treatment are major obstacles to improving treatment outcomes. Stroke has three main causes: i) ischemic stroke (accounting for approximately 65% ​​of all strokes); ii) hemorrhagic stroke (accounting for approximately 10% of all strokes); and iii) cryptogenic stroke (including transient ischemic attacks (TIAs), accounting for approximately 25% of all strokes). Stroke can be considered to have neurogenic and / or cardiac origins.

[0005] There are various approaches available for treating patients who have experienced a stroke. For example, clinicians may administer anticoagulants such as warfarin, or perform endovascular interventions such as thrombectomy to treat ischemic stroke. As another example, clinicians may administer antihypertensive drugs such as beta-blockers (e.g., labetalol) and ACE inhibitors (e.g., enalapril), or perform endovascular interventions such as coil embolization to treat hemorrhagic stroke. Finally, if stroke symptoms resolve spontaneously and neurological examination is negative, clinicians may administer long-term cardiac monitoring (external or implantable) to determine the underlying cardiac origin of cryptogenic stroke.

[0006] Other conditions also affect people. For example, 65 million people worldwide have epilepsy, including 3.4 million in the United States. In the US alone, epilepsy causes approximately 3,400 deaths each year. In some cases, patients may have seizures that are misdiagnosed as epilepsy. For example, one in four patients currently diagnosed with epilepsy is eventually diagnosed with symptoms originating from other causes, such as non-brain-related seizures. People with epilepsy may also have other conditions, as about a quarter of them also have heart rhythm disorders. Treatment for epilepsy can include lifestyle modifications and / or medication. Summary of the Invention

[0007] Generally, this disclosure relates to devices, systems, and techniques for detecting epileptic seizures (e.g., cardiogenic and / or neurogenic epileptic seizures) or strokes via a medical device located on a patient's head (e.g., an implantable medical device (IMD) or an external medical device). For example, an IMD may include multiple electrodes carried by a housing of the device. The IMD may be implanted subcutaneously in a region of the skull, such as the occipital region or the base of the skull. From this location, the IMD is able to record signals from the electrodes carried on the housing. These electrical signals may contain components attributable to brain function and components contributing to cardiac function. The IMD can process the sensed electrical signals to determine a stroke metric indicative of a stroke in the patient and a seizure metric indicative of an epileptic seizure in the patient. Thus, the IMD is capable of detecting stroke and / or epileptic seizure events in a patient from a single device. The IMD can transmit information representing any detected stroke or epileptic seizure to an external device. In some examples, the IMD can transmit information representing a stroke or epileptic seizure to another IMD configured to deliver treatment (such as electrical stimulation therapy and / or drug delivery therapy) to the patient.

[0008] The technology disclosed herein can provide one or more advantages. For example, it may be beneficial for a single subcutaneous implantable device to be configured to detect a stroke or epileptic seizure in a patient. In this way, the IMD can screen patients for potential strokes or epileptic seizures that may inform subsequent treatment. Furthermore, stroke and epileptic seizures may be conditions experienced by a patient simultaneously. For example, a patient with epilepsy may be at higher risk of experiencing a stroke. However, these patients may not have other implantable devices capable of identifying strokes or epileptic seizures. Therefore, clinicians can implant an IMD carrying electrodes in a small package to identify any stroke or epileptic seizure event. In addition, the IMD can transmit indications of a stroke or epileptic seizure to an external device to facilitate treatment of any event that occurs. In this way, the IMD described herein can be used to detect strokes or epileptic seizures in a variety of patients, such as those who have experienced traumatic brain injury, migraine, brain infection, brain tumor, dementia, sleep disorder, or other conditions.

[0009] In one example, a system includes: a memory; a plurality of electrodes; a sensing circuit system configured to: sense electrical signals from a patient via at least two of the plurality of electrodes; and generate physiological information based on the electrical signals; a processing circuit system configured to: receive the physiological information from the sensing circuit system; determine, based on the physiological information, a seizure metric indicating the patient's seizure state and a stroke metric indicating the patient's stroke state; and store the seizure metric and the stroke metric in the memory; and a housing that carries the plurality of electrodes and contains both the sensing circuit system and the processing circuit system.

[0010] In another example, a method includes: sensing an electrical signal from a patient via a sensing circuit system and via at least two of a plurality of electrodes; generating physiological information via the sensing circuit system and based on the electrical signal; receiving the physiological information via and from the sensing circuit system via a processing circuit system; determining a seizure metric indicative of the patient's seizure state and a stroke metric indicative of the patient's stroke state via the processing circuit system and based on the physiological information; and storing the seizure metric and the stroke metric in a memory via the processing circuit system, wherein the housing carries the plurality of electrodes and includes both the sensing circuit system and the processing circuit system.

[0011] In another example, a computer-readable medium including instructions that, when executed, cause a processing circuit system to: control a sensing circuit system to sense electrical signals from a patient via at least two of a plurality of electrodes; control the sensing circuit system to generate physiological information based on the electrical signals; receive the physiological information from the sensing circuit system; determine, based on the physiological information, a seizure measure indicating the patient's seizure state and a stroke measure indicating the patient's stroke state; and store the seizure measure and the stroke measure in a memory, wherein the housing carries the plurality of electrodes and contains both the sensing circuit system and the processing circuit system.

[0012] This summary is intended to provide an overview of the subject matter described in this disclosure. It is not intended to provide an exclusive or exhaustive explanation of the systems, apparatus, and methods described in detail in the following drawings and specification. Further details of one or more examples of this disclosure are set forth in the drawings and the following detailed description. Other features, objectives, and advantages will be apparent from the specification, drawings, and claims. Attached Figure Description

[0013] Figure 1A This is a conceptual diagram of a system configured to detect stroke or epileptic seizures, based on an example of this disclosure.

[0014] Figure 1BThis is a conceptual diagram of a system configured to detect stroke or epileptic seizures, based on an example of this disclosure.

[0015] Figure 1C It is a map of 10-20 mappings used for electroencephalogram (EEG) sensor measurements.

[0016] Figure 2A A top view depicting a sensor device according to an embodiment of the present technology.

[0017] Figure 2B Depicting according to this technology Figure 2A The side view of the sensor device shown.

[0018] Figure 2C A top view depicting another embodiment of the sensor device according to the present technology.

[0019] Figure 2D A side view depicting another embodiment of the sensor device according to the present technology.

[0020] Figure 2E A side view depicting another embodiment of the sensor device according to the present technology.

[0021] Figure 2F A side view depicting another embodiment of the sensor device according to the present technology.

[0022] Figure 2G A top view depicting another exemplary sensor device according to the examples of this disclosure.

[0023] Figure 2H A top view depicting another exemplary sensor device according to the examples of this disclosure.

[0024] Figure 2I A top view depicting another exemplary sensor device including an electrode extension according to an example of this disclosure.

[0025] Figure 2J , Figure 2K , Figure 2L , Figure 2M , Figure 2N , Figure 2P and Figure 2Q An exemplary sensor device including an electrode extension is depicted according to an example of this disclosure.

[0026] Figure 2Q An exemplary sensor device comprising an electrode extension combined with a patient is depicted according to an example of this disclosure.

[0027] Figure 3A , Figure 3B and Figure 3CAnother sensor device is depicted according to an embodiment of the present technology.

[0028] Figure 4 This is a block diagram of an exemplary sensor device configured to detect stroke or epileptic seizures.

[0029] Figure 5 It is configured to be with Figure 4 A block diagram of an exemplary external device for communicating with a sensor device.

[0030] Figure 6 This is a block diagram illustrating an exemplary system according to one or more technologies described herein. The exemplary system includes an access point, a network, an external computing device (such as a server), and one or more other computing devices that may be coupled to the IMD, external devices, and processing circuitry system of FIG1 via the network.

[0031] Figure 7 This is a flowchart of an exemplary technique for detecting at least one of a stroke or epileptic seizure in a patient.

[0032] Figure 8 This is a flowchart of an exemplary technique for timed detection of stroke or epileptic seizures based on one or more triggering events.

[0033] Figure 9 This is a flowchart of an exemplary technique for adjusting the frequency of epileptic seizure detection based on arrhythmia detection.

[0034] Figure 10 This is a flowchart of an example technique for determining the type of epileptic seizure based on electrocardiogram information.

[0035] Many aspects of this disclosure can be better understood by referring to the following accompanying drawings. The components in the drawings are not necessarily drawn to scale. Rather, the focus is on clearly illustrating the principles of the technology. Detailed Implementation

[0036] This disclosure describes various systems, devices, and techniques for detecting stroke and seizures from a device located on a patient's head. It can be difficult to determine whether a patient has had a stroke or has already had one. Current diagnostic techniques typically involve assessing a patient's visible symptoms, such as numbness or tingling in the face, arm, or leg, and difficulty walking, speaking, or understanding (e.g., the FAST (Face, Arm, Speech, Time) visual stroke indicator used to call for emergency help). However, these techniques can lead to undiagnosed strokes, particularly milder strokes that allow the patient to be relatively active after a cursory assessment. Even for relatively mild strokes, prompt treatment is crucial because the effectiveness of stroke treatment is highly time-dependent. Therefore, improved methods for stroke detection are needed. However, such treatments often may be underutilized and / or relatively ineffective due to the failure to promptly identify whether a patient is currently experiencing or has recently experienced a stroke. This poses a specific risk for milder strokes that allow the patient to be relatively active after a cursory assessment.

[0037] Similarly, it can be difficult to detect or identify epileptic seizures from other types of seizures. For example, many conditions can present as recurrent seizures that are misclassified as epileptic seizures. Syncope is an exemplary condition that can be caused by other physiological problems but is often misdiagnosed as epilepsy due to recurrent seizures. Furthermore, some patients exhibit physical manifestations of seizures, such as twitching movements of the arms and legs; other symptoms of seizures can include transient confusion, staring, loss of consciousness or perception, or emotional symptoms such as fear, anxiety, or memory illusions. When a patient is experiencing a seizure, they may not understand the symptoms or accurately identify what is happening. Additionally, patients may not be able to obtain or request interventions such as medication. Therefore, proper diagnosis is even more challenging. In some cases, deep brain stimulation (DBS) devices can detect seizures and deliver electrical stimulation via electrodes implanted in the brain to prevent or reduce the symptoms of seizures. However, such DBS devices require invasive implantation surgery and may not be suitable for patient screening or diagnosis.

[0038] As described herein, medical devices (e.g., IMDs or external devices that can be worn by a patient) can be configured to detect stroke and seizures from a location on or near the patient's head. For example, an IMD can be configured for subcutaneous implantation without requiring any medical leads. In some examples, instead of leads, the IMD may include a housing that directly houses multiple electrodes. Using these housing electrodes, the IMD can sense electrical signals from one or more vectors and generate physiological information representing the patient's condition. This physiological information can indicate brain activity and / or the activity of other organs, such as the heart. The physiological information may include stroke information and / or seizure information. For example, different sensing circuits can generate stroke and seizure information such that appropriate filters and amplifiers can extract relevant components for stroke and seizure detection, respectively. The IMD can then generate a stroke metric indicating whether the patient has experienced a stroke, and / or a seizure metric indicating whether the patient has experienced a seizure, from the appropriate physiological information. For example, IMD can detect and differentiate between different types of epileptic seizures, such as (1) epileptic seizures (with changes observed on an electroencephalogram (EEG)), (2) syncope (epileptic seizures with a cardiac origin and no changes on an EEG), and (3) psychogenic events caused by psychological reasons and without changes on an EEG (e.g., also known as psychogenic nonepileptic seizures (PNES), nonepileptic paroxysmal disorders (NEAD), or psychogenic pseudosyncope (PPS)). In some examples, the seizure metric can identify which type of epileptic seizure has been detected.

[0039] An IMD can store stroke and seizure metrics over time. The IMD can periodically or in response to a triggering event, such as detecting a stroke or seizure that the patient is experiencing, to an external device. In other examples, the IMD can transmit stroke and / or seizure metrics to another IMD or external medical device configured to deliver electrical stimulation therapy and / or drug delivery therapy. In some examples, the IMD can generate stroke and seizure metrics at different frequencies as needed to provide appropriate monitoring for the patient while conserving power. For example, the IMD can generate seizure metrics at a higher frequency than stroke metrics because a seizure may only last a few minutes, while the characteristics of a stroke may last for tens of minutes or even hours. In other examples, the IMD can trigger the generation of seizure and / or stroke metrics separately in response to a triggering event indicating an increased risk of seizure or stroke.

[0040] Conventional EEG electrodes are typically positioned over a large portion of the user's scalp. While electrodes in this region are well-positioned to detect electrical activity from the patient's brain, several drawbacks exist. Sensors in this location interfere with the patient's movement and daily activities, making them impractical for long-term monitoring. Additionally, implanting conventional electrodes under the patient's scalp is difficult and can cause significant patient discomfort. To address these and other drawbacks of conventional EEG sensors, embodiments of this technology include an IMD configured to record electrical signals in areas near the patient's head, such as the posterior part of the neck, the base of the skull, or near the temples. In these locations, implantation under the patient's skin is relatively simple, and temporary applications of the wearable sensor device (e.g., coupled to bandages, clothing, straps, or adhesive components) do not unduly interfere with the patient's movement and activities. Although described primarily in the context of leadless sensor devices, some examples, such as those concerning… Figures 2I-2Q As described, the sensor device may include electrode extensions. Electrode extensions may increase the magnitude of the vector used to sense signals (such as brain and heart signals) via the electrodes, and / or may position the electrodes closer to sources of brain and heart signals, which could enhance the sensitivity of algorithms using such signals to detect and / or predict patient conditions.

[0041] However, EEG signals detected via electrodes positioned at or near the back of a patient's neck can include relatively high noise levels. For example, electrical signals associated with brain activity can interact with electrical signals associated with cardiac activity (e.g., ECG signals) or signals including components associated with the mechanical and muscular activity of the heart (e.g., EMG signals), as well as artifacts from other power sources (such as patient movement or external interference). Therefore, in some embodiments, sensor data can be filtered or otherwise manipulated to separate brain activity data (e.g., EEG signals) and ECG signals (or other cardiac signals) from each other and from other electrical signals (e.g., EMG signals, etc.).

[0042] As described in more detail below, in some implementations, physiological information can be analyzed to determine a stroke or seizure based on one or more thresholds, correlations between signals, or the use of a classification algorithm, which itself can be derived using machine learning techniques applied to a database of data from patients known to have had strokes and / or seizures. One or more detection algorithms can be passive (involving measurements on a patient at complete rest) or active (involving prompting the patient to perform potentially impaired functions, such as moving specific muscle groups (e.g., raising an arm, moving fingers, moving facial muscles, etc.) and / or speaking while recording electrical responses) or derived from electrical stimulation or other stimuli.

[0043] The aspects of the technology described herein can be embodied in a dedicated computer or data processor specifically programmed, configured, or constructed to execute one or more of the computer-executable instructions explained in detail herein. The aspects of the technology can also be practiced in a distributed computing environment where tasks or modules are performed by a remote processing device connected via a communication network (e.g., a wireless communication network, a wired communication network, a cellular communication network, the Internet, a short-range radio network (e.g., via Bluetooth)). In a distributed computing environment, program modules can reside in both local memory storage and remote memory storage.

