Patent Description:
The present technology is directed to medical devices and, more particularly, to systems and methods for detecting strokes.

Stroke is a serious medical condition that can cause permanent neurological damage, complications, and death. Stroke may be characterized as the rapidly developing loss of brain functions due to a disturbance in the blood vessels supplying blood to the brain. The loss of brain functions can be a result of ischemia (lack of blood supply) caused by thrombosis, embolism, or hemorrhage. The decreased blood supply can lead to dysfunction of the brain tissue in that area.

Stroke is the number two cause of death worldwide and the number one cause of disability. Speed to treatment is the critical factor in stroke treatment as <NUM> neurons are lost per minute on average during stroke. Stroke diagnosis and time between event and therapy delivery are the primary barriers to improving therapy effectiveness. Stroke has <NUM> primary etiologies; i) ischemic stroke (representing approximately <NUM>% of all strokes), ii) hemorrhagic stroke (representing approximately <NUM>% of all strokes), and iii) cryptogenic strokes (includes TIA, representing approximately <NUM>% of all strokes). Strokes can be considered as having neurogenic and/or cardiogenic origins.

A variety of approaches exist for treating patients undergoing a stroke. For example, a clinician may administer anticoagulants, such as warfarin, or may undertake intravascular interventions such as thrombectomy procedures to treat ischemic stroke. For example, a clinician may administer antihypertensive drugs, such as beta blockers (e.g., Labetalol) and ACE-inhibitors (e.g., Enalapril) or may undertake intravascular interventions such as coil embolization to treat hemorrhagic stroke. Lastly, if stroke symptoms have resolved on their own with negative neurological work-up, a clinician may administer long-term cardiac monitoring (external or implantable) to determine potential cardiac origins of cryptogenic stroke. However, such treatments may be frequently underutilized and/or relatively ineffective due to the failure to timely identify whether a patient is undergoing or has recently undergone a stroke. This is a particular risk with more minor strokes that leave patients relatively functional upon cursory evaluation.

<CIT> relates to a stroke detection system which can include hardware that is implanted on or within a patient to measure EEG data. The hardware can include, for example, one or more electrodes implanted under the scalp of the patient, and a control unit with computer hardware, software, and/or firmware configured to process, record, and store the EEG data. The control unit can also be implanted under the scalp of the patient. One example of a stroke detection system includes a control unit disposed within a housing and one or more sensor assemblies electrically and physically coupled to the control unit. The sensor assemblies can each comprise, for example, at least one electrode and an electrical lead connecting the electrode to the control unit.

The claimed subject-matter is defined in independent claim <NUM>. Aspects, embodiments and examples of the present disclosure which are not encompassed by the appended claims are not part of the claimed subject-matter and are provided for illustrative purposes,.

Additional features and advantages of the present technology will be set forth in the description below, and in part will be apparent from the description, or may be learned by practice of the subject technology. The advantages of the present technology will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are intended to provide further explanation of the present technology as claimed.

Many aspects of the present disclosure can be better understood with reference to the following drawings. The components in the drawings are not necessarily to scale. Instead, emphasis is placed on illustrating clearly the principles of the present technology. For ease of reference, throughout this disclosure identical reference numbers may be used to identify identical or at least generally similar or analogous components or features.

It can be difficult to determine whether a patient is suffering from a stroke or has suffered from a stroke. Current diagnostic techniques typically involve evaluating a patient for visible symptoms, such as paralysis or numbness of the face, arm, or leg, as well as difficultly walking, speaking, or understanding However, these techniques may result in undiagnosed strokes, particularly more minor strokes that leave patients relatively functional upon cursory evaluation Even for relatively minor strokes, it is important to treat the patient as soon as possible because treatment outcomes for stroke patients are highly time-dependent. Accordingly, there is a need for improved methods for detecting strokes,.

Embodiments of the present technology enable detection of strokes by obtaining patient physiological data using a sensor device and analyzing the physiological data to provide a stroke indication, as described in more detail below. For example, a monitoring device can be equipped with electrodes (e.g., electroencephalogram (EEG) electrodes) that can be used to sense and record a patient's brain electrical activity. The monitoring device can be implantable (e.g., subcutaneously) or configured to be disposed over a patient's skin. The claimed stroke detection system comprises, inter alia, a sensor device comprising a housing carrying a plurality of electrodes, wherein the housing is configured to be subcutaneously implanted above shoulders of a patient.

Conventional EEG electrodes are typically positioned over a large portion of a user's scalp. While electrodes in this region are well positioned to detect electrical activity from the patient's brain. there are certain drawbacks. Sensors in this location interfere with patient movement and daily activities, making them impractical for prolonged monitoring. Additionally, implanting electrodes under the patient's scalp is difficult and may lead to significant patient discomfort. To address these and other shortcomings of conventional EEG sensors, embodiments of the present technology include a sensor device configured to record electrical signals at a region adjacent a rear portion of the patient's neck or base the patient's skull. In this position, implantation under the patient's skin is relatively simple, and a temporary application of a wearable sensor device (e g. , coupled to a bandage, garment, band, or adhesive member) does not unduly interfere with patient movement and activity.

However, the EEG signals detected via electrodes disposed at or adjacent the back of a patient's neck may be relatively noisy. For example, the electrical signals associated with brain activity may be intermixed with electrical signals associated with cardiac activity (e.g., ECG signals) and muscle activity (e.g., EMG signals) among other artifacts. Accordingly, in some embodiments, the sensor data may be filtered or otherwise manipulated to separate the brain activity data (e.g., EEG signals) from other electrical signals (e.g., ECG signals, EMG signals, etc.).

As described in more detail below, in some embodiments, the sensor data can be analyzed to make a stroke determination includes using a classification algorithm, which can itself be derived using machine learning techniques applied to databases of known stroke patient data. The detection algorithm(s) can be passive (involving measurement of a purely resting patient) or active (involving prompting a patient to perform potentially impaired functionality, such as moving particular muscle groups (e.g., raising an arm, moving a finger, moving facial muscles, etc.,) and/or speaking while recording the electrical response).

The following discussion provides a brief, general description of a suitable environment in which the present technology may be implemented. Although not required, aspects of the technology are described in the general context of computer-executable instructions, such as routines executed by a general-purpose computer. Aspects of the technology can be embodied in a special purpose computer or data processor that is specifically programmed, configured, or constructed to perform one or more of the computer-executable instructions explained in detail herein. Aspects of the technology can also be practiced in distributed computing environments where tasks or modules are performed by remote processing devices, which are linked through 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 may be located in both local and remote memory storage devices.

Computer-implemented instructions, data structures, screen displays, and other data under aspects of the technology may 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-transitory data storage media. In some embodiments, aspects of the technology may be distributed over the Internet or over other networks (e.g. a Bluetooth network) on a propagated signal on a propagation medium (e.g., an electromagnetic wave(s), a sound wave) over a period of time, or may be provided on any analog or digital network (packet switched, circuit switched, or other scheme).

<FIG> is a schematic diagram of system <NUM> configured in accordance with an embodiment of the disclosed technology. Although the system <NUM> is shown with certain devices for purposes of explanation, in various examples any one or more of the devices shown in <FIG> can be omitted. Similarly, although the devices shown in <FIG> are illustrated as including certain components, in various examples any one or more of the particular components within these devices can be omitted (e.g., the sensor device <NUM> may omit the accelerometer <NUM>). Moreover, any of the devices can include additional components not specifically shown here.

