Detecting patient conditions using signals sensed on or near the head

Abstract

A system includes a sensor device and processing circuitry. The sensor device includes a housing configured to be disposed over a shoulder of a patient, a plurality of electrodes on the housing, a motion sensor, and sensing circuitry configured to sense, via the electrodes, electroencephalographic signals and electrocardiographic signals and, via the motion sensor, a motion signal. The processing circuitry is configured to determine, from the electroencephalographic signals, values of one or more parameters over time, determine, from the electrocardiographic signals, values of one or more parameters over time, and generate, based on the values and the motion signal, at least one of a detection, a prediction, or a classification of a condition of the patient.

Description

[0001] This application claims the benefit of U.S. Provisional Patent Application Serial No. 63 / 071,997, filed on 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 and / or predicting a patient's condition. Background Technology

[0003] Stroke is a serious medical condition that can lead to permanent neurological damage, complications, and death. Stroke can be characterized by a rapidly developing loss of brain function due to a disruption in the blood vessels supplying blood to the brain. This loss of brain function may result from localized ischemia (insufficient blood supply) caused by thrombosis, embolism, or hemorrhage. Reduced blood supply can lead to dysfunction of the brain tissue in the affected area.

[0004] Stroke is the second leading cause of death and the leading cause of disability worldwide. 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. There are three main causes of stroke: i) ischemic stroke (accounting for approximately 65% ​​of all strokes); ii) hemorrhagic stroke (accounting for approximately 10% of all strokes); and iii) cryptogenic stroke (accounting for approximately 25% of all strokes and including transient ischemic attacks or TIAs). 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 experience seizures that are misdiagnosed as epilepsy. About a quarter of people diagnosed with epilepsy eventually find they have symptoms caused by medical conditions other than epilepsy, such as vasovagal syncope or psychogenic seizures. People with epilepsy may also have other conditions, as about a quarter of them also have arrhythmias. Treatment for epilepsy can include lifestyle modifications and / or medication. Summary of the Invention

[0007] Generally, this disclosure relates to techniques for generating at least one of the detection, prediction, or classification of patient conditions such as stroke, epileptic seizure, vasovagal syncope, or psychogenic attack. In some examples, the detection, prediction, or classification is based on sensor signals sensed by a single sensor device positioned above the patient's shoulder (e.g., at the back of the patient's neck or skull). The technique may include: sensing both brain and cardiac signals via electrodes of the sensor device positioned above the shoulder; determining values ​​of brain and cardiac parameters based on the corresponding signals; and generating the detection, prediction, or classification based on the parameters and motion signals from a motion sensor of the sensing device.

[0008] The technology disclosed herein can provide one or more advantages. For example, it may be advantageous for a system to detect, predict, and / or classify one or more patient conditions among a variety of patient conditions using brain, heart, and motion signals sensed via a single sensor device located above the patient's shoulder. This device can be relatively inconspicuous and usable for extended periods during the patient's daily life compared to other devices typically used to detect such conditions (e.g., multiple devices, devices used in a clinic, or devices prescribed to provide treatment for a specific condition). The sensor device is configured to sense both brain and heart features from its location and additionally sense motion signals to further enhance its ability to detect, predict, or classify certain patient conditions. In some examples, the sensor device includes additional sensors and / or uses identified sensors to sense additional signals, which can allow for the detection, prediction, or classification of additional conditions and / or improve the sensitivity and specificity of algorithms used to detect, predict, or classify conditions.

[0009] In one example, a system includes a sensor device and a processing circuitry system. The sensor device includes a housing configured to be disposed above a patient's shoulder, a plurality of electrodes on the housing, a motion sensor within the housing, and a sensing circuitry system within the housing. The sensing circuitry system is configured to sense brain and cardiac signals from the patient via the plurality of electrodes disposed above the patient's shoulder. The sensing circuitry system is configured to sense motion signals from the patient via the motion sensor disposed above the patient's shoulder. The processing circuitry system is configured to determine the values ​​of one or more parameters from the brain signals over time, and to determine the values ​​of one or more parameters from the cardiac signals over time. The processing circuitry system is configured to generate at least one of a detection, prediction, or classification of the patient's condition based on the values ​​of one or more parameters from the brain signals over time, the values ​​of one or more parameters from the cardiac signals over time, and the motion signals. The processing circuitry system is configured to output an indication of at least one of the detection, prediction, or classification to a computing device.