[0044] The computer-implemented instructions, data structures, screen displays, and other data under various aspects of this technology can be stored or distributed on computer-readable storage media, including magnetically or optically readable computer disks, as microcode on semiconductor memory, nanotechnology memory, organic or optical memory, or other portable and / or non-transient data storage media. In some embodiments, aspects of this technology can be distributed over a period of time on a propagating signal on a propagating medium (e.g., electromagnetic waves, sound waves) on the Internet or other networks (e.g., Bluetooth networks), or can be provided on any analog or digital network (packet switching, circuit switching, or other schemes).

[0045] Figure 1A This is a conceptual diagram of a system configured to detect a stroke or epileptic seizure, according to an example of this disclosure. The exemplary techniques described herein can be used with an implantable medical device (IMD) 106, which can be used with an external device 108, a processing circuitry system 110, and... Figure 1A At least one of the other devices not shown in the diagram performs wireless communication. For example, an external device ( Figure 1A (Not shown) may include at least a portion of processing circuitry system 110, the external device being configured to communicate with IMD 106 and external device 108. For example... Figure 1AAs shown, the IMD 106 is located in the target region 104. The target region 104 may be the back of the user's neck or located at the base of the skull. Although the IMD 106 may be implanted in a position generally centered relative to the head, neck, or target region 104, the IMD 106 may be implanted in an off-center position to obtain the desired vector from electrodes carried on the shell of the IMD 106. In other examples, the target region may be located in other locations on the patient, such as near one or both temples of the user (e.g., above one or both ears) and / or above the temporal portion of the skull. The IMD 106 may be positioned in the target region 104 via implantation (e.g., subcutaneously) or by being placed on the patient's skin, wherein one or more electrodes of the IMD 106 are in direct contact with the patient's skin at or near the target region 104.

[0046] While conventional EEG electrodes are placed on the patient's scalp, this technique advantageously enables the recording of clinically useful brain activity data via electrodes positioned at the target region 104 in the back of the patient's neck or head, or other skull locations (such as the temporal region described herein). This anatomical region is well-suited for implantation of the IMD 106 and temporary placement of the sensor device on the patient's skin. In contrast, conventional EEG electrodes positioned on the scalp are bulky and implantation in the patient's skull is challenging and can cause significant discomfort. As described elsewhere herein, conventional EEG electrodes are typically positioned on the scalp to more easily achieve an appropriate signal-to-noise ratio for detecting brain activity. However, by using specific digital signal processing and dedicated classifier algorithms, clinically useful brain activity data can be obtained using a sensor positioned at the target region 104. Specifically, the electrode can detect regions corresponding to P3, P2, and / or P4 (such as... Figure 1C Electrical activity of brain activity (as shown in the diagram).

[0047] While conventional methods for stroke detection using EEG rely on data from a large number of EEG electrodes, this disclosure describes the ability to determine clinically useful strokes and seizures using relatively few electrodes, such as those carried by the IMD 106. For example, the IMD 106 can extract features from EEG signals indicative of brain or cardiac activity. The IMD 106 can then determine whether a patient has experienced a stroke or seizure based on these extracted features. In some examples, the IMD 106 employs LINQ. TM The insertable cardiac monitor (ICM) is available from Medtronic plc (Dublin, Ireland). Exemplary technology may additionally or alternatively be used with... Figure 1AIt may be used with medical devices not shown, such as another type of IMD, patch monitor device, wearable device (e.g., smartwatch) or another type of external medical device.

[0048] Clinicians sometimes diagnose a patient with a medical condition (e.g., patient 102) and / or determine whether patient 102's condition is improving or worsening based on one or more observed physiological signals collected by physiological sensors (such as electrodes, optical sensors, chemical sensors, temperature sensors, acoustic sensors, and motion sensors). In some cases, clinicians apply non-invasive sensors to the patient to sense one or more physiological signals while the patient is at the clinic for a medical appointment. However, in some examples, events that may alter a patient's condition (such as the administration of treatment) may occur outside the clinic. Therefore, in these examples, clinicians may not be able to simultaneously monitor one or more of the patient's physiological signals during a medical appointment to observe the physiological markers needed to determine whether an event (such as a seizure or stroke) has altered the patient's medical condition and / or to determine whether the patient's medical condition is improving or worsening. Figure 1A In the example shown, IMD 106 is implanted in patient 102 to continuously record one or more physiological signals of patient 102 over an extended period of time.

[0049] In some examples, the IMD 106 includes multiple electrodes. These electrodes are configured to detect signals that enable the processing circuitry of the IMD 106 to determine current values ​​of stroke and seizure metrics associated with the brain and / or cardiovascular function of the patient 102. In some examples, the multiple electrodes of the IMD 106 are configured to detect signals indicating the potential of the tissue surrounding the IMD 106. Furthermore, in some examples, the IMD 106 may additionally or alternatively include one or more optical sensors, accelerometers, impedance sensors, respiratory sensors, temperature sensors, chemical sensors, light sensors, pressure sensors, and acoustic sensors. Such sensors can detect one or more physiological parameters indicative of the patient's condition. In some examples, the IMD 106 may be implanted such that sensors (such as impedance sensors) detect properties of the skin, such as impedance sensors configured to detect changes in the skin that may be related to temperature changes or sweating, which may be a result of one or more types of seizures.

[0050] External device 108 may be a handheld computing device having a display that a user can view and an interface (e.g., a user input mechanism) for providing input to external device 108. For example, external device 108 may include a small display screen (e.g., a liquid crystal display (LCD) or a light-emitting diode (LED) display) that presents information to the user. In addition, external device 108 may include a touchscreen display, a keypad, buttons, peripheral pointing devices, voice activation, or another input mechanism that allows the user to navigate and provide input through the user interface of external device 108. If external device 108 includes buttons and a keypad, the buttons may be dedicated to performing a certain function (e.g., a power button), and the buttons and keypad may be soft keys whose functions change depending on the portion of the user interface currently being viewed by the user, or any combination thereof.

[0051] In other examples, external device 108 may be a separate application within a larger workstation or another multi-functional device, rather than a dedicated computing device. For example, the multi-functional device may be a laptop computer, tablet computer, workstation, one or more servers, cellular phone, personal digital assistant, or another computing device that can run an application that enables the computing device to operate as a secure device.

[0052] When the external device 108 is configured for use by a clinician, it can be used to transmit instructions to the IMD 106. Exemplary instructions may include requests to set electrode combinations for sensing and any other information that may be used to program into the IMD 106. The clinician can also configure and store operating parameters of the IMD 106 within the IMD 106 with the assistance of the external device 108. In some examples, the external device 108 assists the clinician in configuring the IMD 106 by providing a system for identifying potentially beneficial operating parameter values.

[0053] Regardless of whether the external device 108 is configured for use by a clinician or a patient, the external device 108 is configured to communicate wirelessly with the IMD 106 and optionally with another computing device. Figure 1A (Not shown in the image) Communication. For example, external device 108 can communicate via near-field communication technology (e.g., inductive coupling, NFC, or other communication technologies capable of operating within a range of less than 10cm-20cm) and far-field communication technology (e.g., according to 802.11 or...). Communication is achieved through RF telemetry of the specification set or other communication technologies capable of operating beyond the range of near-field communication technologies. In some examples, the external device 108 is the patient 102's smartphone and / or watch or other wearable computing device, which can communicate, for example, via Bluetooth. TMCommunicates with sensor device 106. In some examples, external device 108 is configured to communicate with computer networks (such as those developed by Medtronic, a company based in Dublin, Ireland). (Network) communication. For example, external device 108 can send data (such as data received from IMD 106) to another external device (such as a smartphone, tablet, or desktop computer), and the other external device can then forward the data to the computer network. In other examples, external device 108 can communicate directly with the computer network without any intermediary device.

[0054] In some examples, the processing circuitry 110 may include one or more processors configured to implement functional and / or procedural instructions for execution within the IMD 106. For example, the processing circuitry 110 may be capable of processing instructions stored in a storage device. The processing circuitry 110 may include, for example, a microprocessor, a graphics processing unit (GPU), a tensor processing unit (TPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or an equivalent discrete or integrated logic circuitry system, or a combination of any of the foregoing devices or circuitry systems. Therefore, the processing circuitry 110 may include any suitable structure (whether hardware, software, firmware, or any combination thereof) to perform the functions of the processing circuitry 110 described herein.

[0055] Processing circuitry 110 may refer to a processing circuitry located within either or both of IMD 106 and external device 108. In some examples, processing circuitry 110 may be entirely located within the housing of IMD 106. In other examples, processing circuitry 110 may be entirely located within the housing of external device 108. In still other examples, processing circuitry 110 may be located within IMD 106, external device 108, and... Figure 1A Any other device or group of devices not shown, or any combination thereof. Therefore, the techniques and capabilities attributed herein to processing circuit system 110 are attributed to IMD 106, external device 108, and... Figure 1A Any combination of other devices not shown in the diagram.

[0056] Figure 1AThe medical device system 100A is an example of a system configured to collect electrical signals and generate stroke and seizure measurements according to one or more techniques of this disclosure. In some examples, the processing circuitry system 110 includes sensing circuitry configured to generate physiological information from the sensed electrical signals of the patient 102. In one example, the electrical signals are sensed via one or more electrode combinations of the IMD 106. The electrical signals represent electrical activity of brain function, cardiac function, or other physiological functions as measured by electrodes implanted in the body. For example, among other events, the sensed electrical signals may include characteristics representing cardiac function such as P waves (atrial depolarization), R waves (ventricular depolarization), and T waves (ventricular repolarization). Information related to the aforementioned events, such as the timing of one or more events, can be used for a variety of purposes, such as determining whether an arrhythmia is occurring and / or predicting whether an arrhythmia may occur. A cardiac signal analysis circuitry system that may be implemented as part of the processing circuitry system 110 may perform signal processing techniques to extract information indicative of one or more parameters of the cardiac signals. In other examples, the sensed electrical signals may include features representing brain function, such as the amplitude of frequencies in one or more frequency bands (e.g., alpha, beta, or gamma bands). A brain signal analysis circuitry system, which may be implemented as part of processing circuitry system 110, can perform various processing circuitry systems to extract these brain features from the sensed electrical signals. In some examples, the sensed signals may be alternatives to brain electrical signals and cardiac electrical signals (e.g., EEG or ECG signals). Pulsating signals sensed from the scalp vascular system correspond to ventricular contractions and ECG R waves, albeit with a slight timing delay.

[0057] In some examples, the IMD 106 includes one or more accelerometers. The accelerometers of the IMD 106 can collect accelerometer signals reflecting measurements of one or more of the patient 102's motion, posture, and body angles. In some cases, the accelerometers can collect triaxial accelerometer signals indicating the patient 102's movement in three-dimensional Cartesian space. For example, the accelerometer signals may include a vertical axis accelerometer signal vector, a horizontal axis accelerometer signal vector, and a front axis accelerometer signal vector. The vertical axis accelerometer signal vector may represent the patient 102's acceleration along the vertical axis, the horizontal axis accelerometer signal vector may represent the patient 102's acceleration along the horizontal axis, and the front axis accelerometer signal vector may represent the patient 102's acceleration along the front axis. In some cases, as the patient 102 extends from the neck to the waist, the vertical axis extends substantially along the torso of the patient 102, the horizontal axis extends perpendicularly to the vertical axis across the chest of the patient 102, and the frontal axis extends outward from the chest of the patient 102 and extends through the chest of the patient, perpendicular to both the vertical and horizontal axes.

[0058] The IMD 106 can measure a set of parameters, including the impedance of the patient 102 (e.g., via...). Figures 2A-2Q The parameters described in the diagram include subcutaneous impedance, intrathoracic impedance, or intracardiac impedance; respiratory rate of patient 102 during the nighttime period; respiratory rate of patient 102 during the daytime period; heart rate of patient 102 during the nighttime period; heart rate of patient 102 during the daytime period; atrial fibrillation (AF) load of patient 102; ventricular rate of patient 102 when patient 102 is experiencing AF; or any combination thereof. The processing circuitry system 110 can analyze any one or more parameters in this set to determine whether the patient is experiencing a stroke or seizure and can indicate the effectiveness of treatment regimens administered to patient 102. In some examples, pulsatile signals optically or mechanically sensed from the scalp vascular system, such as via electrodes, optical sensors, accelerometers, pressure sensors, impedance sensors, or phonocardiogram sensors, can provide alternatives to ECG or other cardiac electrical activity signals. In some examples, the treatment regimen may include treatment delivered by one or more medical devices such as: an ICD with intravascular or extravascular leads, a pacemaker, a CRT-D, a neuromodulation device, an LVAD, an implantable sensor, an orthopedic device, or a drug pump. Alternatively or additionally, the treatment regimen may include clinical treatment administered by a medical professional, prescription drug therapy, treatment administered by one or more external medical devices, or any combination thereof. In any case, the processing circuitry 110 may determine the efficacy of the treatment regimen by determining the time at which the treatment regimen was administered (e.g., including the time of start and / or end of the treatment regimen) and analyzing the value of any one or any combination of parameters in that set relative to the time of administration. Alternatively, in some examples, the processing circuitry 110 may determine the efficacy of the treatment regimen by evaluating one or more parameters on a cyclical basis to determine whether one or more parameters have changed over a period of time.

[0059] In some examples, one or more sensors of the IMD 106 (e.g., electrodes, motion sensors, optical sensors, temperature sensors, or any combination thereof) can generate signals indicating parameters of the patient. In some examples, the signals indicating parameters include multiple parameter values, where each of the multiple parameter values ​​represents a parameter measurement value at a corresponding time interval. The multiple parameter values ​​can represent a sequence of parameter values, where each parameter value in the sequence of parameter values ​​is collected by the IMD 106 at the beginning of each time interval of the time interval sequence. For example, the IMD 106 can perform parameter measurements to determine the parameter values ​​of the parameter value sequence according to cyclic time intervals (e.g., daily, nightly, every other day, every twelve hours, hourly, or any other cyclic time interval). In this way, the IMD 106 can be configured to track corresponding patient parameters more efficiently than techniques in which patient parameters are tracked during a patient's visit to a clinic, because the IMD 106 is implanted in the patient 102 and configured to perform parameter measurements according to cyclic time intervals without missing time intervals or performing parameter measurements out of schedule. The processing circuitry 110 can determine these different parameters independently of the seizure metric or stroke metric, or at least in part based on one or more other parameter measurements.

[0060] The IMD 106 may be referred to as a system or device. In one example, the IMD 106 may include a memory, a plurality of electrodes carried by a housing of the IMD 106, and a sensing circuitry configured to sense electrical signals from the patient 10 via at least two of the plurality of electrodes and generate physiological information based on the electrical signals. The IMD 106 may also include a processing circuitry configured to receive the physiological information from the sensing circuitry and, based on the physiological information, determine a seizure metric indicative of the patient's seizure state and a stroke metric indicative of the patient's stroke state. The processing circuitry may be configured to then store the seizure metric and the stroke metric in the memory. The housing of the IMD 106 carries the plurality of electrodes and contains or accommodates both the sensing circuitry and the processing circuitry. In this way, the IMD 106 may be referred to as a leadless sensing device because the electrodes are carried directly by the housing rather than by any leads extending from the housing. However, in some examples, the sensor device 106 may include one or more sensing leads extending from the sensor device and into the patient's tissue. One or more such leads can be used to replace the electrodes of sensor device 106 (e.g., such as...). Figures 2I-2Q Such leads may be used in addition to the electrodes of the sensor device (as depicted in the text), and such leads may perform any function attributed to electrodes herein.