The system <NUM> can be configured to sense physiological patient data and analyze that data to make a stroke determination. In an example, the system <NUM> includes a sensor device <NUM> that is configured to be implanted in a target site of the patient or disposed over the skin of the patient at a target site. The sensor device may be a relatively small device, and may be placed (e.g., inserted) under or over the skin at the back of the patient's neck or base of the skull. Other target sites at which the sensor device may be positioned include other positions on the head, such as over the temporal bone. As described in more detail below, the sensor device <NUM> can detect one more physiological parameters of a patient (e.g., electrical activity corresponding to brain activity in particular regions of the patient's brain, heart rhythm data, motion data, etc.). The sensor device <NUM> can be communicatively coupled to an external device <NUM>, for example via a wireless connection. In some embodiments, the external device <NUM> can be a mobile device (e.g., a smartphone, tablet, smartwatch, etc.) or other computing device with which the patient can interact. In some examples, e.g., when sensor device <NUM> is used intraoperatively or perioperatively, external device <NUM> can be another medical device, such as a respirator, heart-lung machine, EKG machine, or other operating room equipment, or another patient monitoring or therapy device or computing device in the operating room or otherwise in the hospital. In operation, the patient may receive output or instructions from the external device <NUM> that are based at least in part on data received at the external device <NUM> from the sensor device <NUM>. For example, the external device <NUM> may provide an alert to the patient or another entity (e.g., a call center) based on a stroke indication provided by the sensor device <NUM>. Additionally or alternatively, the external device <NUM> may output user prompts which can be synchronized with data collection via the sensor device <NUM>. For example, the external device <NUM> may instruct the user to lift an arm, make a facial expression, etc., and the sensor device <NUM> may record physiological data while the user performs the requested actions. Moreover, the external device <NUM> may itself analyze the patient (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 to detect any other indicia of stroke. In some embodiments, such indicia can be compared against pre-stroke inputs (e.g., a stored baseline facial image or voice-print with baseline speech recording).

The sensor device <NUM> and/or the external device <NUM> can also be communicatively coupled with one or more external computing devices <NUM> (e.g., over network <NUM>). In some examples, the external computing devices <NUM> can take the form of servers, personal computers, tablet computers or other computing devices associated with one or more healthcare providers (e.g., hospitals, medical data analytic companies, device manufacturers, etc.). These external computing devices <NUM> can collect data recorded by the sensor device <NUM> and/or the external device <NUM>. In some embodiments, such data can be anonymized and aggregated to perform large-scale analysis (e.g., using machine-learning techniques or other suitable data analysis techniques) to develop and improve stroke detection algorithms using data collected by a large number of sensor devices <NUM>. Additionally, the external computing devices <NUM> may transmit data to the external device <NUM> and/or the sensor device <NUM>. For example, an updated algorithm for making stroke determinations may be developed by the external computing devices <NUM> (e.g., using machine learning or other techniques) and then provided to the sensor device <NUM> and/or the external device <NUM> via the network (e.g., as an over-the-air update), and installed on the sensor device <NUM> and/or external device <NUM>.

In some embodiments, the system <NUM> can also include additional implantable devices, such as an implantable cardiac monitors, an implantable pacemaker, an implantable cardiac defibrillator, a cardiac resynchronization therapy (CRT) device (e.g., CRT-D defibrillator or CRT-P pacemaker), a neurostimulator, a deep-brain stimulation device, a nerve stimulator, a drug pump (e.g., an insulin pump), a glucose monitor, or other devices. Other devices that may support and enhance a personal ecosystem to reduce stroke risk include fitness monitors, nutrition devices, etc. Additionally or alternatively, a stroke detection device can be used in conjunction with other disease therapies with high risk of stroke as an adverse event (e.g., LVAD devices, TAVI/TAMR surgery, bariatric/gastric surgery, etc.) Another example of adjunct therapy with high risk of stroke is ventilation, such as during treatment of COVID-<NUM> or other infections, or Acute Respiratory Distress Syndrome (ARDS).

As noted previously, the sensor device <NUM> is configured to be coupled to a patient for recording physiological data relevant to a stroke determination. For example, the sensor device <NUM> can be implanted within the body of a patient, may be disposed directly over a patient's skin (e.g., held in place via an adhesive or fastener), or may be removably worn by the patient. The claimed stroke detection system comprises, inter alia, a sensor device comprising a housing being configured to be subcutaneously implanted above shoulders of a patient. The sensor device <NUM> includes sensing components <NUM>, which can include a number of different sensors and/or types of sensors. For example, the sensing components <NUM> can include a plurality of electrodes <NUM>, an accelerometer <NUM>, and optionally other sensors <NUM>. Examples of other sensors <NUM> include a blood pressure sensor, a pulse oximeter. an ECG sensor or other heart-recording device, an EMG sensor or other muscle-activity recording device, a temperature sensor, a skin galvanometer, hygrometer, altimeter, gyroscope, magnetometer, proximity sensor, hall effect sensors, or any other suitable sensor for monitoring physiological characteristics of the patient. These particular sensing components <NUM> are exemplary, and in various embodiments the sensors employed can vary.

The electrodes <NUM> can be configured to detect electrical activity such as brain activity (e.g., EEG data), heart activity (e.g., ECG data), and/or muscle activity (e.g., EMG data). The electrodes <NUM> may be formed from any suitable conductive material or materials to enable the electrodes to perform electrical measurements on the patient. In some embodiments, the sensor device <NUM> can be configured to analyze data from the electrodes <NUM> to extract both brain activity data (e.g., EEG signals) and heart activity data (e.g., ECG signals). The brain activity data may be evaluated to provide a stroke determination or other assessment of brain condition, while the heart activity data may be evaluated to provide an assessment of heart condition or to detect certain cardiac events (e.g., heart rate variability, arrhythmias (e.g., tachyarrhythmias or bradycardia), ventricular or atrial fibrillation episodes, etc..

In some embodiments, the sensor device <NUM> is configured to analyze data from the electrodes <NUM> to extract brain activity data and to discard or reduce any contribution from heart or muscle activity. In some embodiments, the electrodes <NUM> are configured to be disposed over the patient's skin. In such embodiments, the electrodes <NUM> can include protrusions (e.g.. microneedles or other suitable structures) configured to at least partially penetrate the patient's skin so as to improve detection of subcutaneous electrical activity. In some embodiments, the sensor device <NUM> can be configured to be implanted within the body (e.g., subcutaneously), and as such the electrodes <NUM> can include a conductive surface exposed along at least a portion of the sensor device <NUM> so as to detect electrical activity within the body.

The sensor device <NUM> may be configured to calculate physiological characteristics relating to one or more electrical signals received from the electrodes <NUM>. For example, the sensor device <NUM> may be configured to algorithmically determine the presence or absence of a stroke or other neurological condition from the electrical signal. In certain embodiments, the sensor device <NUM> may make a stroke determination for each electrode <NUM> (e.g., channel) or may make a stroke determination using electrical signals acquired from two or more selected electrodes <NUM>.