[0010] In another example, a method includes sensing brain and cardiac signals of a patient via multiple electrodes of a sensor device positioned above the patient's shoulder, and sensing motion signals of the patient via a motion sensor of the sensor device positioned above the patient's shoulder. The method further includes determining values ​​of one or more parameters from the brain signals over time, and determining values ​​of one or more parameters from the cardiac signals over time. The method also includes generating at least one of a detection, prediction, or classification of the patient's condition based on the values ​​of one or more parameters from the electroencephalogram (EEG) signals over time, the values ​​of one or more parameters from the electrocardiogram (ECG) signals over time, and the motion signals; and outputting an indication of at least one of the detection, prediction, or classification to a computing device.

[0011] In another example, a computer-readable storage medium includes instructions that, when executed, cause a processing circuit system to perform a method comprising the steps of: determining the value over time of one or more parameters of brain signals sensed by a plurality of electrodes of a sensor device positioned above the patient's shoulder; determining the value over time of one or more parameters of brain signals sensed by a plurality of electrodes of a sensor device positioned above the patient's shoulder; determining the value over time of one or more parameters of cardiac signals sensed by a plurality of electrodes of a sensor device positioned above the patient's shoulder; generating at least one of detection, prediction, or classification of a patient's condition based on the values ​​over time of one or more parameters of brain signals, the values ​​over time of one or more parameters of cardiac signals, and motion signals sensed by a motion sensor of a sensor device positioned above the patient's shoulder; and outputting an indication of at least one of detection, prediction, or classification to a computing device.

[0012] In another example, the system includes means for performing any of the methods described herein.

[0013] 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. Attached Figure Description

[0014] Figure 1A This is a conceptual diagram of a system configured to detect a patient's medical condition, based on an example of this disclosure.

[0015] Figure 1B This is a conceptual diagram of another system configured to detect a patient's medical condition, based on an example of this disclosure.

[0016] Figure 1C It is a map of 10-20 mappings used for electroencephalogram (EEG) sensor measurements.

[0017] Figure 2A A top view depicting a sensor device according to an example of this disclosure.

[0018] Figure 2B Depicting examples according to this disclosure Figure 2A The side view of the sensor device shown.

[0019] Figure 2C A top view depicting another exemplary sensor device according to the examples of this disclosure.

[0020] Figure 2D A side view of another exemplary sensor device according to the examples of this disclosure is depicted.

[0021] Figure 2E A side view of another exemplary sensor device according to the examples of this disclosure is depicted.

[0022] Figure 2F A side view of another exemplary sensor device according to the examples of this disclosure is depicted.

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

[0024] Figure 2H A top view depicting another exemplary sensor device according to the examples of this disclosure.

[0025] Figure 2I A top view depicting another exemplary sensor device including an electrode extension according to an example of this disclosure.

[0026] Figures 2J-2N and Figure 2P An exemplary sensor device including an electrode extension is depicted according to an example of this disclosure.

[0027] Figure 2Q An exemplary sensor device, including an electrode extension that is coupled to a patient, is depicted according to an example of this disclosure.

[0028] Figure 2R An exemplary sensor device including an optical sensor is depicted according to an example of this disclosure.

[0029] Figures 3A-3C Other sensor devices are depicted according to examples of this disclosure.

[0030] Figure 3D Another exemplary sensing device including an optical sensor is depicted according to an example of this disclosure.

[0031] Figure 4 This is a block diagram illustrating an exemplary configuration of a sensor device.

[0032] Figure 5 It is configured to be with Figure 4 A block diagram illustrating an exemplary configuration of an external device communicating with a sensor device.

[0033] 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, external computing devices (such as servers), and one or more other computing devices that can be coupled to the sensor, external device, and processing circuitry system of FIG1 via the network.

[0034] Figure 7 This is a flowchart illustrating an exemplary technique for generating at least one of detection, prediction, or classification of a patient's condition based on information from multiple sensors.

[0035] Figure 8 This is a flowchart illustrating exemplary operations in a technique for generating at least one of detection, prediction, or classification of a patient's condition based on information from multiple sensors.