[0061] Physiological data may include brain electrical activity data and / or cardiac electrical activity data. In some examples, multiple electrodes are configured to detect brain activity information corresponding to activity in at least one of the P3, Pz, or P4 brain regions located in the occipital or upper cervical region, such as... Figure 1C As shown in the diagram. In this way, the shell of the IMD 106 can be configured to be disposed in or near the neck or posterior part of the skull of the patient 102. The shell of the IMD 106 can be configured to be implanted into the patient 104, such as through subcutaneous implantation. In other examples, the shell of the IMD 106 can be configured to be disposed on the outer surface of the skin of the patient 102.

[0062] In some examples, IMD 106 may include a single sensing circuitry configured to generate information from sensed electrical signals that includes both electroencephalographic activity data (e.g., electroencephalogram (EEG) data) and electrocardiographic activity data (e.g., electrocardiogram (ECG) data or heart contractions). In other examples, the processing circuitry of IMD 106 may include separate hardware that generates different information from the sensed signals. For example, IMD 106 may include: a first circuitry configured to generate brain activity from electrical signals; and a second circuitry, distinct from the first circuitry and configured to generate cardiac activity data from the electrical signals. Even if the first and second circuitry are configured to generate different information or data, in some examples, the sensed electrical signals may be modulated or processed by one or more electrical components (e.g., filters or amplifiers) before being processed by the first and second circuitry. In some examples, the brain electrical activity data may include features, such as spectral characteristics, indicating signal strength in various frequency bands or at various frequencies. In this way, IMD 106 can generate seizure metrics based on this EEG activity data. In some examples, cardiac electrical activity data may include features such as the timing and / or amplitude of P waves and R waves, or any other features representing cardiac function.

[0063] Each of the stroke metric and the seizure metric can indicate the likelihood (or risk) that the patient 102 has experienced or is experiencing a stroke or seizure. For example, each stroke metric and seizure metric can include a numerical value representing the probability that the patient 102 has experienced a stroke or seizure. The IMD 106 can then compare the metric to a corresponding threshold or monitor the relative change of the metric value over time to determine whether a stroke or seizure has occurred. In other examples, the stroke metric and / or seizure metric can be binary values ​​indicating whether no event occurred or whether an event did occur. In some examples, the IMD 106 can generate each stroke metric and / or seizure metric based on sensed data in addition to sensed signals from electrodes carried on the housing of the IMD 106.

[0064] In one example, IMD 106 may include one or more accelerometers within a housing. The accelerometers may be configured to generate motion data representing the movement of patient 102. IMD 106 may then be configured to determine based on physiological data including motion data, seizure metrics, and stroke metrics. For example, body movement or lack of body movement may indicate the type of seizure experienced by patient 102. In one example, IMD 106 may utilize signals generated by the accelerometers to at least partially classify seizures as motor seizures or non-motor seizures. For motor seizures, IMD 106 may also utilize accelerometer signals to classify motor seizures as tonic-clonic seizures or some other type of motor seizure. As another example, certain body movements or behaviors (e.g., movement patterns) may indicate a stroke. In one example, the processing circuitry of IMD 106 may be configured to determine, based on motion data, that patient 102 has fallen. In response to determining that patient 102 has fallen, the processing circuitry may be configured to determine or notify of a stroke metric based on the determination that the patient has fallen. The IMD 106 can correlate motion data from fall detection with stroke metrics based on time. For example, the IMD 106 can consider a time window from the detection of a fall to other factors associated with a stroke metric. In this way, if a fall detection occurs within a predetermined or calculated time window based on one or more other factors associated with a stroke metric, the fall detection can be included or further weighted, as it can predict stroke. Fall detection can be a factor in a stroke metric because a stroke can cause a patient to fall. Therefore, by combining other features extracted from the sensed electrical signals, the IMD 106 can determine from the fall indication a stroke metric indicating a stroke detection. In other examples, the IMD 106 can determine that a characteristic of the motion data exceeds a threshold. For example, the threshold could be an acceleration value indicating a fall. In response to determining that a characteristic of the motion data exceeds a threshold, the processing circuitry of the IMD 106 can determine at least one of a seizure metric or a stroke metric. For example, for a seizure, the frequency of motion data exceeding a frequency threshold can indicate body movements caused by a seizure.

[0065] In some examples, the physiological information generated from the sensed electrical signals may include ECG information. IMD 106 can extract various features from ECG information (such as heart rate, heart rate variability, etc.). IMD 106 can determine that patient 102 has experienced a seizure based on seizure metrics and select a seizure type representing the patient's experience from multiple seizure types based on ECG information. For example, seizure types may include single seizures, stroke-induced seizures, epilepsy-related seizures, loss-of-consciousness seizures, tonic-clonic or convulsive seizures, atonic seizures, clonic seizures, tonic seizures, and myoclonic seizures. In some examples, IMD 106 may also determine the seizure type based on accelerometer data, temperature data, or any other parameters extracted from one or more sensors.

[0066] The IMD 106 can generate seizure and stroke metrics at the same or different frequencies. In some examples, these frequencies may refer to the frequency at which the sensing circuitry generates appropriate information to determine either the stroke or seizure metric. In other examples, the IMD 106 can continuously generate physiological information from which both the stroke and seizure metrics can be determined. However, the frequency may refer to the frequency at which the processing circuitry generates the stroke or seizure metric from the physiological information. The seizure detection frequency may differ from the stroke detection frequency due to the duration and / or impact of a seizure or stroke. For example, a seizure may last only a few minutes, but a stroke may last for hours. Therefore, in some examples, the seizure detection frequency is higher than the stroke detection frequency. In this way, a timer can be used to trigger the detection of both stroke and seizure. Such variations in detection frequency allow the IMD 106 to conserve power and monitor for stroke or seizures only when appropriate.

[0067] In other examples, triggering events for seizure or stroke detection can be identified from various sensed data. For instance, the processing circuitry of IMD 106 can be configured to determine cardiac arrhythmias in patient 102 based on ECG signals containing physiological information. For some patients, arrhythmias can trigger or be caused by seizures. In response to the determination of an arrhythmia, IMD 106 can therefore increase the determined seizure detection frequency that controls the seizure metric and determine the seizure metric based on the seizure detection frequency. In this way, IMD 106 can adjust the seizure monitoring frequency based on the presence of any arrhythmia in the heart.

[0068] Figure 1B This is a conceptual diagram of a system 100B configured to detect stroke or epileptic seizures, according to an example of this disclosure. System 100B can be integrated with... Figure 1A System 100B is substantially similar to system 100A. However, system 100B can be configured to be implanted in target region 120, located on the back of the head of patient 102 at the temple, for example, above the ear and / or above the temporal portion of the skull. The IMD 106 implanted in target region 120 can be configured to generate stroke and seizure metrics based on electrical signals sensed in that region. In such examples, if the electrodes of sensor device 106 are implanted on the opposite side of the patient's head, the electrodes can detect regions corresponding to T3 (such as...). Figure 1C The IMD 106 may detect electrical activity in the brain in the T4 region (as shown in the diagram) or bilaterally implanted in the temporal region. In some examples, due to different types of noise at target region 120 (such as muscle activity due to jaw movements or other types of electrical activity), the IMD 106 may need to employ different filters or other processing or signal modulation techniques than those at target region 104. In other examples, the IMD 106 may be configured to determine seizure and stroke metrics from other regions of the patient 102's head, which may be located outside of target regions 104 and 120.

[0069] Figure 1C This is a graph of 10-20 mappings used in electroencephalography (EEG) sensor measurements. For example... Figure 1C As shown, electrodes carried by the IMD 106 can be used to target various locations on the head of patient 12. In the occipital region (such as in...) Figure 1A Within the target area 104, the IMD 106 can sense electrical signals at at least one of P3, Pz, or P4. On the sides of the head (such as in...) Figure 1B Within the target region 120, the IMD 106 can sense electrical signals at at least one of F7, T3, or T5 in the left hemisphere of the brain. Alternatively, the IMD 106 can sense electrical signals at at least one of F8, T4, or T6 in the right hemisphere of the brain.

[0070] Figure 2A A top view depicting a sensor device (e.g., an implantable medical device) according to an embodiment of the present technology. Figure 2B Depicting according to this technology Figure 2A The side view of the sensor device 210 shown. Figure 2A A plan view of an exemplary sensor device 210 is shown. In some embodiments, sensor device 210 may include the components described above. Figure 1A and Figure 1B The description of IMD 106 and / or the following regarding Figures 3A-3C and Figure 4The described IMD 310, 360B, 360C or 400 features are some or all of the features and are similar to these features, and may include features such as combinations Figure 2A Additional features described. In the example shown, sensor device 210 includes a housing 201 in which multiple electrodes 213A, 213B, 213C, 213D (collectively referred to as “electrodes 213”) are carried. Although four electrodes are shown for sensor device 210, in other examples, housing 201 may carry only two or three electrodes, or may carry more than four electrodes. In some examples, each electrode may be configured to sense both ECG or other cardiac signals and EEG signals. In other examples, each electrode may be wired to detect only a single type of signal, such as ECG or EEG. In this way, the sensing circuitry system can utilize different electrodes to sense EEG and ECG. In such a configuration, two different and independently wired electrodes may be located in Figure 2A The electrodes 213 shown are positioned at the locations of each of the electrodes 213. In operation, the electrodes 213 can be positioned to make direct contact with tissue at the target site (e.g., if placed on a user's skin, the electrode makes direct contact with the user's skin, or if the sensor device 210 is implanted, the electrode makes direct contact with subcutaneous tissue). The housing 201 further encloses the electronic circuitry located within the sensor device 210 and protects the contained circuitry (e.g., processing circuitry, sensing circuitry, communication circuitry, sensors, and power supply) from the influence of bodily fluids. In various embodiments, the electrodes 213 can be disposed along any surface of the sensor device 210 (e.g., front surface, rear surface, left side surface, right side surface, upper surface, lower surface, or other surface), and this surface can take any suitable form.

[0071] exist Figure 2A and Figure 2BIn the example, the housing 201 may be a biocompatible material having a relatively flat shape, the relatively flat shape including: a first main surface 203 configured to face the tissue of interest (e.g., facing forward when positioned at the back of a patient's neck); a second main surface 204 opposite to the first main surface; and a depth D or thickness of the housing 201 extending between the first and second main surfaces. The housing 201 may define an upper surface 206 (e.g., configured to face upward when the sensing device 210 is implanted in or placed in the patient's head or neck) and an opposing lower surface 208. The housing 201 may also include a central portion 205, a first lateral portion (or left side portion) 207, and a second lateral portion (or right side portion) 209. Electrodes 213 are distributed around housing 201 such that a central electrode 213B is disposed within a central portion 205 (e.g., substantially centered along the horizontal axis of the device), a back electrode 213D is disposed on a lower surface, a left electrode 213A is disposed within a left portion 207, and a right electrode 213C is disposed within a right portion 209. As shown, housing 201 may define its central portion 205 as a boomerang or herringbone shape with a apex, its first lateral portions 207 and second lateral portions 209 both laterally outward and extending from the central portion 205, and also extending downward at a downward angle relative to the horizontal axis of the device. In other examples, housing 201 may be formed in other shapes, which may be determined by the desired distance or angle between the different electrodes 213 carried by housing 201.

[0072] The configuration of the outer shell 201 can facilitate placement, either in a bandage-like form or for subcutaneous implantation on the patient's skin. Therefore, a relatively thin outer shell 201 may be advantageous. Additionally, in some embodiments, the outer shell 201 can be flexible, allowing it to be at least partially bent to correspond to the anatomy of the patient's neck (e.g., the left and right portions 207 and 209 of the outer shell 201 bend forward relative to the central portion 205 of the outer shell 201).

[0073] In some examples, the housing 201 may have a length L of about 15 mm to about 50 mm, about 20 mm to about 30 mm, or about 25 mm. The housing 201 may have a width W of about 2.5 mm to about 15 mm, about 5 mm to about 10 mm, or about 7.5 mm. In some embodiments, the housing 201 may have a thickness of less than about 10 mm, about 9 mm, about 8 mm, about 7 mm, about 6 mm, about 5 mm, about 4 mm, or about 3 mm. In some embodiments, the thickness of the housing 201 may be about 2 mm to about 8 mm, about 3 mm to about 5 mm, or about 4 mm. The housing 201 may have a volume of less than about 1.5 cc, about 1.4 cc, about 1.3 cc, about 1.2 cc, about 1.1 cc, about 1.0 cc, about 0.9 cc, about 0.8 cc, about 0.7 cc, about 0.6 cc, about 0.5 cc, or about 0.4 cc. In some implementations, the housing 201 may have a size suitable for implantation via a cannula guide or any other suitable implantation technique.

[0074] As shown in the figure, the electrodes 213, carried by the housing 201, are arranged such that all three electrodes 213 are not located on a common axis. In this configuration, the electrodes 213 can realize multiple signal vectors, which can provide one or more improved signals compared to electrodes all aligned along a single axis. This can be particularly useful in sensor device 210, which is configured to be implanted in the neck or head to detect electrical activity in the brain. In some embodiments, this electrode configuration also provides improved cardiac ECG sensitivity by integrating three potential signal vectors. In some examples, the processing circuitry system can generate a virtual signal vector by weighting and summing two or more physical signal vectors (such as physical signal vectors that can be obtained from the electrodes 213 of sensor device 210 or the electrodes of any other sensor device described herein).

[0075] In such Figure 2B In the example shown, all electrodes 213 are located on the first main surface 203 and are substantially flat and outward-facing. However, in other examples, one or more electrodes 213 may utilize a three-dimensional configuration (e.g., curved around the edge of the device 210). Similarly, in other examples (such as... Figure 2B (As shown in the example), one or more electrodes 213 may be disposed on a second main surface opposite to the first main surface. Various electrode configurations allow for configurations in which the electrodes 213 are located on both the first and second main surfaces. In other configurations (such as...) Figure 2BIn the configuration shown, electrode 213 is disposed on only one main surface of housing 201. Electrode 213 can be formed of a variety of different types of biocompatible conductive materials (e.g., stainless steel, titanium nitride, platinum-iridium, iridium, or alloys thereof) and can utilize one or more coatings, such as titanium nitride or fractal titanium nitride. In some embodiments, the material selection for the electrode may also include materials with high surface area (e.g., to provide better electrode capacitance for better sensitivity) and roughness (e.g., to contribute to implant stability). Although Figure 2A and Figure 2B The example shown includes four electrodes 213, but in some embodiments, the sensor device 210 may include one, two, three, four, five, six or more electrodes carried by the housing 201.

[0076] Figure 2C A top view depicting another example of a sensor device 220 according to the present technology. Figure 2C A sensor device 220 is shown that is substantially similar to sensor device 210, but sensor device 220 includes electrodes 213 that are not exposed along the first main surface 203 of housing 201. Instead, as... Figure 2D and Figure 2E As shown, electrode 213 may be exposed along the upper and lower surfaces (e.g., facing upper and lower when implanted in or on the patient's neck). Figure 2F Sensor device 230 is shown, which is substantially similar to sensor devices 210 and 220, but housing 201 is configured with a curved configuration, wherein electrodes can be positioned along the upper and / or lower surfaces of housing 201. In some embodiments, the curved configuration can improve patient comfort and more easily conform to the anatomy of the patient's neck region. In some examples, any of sensor devices 210, 220, or 230 may be flexible to conform to the patient's anatomy at the desired implantation location or outer surface location. Additionally, examples including electrode extensions (e.g., such as...) Figure 2I-2Q The sensor devices (as depicted) are inherently flexible, allowing them to conform to the anatomy of the neck and / or skull. In some examples, sensor device 220 and / or sensor device 230 may be implanted in a location substantially centered relative to the chest, head (e.g., the occipital region or temporal region), neck, or another target region. In some examples, sensor device 220 and / or sensor device 230 may be placed on the outer surface of the patient's skin.