In various embodiments, the number and configuration of electrodes <NUM> can vary. For example, the sensor device <NUM> can include at least <NUM>, at least <NUM>, at least <NUM>, at least <NUM>, or more electrodes <NUM> in an array. In some embodiments, the sensor device <NUM> includes fewer than <NUM>, fewer than <NUM>, fewer than <NUM>, or fewer than <NUM> electrodes <NUM> in an array. As described in more detail below, although conventional EEG arrays include a large number of electrodes disposed over the top of a patient's head, some embodiments of the present technology include a relatively small number of electrodes (e.g., three electrodes) configured to be placed over the rear portion of the patient's neck or skull, or another target region of the patient. In this position, electrical data collected via these electrodes <NUM> may correspond to brain activity in regions determined to be of interest for stroke determination (e.g., the P3, Pz, and/or P4 regions in the case of the rear portion of the patient's neck or skull).

The claimed stroke detection system comprises, inter alia, a sensor device comprising a housing carrying a plurality of electrodes and enclosing processing ciruitry. In some embodiments, the electrodes <NUM> may all reside within a single housing of the sensor device <NUM>. In some embodiments, the electrodes <NUM> may extend away from a housing of the sensor device <NUM> and be connected via leads or other connective components. For example, the sensor device <NUM> can include a housing that encompasses certain components (e.g., the power <NUM>, communications link <NUM>, processing circuitry <NUM>, and/or memory <NUM>), and the electrodes <NUM> (and/or other sensing components <NUM>) can be coupled to the housing via electrical leads or other suitable connections. In such configurations, the electrodes <NUM> can be positioned at locations spaced apart from the housing of the sensor device <NUM>. In some embodiments, the electrodes <NUM> can be disposed within discrete housings that are in turn coupled to a housing containing the other components of the sensor device <NUM>. Such a configuration, in which multiple housings (or sub-housings) are coupled together via flexible or other connectors, may facilitate placement of the sensor device <NUM> at a desired location to improve patient comfort. Additionally, this may facilitate placement of electrodes <NUM> at desirable positions for detecting clinically useful brain activity data.

The accelerometer <NUM> can be configured to detect patient movement. In some embodiments, patient movement data collected via the accelerometer <NUM> can be used to make a fall determination. Fall detection can be particularly valuable when assessing potential stroke patients, as a large percentage of patients admitted for ischemic or hemorrhagic stroke have been found to have had a significant fall within <NUM> days of the stroke event. Accordingly, in some embodiments, the sensor device <NUM> can be configured to initiate monitoring of brain activity via the electrodes <NUM> upon fall detection using the accelerometer <NUM>. In some embodiments, the sensing performed via the electrodes <NUM> can be modified in response to a fall determination, for example with an increased sampling rate or other modification. In addition to fall detection, the accelerometer <NUM> (or similar sensor) can be used to determine potential body trauma due to sudden acceleration and/or deceleration (e.g., a vehicular accident, sports collision, concussion, etc.). These events could be thrombolytic, a precursor to stroke.

The sensor device <NUM> can also include a power source <NUM> (e.g., a battery, capacitors). In some embodiments, the power source <NUM> can be rechargeable, for example using inductive charging or other wireless charging techniques. Such rechargeability can facilitate long-term placement of the sensor device <NUM> on or within a patient.

A communications link <NUM> enables the sensor device <NUM> to transmit to and/or receive data from external devices (e.g., external device <NUM> or external computing devices <NUM>). The communications link <NUM> can include a wired communication link and/or a wireless communication link (e.g., Bluetooth, Near-Field Communications, LTE, <NUM>, Wi-Fi, infrared and/or another wireless radio transmission network).

The processing circuitry <NUM> can include one or more CPUs, ASICs, digital signal processing circuitry, or any other suitable electrical components configured to process data from the sensing components <NUM> and control operation of the sensor device <NUM>. In some embodiments, the processing circuitry <NUM> includes hardware particularly adapted for artificially intelligence and/or machine learning applications, for example, a tensor processing unit (TPU) or other such hardware. In certain embodiments, the processing circuitry of the sensor device <NUM> may include one or more input protection circuits to filter the electrical signals and may include amplifier/filter circuitry to remove DC and high frequency components, one or more analog-to-digital (A/D) converters, or any other suitable components.

The sensor device <NUM> can further include memory <NUM>, which can take the form of one or more computer readable storage modules configured to store information (e.g., signal data, subject information or profiles, environmental data, data collected from one or more sensing components, media files) and/or executable instructions that can be executed by the processing circuitry <NUM>. The memory <NUM> can include, for example, instructions for analyzing patient data to determine whether a patient is undergoing or has recently or previously undergone a stroke. In some embodiments, the memory <NUM> stores data (e.g., signal data acquired from the sensing components <NUM>) used in the stroke detection techniques disclosed herein.

As noted above, in some embodiments, the sensor device <NUM> may also communicate with an external device <NUM>. The external device <NUM> can be, for example, a smartwatch, smartphone, laptop, tablet, desktop PC, or any other suitable computing device and can include one or more features, applications and/or other elements commonly found in such devices. For example, the external device <NUM> can include display <NUM>, a communications link <NUM> (e.g., a wireless transceiver that may include one or more antennas for wirelessly communicating with, for example, other devices, websites, and the sensor device <NUM>). Communication between the external device <NUM> and other devices can be performed via, e.g., a network <NUM> (which can include the Internet, public and private intranet, a local or extended Wi-Fi network, cell towers, the plain old telephone system (POTS), etc.), direct wireless communication, etc. The external device <NUM> can additionally include well-known input components <NUM> and output components <NUM>, including, for example, a touch screen, a keypad, speakers, a camera, etc..

In operation, the patient may receive output or instructions from the external device <NUM> that are based at least in part on data received at the external device <NUM> from the sensor device <NUM>. For example, the sensor device <NUM> may generate a stroke indication based on analysis of data collected via sensing components <NUM>. The sensor device <NUM> may then instruct the external device <NUM> to output an alert to the patient (e.g., via display <NUM> and/or output <NUM>) or another entity. In some embodiments, the alert can both be displayed to the user (e.g., via display <NUM> of the external device) and can also be transmitted to an appropriate emergency medical response service (e.g., a <NUM>-<NUM>-<NUM> call may be placed with location data from the external device <NUM> used to direct responders to locate the patient), and/or to other healthcare provider entities or individuals (e.g. a hospital, emergency room, or physician). In some embodiments, embedded circuitry that provides location data (e.g., a GPS unit) can be included within the sensor device <NUM>.

Additionally or alternatively, the external device <NUM> may output user prompts which may be used in conjunction with physiological data collection via the sensor device <NUM>. For example, the external device <NUM> may instruct the user to perform an action (e.g., lift an arm, make a facial expression, etc.), and the sensor device <NUM> may record physiological data while the user performs the requested actions. In some embodiments, the external device <NUM> may itself analyze physiological parameters of the patient, for example using a camera to detect facial drooping or other indicia of stroke. In some embodiments, such physiological data collected via the external device <NUM> can be combined with data collected via the sensing components <NUM> and analyzed together to make a stroke determination.