[0036] Figure 9 This is a flowchart illustrating an exemplary technique for determining the value of one or more parameters based on brain signals.

[0037] Figure 10 This is a flowchart illustrating an exemplary technique for determining the value of one or more parameters based on cardiac signals.

[0038] Figure 11This is a flowchart illustrating an exemplary technique for updating baseline and condition reference values ​​for one or more parameters.

[0039] Figure 12 This is a flowchart illustrating an exemplary technique for determining whether a detected or predicted patient condition is cardiac or neurogenic.

[0040] Figure 13 This is a flowchart illustrating an exemplary technique for adjusting the operating point of a stroke detection / prediction algorithm based on the detection of falls or near falls.

[0041] Figure 14 This is a flowchart illustrating an exemplary technique for determining and implementing a patient's preferred treatment pathway based on the generated detection, prediction, or classification of the patient's condition.

[0042] 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 emphasis is on clearly illustrating the principles of the inventive technique. Detailed Implementation

[0043] This disclosure describes various systems, devices, and techniques for detecting, predicting, and / or classifying one or more patient conditions from a device located on a patient's head. It may be difficult to determine whether a patient has or will have certain conditions, such as stroke or epileptic seizures, vasovagal syncope, or psychogenic attacks. It may also be difficult to classify the conditions experienced by the patient, such as between different conditions (e.g., epileptic seizures and vasovagal syncope) or between different types of conditions (e.g., different types or origins of stroke or epileptic seizures).

[0044] Current diagnostic techniques typically involve assessing a patient's visible symptoms, such as numbness or tingling in the face, arms, or legs, and difficulty walking, speaking, or understanding in the event of a stroke. The visual stroke indicator is abbreviated as FAST: face, arms, and speech—time to call 911. However, these techniques can lead to undiagnosed strokes, particularly milder strokes that allow the patient to remain relatively active after a cursory assessment. Even for relatively mild strokes, prompt treatment is crucial, as the effectiveness of stroke treatment is highly time-dependent. Therefore, improved methods for stroke detection are needed. However, such treatments are often 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 particular risk for milder strokes that allow the patient to remain relatively active after a cursory assessment.

[0045] Similarly, seizures can be difficult to detect or identify, such as those occurring in patients with epilepsy. Some patients exhibit physical manifestations of epileptic seizures, such as twitching movements of the arms and legs. Other symptoms of epileptic 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 an epileptic seizure, they may not understand the symptoms or accurately identify what is happening. Furthermore, patients may not be able to obtain or request interventions, such as medication. In some cases, deep brain stimulation (DBS) devices can detect epileptic 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.

[0046] As described herein, sensor devices can be used to detect, predict, and / or classify patient conditions from locations on or near the patient's head. Sensor devices can be configured for subcutaneous implantation or placement outside the patient's body (e.g., wear) without the need for any medical leads. In some examples, instead of leads, sensor devices may include a housing that carries multiple electrodes directly on the housing, and one or more other sensors on or within the housing. By using these housing electrodes, the sensor device can sense electrical signals from one or more vectors, and a processing circuitry system can determine values ​​of physiological parameters representing the patient's condition. These signals and parameters can indicate brain activity and / or the activity of other organs (such as the heart). Based on the parameter values, the processing circuitry system can detect, predict, and / or classify the patient's condition. The processing circuitry system can output indications of detection, prediction, and / or classification to a computing device, for example, to facilitate treatment or intervention.

[0047] Conventional electroencephalography (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. Furthermore, 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, sensor devices according to the techniques described herein sense electrical signals from near the patient's head or from smaller areas on the patient's head, such as the back of the neck, the back of the skull, or a smaller area near one or both 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 primarily described in the context of leadless sensor devices, in some examples, such as those concerning… Figures 2I-2N , Figure 2P and Figure 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.

[0048] EEG signals detected via electrodes positioned at or near the back of a patient's neck may include other signals and relatively high noise levels. For example, electrical signals associated with brain activity may be intermingled with electrical signals associated with cardiac activity (e.g., electrocardiogram (ECG) signals or signals including components associated with the mechanical activity of the heart) and electrical signals associated with muscle activity (e.g., electromyography (EMG) signals), as well as artifacts from other power sources (such as patient movement or external interference). Therefore, in some examples, the signals may be filtered or otherwise manipulated to separate brain activity data (e.g., EEG signals) and electrocardiogram signals (e.g., ECG signals or other cardiac signals) from each other and from other electrical signals (e.g., EMG signals, etc.). The sensor device of this disclosure may include multiple electrodes having non-parallel vector axes for sensing differential signals, and the circuitry within the device may be configured to generate signals, such as cardiac and brain signals, based on the differential signals.