[0077] In operation, electrode 213 is used to sense electrical signals (e.g., EEG and / or ECG signals), which can be submuscular or subcutaneous. The sensed electrical signals can be stored in the memory of sensor device 210, and the signal data can be transmitted to another device (e.g., via a communication link). Figure 1A External device 108). The sensed electrical signals may be time-coded or otherwise correlated with time data and stored in such a form that the recentity, frequency, time of day, time span, or date of a particular signal data point or sequence of signal data (or an indicator or statistic calculated based thereon) can be determined and / or reported. In some examples, electrode 213 may additionally or alternatively be used to sense any biopotential signals of interest from any implantation site, such as electrocardiogram (ECG), intracardiac electrogram (EGM), electromyography (EMG), or neural signals. This data may be time-coded or time-correlated and stored in the same form as described above for EEG signal data.

[0078] Figure 2G and Figure 2H A top view depicting an example of an apparatus according to this disclosure. Figure 2G A housing 201 of sensor device 210 is depicted, comprising electrodes 213A-213C disposed around the periphery of housing 201. Each of electrodes 213A-213C may be configured to receive raw signals including cardiac and brain components. Sensor device 210 may include a circuitry configured to filter the raw signals received by electrodes 213A-213C to generate cardiac and brain signals (e.g., ECG and EEG signals). In some examples, this circuitry may be located externally to sensor device 210.

[0079] Figure 2H The housing 241 of the sensor device 240 is depicted, which includes electrodes 253A-253C and electrodes 254A-254C. Electrodes 253A and 254A together can be referred to as segmented electrodes. Similarly, electrodes 253B and 254B can be referred to as segmented electrodes, and electrodes 253C and 254C can be referred to as segmented electrodes. Insulating material can separate the conductive portions of the segmented electrodes (e.g., electrodes 253A and 254A).

[0080] The circuit system can be configured to generate a first heart (e.g., ECG) signal based on differential signals received at electrodes 253A and 253B, a second heart signal based on differential signals received at electrodes 253B and 253C, and / or a third heart signal based on differential signals received at electrodes 253C and 253A. Similarly, the circuit system can be configured to generate a first brain (e.g., EEG) signal based on differential signals received at electrodes 254A and 254B, a second brain signal based on differential signals received at electrodes 254B and 254C, and / or a third brain signal based on differential signals received at electrodes 254C and 254A.

[0081] Figure 2I A top view of another exemplary sensor device 250 is depicted, which includes electrodes 263A-236D, 267, and 269. Each of electrodes 263A-236D, 267, and 269 can be configured to receive both cardiac and brain components (e.g., ECG and EEG). Sensor device 250 may include circuitry configured to filter the raw signals received by electrodes 263A-236D, 267, and 269 to generate cardiac and brain signals (e.g., ECG and EEG signals). Sensor device 250 may also include circuitry configured to measure tissue impedance via electrodes 263A-236D, 267, and 269.

[0082] exist Figure 2I In the example, sensor device 250 includes a housing 251, which includes an upper surface 256, an opposing lower surface 258, a central portion 255, a first lateral portion (or left side portion) 257, and a second lateral portion (or right side portion) 259. Electrodes 263 are distributed around housing 251 such that a central electrode 263B is disposed within the central portion 255 (e.g., substantially centered along the horizontal axis of the device), a left electrode 263A is disposed within the left side portion 257, and a right electrode 263C is disposed within the right side portion 259.

[0083] The sensor device 250 also includes electrode extensions 265A and 265B (collectively referred to as "electrode extensions 265"). Figure 2IAs shown, electrode extension 265A includes blades 268 such that one or more electrodes 267 are distributed on the blades 268. Electrode extension 265B includes one or more annular electrodes 269. In some examples, electrode extension 265 may be connected to housing 256 of sensor device 250 via a head pin. In some examples, electrode extension 265 may be permanently attached to housing 256 of sensor device 250. In other examples, the number and type of electrodes on electrode extension 265 and such extensions and on housing 251 may differ. Figure 2I The numbers and types shown.

[0084] In some examples, the electrode extension 265 may have a length L1 of about 15 mm to about 50 mm, about 20 mm to about 30 mm, or about 25 mm. One or more electrode extensions 265 may provide the sensor device 250 with a larger sensing vector for sensing signals via the electrodes. A larger (longer) sensing vector, relative to a smaller (shorter) sensing vector, can help improve signal quality by including one or more electrodes on one or more extensions.

[0085] Electrode extensions 265 may be inherently flexible, allowing them to conform to neck and / or cranial anatomy. Additionally, the length and flexibility of one or more electrode extensions 265 may allow electrodes on the extensions to be advantageously positioned near certain brain structures or locations, vascular structures, or other anatomical structures or locations, which may also contribute to improved signal quality, for example, when the signal originates from or is influenced by that structure. For example, electrode extensions 265A and 265B may extend upward from sensor device 250 to enhance brain signal sensing and detection. Improved signal quality can improve the performance of algorithms used to predict or detect patient conditions using such signals. In examples where one or more electrode extensions 265 are implanted, the extensions may pass under the scalp to position one or more electrodes on the extensions at desired locations in the skull.

[0086] Figures 2J-2P Exemplary sensor devices 270J, 270K, 270L, 270M, 270N, and 270P (collectively, “Electrode Extensions 270”) are depicted according to examples of this disclosure, including electrode extensions 272J-272P, 276K, 276N, 284M-284P, 285M-285P, and 286M-286P. Sensor devices 270 may otherwise be similar to those described above. Figure 1A-Figure 2I The sensor device shown and described may, for example, include electronics within a housing and electrodes on the housing. Figures 2J-2PThe electrode extension shown can also be referred to as a lead. As an example, the electrodes on electrode extensions 272J-272P, 276K, 276N, 284M-284P, 285M-285P and 286M-286P can be ring electrodes or paddle electrodes.

[0087] Figure 2J-Figure 2L Examples of electrode extensions 272J-272L and 276K are shown, which can be positioned to extend from sensor devices 270J-270L in a first direction, and Figure 2M-Figure 2P Examples of electrode extensions 272M-272P and 276N are shown, which can be positioned to extend from sensor devices 270M-270P in a first direction, and examples of electrode extensions 284M-284P, 285M-285P, and 286M-286P, which can be positioned to extend from sensor devices 270M-270P in a second direction opposite to the first direction. In some examples, the first direction can be a downward direction, for example, toward the patient's neck and shoulders, and the second direction can be an upward direction, for example, toward the patient's upper skull and scalp. For example, a first electrode extension can be positioned to extend toward a first temporal region, and a second electrode extension can be positioned to extend toward a second temporal region. Figures 2J-2P The electrode extensions 272J-272P and 276K shown can extend toward the neck and shoulders to enhance cardiac signal sensing and detection. Figure 2M-Figure 2P The electrode extensions 284M-284P, 285M-285P and 286M-286P shown can be positioned to extend upward toward the upper skull and scalp to enhance brain signal sensing.

[0088] Figure 2J An example of a single electrode extension 272J extending from the center of the sensor device 270J is shown. Figure 2K Examples of two electrode extensions 272K and 276K extending from opposite ends of sensor device 270K are shown. Electrode 274K on electrode extension 276K may have a positive polarity, and electrode 278K on electrode extension 272K may have a negative polarity, or the polarities of the electrodes may be interchanged, such that the electrodes have opposite polarities. Figure 2L An example of a single electrode extension 272L extending from the center of the sensor device 270L is shown. Two electrodes 274L and 278L with opposite polarities are present on the single electrode extension 272L. Therefore, Figure 2K and Figure 2LThe sensor devices 270K and 270L shown can be configured to receive differential signals via electrodes 274K, 278K, 274L, and 278L on one or more electrode extensions 272K, 276K, and 272L. Figure 2J In the example, a generally vertical or non-horizontal sensing vector can be formed between the electrodes on the extension 272J and the electrodes on the housing of the sensor device 270J, and Figure 2L In the example, a roughly perpendicular sensing vector can be formed between electrodes 274L and 278L on the extension 272L. Figure 2K In the example, a roughly horizontal sensing vector can be formed between the electrode 274K on the extension 272K and the electrode 278K on the extension 276K.

[0089] Figure 2M-Figure 2P Each of the sensor devices 270M-270P shown includes one or more electrode extensions 272M-272P and 276N extending in a first direction, and three electrode extensions 284M-284P, 285M-285P and 286M-286P extending in a second direction opposite to the first direction. Figure 2M A single electrode extension 272M is shown extending from the center of the sensor device 270M along a first direction. Figure 2N Two electrode extensions 272N and 276N are shown extending from opposite ends of the sensor device 270N along a first direction. Figure 2P A single electrode extension 272P extending from the center of the sensor device 270P is shown, wherein the single electrode extension 272P includes two electrodes 274P and 278P with opposite polarities.

[0090] Figure 2Q A sensor device 270Q is shown on the back of a patient's neck. In the example shown, the sensor device 270Q is configured similar to Figure 2N The sensor device is 270N. However, including information about... Figures 2I-2P Any of the sensor devices in the described extension can be used as Figure 2Q The sensor device 270Q is positioned in the manner shown. Additionally, this includes information about... Figures 2I-2P Any of the sensor devices in the described extension can be positioned at other locations described herein, such as temporarily as per [the relevant context]. Figure 1B As shown.

[0091] Such sensor devices may include one or more extensions extending toward the patient's neck or shoulder in a first downward direction. The extensions extending along this first direction may position electrodes to facilitate the sensing of cardiac signals (e.g., ECG). Such sensor devices may also include one or more extensions extending toward the patient's upper skull and scalp in a second upward direction opposite to the first direction. The extensions extending in this second direction may facilitate the sensing of brain signals (e.g., EEG). Each extension may include one or more electrodes to provide one or more sensing vectors of one or more orientations, together with the same extension, different extensions, or another electrode on the housing of the sensor device.

[0092] Figures 3A-3C Exemplary sensor devices 310, 360B, and 360C according to embodiments of the present technology are depicted. In some examples, sensor device 310 may include some or all of the features of IMD 106 or 400 and sensor devices 210, 220, and 230 described herein according to embodiments of the present technology, and may include features such as those combined with… Figure 3A Additional features described. Figure 3A In the example shown, sensor device 310 may be embodied as a monitoring device having a housing 314, a proximal electrode 313A, and a distal electrode 313B (referred individually or collectively as "electrode 313" or "electrodes 313"). Housing 314 may further include a first main surface 318, a second main surface 320, a proximal end 322, and a distal end 324. Housing 314 encloses the electronic circuitry system located within sensor device 310 and protects the circuitry system contained therein from bodily fluids. Electrical feedthrough provides electrical connection to electrodes 313. In one example, sensor device 310 may be embodied as an external monitor, such as a patch that can be positioned on the outer surface of a patient, or another type of medical device (e.g., instead of an ICM), such as those further described herein.

[0093] exist Figure 3A In the example shown, sensor device 310 is defined by a length "L", a width "W", and a thickness or depth "D". Sensor device 310 may be in the form of an elongated rectangular prism, where the length L is significantly greater than the width W, and the width W is greater than the depth D. In one example, the geometry of sensor device 310 (specifically, the width W being greater than the depth D) is chosen to allow sensor device 310 to be inserted under the patient's skin using minimally invasive surgery and to remain in the desired orientation during insertion. For example, Figure 3AThe device shown includes radial asymmetry (particularly a rectangular shape) along the longitudinal axis, which holds the device in the correct orientation after insertion. For example, in one example, the spacing between the proximal electrode 313a and the distal electrode 313B can range from 30 mm to 55 mm, 35 mm to 55 mm, and 40 mm to 55 mm, and can be any range or a single spacing from 25 mm to 60 mm. In some examples, the length L can be from 30 mm to about 70 mm. In other examples, the length L can range from 40 mm to 60 mm, 45 mm to 60 mm, and can be any length or length range between about 30 mm and about 70 mm. Furthermore, the width W of the first main surface 18 can range from 3 mm to 10 mm, and can be any single width or width range between 3 mm and 10 mm. The thickness of the depth D of the sensor device 310 can range from 2 mm to 9 mm. In other examples, the depth D of the sensor device 310 can range from 2 mm to 5 mm, and can be any single depth or depth range from 2 mm to 9 mm. Furthermore, the sensor device 310 according to examples of this disclosure has a geometry and dimensions designed for ease of implantation and patient comfort. Examples of the sensor device 310 described in this disclosure can have a volume of 3 cc or less, 2 cc or less, 1 cc or less, 0.9 cc or less, 0.8 cc or less, 0.7 cc or less, 0.6 cc or less, 0.5 cc or less, or 0.4 cc or less, any volume between 3 cc and 0.4 cc, or any volume less than 0.4 cc. In addition, in Figure 3A In the example shown, the proximal end 322 and the distal end 324 are rounded to reduce discomfort and irritation to surrounding tissues once inserted under the patient's skin.

[0094] exist Figure 3A In the example shown, once inserted into the patient, the first primary surface 318 faces outward toward the patient's skin, while the second primary surface 320 is positioned opposite the first primary surface 318. Therefore, both the first and second primary surfaces can face a direction along the patient's sagittal axis, and this orientation can be maintained during implantation due to the size of the sensor device 310. Additionally, the accelerometer or its axis can be oriented along the sagittal axis.

[0095] The proximal electrode 313A and distal electrode 313B are used to sense electrical signals (e.g., EEG signals, ECG signals, other brain signals and / or cardiac signals or impedance), which can be submuscular or subcutaneous. The electrical signals can be stored in the memory of the sensor device 310, and the signal data can be transmitted via the integrated antenna 326 to another medical device, which can be another implantable device or external device, such as external device 108. Figure 1A In some examples, electrodes 313A and 313B can be additionally or alternatively used to sense any biopotential signals of interest from any implantation site, such as electrocardiogram (ECG), intracardiac electrogram (EGM), electromyography (EMG), or neural signals.