As noted previously, the external computing device(s) <NUM> can take the form of servers or other computing devices associated with healthcare providers or other entities. The external devices can include a communications link <NUM> (e.g., components to facilitate wired or wireless communication with other devices either directly or via the network <NUM>), a memory <NUM>, and processing circuitry <NUM>. These external computing devices <NUM> can collect data recorded by the sensor device <NUM> and/or the external device <NUM>. In some embodiments, such data can be anonymized and aggregated to perform large-scale analysis (e.g., using machine-learning techniques or other suitable data analysis techniques) to develop and improve stroke detection algorithms using data collected by a large number of sensor devices <NUM> associated with a large population of patients. Additionally, the external computing devices <NUM> may transmit data to the external device <NUM><NUM> and/or the sensor device <NUM>. For example, an updated algorithm for making stroke determinations may be developed by the external computing devices <NUM> (e.g., using machine learning or other techniques) and then provided to the sensor device <NUM> and/or the external device <NUM> via the network <NUM>, and installed on the recipient device <NUM>/<NUM>.

<FIG> illustrates a plan view of an example sensor device <NUM>. In some embodiments, the sensor device <NUM> can include some or all of the features of the sensor device <NUM> described above with respect to <FIG> and/or the sensor device <NUM> described below with respect to <FIG>, and can include additional features as described in connection with <FIG>. In the illustrated example, the sensor device <NUM> includes a housing <NUM> that carries a plurality of electrodes 213a-c (collectively "electrodes <NUM>") therein. In operation, the electrodes 213a--c can be placed in direct contact with tissue at the target site (e.g., with the user's skin if placed over the user's skin, or with subcutaneous tissue if the sensor device <NUM> is implanted). The housing <NUM> additionally encloses electronic circuitry located inside the sensor device <NUM> and protects the circuitry contained therein from body fluids. In various embodiments, the electrodes <NUM> can be disposed along any surface of the sensor device <NUM> (e.g., anterior surface, posterior surface, left lateral surface, right lateral surface, superior side surface, inferior side surface, or otherwise), and the surface in turn may take any suitable form.

In the example of <FIG>, the housing <NUM> can be a biocompatible material having a relatively planar shape including a first major surface <NUM> configured to face towards the tissue of interest (e.g., to face anteriorly when positioned at the back of the patient's neck) a second major surface <NUM> opposite the first, and a depth D or thickness of the housing <NUM> extending between the first and second major surfaces. The housing <NUM> can define a superior side surface <NUM> (e.g., configured to face superiorly when the device <NUM> is implanted in or at the patient's neck) and an opposing inferior side surface <NUM>. The housing <NUM> can further include a central portion <NUM>, a first lateral portion (or left portion) <NUM>, and a second lateral portion (or right portion) <NUM>. The electrodes <NUM> are distributed about the housing <NUM> such that a central electrode 213b is disposed within the central portion <NUM> (e.g., substantially centrally along a horizontal axis of the device), a left electrode 213a electrode is disposed within the left portion <NUM>, and a right electrode 213c is disposed within the right portion <NUM>. As illustrated, the housing <NUM> can define a boomerang or chevron-like shape in which the central portion <NUM> includes a vertex, with the first and second lateral portions <NUM> and <NUM> extending both laterally outward and from the central portion <NUM> and also at a downward angle with respect to a horizontal axis of the device.

The configuration of the housing <NUM> can facilitate placement either over the user's skin in a bandage-like form or for subcutaneous implantation. As such, a relatively thin housing <NUM> can be advantageous. Additionally, the housing <NUM> can be flexible in some embodiments, so that the housing <NUM> can at least partially bend to correspond to the anatomy of the patient's neck (e.g., with left and right lateral portions <NUM> and <NUM> of the housing <NUM> bending anteriorly relative to the central portion <NUM> of the housing <NUM>).

In some embodiments, the housing <NUM> can have a length L of between about <NUM>-<NUM>, between about <NUM>-<NUM>, or about <NUM>. The housing <NUM> can have a width W of between about <NUM>-<NUM>, between about <NUM>-<NUM>, or about <NUM>. In some embodiments, the housing <NUM> can have a thickness less than about <NUM>, about <NUM>, about <NUM>, about <NUM>, about <NUM>, about <NUM>, about <NUM>, or about <NUM>. In some embodiments, the thickness of the housing <NUM> can be between about <NUM>-<NUM>, between <NUM>-<NUM>, or about <NUM>. The housing <NUM> can have a volume of less than about <NUM> cc, about <NUM> cc, about <NUM> cc, about <NUM> cc, about <NUM> cc, about <NUM> cc, about <NUM> cc, about <NUM> cc, about <NUM> cc, about <NUM> cc, about <NUM> cc, or about <NUM> cc. In some embodiments, the housing <NUM> can have dimensions suitable for implantation through a trocar introducer or any other suitable implantation technique.

As illustrated, the electrodes <NUM> carried by the housing <NUM> are arranged so that all three electrodes <NUM> do not lie on a common axis. In such a configuration, the electrodes <NUM> can achieve a better signal vector as compared to electrodes that are all aligned along a single axis. This can be particularly useful in a sensor device <NUM> configured to be implanted at the neck while detecting electrical activity in the brain. In some embodiments, this electrode configuration also provides for improved cardiac ECG sensitivity by integrating <NUM> potential signal vectors.

In the example shown in <FIG>, all three electrodes <NUM> are located on the first major surface <NUM> and are substantially flat and outwardly facing. However, in other examples one or more electrodes <NUM> may utilize a three-dimensional configuration (e.g., curved around an edge of the device <NUM>). Similarly, in other examples one or more electrodes <NUM> may be disposed on the second major surface opposite the first. The various electrode configurations allow for configurations in which electrodes <NUM> are located on both the first major surface and the second major surface. In other configurations, such as that shown in <FIG>, electrodes <NUM> are only disposed on one of the major surfaces of the housing <NUM>. The electrodes <NUM> may be formed of a plurality of different types of biocompatible conductive material (e.g., stainless steel, titanium, platinum, iridium, or alloys thereof), and may utilize one or more coatings such as titanium nitride or fractal titanium nitride. In some embodiments, the material choice for electrodes can also include materials having a high surface area (e.g., to provide better electrode capacitance for better sensitivity) and roughness (e.g., to aid implant stability). Although the example shown in <FIG> includes three electrodes <NUM>, in some embodiments the sensor device <NUM> can include <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, or more electrodes carried by the housing <NUM>.

<FIG> illustrates another example embodiment in which the electrodes <NUM> are not exposed along the first major surface <NUM> of the housing <NUM>. Instead, the electrodes <NUM> can be exposed along superior and inferior side surfaces (e.g., facing superiorly and inferiorly when implanted at or on a patient's neck), as shown in <FIG> illustrates another example in which the housing <NUM> assumes a curved configuration, and in which the electrodes can be place along the superior and/or inferior side surfaces of the housing <NUM>. In some embodiments, a curved configuration can improve patient comfort and more readily conform to the anatomy of the patient's neck region.

In operation, the electrodes <NUM> are used to sense electrical signals (e.g., EEG signals) which may be submuscular or subcutaneous. The sensed electrical signals may be stored in a memory of the sensor device <NUM>, and signal data may be transmitted via a communications link to another device (e.g., external device <NUM> of <FIG>). The sensed electrical signals may be time-coded or otherwise correlated with time data, and stored in this form, so that the recency, frequency, time of day, time span, or date(s) of a particular signal data point or data series (or computed measures or statistics based thereon) may be determined and/or reported. In some examples, electrodes <NUM> may additionally or alternatively be used for sensing any bio-potential signal of interest, such as an electrocardiogram (ECG), intracardiac electrogram (EGM), electromyogram (EMG), or a nerve signal, from any implanted location. These data may be time-coded or time-correlated, and stored in that form, in the manner described above with respect to EEG signal data.