[0096] exist Figure 3A In the example shown, the proximal electrode 313A is closely adjacent to the proximal end 322, and the distal electrode 313B is closely adjacent to the distal end 324. In this example, the distal electrode 313B is not limited to a flat, outward-facing surface, but can extend from the first main surface 318 around the circular edge 328 or the end surface 330 onto the second main surface 320, such that the electrode 313B has a three-dimensional curved configuration. Figure 3A In the example shown, the proximal electrode 313A is located on the first main surface 318 and is substantially flat and outward-facing. However, in other examples, the proximal electrode 313A may utilize a three-dimensional curved configuration of the distal electrode 313B, thereby providing a three-dimensional proximal electrode (not shown in this example). Similarly, in other examples, the distal electrode 313B may utilize a substantially flat and outward-facing electrode located on the first main surface 318, similar to the electrode shown with respect to the proximal electrode 313A. Various electrode configurations allow for configurations in which the proximal electrode 313A and the distal electrode 313B are located on both the first main surface 318 and the second main surface 320. In other configurations, such as Figure 3A In the configuration shown, only one of the proximal electrode 313A and the distal electrode 313B is located on both main surfaces 318 and 320. In other configurations, both the proximal electrode 313A and the distal electrode 313B are located on either the first main surface 318 or the second main surface 320 (i.e., the proximal electrode 313A is located on the first main surface 318, and the distal electrode 313B is located on the second main surface 320). In another example, the sensor device 310 may include electrodes 313 on both the first main surface 318 and the second main surface 320 at or near the proximal and distal ends of the device, such that a total of four electrodes 313 are included on the sensor device 310. The electrodes 313 may be formed of a variety of different types of biocompatible conductive materials (e.g., stainless steel, titanium nitride, platinum, iridium, or alloys thereof) and may utilize one or more coatings, such as titanium nitride or fractal titanium nitride. Although Figure 3A The example shown includes two electrodes 313, but in some embodiments, the sensor device 310 may include three, four, five or more electrodes carried by a housing 314.

[0097] exist Figure 3A In the example shown, the proximal end 322 includes a head assembly 332 that includes one or more of a proximal electrode 313A, an integrated antenna 326, an anti-migration protrusion 334, and / or a stitching hole 336. The integrated antenna 326 is located on the same main surface as the proximal electrode 313A (i.e., the first main surface 318) and is also included as part of the head assembly 332. The integrated antenna 326 allows the sensor device 310 to transmit and / or receive data. In other examples, the integrated antenna 326 may be formed on the main surface opposite the proximal electrode 313A, or it may be integrated within the housing 314 of the sensor device 310. Figure 3A In the example shown, the anti-migration protrusion 334 is located adjacent to the integrated antenna 326 and protrudes away from the first main surface 318 to prevent longitudinal movement of the device. Figure 3A In the example shown, the anti-migration protrusion 334 includes a plurality (e.g., six or nine) of small bumps or protrusions extending away from the first main surface 318. As discussed above, in other examples, the anti-migration protrusion 334 may be located on the main surface opposite the proximal electrode 313A and / or the integrated antenna 326. Furthermore, in Figure 3A In the example shown, the head assembly 332 includes a suture hole 336, which provides another means of securing the sensor device 310 to the patient to prevent movement after insertion. In the example shown, the suture hole 336 is located near the proximal electrode 313A. In one example, the head assembly 332 is a molded head assembly made of polymer or plastic material, which can be integrated with or detached from the main part of the sensor device 310.

[0098] Figure 3B A third electrode 392B is shown at the midpoint between electrodes 390B and 391B. The dimension D of the housing 374B of the sensor device 360B can be increased to adjust the angle α, achieving a more orthogonal orientation of the triangular configuration of electrodes 390B-392B. In some examples, the sensor device 360B may have the same shape and dimensions as the sensor device 310, except where electrode 392B is added to the side or rear surface of the housing 374B to create a triangular electrode configuration. Furthermore, Figure 3CA sensor device 360C with an extended third dimension D is shown. A third electrode 392C is positioned at a corner to create a triangular electrode configuration together with electrodes 390C and 391C. Dimension D can be designed to achieve a specific angle for the triangular configuration of electrodes 390C-392C.

[0099] Figure 4 This is a block diagram of an exemplary IMD 400 configured to detect stroke or epileptic seizures. IMD 400 may be an example of IMD 106 or any of sensor devices 210, 220, 230, 240, 250, 270, 310, 360B, or 360C. In the example shown, IMD 400 includes electrodes 418A-418C (collectively referred to as “electrodes 418”), antenna 405, processing circuitry 402, sensing circuitry 406, communication circuitry 404, storage device 410, switching circuitry 408, sensor 414 (including one or more motion sensors 416), and power supply 412. Although not explicitly stated... Figures 3A-3C As shown in the figure, sensor 414 may include one or more photodetectors.

[0100] Processing circuit system 402 may include fixed-function circuit systems and / or programmable processing circuit systems. Processing circuit system 402 may include any or more of a microprocessor, GPU, TPU, controller, DSP, ASIC, FPGA, or equivalent discrete or analog logic circuit systems. In some examples, processing circuit system 402 may include multiple components (such as any combination of one or more microprocessors, one or more GPUs, one or more TPUs, one or more controllers, one or more DSPs, one or more ASICs, or one or more FPGAs), as well as other discrete or integrated logic circuit systems. The functionality attributed herein to processing circuit system 402 may be embodied in software, firmware, hardware, or any combination thereof. Processing circuit system 402 may be processing circuit system 110 ( Figure 1A and Figure 1B Examples or components of, and can be processing circuitry systems of any of the sensor devices 106, 210, 220, 230, 240, 250, 270, 310, 360A and 360B.

[0101] Sensing circuitry 406 and communication circuitry 404 can be selectively coupled to electrodes 418A-418C via switching circuitry 408, as controlled by processing circuitry 402. Sensing circuitry 406 can monitor signals from electrodes 418A-418C to monitor electrical activity in the brain (e.g., to generate an EEG) and / or electrical activity in the heart (e.g., to generate an ECG), from which processing circuitry 402 can generate stroke and seizure metrics. Sensing circuitry 406 can also sense physiological characteristics (such as subcutaneous tissue impedance) indicating at least some aspects of the patient 102's breathing pattern, and EMG or ECG indicating at least some aspects of the patient 102's cardiac pattern. Sensing circuitry 406 can also monitor signals from sensor 414, which may include one or more motion sensors 416 and any additional sensors (such as photodetectors or optical sensors, pressure sensors, or acoustic sensors) that may be positioned on sensor device 400.

[0102] In some examples, subcutaneous impedance signals collected by IMD 400 can indicate the respiratory rate and / or respiratory intensity of patient 102, and EMG collected by IMD 400 can indicate the heart rate of patient 102 and atrial fibrillation (AF) load or other arrhythmias of patient 102. In some examples, the respiratory component can be sensed additionally (using hybrid sensor technology) or alternatively in other signals (such as motion sensor signals, optical signals), or as a component of cardiac signals sensed via electrode 418 (e.g., baseline shift). Sensing circuitry 406 can also monitor signals from sensor 414, which may include one or more motion sensors 416 and any additional sensors (such as photodetectors or pressure sensors) that may be positioned on IMD 400. Sensor 414 can also or alternatively detect heart sounds, respiration (e.g., rate or timing), impedance, or blood pressure. Therefore, sensor 414 can also or alternatively include sensors (such as one or more microphones, pressure sensors, electrodes, etc.). The IMD 400 can utilize any of these sensors 414 to determine one or more physiological signals of the patient, which can be used to detect a certain type of seizure or stroke. In some examples, the sensing circuitry 406 may include one or more filters and amplifiers for filtering and amplifying signals received from one or more of the electrodes 418A-418C and / or one or more motion sensors 42. In some examples, the sensing circuitry 406 may include separate hardware (e.g., separate circuitry) configured to modulate and process the sensed electrical signals from which it generates seizure and stroke metrics. In this way, each separate circuitry may perform one or more filters and amplifiers configured to extract relevant features or signal components from the sensed electrical signals. Furthermore, the processing circuitry 402 may selectively control each separate circuitry depending on whether a seizure or stroke metric should be generated.

[0103] The communication circuitry 404 may include any suitable hardware, firmware, software, or any combination thereof for communicating with another device (such as external device 108) or another IMD or sensor (such as a pressure sensing device). Under the control of the processing circuitry 402, the communication circuitry 404 may receive downlink telemetry from external device 108 or another device and transmit uplink telemetry to such external device or other device by means of, for example, an internal or external antenna of antenna 405. In some examples, the communication circuitry 404 may use two or more electrodes 418 (e.g., selected by the processing circuitry 402 via switching circuitry 408) to receive downlink telemetry from external device 108 or another device and transmit uplink telemetry to such external device or other device via tissue conduction communication (TCC). In addition, the processing circuitry 402 may communicate with external devices (e.g., external device 108) and computer networks (such as those developed by Medtronic, Inc. of Dublin, Ireland). The communication circuitry 404 communicates with networked computing devices. In some examples, the communication circuitry 404 can be configured to communicate with other devices within the IMD 400 using tissue conduction communication (TCC).

[0104] Clinicians or other users can retrieve data from the IMD 400 using external device 108 or by using another local or networked computing device configured to communicate with the processing circuitry 402 via the communication circuitry 404. Clinicians can also use external device 108 or another local or networked computing device to program the parameters of the IMD 400.

[0105] In some examples, storage device 410 may be referred to as memory and includes computer-readable instructions that, when executed by processing circuitry system 402, cause IMD 400 and processing circuitry system 402 to perform various functions accorded to IMD 400 and processing circuitry system 402 herein. Storage device 410 may include any volatile, non-volatile, magnetic, optical, or electrical medium, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically erasable programmable ROM (EEPROM), flash memory, or any other digital medium. Storage device 410 may also store data generated by sensing circuitry system 406, such as physiological information, or data generated by processing circuitry system 402 (such as stroke measurements and seizure measurements).

[0106] Power source 412 is configured to deliver operating power to components of IMD 400. Power source 412 may include a battery and power generation circuitry for generating operating power. In some examples, the battery is rechargeable to allow for long-term operation. In some examples, recharging is achieved through proximal inductive interaction between an external charger and an inductive charging coil within external device 108. Power source 412 may include any one or more of a variety of battery types, such as nickel-cadmium batteries and lithium-ion batteries. Non-rechargeable batteries may be selected to last for several years, while rechargeable batteries may be inductively charged from an external device, for example, on a daily or weekly basis.

[0107] As described herein, the IMD 400 can be configured to sense electrical signals and generate a stroke metric indicating whether the patient 102 has experienced a stroke. In some examples, the processing circuitry 402 is configured to use electrodes 418 to analyze data from one or more electrode combinations to extract brain activity data and to discard or reduce any contributions from cardiac or muscle activity. In some examples, electrodes 418 are configured to be placed on the patient's skin. In such embodiments, electrodes 418 may include protrusions (e.g., micromanipulation needles or other suitable structures) configured to at least partially penetrate the patient's skin to improve the detection of subcutaneous electrical activity.

[0108] In some examples, sensing circuitry 406 senses brain signals via electrodes 418. Brain signals can represent the electrical activity of the brain and can be EEG. Processing circuitry 402 can determine parameter values ​​from the brain signals, such values ​​being determined based on the amplitude of the signal in one or more frequency bands. Sensing circuitry 406 may include filters and other sensing circuitry to isolate the brain signals of interest.

[0109] In some examples, sensing circuitry 406 senses cardiac signals, and processing circuitry 402 can determine parameter values ​​from the cardiac signals. Exemplary parameter values ​​as described herein (such as heart rate or heart rate variability) can be determined based on the detection of the presence of a heartbeat in the cardiac signal. Sensing circuitry 406 can be configured to sense a variety of different signals, within which a heartbeat can be identified and the values ​​of cardiac parameters can be determined.

[0110] For example, sensing circuitry 406 can be configured to sense cardiac signals representing the electrical activity (depolarization and repolarization) of the heart, such as subcutaneous ECG signals, via electrodes 418. As another example, sensing circuitry 406 can be configured to sense cardiac signals representing the mechanical activity of the heart via electrodes 418. Components of the signal sensed via electrodes 418 (e.g., on or under the scalp of a patient) can vary based on vibration, blood flow, or changes in impedance associated with cardiac contraction. Filtering to isolate this component can include a 0.5 Hz to 3 Hz bandpass filter, although other filter types, ranges, and cutoff values ​​are also possible. In some examples, sensing circuitry 406 can be configured to sense cardiac signals representing the mechanical activity of the heart via other sensors 414, such as optical sensors, pressure sensors, or motion sensors 416.

[0111] For example, sensing circuitry 406 and / or processing circuitry 402 can detect a heart pulse via an optical sensor. Processing circuitry 402 can determine heart rate or heart rate variability based on the detection of the heart pulse via the optical sensor, for example, in conjunction with an ECG signal or in the absence of an ECG signal (e.g., if the ECG signal quality is poor). The optical sensor signal can be additionally or alternatively used for other purposes, such as determining blood oxygenation, local tissue perfusion, or blood pressure, any of which can be used for stroke detection or prediction and / or differentiation between ischemic and hemorrhagic strokes.

[0112] For example, during the implantation of sensor device 400, one or more electrodes 418 may be positioned to facilitate sensing cardiac signals via the electrodes. In some examples, sensor device 400 may include one or more electrode extensions 265, 272, 276, 284, 285, 286 to position one or more electrodes 418 (e.g., via passing under the scalp) at desired locations for sensing brain and / or cardiac signals. The desired locations for sensing brain and cardiac signals using electrodes 418 may be determined before implanting sensor device 406 in a specific patient using external sensing devices such as standard multielectrode ECG and EEG devices, or determined experimentally in a specific patient, or in multiple subjects. In some examples, one or more housing-based electrodes 418 of sensor device 400 are positioned at desired locations for sensing brain signals, and one or more extension-based electrodes 418 are positioned at desired locations for sensing cardiac signals, or vice versa. Reference Figure 1C Exemplary locations for positioning electrodes used to sense cardiac signals include P3, PQ3, PQ7, F3, F2, AF3, or C2.

[0113] In some examples, the processing circuitry 402 may integrate both electrical (e.g., ECG) cardiac signals and pulsatile cardiac signals to detect, predict, and / or classify conditions. In some examples, such integration may result in an "enhanced" ECG signal. For example, the processing circuitry 402 may identify features within the ECG signal based on pulse timing in the pulsatile signal. In some examples, the processing circuitry 402 may address the delay in pulse timing relative to the ECG in such integration.

[0114] For example, optical sensor signals (e.g., photoplethysmography pulse wave signals) can be used as a time base for ensemble averaging or other means of incorporating heart rate information to improve the signal-to-noise ratio of cardiac signals. Therefore, optical sensor signals can be considered as a substitute for cardiac signals and / or for deriving enhanced cardiac signals, which may be particularly useful when ECG quality is poor. The first or second derivative of the optical sensor signal can be used as a trigger for ensemble averaging (e.g., ECG signals) by, for example, determining the time associated with, the maximum / minimum and / or the zero intersection of the first or second derivative. Sharp high-frequency points can be used as trigger points to increase the resolution of the ensemble signal, while lower-frequency trigger points may blur or distort the ensemble average. The cardiac waveform aligned with the trigger point can be stored and averaged to generate the ensemble signal.

[0115] The processing circuitry system 402 can be configured to calculate physiological characteristics, such as stroke metrics, associated with one or more electrical signals received from the electrodes 418. For example, the processing circuitry system 402 can be configured to determine the presence or absence of a stroke (via the generation of a stroke metric) or other neurological condition from the electrical signals using an algorithm. In some examples, the processing circuitry system 402 can make a stroke determination for each electrode 418 (e.g., a channel), or it can make a stroke determination using electrical signals obtained from two or more selected electrodes 418.