<FIG> illustrates another example sensor device <NUM>. In some embodiments, the sensor device <NUM> can include some or all of the features of the sensor devices <NUM> and <NUM> described above with respect to <FIG> and <FIG> in accordance with embodiments of the present technology, and can include additional features as described in connection with <FIG>. In the example shown in <FIG>, sensor device <NUM> may be embodied as a monitoring device having housing <NUM>, proximal electrode 313a and distal electrode 313b (individually or collectively "electrode <NUM>" or "electrodes <NUM>"). Housing <NUM> may further comprise first major surface <NUM>, second major surface <NUM>, proximal end <NUM>, and distal end <NUM>. Housing <NUM> encloses electronic circuitry located inside the sensor device <NUM> and protects the circuitry contained therein from body fluids. Electrical feedthroughs provide electrical connection of electrodes <NUM>. In an example, sensor device <NUM> may be embodied as an external monitor, such as patch that may be positioned on an external surface of the patient, or another type of medical device (e.g., instead of as an ICM), such as described further herein.

In the example shown in <FIG>, sensor device <NUM> is defined by a length "L," a width "W," and thickness or depth "D. " sensor device <NUM> may be in the form of an elongated rectangular prism wherein the length L is significantly larger than the width W, which in turn is larger than the depth D. In one example, the geometry of the sensor device <NUM>-in particular, a width W being greater than the depth D-is selected to allow sensor device <NUM> to be inserted under the skin of the patient using a minimally invasive procedure and to remain in the desired orientation during insertion. For example, the device shown in <FIG> includes radial asymmetries (notably, the rectangular shape) along the longitudinal axis that maintains the device in the proper orientation following insertion. For example, in one example the spacing between proximal electrode 313a and distal electrode 313b may range from <NUM> millimeters (mm) to <NUM>, <NUM> to <NUM>, and from <NUM> to <NUM> and may be any range or individual spacing from <NUM> to <NUM>. In-some examples, the length L may be from <NUM> to about <NUM>. In other examples, the length L may range from <NUM> to <NUM>, <NUM> to <NUM> and may be any length or range of lengths between about <NUM> and about <NUM>. In addition, the width W of first major surface <NUM> may range from <NUM> to <NUM> and may be any single or range of widths between <NUM> and <NUM>. The thickness of depth D of sensor device <NUM> may range from <NUM> to <NUM>. In other examples, the depth D of sensor device <NUM> may range from <NUM> to <NUM> and may be any single or range of depths from <NUM> to <NUM>. In addition, sensor device <NUM> according to an example of the present disclosure is has a geometry and size designed for ease of implant and patient comfort. Examples of sensor device <NUM> described in this disclosure may have a volume of <NUM> cc or less, <NUM> cc or less, <NUM> cc or less, <NUM> cc or less, <NUM> cc or less, <NUM> cc or less, <NUM> cc or less, <NUM> cc or less, or <NUM> cc or less, any volume between <NUM> and <NUM> cc. In addition, in the example shown in <FIG>, proximal end <NUM> and distal end <NUM> are rounded to reduce discomfort and irritation to surrounding tissue once inserted under the skin of the patient.

In the example shown in <FIG>, once inserted within the patient, the first major surface <NUM> faces outward, toward the skin of the patient while the second major surface <NUM> is located opposite the first major surface <NUM>. Consequently, the first and second major surfaces may face in directions along a sagittal axis of patient, and this orientation may be consistently achieved upon implantation due to the dimensions of sensor device <NUM>. Additionally, an accelerometer, or axis of an accelerometer, may be oriented along the sagittal axis.

Proximal electrode 313a and distal electrode 313b are used to sense electrical signals (e.g., EEG signals) which may be submuscular or subcutaneous. Electrical signals may be stored in a memory of the sensor device <NUM>, and signal data may be transmitted via integrated antenna <NUM> to another medical device, which may be another implantable device or an external device, such as external device <NUM> (<FIG>). In some examples, electrodes 313a and 313b may additionally or alternatively be used for sensing any bio-potential signal of interest, such as an electrocardiogram (ECG), intracardiac electrogram (EGM), electromyogram (EMG), or a nerve signal, from any implanted location.

In the example shown in <FIG>, proximal electrode 313a is in close proximity to the proximal end <NUM>, and distal electrode 313b is in close proximity to distal end <NUM>. In this example, distal electrode 313b is not limited to a flattened, outward facing surface, but may extend from first major surface <NUM> around rounded edges <NUM> or end surface <NUM> and onto the second major surface <NUM> so that the electrode 313b has a three-dimensional curved configuration. In the example shown in <FIG>, proximal electrode 313a is located on first major surface <NUM> and is substantially flat, outward facing. However, in other examples proximal electrode 313a may utilize the three-dimensional curved configuration of distal electrode 313b, providing a three-dimensional proximal electrode (not shown in this example). Similarly, in other examples distal electrode 313b may utilize a substantially flat, outward facing electrode located on first major surface <NUM> similar to that shown with respect to proximal electrode 313a. The various electrode configurations allow for configurations in which proximal electrode 313a and distal electrode 313b are located on both first major surface <NUM> and second major surface <NUM>. In other configurations, such as that shown in <FIG>, only one of proximal electrode 313a and distal electrode 313b is located on both major surfaces <NUM> and <NUM>, and in still other configurations both proximal electrode 313a and distal electrode 313b are located on one of the first major surface <NUM> or the second major surface <NUM> (e.g., proximal electrode 313a located on first major surface <NUM> while distal electrode 313b is located on second major surface <NUM>). In another example, sensor device <NUM> may include electrodes <NUM> on both first major surface <NUM> and second major surface <NUM> at or near the proximal and distal ends of the device, such that a total of four electrodes <NUM> are included on sensor device <NUM>. Electrodes <NUM> may be formed of a plurality of different types of biocompatible conductive material (e.g., stainless steel, titanium, platinum, iridium, or alloys thereof), and may utilize one or more coatings such as titanium nitride or fractal titanium nitride. Although the example shown in <FIG> includes two electrodes <NUM>, in some embodiments the sensor device <NUM> can include <NUM>, <NUM>, <NUM>, or more electrodes carried by the housing <NUM>.

In the example shown in <FIG>, proximal end <NUM> includes a header assembly <NUM> that includes one or more of proximal electrode 313a, integrated antenna <NUM>, anti-migration projections <NUM>, or suture hole <NUM>. Integrated antenna <NUM> is located on the same major surface (i.e., first major surface <NUM>) as proximal electrode 313a and is also included as part of header assembly <NUM>. Integrated antenna <NUM> allows sensor device <NUM> to transmit or receive data. In other examples, integrated antenna <NUM> may be formed on the opposite major surface as proximal electrode 313a, or may be incorporated within the housing <NUM> of sensor device <NUM>. In the example shown in <FIG>, anti-migration projections <NUM> are located adjacent to integrated antenna <NUM> and protrude away from first major surface <NUM> to prevent longitudinal movement of the device. In the example shown in <FIG> anti-migration projections <NUM> includes a plurality (e.g., six or nine) small bumps or protrusions extending away from first major surface <NUM>. As discussed above, in other examples anti-migration projections <NUM> may be located on the opposite major surface as proximal electrode 313a or integrated antenna <NUM> In addition, in the example shown in <FIG> header assembly <NUM> includes suture hole <NUM>, which provides another means of securing sensor device <NUM><NUM> to the patient to prevent movement following insert. In the example shown, suture hole <NUM> is located adjacent to proximal electrode 313a. In one example, header assembly <NUM> is a molded header assembly made from a polymeric or plastic material, which may be integrated or separable from the main portion of sensor device <NUM>.

<FIG> illustrates an exemplary target region <NUM> for positioning a sensor device (e.g., sensor devices <NUM>, <NUM>, <NUM> described elsewhere herein). As illustrated, the target region <NUM> can be a rear portion of a user's neck or skull. The target region <NUM> can be positioned above the patient's shoulders and at or below the patient's occipital bone. As noted previously, a sensor device can be disposed in this region either via implantation (e.g., subcutaneously) or by being placed over the patient' s skin with one or more electrodes of a sensor device being in direct contact with the patient's skin at or adjacent the target region <NUM>. The claimed stroke detection system comprises, inter alia, a sensor device comprising a housing being configured to be subcutaneously implanted. A sensor device may similarly be disposed at other target regions, such as over a temporal bone or other cranial regions.

While conventional EEG electrodes are placed over the patient's scalp. the present technology advantageously enables recording of clinically useful brain activity data via electrodes positioned at the target region <NUM>, e.g., at the rear of the patient's neck. This anatomical area is well suited to suited both to implantation of a sensor device and to temporary placement of a sensor device over the patient's skin In contrast, EEG electrodes positioned over the scalp are cumbersome, and implantation over the patient's skull is challenging and may introduce significant patient discomfort. As noted elsewhere here, conventional EEG electrodes are typically positioned over the scalp to more readily achieve a suitable signal-to-noise ratio for detection of brain activity. However, by using certain digital signal processing, and a special-purpose classifier algorithm, clinically useful brain activity data can be obtained using sensors disposed at the target region <NUM>. Specifically, the electrodes can detect electrical activity that corresponds to brain activity in the P3, Pz, and/or P4 regions (see <FIG>).

While conventional approaches to stroke detection utilizing EEG have relied on data from a large number of EEG electrodes, the inventors have discovered that clinically useful stroke determinations may be made utilizing relatively few electrodes. In an experiment conducted by the inventors, data from a base set of <NUM> patients (<NUM> stroke and <NUM> non-stroke) was used. EEG data was recorded somewhere between <NUM> and <NUM> hours post-event using a conventional EEG array with a sampling frequency of <NUM> over a period of <NUM> minutes. The EEG data was detrended, then bandpass filtered (e.g., filter <NUM>-<NUM> to remove high-frequency noise), followed re-referencing to Pz, wavelet denoising, and finally low-pass filtering below <NUM>. With an EEG array of <NUM> contacts (with Pz serving as ground), and <NUM> power bins, a total of <NUM> features were extracted.

A gradient boosting algorithm was trained on the data set following feature extraction to generate a classifier algorithm. The classifier was tuned by paring down features to only those related to the stroke/non-stroke condition. A sequentially backward floating feature selection approach was employed, which sequentially removes individual features using a classifier performance metric. The classifier was further tuned by adjusting the frequency bins. The result of this analysis was five features that effectively discriminate between stroke and non-stroke conditions. These features were three frequency bins associated with the P3 electrode (<NUM>-<NUM>, <NUM>-<NUM>, and <NUM>-<NUM>) and two frequency bins associated with the P4 electrode (<NUM>-<NUM> and <NUM>-<NUM>). <FIG> is a graph of normalized power over the relevant frequency bins for the P3 and P4 electrodes. The relevant frequency bins are indicated in the graph with shading.

The resulting classifier succeeded in making stroke/non-stroke determinations with an accuracy of approximately <NUM>%. <FIG> graphically illustrates these results mapped using two features (the P4 electrode in the <NUM>-<NUM> range along the x-axis and the P3 electrode in the <NUM>-<NUM> range along the y-axis). The "+" and "-" symbols in the graph reflect the actual stroke/non-stroke condition, and the shaded regions in the graph reflect the predictions made by the classifier. As seen in <FIG>, the majority of the "+" symbols are grouped within the predicted stroke region and the majority of the "-" symbols are grouped in the predicted non-stroke region. Significantly and surprisingly, this classifier achieved relatively high accuracy while only relying on data from three electrodes: P3, P4, and the ground electrode, Pz. As such, the inventors successfully demonstrated that clinically useful stroke determinations are possible without requiring data from a full array of <NUM> or more EEG electrodes as found in conventional approaches.

The accuracy of such a classifier can be improved by training the algorithm on larger sets of data corresponding to stroke and non-stroke EEG readings. Additionally, other physiological parameters can be added to the classifier model (e.g., fall detection as determined using an accelerometer, particular heart rhythms, gender, age, medical history, etc.). Additionally, in some embodiments a classifier can be used to discriminate between ischemic and hemorrhagic strokes. Such discrimination can be particularly useful as the interventions may differ. For example, an ischemic stroke may be treated using thrombectomy, while a hemorrhagic stroke may be treated using surgery or another suitable technique.

<FIG> is a flow diagram of a method <NUM> for making a stroke determination. The process <NUM> can include instructions stored, for example, in the memory (e.g., memory <NUM>, <NUM>, and/or <NUM> of <FIG>) that are executable by the one or more processors (e.g., processing circuitry <NUM>, <NUM>, and/or <NUM> of <FIG>). In some embodiments, portions of the process <NUM> are performed by one or more hardware components (e.g., the sensing components <NUM> of <FIG>). In certain embodiments, portions of the process <NUM> are performed by a device external to the system <NUM> of <FIG>.

As illustrated, the process <NUM> begins in block <NUM> with collecting EEG sensor data via electrodes disposed at or adjacent the back of the neck or base of the skull (e.g., the target region <NUM> shown in <FIG>). In some embodiments, the EEG sensor data can include electrical signals detected using electrodes of a sensor device <NUM>, <NUM>, or <NUM> as described above with respect to <FIG>. Such a device can be disposed (e.g., either implanted subcutaneously or positioned over the patient's skin) at the target region <NUM> (<FIG>).

The process <NUM> continues in block <NUM> with filtering the EEG sensor data to remove ECG artifacts. Conventionally, EEG data has been obtained via electrodes positioned over the scalp because it is a relatively noise-free location for signal acquisition. Other anatomical locations such as back of the neck have not been used, not because EEG signal isn't present, but because of the noisier environment and band-overlap with other physiologic signals such as ECG. However, recent techniques for machine learning/adaptive neural network processing have enhanced the signal extraction capability (e.g., to filter out or reduce the contribution of ECG signals from the EEG signals). One such methodology is described in "ECG Artifact Removal of EEG signal using Adaptive Neural Network" as published in IEEE Xplore <NUM> May <NUM>,.

Similarly, electrical signals associated with muscle activity may also be filtered from the EEG sensor data to remove such artifacts.