[0116] IMD 400 can also be configured to sense electrical signals and generate a seizure metric indicating whether patient 102 has experienced a seizure. For example, processing circuitry 402 can be configured to analyze physiological information (e.g., EEG information) received from sensing circuitry 406. Processing circuitry 402 can search the physiological information for one or more features indicative of one or more types of seizures. For example, processing circuitry 402 can identify frequency bands including oscillations or amplitudes exceeding a corresponding threshold. In other examples, processing circuitry 402 can apply one or more machine learning algorithms or other algorithms to the physiological information to identify when a patient's physiological information indicates a seizure. In one example, processing circuitry 402 can normalize patient data to baseline characteristics. For example, because each patient is different, sensed data from patients can be normalized to baseline measurements, allowing machine learning algorithms to similarly interpret data from different patients and identify specific metrics or risk scores for each patient.

[0117] In some examples, the processing circuitry 402 may incorporate patient movement information as part of seizure detection and stroke detection. For example, motion sensor 416 may include one or more accelerometers configured to detect patient movement. The processing circuitry 402 or sensing circuitry 406 may determine whether a patient has fallen based on patient movement data collected via the accelerometers. Fall detection can be particularly beneficial when assessing potential stroke patients, as it has been found that a significant number of patients admitted for ischemic or hemorrhagic stroke have experienced a significant fall within 15 days of the stroke event. Therefore, in some embodiments, the processing circuitry 402 may be configured to initiate monitoring of brain activity via electrode 418 when fall detection is performed using the accelerometers. In some examples, sensing performed via electrode 418 may be modified in response to a fall determination, for example, by utilizing an increased sampling rate or other modifications. In addition to fall detection, the accelerometer 115 (or a similar sensor) may also be used to determine potential physical trauma due to sudden acceleration and / or deceleration (e.g., vehicle accidents, sports collisions, concussions, etc.). These events may be thrombolytic events (precursors to stroke). Similar to stroke determination, the processing circuitry system 402 can employ these fall determinations or other movements when determining a seizure metric or otherwise determining whether a patient is experiencing or has experienced a seizure. For example, sensor 414 can detect the frequency of head movements indicative of a seizure and initiate or increase the sensing frequency generated by electrical signal sensing and seizure metric. In one example, IMD 106 can utilize signals generated by an accelerometer to at least partially classify a seizure into a motor seizure or a non-motor seizure. For motor seizures, IMD 106 can also utilize the accelerometer signal to classify the motor seizure into a tonic-clonic seizure or some other type of motor seizure.

[0118] Figure 5 This is a block diagram of an exemplary external device 500 configured to communicate with an IMD (e.g., IMD 106 or IMD 400) or a sensor device described herein. External device 500 is an example of external device 108 of FIG1. Figure 5 In the example, the external device 500 includes a processing circuit system 502, a communication circuit system 504, a storage device 510, a user interface 506, and a power supply 508.

[0119] In one example, processing circuitry system 502 may include one or more processors configured to implement functional and / or procedural instructions for execution within external device 500. For example, processing circuitry system 502 may be able to process instructions stored in storage device 510. Processing circuitry system 502 may include, for example, a microprocessor, GPU, TPU, DSP, ASIC, FPGA, or equivalent discrete or integrated logic circuitry system, or a combination of any of the foregoing devices or circuitry systems. Therefore, processing circuitry system 502 may include any suitable structure, whether hardware, software, firmware, or any combination thereof, to perform the functions of processing circuitry system 502 as described herein.

[0120] The communication circuitry 504 may include any suitable hardware, firmware, software, or any combination thereof for communicating with another device, such as IMD 400. Under the control of the processing circuitry 502, the communication circuitry 504 may receive downlink telemetry from IMD 400 or another device, and send uplink telemetry to it. In other examples, the communication circuitry 504 may also employ TCC to communicate with other devices.

[0121] Storage device 510 can be configured to store information within external device 500 during operation. Storage device 510 may include a computer-readable storage medium or a computer-readable storage device. In some examples, storage device 510 includes one or more of short-term memory or long-term memory. Storage device 510 may include, for example, RAM, dynamic random-access memory (DRAM), static random-access memory (SRAM), magnetic disk, optical disk, flash memory, or various forms of electrically programmable memory (EPROM) or EEPROM. In some examples, storage device 510 is used to store data indicating instructions executed by processing circuitry system 502. Storage device 510 may be used by software or applications running on external device 500 to temporarily store information during program execution.

[0122] The data exchanged between the external device 500 and the IMD 400 may include operating parameters. The external device 500 may transmit data including computer-readable instructions that, when implemented by the IMD 400, can control the IMD 400 to change one or more operating parameters and / or export collected data. For example, the processing circuitry system 502 may transmit instructions to the IMD 400 requesting it to export collected data (e.g., data corresponding to one or more of physiological information, seizure metrics, stroke metrics, or accelerometer signals) to the external device 500. Furthermore, the external device 500 can receive the collected data from the IMD 400 and store it in the storage device 510. Alternatively or additionally, the processing circuitry system 502 may export instructions to the IMD 400 requesting it to update the electrode assembly for stimulation or sensing.

[0123] Users (such as clinicians or patients 102) can interact with external devices 500 through user interface 506. User interface 506 includes a display (such as an LCD or LED display or other type of screen) (not shown), which the processing circuitry system 502 can utilize to present information related to the IMD 400 (e.g., stroke metrics and / or seizure metrics). In addition, user interface 506 may include input mechanisms for receiving input from the user. Input mechanisms may include any one or more of the following: buttons, a keypad (e.g., an alphanumeric keypad), a peripheral pointing device, a touchscreen, or another input mechanism that allows the user to navigate and input through the user interface presented by the processing circuitry system 502 of external device 500. In other examples, user interface 506 may also include an audio circuitry system for providing auditory notifications, instructions, or other sounds to patient 102, receiving voice commands from patient 102, or both. Storage device 510 may include instructions for operating user interface 506 and for managing power supply 508.

[0124] Power source 508 is configured to deliver operating power to components of external device 500. Power source 508 may include a battery and a power generation circuit for generating operating power. In some examples, the battery is rechargeable to allow for long-term operation. Recharging can be achieved by electrically coupling power source 508 to a bracket or plug connected to an alternating current (AC) outlet. Alternatively, recharging can be achieved through near-end inductive interaction between an external charger and an inductive charging coil within external device 500. In other examples, conventional batteries (e.g., nickel-cadmium or lithium-ion batteries) may be used. Furthermore, external device 500 may be directly coupled to an AC outlet for operation.

[0125] In some examples, external device 500 can provide alerts to a patient or another entity (e.g., a call center) based on stroke indicators or seizure indicators provided by IMD 400. In some examples, user interface 506 can provide an interface for presenting alerts for the detection, prediction, or classification of a condition (e.g., stroke) and provide input to the user (e.g., a patient, caregiver, or clinician) beyond detection, prediction, or classification. In this way, the system described herein can avoid unnecessary emergency activity caused by false detections by the system. Additionally or alternatively, external device 500 can output user prompts that can be synchronized with data collection via IMD 400. For example, external device 500 can instruct the user to raise an arm, make a facial expression, etc., and IMD 400 can record physiological data as the user performs the requested action. Furthermore, external device 500 itself can analyze the patient's (e.g., the patient's activity or condition in response to such prompts) for example, using a camera to detect facial drooping, using a microphone to detect slurred speech, or detecting any other stroke indicators. In some implementations, such indicators can be compared to input prior to the stroke (e.g., a stored baseline facial image or a voiceprint with a baseline speech recording). Similarly, the external device 500 can use one or more sensors to detect patient movement or facial activity to provide data indicating a seizure or impending seizure.

[0126] Figure 6 This is a block diagram illustrating an exemplary system according to one or more technologies described herein. The exemplary system includes an access point 600, a network 602, an external computing device (such as a server 604), and one or more other computing devices 610A-610N, which can be coupled to an IMD 106, an external device 108, and a processing circuitry system 110 via the network 602. In this example, the IMD 106 can communicate with the external device 108 via a first wireless connection and with the access point 600 via a second wireless connection using a communication circuitry system. Figure 6 In the example, access point 600, external device 108, server 604 and computing devices 610A-610N are interconnected and can communicate with each other via network 602.

[0127] Access point 600 may include a device connected to network 602 via any of a variety of wired or wireless network connections, such as dial-up, digital subscriber line (DSL), or cable modem connections. In other examples, access point 600 may be coupled to network 602 via different forms of connection, including wired or wireless connections. In some examples, access point 600 may be a user device that can be co-located with a patient, such as a tablet or smartphone. As discussed above, IMD 106 may be configured to transmit data to external device 108, such as any or a combination of EGM signals, accelerometer signals, and tissue impedance signals. In addition, access point 600 may, for example, periodically or in response to commands from the patient or network 602, query IMD 106 to retrieve parameter values ​​determined by the processing circuitry of IMD 106 or other operational or patient data from IMD 106. Access point 600 may then transmit the retrieved data to server 604 via network 602.

[0128] In some cases, server 604 can be configured to provide a secure storage site for data already collected from IMD 106 and / or external device 108. In some cases, server 604 can aggregate data in web pages or other documents for viewing by trained professionals (such as clinicians) via computing devices 610A–610N. Figure 6 One or more aspects of the system shown can be used in a manner similar to Medtronic plc, Dublin, Ireland. The network provides general network technologies and functions for implementation.

[0129] Server 604 may include processing circuitry system 606. Processing circuitry system 606 may include fixed-function circuitry system and / or programmable processing circuitry system. Processing circuitry system 606 may include any or more of a microprocessor, GPU, TPU, controller, DSP, ASIC, FPGA, or equivalent discrete or analog logic circuitry system. In some examples, processing circuitry system 606 may include multiple components (such as any combination of one or more microprocessors, one or more controllers, one or more DSPs, one or more ASICs, or one or more FPGAs), as well as other discrete or integrated logic circuitry systems. The functionality attributed herein to processing circuitry system 606 may be embodied in software, firmware, hardware, or any combination thereof. In some examples, by way of example, processing circuitry system 606 may perform one or more of the techniques described herein based on: EGM signals, impedance signals, accelerometer signals, or other sensor signals received from IMD 106, or parameter values ​​determined by IMD 106 based on such signals and received from IMD 106. For example, the processing circuit system 606 can perform one or more of the techniques described herein to identify significant changes in one or more physiological parameters caused by an event resulting from medical treatment.

[0130] Server 604 may include memory 608. Memory 608 includes computer-readable instructions that, when executed by processing circuitry 606, cause IMD 106 and processing circuitry 606 to perform various functions borne by IMD 106 and processing circuitry 606 herein. Memory 608 may include any volatile, non-volatile, magnetic, optical, or electrical medium, such as RAM, ROM, NVRAM, EEPROM, flash memory, or any other digital medium.

[0131] In some examples, one or more of the computing devices 610A-610N (e.g., device 610A) may be a tablet computer or other smart device located with the clinician, through which the clinician can program to receive alerts and / or query the IMD 106. For example, when patient 102 is between clinician visits, the clinician may access data corresponding to any or a combination of the following to check the status of the medical condition via device 610A: sensed physiological signals, accelerometer signals, seizure measurements, stroke measurements, and other types of signals collected by the IMD 106, or parameter values ​​determined by the IMD 106 based on such signals. In some examples, the clinician may input instructions for medical interventions for patient 102 into an application in device 610A, such as based on the status of the patient's condition determined by the IMD 106, external device 108, processing circuitry system 110, or any combination thereof, or based on other patient data known to the clinician. Device 610A can then transmit instructions for medical intervention to another computing device 610A-610N (e.g., device 610B) located on patient 102 or patient 102's caregiver. For example, such instructions for medical intervention may include instructions to change medication dosage, timing, or selection, to schedule a clinician visit, or to seek medical care. In another example, device 610B may generate an alert for patient 102 based on the status of patient 102's medical condition determined by IMD 106, which could enable patient 102 to proactively seek medical care before receiving instructions for medical intervention. In this way, patient 102 can be authorized to take action as needed to address his or her medical condition, which could help improve patient 102's clinical outcomes.

[0132] Figure 7 This is a flowchart of an exemplary technique for detecting at least one of a stroke or epileptic seizure in a patient. The sensing circuitry 406 and processing circuitry 402 of the IMD400 will be described as performing an example. Figure 7 While this technology is applicable, in other examples, other components, devices, and systems (e.g., IMD 106 or sensor devices 210, 220, or 230) can perform similar functions. Figure 7As shown in the example, sensing circuitry 406 senses electrical signals from a patient (700). Sensing circuitry 406 can sense these electrical signals from sensing vectors determined by electrodes 418 selected for sensing. In this way, sensing circuitry 406 can use different vectors (e.g., different electrode combinations) to obtain different electrical information from the patient. In some examples, sensing circuitry 406 can use different electrode combinations to detect stroke and epileptic seizures, respectively. Sensing circuitry 406 can then generate physiological information (702) based on the sensed electrical signals. Generating physiological information may include various filtering, amplification, transformation, digitization, or any other conditioning and processing that generates physiological information that can be analyzed by processing circuitry 402.

[0133] The processing circuitry 402 then receives physiological information from the sensing circuitry 406 and determines a seizure metric and a stroke metric from the physiological information (704). In some examples, the processing circuitry 402 determines the seizure metric and the stroke metric at the same frequency and / or from the same physiological information. In other examples, the processing circuitry 402 may determine the seizure metric or the stroke metric more frequently than other metrics. In some cases, a seizure may only last a few minutes, so the processing circuitry 402 may determine the seizure metric more frequently than the stroke metric. The processing circuitry 406 then stores the seizure metric and the stroke metric in memory (706). If the processing circuitry 402 has an instruction to transmit the metric information to an external device (such as external device 108) (the "Yes" branch of block 708), the processing circuitry 402 may control the communication circuitry to transmit the metric information to external device 108 (710). For example, the processing circuitry 706 can receive a trigger to send information, such as a seizure metric or stroke metric exceeding a corresponding threshold indicating that a seizure or stroke is occurring or has occurred. In this way, the external device 108 can notify the patient or clinician that the patient may require assistive or therapeutic intervention. If the processing circuitry 402 does not have an instruction to transmit the metric information to an external device (such as external device 108) (the "No" branch of block 708), the processing circuitry 402 continues to sense electrical signals from the patient (700).

[0134] The processing circuit system 402 can employ various techniques to determine stroke and seizure metrics. For example, the processing circuit system 402 can use one or more different algorithms (such as machine learning algorithms) to generate stroke metrics. In one example, after feature extraction, a gradient advancement algorithm can be trained on the dataset to generate a classifier algorithm. The classifier can be tuned by reducing the features to only those relevant to stroke / non-stroke conditions. A sequential backward floating feature selection method can be employed, which sequentially removes individual features using classifier performance metrics. The classifier can then be further tuned by adjusting the frequency windows. The results of this analysis can include five features that effectively distinguish between stroke and non-stroke conditions. These exemplary features are three frequency windows (5.5Hz–7.5Hz, 8Hz–9.5Hz, and 13.5Hz–15Hz) associated with the P3 electrode and two frequency windows (5.5Hz–7.5Hz and 13.5Hz–15Hz) associated with the P4 electrode.

[0135] The resulting classifier can be used by processing circuitry systems to make stroke / non-stroke determinations with high accuracy (such as approximately 85%). Using this method and the features discussed above, this exemplary classifier can achieve high accuracy while relying solely on data from... Figure 1C Data from the three electrodes (P3, P4, and ground electrode Pz) is used. Therefore, for example, IMD 106 or sensor device 210 can generate a stroke metric that identifies a stroke without requiring data from a full array of 16 or more EEG electrodes as found in conventional methods. As described herein, the stroke metric can be, for example, a binary output of stroke / non-stroke condition, a probability indication of stroke likelihood, or other outputs relating to the patient's condition and the likelihood of having already had a stroke. This stroke metric can be calculated using a classifier model as described elsewhere herein.