In block <NUM>, a patient stroke indicator is provided. The patient stroke indicator can be, for example, a binary output of stroke condition/non-stroke condition, a probabilistic indication of stroke likelihood, or other output relating to the patient's condition and likelihood of having suffered a stroke. This stroke indicator can be calculated using a classifier model as described elsewhere herein. In addition to providing the patient stroke indicator, information or instructions can also be output to a patient or user. The information or instructions can be output via a display device (e.g., the display <NUM> of <FIG>). For example, if a stroke is identified in block <NUM>, then the system may provide instructions to route the patient to a comprehensive stroke treatment center or otherwise flag the patient for treatment. In embodiments in which the process <NUM> is performed while the patient is in an ambulance, the process <NUM> can output information or instructions to an emergency medical technician (EMT) or other personnel in the rear of the ambulance and/or to the ambulance driver. In some embodiments, the display to the ambulance driver can include navigational information such as a map and instructions to take the patient to a particular hospital or facility with a stroke center. In embodiments in which the process <NUM> is performed intraoperatively or perioperatively, the stroke indicator may be provided via the sensor device or other external devices in the operating room or hospital in wireless communication with the sensor device.

In some embodiments, prior to, concurrently with, or after providing the stroke indicator in block <NUM>, the method <NUM> can include triggering an automatic data transmission, for example of a stroke determination which can be output to the patient or another entity (e.g., a call center, emergency response personnel, etc.). A call center may contact the patient or a patient's designated contact to inquire as the patient's status, and/or to confirm a patient stroke. If the patient stroke is confirmed (or if the call center is unable to reach the patient), a <NUM>-<NUM>-<NUM> emergency call can be initiated, either manually by call center personnel or automatically.

<FIG> is a flow diagram of another method <NUM> for making a stroke determination. The process <NUM> can include instructions stored, for example, in the memory (e.g., memory <NUM>, <NUM>, or <NUM> of <FIG>) that are executable by the one or more processors (e.g., processing circuitry <NUM>, <NUM>, or <NUM> of <FIG>). In some embodiments, portions of the process <NUM> are performed by one or more hardware components (e.g., the display <NUM>, input <NUM>, and/or output <NUM> of the external device <NUM>; the sensing components <NUM> of the sensor device <NUM> (<FIG>)). In certain embodiments, portions of the process <NUM> are performed by a device external to the system <NUM> of <FIG>.

In block <NUM>, instructions are output to a patient to perform an action. For example, instructions may be output via external device <NUM> (<FIG>), using a display, speaker, or other suitable output. The instructions can include patient prompts for the patient to perform particular acts or movements, such as lifting an arm or leg, moving a hand or fingers, speaking, smiling, recognition of an image, clapping, etc. In some embodiments, these prompts can be provided in succession, and patient data can be obtained after each prompt while the patient responds (or fails to respond) to the particular instructions. In some embodiments, accelerometer data (e.g., an accelerometer within external device <NUM>) can be used to monitor patient movement in response to the provided prompts.

In block <NUM>, EEG sensor data is collected while the patient performs the actions included in the instructions of block <NUM>. In some embodiments, the EEG sensor data can be collected via electrodes disposed at or adjacent the back of the neck or base of the skull (e.g., the target region <NUM> shown in <FIG>) as described elsewhere herein. In some embodiments, the EEG sensor data can include electrical signals detected using electrodes of a sensor device <NUM>, <NUM>, or <NUM> as described above with respect to <FIG>. Such a device can be disposed (e.g., either implanted subcutaneously or positioned over the patient's skin) at a target region of the patient.

In block <NUM>, the sensor data is analyzed and, based on the analysis, the system can provide a patient stroke indicator. The analysis can include, for example, using a classifier algorithm as described elsewhere herein. The patient stroke indicator can be, for example, a binary output of stroke condition/non-stroke condition, a probabilistic indication of stroke likelihood, or other output relating to the patient's condition and likelihood of having suffered a stroke. In some embodiments, if a stroke is indicated, the system can output appropriate information or instructions via a display device (e.g., the display <NUM> of <FIG> or the display <NUM> of <FIG>). For example, if a stroke is identified in block <NUM>, then the system may provide instructions to route the patient to a comprehensive stroke treatment center or otherwise flag the patient for treatment.

<FIG> illustrates an example method <NUM> for enhanced stroke detection. As illustrated, the process <NUM> begins in block <NUM> with collecting EEG sensor data via electrodes disposed at or adjacent the back of the neck or base of the skull (e.g., the target region <NUM> shown in <FIG>). In some embodiments, the EEG sensor data can include electrical signals detected using electrodes of a sensor device <NUM>, <NUM>, or <NUM> as described above with respect to <FIG>. Such a device can be disposed (e.g., either implanted subcutaneously or positioned over the patient's skin) at the target region <NUM> (<FIG>).

The process <NUM> continues in block <NUM> with filtering the EEG sensor data to remove ECG artifacts as described elsewhere herein. In block <NUM>, a classification algorithm is applied. The classification algorithm can be, for example, an algorithm adapted from the use of artificial intelligence (e.g., machine learning, neural networks, etc.) as applied to patient stroke data, for example as described above with respect to <FIG>. Based on the classification algorithm, in block stroke determination is made, which may be binary or probabilistic. In block <NUM>, if a stroke is detected (e.g., a probabilistic determination falls below some pre-determined threshold, for example less than <NUM>% probability of a patient stroke based on the classifier algorithm), the result can be output in block <NUM>. If a stroke is detected in block <NUM> (e.g., a probabilistic determination exceeds some pre-determined threshold, for example <NUM>% likelihood of stroke), then the process <NUM> continues to block <NUM> to apply an etiology classifier. In some embodiments, such an etiology classifier can make a determination (probabilistic or definitive) of the origin of the stroke (e.g., ischemic or hemorrhagic). Such determinations can be made based on collected EEG sensor data alone or in conjunction with additional physiological parameters or patient data. In block <NUM>, a location classifier is applied. This classifier can determine a location of the stroke. For example, the location determination can include a left-versus-right hemisphere determination (e.g., a binary output or probabilistic result). In some embodiments, the location determination can include a more precise mapping of brain regions with particular probabilities assigned, for example a <NUM>% probability of the stroke location being at a particular point on the patient's brain. The stroke location may be output along a spherical surface map or other suitable coordinate system for identifying the location in the patient's brain. In block <NUM>, the result of the classifiers can be output, for example via graphical display, automatic alert to call center, patient, or other entity, etc. In some embodiments, the output can include a graphical representation of the stroke location, for example as a superimposed location over a graphical representation of a brain.

In addition to outputting the results, information or instructions can also be output to a patient or user. The information or instructions can be output via a display device (e.g., the display <NUM> of <FIG>). For example, if a stroke is identified in block <NUM>, then the system may provide instructions to route the patient to a comprehensive stroke treatment center or otherwise flag the patient for treatment. In embodiments in which the process <NUM> is performed while the patient is in an ambulance, the process <NUM> can output information or instructions to an emergency medical technician (EMT) or other personnel in the rear of the ambulance and/or to the ambulance driver. In some embodiments, the display to the ambulance driver can include navigational information such as a map and instructions to take the patient to a particular hospital or facility with a stroke center.

<FIG> illustrates an example method <NUM> for detecting a stroke and/or determining a patient's risk of stroke. In block <NUM>, <NUM>, and <NUM>, the method <NUM> includes collecting EEG sensor data, ECG sensor data, and accelerometer sensor data, respectively. In some embodiments, fewer data can be collected, and in other embodiments additional data can also be collected (e.g., body temperature, blood pressure, etc.). In blocks <NUM>, <NUM>, and <NUM>, classification algorithms are applied to the respective data collected, and based on the classification algorithms, pre-stroke patterns are identified in blocks <NUM>, <NUM>, and <NUM>. In some embodiments, the classification algorithms can be generated from adaptive neural network models or other machine learning techniques trained on large samples of patient stroke data to identify particular patterns that are indicative of pre-stroke states. Such data may be more readily collected by the use of implantable monitoring devices as described herein.