[0136] The accuracy of any classifier can be improved by training the algorithm on a large dataset corresponding to stroke and non-stroke EEG readings. Additionally, other physiological parameters (e.g., fall detection determined using accelerometers, specific heart rhythm, sex, age, medical history, etc.) can be added to the classifier model. Furthermore, in some examples, the classifier can be used to distinguish between ischemic stroke and hemorrhagic stroke. Such differentiation can be particularly useful because interventions may differ. For example, ischemic stroke can be treated with thrombectomy, while hemorrhagic stroke can be treated with surgery or another suitable technique.

[0137] In some examples of circuitry systems dedicated to generating physiological information (e.g., stroke information) used by processing circuitry system 402 to generate stroke measurements, sensing circuitry system 406 may filter the electrical signal to remove ECG artifacts. Typically, EEG data is obtained via electrodes positioned on the scalp, as the scalp is a relatively noise-free location for signal acquisition. Other anatomical locations (such as the back of the neck) have not been used, not because EEG signals are absent, but because of the noisier environment and band overlap with other physiological signals (such as ECG). However, processing circuitry system 402 may employ machine learning / adaptive neural network techniques to improve signal extraction capabilities (e.g., filtering out or reducing the contribution of ECG signals from EEG signals). One such method is described in “ECG Artifact Removal of EEG Signal Using Adaptive Neural Network,” published on May 27, 2019, at IEEE Xplore 27, which is incorporated herein by reference in its entirety. Similarly, electrical signals associated with muscle activity can be filtered from EEG sensor data to remove such artifacts.

[0138] In some examples, the classification algorithm used to determine the stroke metric may be, for example, an algorithm adapted from the use of artificial intelligence (e.g., machine learning, neural networks, etc.) applied to patient stroke data to determine what type of stroke was detected. Based on the classification algorithm, a stroke determination is made in the box; this determination may be binary or probabilistic. If a stroke is detected (e.g., the probabilistic determination exceeds a predetermined threshold, such as an 85% probability of stroke), the processing circuitry system 402 may apply a causative classifier. In some examples, such a causative classifier may (probabilistically or deterministically) determine the origin of the stroke (e.g., ischemic or hemorrhagic). This determination may be based on separately collected EEG sensor data or in combination with additional physiological parameters or patient data. For example, the causative classifier may determine the location of the stroke. For example, location determination may include left-hemispheric versus right-hemispheric determination (e.g., binary output or probabilistic result). In one example of hemisphere-specific signals, one or more electrodes may be positioned nearby and configured to detect brain activity data corresponding to activity in the T3 brain region, and another one or more electrodes may be positioned nearby and configured to detect brain activity data corresponding to activity in the T4 brain region. The processing circuitry system can then be configured to determine a seizure metric indicative of a patient's seizure state and a stroke metric indicative of a patient's stroke state based on physiological information representing hemispheric activity associated with corresponding electrodes at T3 or T4 brain regions. In other words, as an example, the system can utilize information about which hemisphere of the brain the seizure or stroke originated from as at least part of the stroke location. In other examples, this hemisphere-specific information can be obtained from locations other than T3 and T4. In some examples, location determination can include a more precise mapping of brain regions assigned specific probabilities, such as a 70% probability that the stroke location is at a specific point on the patient's brain. The stroke location can be output along a spherical map or other suitable coordinate system used to identify the location of the patient's brain. The processing circuitry system 402 can output the results of these classifiers in the form of patient metrics or other values ​​indicating the type of stroke detected.

[0139] The processing circuitry system 402 can detect seizures by generating a seizure metric, which can be a binary output of a seizure / non-seizure state, a probability indication of stroke likelihood, the type of seizure experienced, or other outputs related to the patient's condition and the likelihood of experiencing a seizure. The seizure metric can be computed by comparing one or more features extracted from physiological information with corresponding thresholds, lookup tables, equations, or by applying one or more classifiers to the physiological information to apply a machine learning model to the data. The machine learning model can be trained on data sensed, for example, by IMD 106 or sensor device 210 in a specific patient and / or other patients who may or may not have experienced seizures.

[0140] In one example, the IMD 400 and processing circuitry 402 can generate a seizure metric based on the following techniques. The processing circuitry 402 can control the sensing circuitry 406 to sense electrical signals via an electrode assembly of electrodes 418 and generate the physiological information to be analyzed. The processing circuitry 402 can perform a Fast Fourier Transform (FFT) and use a one-second buffer of electrical signal data to determine the spectral power in a band of interest at approximately 2 Hz (e.g., a band from approximately 5 Hz to approximately 45 Hz). In some examples, the overlap of the buffered data can be approximately 50% or approximately 0.5 seconds. This results in a one-second average power for that specific band, i.e., a short-term average of the power. In other examples, the processing circuitry 402 can analyze different (e.g., smaller or larger) bands, using different sampling frequencies, or different buffer sizes. As an example, the processing circuitry 402 can average the one-second average power over a period of time (e.g., 30 minutes). The average of the one-second average power can be averaged using a cascaded filter (such as a three-stage cascaded averaging filter) to form a long-term average or background signal. An exemplary three-stage cascaded averaging filter may include a first-stage mean filter, a second-stage median filter, and a third-stage state mean filter. The processing circuitry 402 can generate a ratio of the short-term average to the long-term average to identify changes in the patient's brain state associated with seizures.

[0141] The processing circuitry system 402 can then apply the ratio of the short-term average to the long-term average to one or more thresholds to identify whether a seizure has occurred. In one example, the processing circuitry system 402 compares this ratio to a dual threshold (e.g., using a dual threshold detector) to determine whether a seizure has occurred and its duration. For example, if the ratio increases above a seizure threshold, the processing circuitry system 402 determines that a seizure has begun. The processing circuitry system 402 can calculate this time until the ratio drops below a duration threshold. In some examples, the seizure threshold and the duration threshold can be set to the same value, but in other examples, the seizure threshold and the duration threshold can be different. For example, the seizure threshold can be higher than the duration threshold to prevent premature detection of a seizure and ensure that the duration of the seizure captures the entire event. In some examples, the processing circuitry system 402 may also employ a seizure count threshold and / or a duration count threshold before determining that a seizure threshold or a duration threshold has been exceeded. Both the seizure count threshold and the duration count threshold can be established, respectively, for the number of consecutive times the ratio must exceed the seizure threshold or the duration threshold before the processing circuitry system 402 can determine that a seizure threshold or a duration threshold has been exceeded. In this way, a brief period of time shorter than the seizure count threshold (i.e., a ratio exceeding the seizure threshold) will not be identified as the start of a seizure. Similarly, a brief period of time shorter than the duration count threshold (i.e., a ratio falling below the seizure threshold) will not be identified as the end of a seizure. Seizure measurement may include values ​​indicating whether a seizure was detected using the techniques described above and / or the duration of any detected seizures.

[0142] The processing circuitry 402 can also perform additional processing or analysis to determine a seizure metric using an IMD 400 positioned at the back or occipital region of the neck, where added noise may affect one or more sensing vectors. For example, the processing circuitry 402 (or sensing circuitry 406) can average electrical signals sensed from two or more different electrode combinations (or average the spectral power calculated from the sensed electrical signals). The processing circuitry 402 can then apply the averaged electrical signal or spectral power to the techniques described above, including a seizure threshold. This process can enhance common-mode suppression of any artifacts in the sensed electrical signals. The processing circuitry 402 can provide vector summation to form a virtual signal using any linear combination (e.g., a general weighted average) of any two physical signals. In another example, the processing circuitry 402 may include one or more additional median and / or mean filters from a cascaded averaging filter of the techniques described above.

[0143] In other examples, the processing circuitry system 402 can apply one or more ratios derived from the corresponding electrode combinations (i.e., sensing vectors) to a decision tree seizure classifier. For example, the processing circuitry system 402 can determine ratios for multiple frequency bands from each available sensing vector (e.g., three vectors in the example of IMD 400 or sensor device 210) and then feed these ratios into an ensemble of the decision tree seizure classifier. In one example, the decision tree seizure classifier can be trained offline using gradient advancement (e.g., similar to the gradient advancement discussed above regarding stroke metric determination). The processing circuitry system 402 can employ any of these techniques to generate a seizure metric indicating whether a patient has experienced a seizure. In this way, the processing circuitry system 402 can utilize many different sensed signals that can be used for the IMD 400, which can reduce the impact of extraneous noise or interfering signals on the computation in order to extract relevant features associated with brain activity.

[0144] In some examples, when processing circuitry 402 transmits stroke and / or seizure measurements to an external device, the external device may be associated with an emergency service. In some examples, the external device may include Global Positioning System (GPS) capabilities or other location detection technologies (e.g., WiFi triangulation) enabling it to identify, store, and / or transmit the geographic location where the stroke or seizure measurement occurred. The external device can then transmit the location information and / or seizure and / or stroke measurements to another device or system via cell phone base stations, satellites, or other technologies. Other systems may be emergency services, such as 911 or other medical services. If performed in an ambulance... Figure 7 The technology, such as a device carried by an ambulance or technician, can receive the measurement and output information or instructions to the emergency medical technician (EMT) or other personnel behind the ambulance and / or to the ambulance driver. In some embodiments, the content displayed to the ambulance driver may include navigation information (such as a map) and instructions for taking the patient to a specific hospital or facility with a stroke center or a seizure specialist.

[0145] Figure 8 This is a flowchart of an exemplary technique for timing detection of stroke or epileptic seizures based on one or more triggering events. The sensing circuitry 406 and processing circuitry 402 of the IMD 400 will be described as performing an example. Figure 8 While this technology is applicable, in other examples, other components, devices, and systems (e.g., IMD 106 or sensor devices 210, 220, or 230) can perform similar functions. Figure 8As shown in the example, the processing circuitry 402 can operate in monitoring mode for both seizures and strokes (800). If the processing circuitry 402 receives a trigger to detect a seizure (the "Yes" branch of block 802), the processing circuitry 402 controls the sensing circuitry 406 to sense electrical signals and generate seizure information (which may be part of physiological information) (804). Using the seizure information, the processing circuitry 402 then generates and stores seizure metrics according to any of the exemplary techniques described herein (806). The trigger may be a triggering event based on the time of day, the frequency of seizure detection (e.g., triggering a determined timer when it expires), sensed patient activity or movement, stroke determination, or any other information indicating that the system should perform an analysis of a potential seizure.

[0146] If the processing circuitry 402 does not receive a trigger for detecting a seizure (the "No" branch of block 802), the processing circuitry determines whether a stroke trigger event has been received (808). If the processing circuitry 402 has received a trigger for detecting a stroke (the "Yes" branch of block 808), the processing circuitry 402 controls the sensing circuitry 406 to sense an electrical signal and generate stroke information (which may be part of physiological information) (810). Using the stroke information, the processing circuitry 402 then generates and stores a stroke metric according to any of the exemplary techniques described herein (812). The trigger may be a triggering event based on the time of day, the frequency of stroke detection (e.g., triggering a determined timer when it expires), sensed stroke-related activity or movement of the patient, a seizure determination, or any other information that indicates the system should perform an analysis of a potential stroke. The processing circuitry 402 then continues to operate in monitoring mode (800).

[0147] In other examples, processing circuitry 402 may determine whether a trigger for a seizure or a trigger for a stroke has occurred in parallel with each other or in a different order in the control loop (e.g., looking for a stroke trigger before a seizure trigger). It should be noted that processing circuitry 402 may calculate stroke and seizure metrics in any order or simultaneously with each other.

[0148] Figure 9 This is a flowchart of an exemplary technique for adjusting the frequency of epileptic seizure detection based on arrhythmia detection. The sensing circuitry 406 and processing circuitry 402 of the IMD 400 will be described as implementing an example. Figure 9 While this technology is applicable, in other examples, other components, devices, and systems (e.g., IMD 106 or sensor devices 210, 220, or 230) can perform similar functions. Figure 9As shown in the example, the processing circuitry 402 can control the sensing circuitry 406 to sense electrical signals and generate seizure information (900). The processing circuitry 402 then determines a seizure metric from the seizure information that indicates whether a seizure has occurred, and stores the seizure metric (902).

[0149] The processing circuitry 402 then analyzes the ECG information (904) for any arrhythmias related to the patient's heart. In some examples, the processing circuitry 402 may extract features of cardiac function (e.g., heart rate or heart rate variability) from the same physiological information generated for the purpose of calculating a seizure metric or stroke metric. In some examples, the processing circuitry 402 may determine the RR interval (the time between adjacent R waves in the ECG) and then determine additional information from the RR interval (such as heart rate variability, marginal intervals or ectopic heartbeats, the presence of tachycardia, a trend of increasing or decreasing RR intervals, or other features). In other examples, various filters may be employed to remove ECG features from the sensed signal. In this case, the processing circuitry 402 may control the sensing circuitry 406 to sense the electrical signal and employ specific conditioning or processing to maintain a frequency associated with cardiac function, and include this information as part of the seizure information to identify arrhythmias.

[0150] If the processing circuitry 402 determines that an arrhythmia is present in the patient (the "Yes" branch of box 906), the processing circuitry 402 increases the seizure detection frequency, which controls the frequency or timing at which the processing circuitry 402 identifies the presence or absence of a seizure (908). An increase in the seizure detection frequency may cause the next detection window to appear earlier than in other cases. If the processing circuitry 402 determines that no arrhythmia is present in the patient (the "No" branch of box 906), the processing circuitry 402 waits until the next detection window (910) before generating additional seizure information (900) and determining the next seizure metric (902). In some examples, the absence of any arrhythmia in the patient may trigger the processing circuitry 402 to set the seizure detection frequency back to baseline or normal values ​​even if the seizure detection frequency was previously increased due to a detected arrhythmia. The processing circuitry 402 may wait for a predetermined time before setting the seizure detection frequency back to baseline or normal values, either during or after the arrhythmia occurs.

[0151] Figure 10 This is a flowchart of an example technique for determining the type of epileptic seizure based on electrocardiogram information. The sensing circuitry 406 and processing circuitry 402 of the IMD 400 will be described as performing the example. Figure 10While this technology is applicable, in other examples, other components, devices, and systems (e.g., IMD 106 or sensor devices 210, 220, or 230) can perform similar functions. Figure 10 As shown in the example, the processing circuitry 402 can control the sensing circuitry 406 to sense electrical signals and generate seizure information (1000) from the sensed EEG. The processing circuitry 402 then determines a seizure metric (1002) from the seizure information that indicates whether a seizure has occurred.

[0152] If the seizure metric indicates that no seizure occurred (No branch of box 1004), the processing circuitry 402 stores the seizure metric (1010) and continues to generate seizure information as programmed (1000). If the seizure metric indicates that a seizure has occurred (Yes branch of box 1004), the processing circuitry 402 obtains ECG information (1006). Figure 9 As described above, the processing circuitry 402 can control the sensing circuitry 406 to sense electrical signals between one or more vectors, and then modulate or process the sensed signals to at least retain ECG information. In some examples, the sensing circuitry 406 and / or the processing circuitry 402 can filter the sensed signals to extract cardiac features (such as R waves or P waves) to determine the RR intervals and / or heart rate information that can be included in the ECG information. Thus, the ECG information can indicate cardiac function. The processing circuitry 402 can then determine the type of seizure based on one or more features of the ECG information (1008). For example, the processing circuitry 402 can classify a seizure as an epileptic seizure in response to determining a decrease in heart rate variability or other changes in heart rate variability during a seizure. In other examples, the processing circuitry 402 can determine that a seizure is a tonic-clonic seizure in response to determining that the patient has an increased heart rate during a seizure. In some cases, the processing circuitry 402 can distinguish between epileptic seizures and other types of seizures (such as syncope and psychogenic events). After classifying the epileptic seizures, the processing circuitry 402 stores the seizure metric and seizure classification (1010) before continuing to monitor the patient (1000). In some examples, the processing circuitry 402 may also initiate communication with an external device in response to recognizing that a seizure or a specific type of seizure has occurred.

[0153] This document describes the following embodiments. Embodiment 1: A system comprising: a memory; a plurality of electrodes; a sensing circuit system configured to: sense electrical signals from a patient via at least two of the plurality of electrodes; and generate physiological information based on the electrical signals; a processing circuit system configured to: receive the physiological information from the sensing circuit system; determine, based on the physiological information, a seizure metric indicating the patient's seizure state and a stroke metric indicating the patient's stroke state; and store the seizure metric and the stroke metric in the memory; and a housing that carries the plurality of electrodes and includes both the sensing circuit system and the processing circuit system.

[0154] Example 2: The system according to Example 1, wherein the physiological data includes brain activity data.

[0155] Example 3: The system according to any one of Examples 1 or 2, wherein the plurality of electrodes are configured to detect brain activity data corresponding to activity in at least one of the P3, Pz or P4 brain regions.

[0156] Example 4: The system according to any one of Examples 1 to 3, wherein the housing is configured to be disposed at or near the back of the patient's neck or skull.

[0157] Example 5: The system according to any one of Examples 1 to 4, wherein the plurality of electrodes are configured to detect brain activity data corresponding to activity in at least one of the T3 or T4 brain regions.

[0158] Example 6: According to the system described in Example 5, the processing circuitry is configured to determine a seizure metric indicating the patient's seizure state and a stroke metric indicating the patient's stroke state based on physiological information representing hemispheric activity associated with corresponding electrodes in the T3 or T4 brain regions.

[0159] Example 7: The system according to any one of Examples 1 to 6, wherein the housing is configured to be implanted in a patient.

[0160] Example 8: The system according to Example 7, wherein the shell is configured for subcutaneous implantation.

[0161] Example 9: The system according to any one of Examples 1 to 8, wherein the housing is configured to be disposed on the outer surface of the patient's skin.

[0162] Example 10: The system according to any one of Examples 1 to 9, wherein the physiological data includes electroencephalogram (EEG) activity data and electrocardiogram (ECG) activity data, and wherein the sensing circuit system includes: a first circuit system configured to generate EEG activity from electrical signals; and a second circuit system different from the first circuit system and configured to generate ECG activity data from electrical signals.

[0163] Example 11: The system according to any one of Examples 1 to 10 further includes an accelerometer within the housing, the accelerometer being configured to generate motion data representing the patient's movements, and wherein the processing circuitry is configured to determine seizure and stroke metrics based on physiological data including the motion data.

[0164] Example 12: The system according to any one of Examples 1 to 11, wherein the processing circuitry is configured to determine that a patient has fallen based on motion data, and wherein the processing circuitry is configured to determine a stroke metric based on the determination that the patient has fallen.

[0165] Example 13: The system according to Example 12, wherein the processing circuit system is configured to: determine that a characteristic of motion data exceeds a threshold; and in response to determining that a characteristic of motion data exceeds a threshold, determine at least one of an epileptic seizure metric or a stroke metric.

[0166] Example 14: The system according to any one of Examples 1 to 13, wherein the physiological information includes electrocardiogram information, and wherein the processing circuitry is configured to: determine that the patient has experienced a seizure based on a seizure metric; and select, based on the electrocardiogram information, a seizure type representing the seizure experienced by the patient from a variety of seizure types.

[0167] Example 15: The system according to any one of Examples 1 to 14, wherein the processing circuit system is configured to determine the epileptic seizure metric at a seizure detection frequency different from the stroke metric.

[0168] Example 16: The system according to Example 15, wherein the frequency of epileptic seizure detection is greater than the frequency of stroke detection.

[0169] Example 17: The system according to any one of Examples 1 to 16, wherein the processing circuit system is configured to: determine a cardiac arrhythmia of a patient's heart based on electrocardiogram signals of physiological information; increase the determined seizure detection frequency of the seizure control measure in response to the determination of the arrhythmia; and determine the seizure measure based on the seizure detection frequency.

[0170] Example 18: The system according to any one of Examples 1 to 17 further includes determining the patient's geographic location; and transmitting the geographic location and at least one of a seizure metric or a stroke metric to an emergency service.

[0171] Example 19: A method comprising: sensing an electrical signal from a patient via a sensing circuit system and via at least two of a plurality of electrodes; generating physiological information via the sensing circuit system and based on the electrical signal; receiving the physiological information via and from the sensing circuit system via a processing circuit system; determining, via the processing circuit system and based on the physiological information, a seizure metric indicative of the patient's seizure state and a stroke metric indicative of the patient's stroke state; and storing the seizure metric and the stroke metric in a memory via the processing circuit system, wherein a housing carries the plurality of electrodes and includes both the sensing circuit system and the processing circuit system.

[0172] Example 20: The method according to Example 19, wherein the physiological data includes brain activity data.

[0173] Example 21: The method according to any one of Examples 19 and 20, wherein the plurality of electrodes are configured to detect brain activity data corresponding to activity in at least one of the P3, Pz or P4 brain regions.

[0174] Example 22: The method according to any one of Examples 19 to 21, wherein the housing is configured to be disposed at or near the back of the patient's neck or skull.

[0175] Example 23: The method according to any one of Examples 19 to 22, wherein the plurality of electrodes are configured to detect brain activity data corresponding to activity in at least one of the T3 or T4 brain regions.

[0176] Example 24: According to the method of Example 23, wherein determining the seizure metric indicating the patient's seizure state and the stroke metric indicating the patient's stroke state includes: determining the seizure metric indicating the patient's seizure state and the stroke metric indicating the patient's stroke state based on physiological information representing hemispheric activity associated with corresponding electrodes at T3 or T4 brain regions.

[0177] Example 25: The method according to any one of Examples 19 to 24, wherein the shell is configured to be implanted in the patient's body.

[0178] Example 26: The method according to Example 25, wherein the shell is configured for subcutaneous implantation.

[0179] Example 27: The method according to any one of Examples 19 to 26, wherein the housing is configured to be disposed on the outer surface of the patient's skin.

[0180] Example 28: The method according to any one of Examples 19 to 27, wherein the physiological data includes electroencephalogram (EEG) activity data and electrocardiogram (ECG) activity data, and wherein the method further includes: generating EEG activity from electrical signals through a first circuit system of a sensing circuit system; and generating ECG activity data from electrical signals through a second circuit system different from the first circuit system.

[0181] Example 29: The method according to any one of Examples 19 to 28 further includes generating motion data representing the patient's movement via an accelerometer within the housing, and wherein determining the seizure measure and stroke measure includes determining the seizure measure and stroke measure based on physiological data including the motion data.

[0182] Example 30: The method according to Example 29 further includes determining that the patient has fallen based on motion data, and wherein determining the stroke metric includes determining the stroke metric based on the determination that the patient has fallen.

[0183] Example 31: The method according to Example 30, wherein determining the stroke metric includes determining the stroke metric based on the determination that the patient has fallen and a time window representing the duration from when the patient is determined to have fallen.

[0184] Example 32: The method according to any one of Examples 29 to 31 further includes: determining that a characteristic of the motion data exceeds a threshold; and in response to determining that a characteristic of the motion data exceeds a threshold, determining at least one of an epileptic seizure metric or a stroke metric.

[0185] Example 33: The method according to any one of Examples 19 to 32, wherein the physiological information includes electrocardiogram information, and wherein the method further includes: determining that the patient has experienced a seizure based on a seizure metric; and selecting a seizure type representing the seizure experienced by the patient from a plurality of seizure types based on the electrocardiogram information.

[0186] Example 34: The method according to any one of Examples 19 to 33, wherein determining the seizure metric includes determining the seizure metric at a seizure detection frequency different from the stroke metric.

[0187] Example 35: The method described in Example 34, wherein the frequency of epileptic seizure detection is greater than the frequency of stroke detection.

[0188] Example 36: The method according to any one of Examples 19 to 35 further includes: determining a cardiac arrhythmia of the patient's heart based on electrocardiogram signals of physiological information; and increasing the determined seizure detection frequency of the seizure control measure in response to the determination of the arrhythmia, wherein determining the seizure control measure includes determining the seizure control measure based on the seizure detection frequency.

[0189] Example 37: The method according to any one of Examples 19 to 36 further includes controlling a telemetry circuit system to transmit at least one of a seizure measurement or a stroke measurement to an external device; determining the patient's geographic location via the external device; and transmitting the geographic location and at least one of the seizure measurement or stroke measurement to emergency services via the external device.

[0190] Example 38: A computer-readable medium includes: controlling a sensing circuit system to sense electrical signals from a patient via at least two of a plurality of electrodes; controlling the sensing circuit system to generate physiological information based on the electrical signals; receiving the physiological information from the sensing circuit system; determining a seizure metric indicative of the patient's seizure state and a stroke metric indicative of the patient's stroke state based on the physiological information; and storing the seizure metric and the stroke metric in a memory, wherein the housing carries the plurality of electrodes and includes both the sensing circuit system and the processing circuit system.

[0191] The techniques described in this disclosure can be implemented, at least in part, in hardware, software, firmware, or any combination thereof. For example, aspects of these techniques can be implemented in one or more processors, DSPs, ASICs, FPGAs, GPUs, TPUs, or any other equivalent integrated or discrete logic QRS circuit systems, and any combination of such components embodied in external devices (such as doctor or patient programmers, simulators, or other devices). The terms "processor" and "processing circuit system" can generally refer to any of the aforementioned logic circuit systems, alone or in combination with other logic circuit systems, or any other equivalent circuit system, alone or in combination with other digital or analog circuit systems.

[0192] For each aspect implemented in software, at least some of the functions of the systems and apparatus described in this disclosure can be embodied in instructions on a computer-readable storage medium, such as RAM, DRAM, SRAM, magnetic disk, optical disk, flash memory, or various forms of EPROM or EEPROM. The instructions can be executed to support one or more aspects of the functions described in this disclosure.

[0193] Additionally, in some aspects, the functions described herein can be housed within dedicated hardware and / or software modules. Describing different features as modules or units is intended to highlight different functional aspects and does not necessarily imply that such modules or units must be implemented by separate hardware or software components. Rather, the functions associated with one or more modules or units can be performed by separate hardware or software components, or integrated into common or separate hardware or software components. Furthermore, this technology can be fully implemented in one or more circuit or logic elements. The technology disclosed herein can be implemented in a variety of devices or apparatuses, including IMDs, external programmers, combinations of IMDs and external programmers, integrated circuits (ICs), or a set of ICs and / or discrete circuit systems residing in IMDs and / or external programmers.

Claims

1. A medical system comprising: Memory; Multiple electrodes; Sensing circuit system, the sensing circuit system being configured to: Electrical signals from the patient are sensed via at least two of the plurality of electrodes; as well as Physiological information is generated based on the electrical signals; Processing circuitry system, the processing circuitry system being configured to: Receive the physiological information from the sensing circuit system; Based on the physiological information, a seizure metric indicating the patient's seizure state is determined; as well as The epileptic seizure metric is stored in the memory; as well as The housing carries the plurality of electrodes and includes both the sensing circuitry and the processing circuitry. The processing circuit system is configured as follows: The patient's cardiac arrhythmia is determined based on the electrocardiogram signal containing the physiological information. In response to determining the arrhythmia, the frequency of seizure detection is increased, wherein the seizure detection frequency is used to control the determination of the seizure metric; and The epileptic seizure metric is determined based on the frequency of epileptic seizure detection.

2. The medical system according to claim 1, wherein the physiological information includes brain activity data.

3. The medical system according to any one of claims 1 and 2, wherein the plurality of electrodes are configured to detect brain activity data corresponding to activity in at least one of the P3, Pz, or P4 brain regions.

4. The medical system according to any one of claims 1 and 2, wherein the housing is configured to be disposed at or near the back of the patient's neck or skull.

5. The medical system according to any one of claims 1 and 2, wherein the plurality of electrodes are configured to detect brain activity data corresponding to activity in at least one of the T3 or T4 brain regions.

6. The medical system of claim 5, wherein the processing circuitry is configured to: determine, based on the physiological information representing hemispheric activity associated with corresponding electrodes at the T3 or T4 brain regions, a seizure metric indicating the patient's seizure state and a stroke metric indicating the patient's stroke state; and store the stroke metric in the memory.

7. The medical system according to any one of claims 1 and 2, wherein the housing is configured to be implanted in the patient.

8. The medical system of claim 7, wherein the housing is configured for subcutaneous implantation.

9. The medical system according to any one of claims 1 and 2, wherein the physiological information includes electroencephalogram (EEG) activity data and electrocardiogram (ECG) activity data, and wherein the sensing circuitry system comprises: A first circuit system configured to generate the brainwave activity data from the electrical signal; as well as A second circuit system, different from the first circuit system, is configured to generate the electrocardiogram activity data from the electrical signal.

10. The medical system of any one of claims 1 and 2, further comprising an accelerometer within the housing, the accelerometer being configured to generate motion data representing the patient's movements, and wherein the processing circuitry is configured to: determine a seizure metric and a stroke metric indicating the patient's stroke state based on the physiological information including the motion data; and store the stroke metric in the memory.

11. The medical system of claim 10, wherein the processing circuitry is configured to determine, based on the motion data, that the patient has fallen, and wherein the processing circuitry is configured to determine the stroke metric based on the determination that the patient has fallen.

12. The medical system according to any one of claims 1 and 2, wherein the physiological information includes electrocardiogram information, and wherein the processing circuitry is configured to: Based on the epileptic seizure metric, it is determined that the patient has experienced an epileptic seizure; and Based on the electrocardiogram information, a seizure type is selected from a variety of seizure types to represent the seizure experienced by the patient.

13. The medical system according to any one of claims 1 and 2, wherein the processing circuitry is configured to: Based on the physiological information, a stroke metric indicating the patient's stroke status is determined; The seizure metric is determined by a seizure detection frequency that is different from the stroke metric, wherein the seizure detection frequency is greater than the stroke detection frequency. as well as The stroke measurement is stored in the memory.

14. The medical system according to any one of claims 1 and 2, further comprising a telemetry circuitry system and an external device within the housing, wherein the processing circuitry system is configured to determine a stroke metric indicative of the patient's stroke status based on the physiological information; The stroke measurement is stored in the memory; and controlling the telemetry circuitry to transmit at least one of the epileptic seizure measure or the stroke measure to the external device, wherein the external device is configured to: Determine the patient's geographical location; as well as The geographic location and at least one of the epilepsy seizure metric or the stroke metric are transmitted to the emergency services.

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