In block <NUM>, the identified patterns can be integrated or otherwise combined and a stroke risk parameter can be calculated. The stroke risk can be based on the physiological data as well as other patient parameters (e.g., gender, age, history of stroke or heart conditions, etc.), and can include a probabilistic output of a patient's risk for stroke over. If, in block <NUM>, there is no risk of stroke identified (e.g., the stroke risk parameter falls below a pre-defined threshold), then no action is taken in block <NUM>. Optionally, a result of "no risk" or "low risk" can be output to the patient or other entity. If, in block <NUM>, a stroke risk is identified (e.g., the stroke risk parameter exceeds a pre-determined threshold) then an alert can be output in block <NUM>. Such an alert can be provided to the patient (e.g., via the external device <NUM>), to a call center, a patient's medical team, or any other suitable entity.

Intraoperative monitoring for stroke has been implemented for high-risk procedures, such as transcatheter valve replacement and carotid endarterectomy. Techniques for intraoperative stroke monitoring have included monitoring EEG and/or ultrasound. Typically, intraoperative EEG monitoring for stroke is performed using large arrays of cranial electrodes, such as <NUM> to <NUM> electrodes, and a large desktop/rack mounted signal acquisition and processing system.

Using the signal processing and analysis techniques described herein, the systems and sensor devices described herein are configured to provide a stroke indication using a relatively smaller number of electrodes, e.g., three electrodes, and a relatively smaller sensor device package. In some examples, a sensor device as described herein may be positioned externally in one of the target locations described herein for intraoperative and/or perioperative stroke monitoring, during an interoperative or perioperative period of the patient. One possible target location is the back of the head or neck. Other possible target locations include the forehead and/or the neck just behind the ear, which may eliminate a need to shave hair prior to placement of the sensor device. Further, such sensor device, systems, and techniques may be used for temporary stroke monitoring during other periods in which a patient may be at relatively higher risk of a stroke, such as certain periods of cardiac arrhythmia, when on a respirator, or due to complications arising from Covid-<NUM> or other infection.

Some example sensor devices may include one or more flexible electrode extensions or leads attached to its housing to allow the housing to be positioned at one of these target locations and an electrode on the extension at another of these target locations. Electrode extensions are inherently flexible, allowing conformance to neck and/or head anatomy. Additionally, the length and flexibility of one or more electrode extensions may allow an electrode on the extension to advantageously be positioned proximate to certain brain structures or locations, vascular structures, or other anatomical structures or locations, which may also facilitate improved signal quality, e.g., when the signal originates from or is affected by the structure. Furthermore, electrode extensions may extend superiorly and/or inferiorly from the sensor device housing for improved brain signal and/or cardiac signal sensing and detection. Improved signal quality may result in improved performance of algorithms for predicting or detecting stroke using such signals.

For example, <FIG> and <FIG> depict a sensor device <NUM> that includes a housing <NUM> and a plurality of flexible electrode extensions 1204a-1204e (collectively, "electrode extensions <NUM>") extending from the housing. Sensor device <NUM> includes electrodes 213a-213c distributed on housing <NUM>. Housing <NUM> and electrodes 213a-213c may be substantially as described above with respect to <FIG>. Each of electrode extensions <NUM> includes a respective one of electrodes 213d-<NUM>. In some examples, electrode extensions <NUM> may include more than one electrode and/or include other sensing elements instead of in addition to electrodes. The numbers and configurations of electrode extensions <NUM> and electrodes <NUM> illustrated in <FIG> and <FIG> are merely examples. As illustrated by <FIG>, sensor device <NUM> may be implanted at target location <NUM> that, as described herein, may be at the back of the patients neck or skull, a temporal location, or another location of the patient.

In some examples, one or more of electrode extensions <NUM> may include a paddle with one or more electrodes <NUM> distributed thereon. In some examples, electrodes <NUM> on electrode extensions <NUM> may include ring electrodes or segmented ring electrodes. In the example illustrated by <FIG> and <FIG>, electrode extensions 1204a-1204c extend from housing <NUM> in a first, superior direction, and electrode extensions 1204d and 1204e extend from housing <NUM> in a second, inferior direction, that is opposite the first direction. When positioned at target site <NUM>, the first direction may be towards the upper cranium and scalp of the patient, e.g., to better sense brain signals, and the second direction may be towards the neck and/or shoulders of the patient, e.g., to better sense cardiac signal.

In some examples, the sensor device may take the form of a wearable patch, e.g., attached to the patient with an adhesive. In some examples, the patch may be configured to be adhered to the patient for an intraoperative and/or perioperative period. Depending on the target location for sensor device placement, "wet electrodes," e.g., including a conductive gel at the electrode skin interface, may require shaving of hair on the head. "Dry electrodes," e.g., not include a conductive gel, may provide adequate signal quality and be used in some examples. Dry electrodes may be integrated into a senor device having a hat-like form factor or otherwise head-worn, e.g., a baseball cap with dry electrodes at the back of the head.

This disclosure is not intended to be exhaustive or to limit the present technology to the precise forms disclosed herein. Although specific embodiments are disclosed herein for illustrative purposes, various equivalent modifications are possible without deviating from the present technology, as those of ordinary skill in the relevant art will recognize. In some cases, well-known structures and functions have not been shown and/or described in detail to avoid unnecessarily obscuring the description of the embodiments of the present technology. Although steps of methods may be presented herein in a particular order, in alternative embodiments the steps may have another suitable order. Similarly, certain aspects of the present technology disclosed in the context of particular embodiments can be combined or eliminated in other embodiments. Furthermore, while advantages associated with certain embodiments may have been disclosed in the context of those embodiments, other embodiments can also exhibit such advantages, and not all embodiments need necessarily exhibit such advantages or other advantages disclosed herein to fall within the scope of the present technology. Accordingly, this disclosure and associated technology can encompass other embodiments not expressly shown and/or described herein.

Unless otherwise indicated, all numerical values used in the specification and claims, are to be understood as being modified in all instances by the term "about. " Accordingly, unless indicated to the contrary, the numerical parameters set forth in the following specification and attached claims are approximations that may vary depending upon the desired properties sought to be obtained by the present technology. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of the claims, each numerical parameter should at least be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Additionally, all ranges disclosed herein are to be understood to encompass any and all subranges subsumed therein. For example, a range of "<NUM> to <NUM>" includes any and all subranges between (and including) the minimum value of <NUM> and the maximum value of <NUM>, i.e., any and all subranges having a minimum value of equal to or greater than <NUM> and a maximum value of equal to or less than <NUM>, e.g., <NUM> to <NUM>.

Claim 1:
A stroke detection system (<NUM>) comprising:
a sensor device (<NUM>) configured to obtain physiological data from a patient, wherein the sensor device comprises a housing carrying a plurality of electrodes (<NUM>), wherein the housing is configured to be subcutaneously implanted above shoulders of the patient, and the physiological data comprises electrical brain activity data and electrical heart activity data of the patient sensed via the plurality of electrodes;
and
processing circuitry (<NUM>) enclosed by the housing and configured to:
analyze the physiological data; and
based on the analysis, provide a patient stroke indicator.