[0049] As described in more detail below, parameter values ​​can be analyzed to detect, predict, or classify one or more conditions based on one or more thresholds, correlations between signals, or the use of classification algorithms. These classification algorithms themselves can be derived using machine learning techniques applied to databases of patient data known to represent conditions or classifications. One or more detection algorithms can be passive (involving measurements on a fully resting patient) 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.

[0050] 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, or a short-range radio network (such as via Bluetooth)). In a distributed computing environment, program modules can reside in both local memory storage and remote memory storage.

[0051] 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 via the Internet or other networks (e.g., Bluetooth networks) on a propagation medium (e.g., one or more electromagnetic waves, sound waves), or can be set on any analog or digital network (packet switching, circuit switching, or other schemes).

[0052] Figure 1A This is a conceptual diagram of a system 100A configured to detect medical conditions according to an example of this disclosure. The exemplary techniques described herein can be used with a sensor device 106, which in the illustrated example is an implantable medical device (IMD) and can be integrated 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 the processing circuitry system 110.

[0053] like Figure 1AAs shown, sensor device 106 is located in target region 104. Target region 104 may be the back of the user's neck or the back of the skull. In other examples, the target region may be located at other locations on the patient, such as near one or both temples (e.g., above one or both ears) and / or above the temporal portion of the skull. Although sensor device 106 may be implanted in a generally central location relative to the head, neck, or target region 104, sensor device 106 may be implanted in an off-center location to obtain a desired vector from electrodes carried on the housing of sensor device 106. Sensor device 106 may be positioned in target region 104 via implantation (e.g., subcutaneously) or by being placed on the patient's skin, wherein one or more electrodes of sensor device 106 are in direct contact with the patient's skin at or near target region 104.

[0054] While conventional EEG electrodes are placed on the patient's scalp, and ECG electrodes are positioned elsewhere on the patient's body, this technique advantageously enables the recording of clinically useful brain and cardiac activity signals via electrodes positioned in a target area 104 at the back of the patient's neck or head, or other skull locations (such as the temporal location described herein). This anatomical region is well-suited for the implantation of the sensor device 106 and its temporary placement 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.

[0055] As described elsewhere in this article, conventional EEG electrodes are typically positioned on the scalp to more easily achieve a suitable signal-to-noise ratio for detecting brain activity. However, by using some form of digital signal processing and a dedicated classifier algorithm, electrodes positioned at target regions 104 can be used to obtain clinically useful brain and cardiac activity signals. Specifically, the electrodes can detect signals corresponding to regions P3, P2, and / or P4 (e.g., Figure 1C Electrical activity of brain activity (as shown in the diagram).

[0056] The processing circuitry system 110 can extract values ​​of one or more parameters (e.g., characteristics) from signals indicative of brain and / or heart activity. The processing circuitry system 110 can then determine, based on these parameter values, whether the patient has experienced a stroke (or has an over-threshold risk of experiencing a stroke), has an epileptic seizure, or has another condition. In some examples, the sensor device 106 employs LINQ. TMThe technology can take the form of an insertable cardiac monitor (ICM) (available from Medtronic plc, Dublin, Ireland) or a device with a similar implantable volume and similar sensing capabilities. Exemplary technologies may additionally or alternatively be used with... Figure 1A It 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.

[0057] 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 having a medical appointment at the clinic. 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 monitor one or more of the patient's physiological signals during a medical appointment while simultaneously observing the physiological markers needed to determine whether an event (such as a seizure or stroke) has altered the patient's medical condition and / or whether the patient's medical condition is improving or worsening. Figure 1A In the example shown, sensor device 106 is implanted in or attached to patient 102 to continuously record one or more physiological signals of patient 102 over an extended period of time.

[0058] In some examples, sensor device 106 includes multiple electrodes. In some examples, sensor device 106 can sense brain activity signals and heart activity signals, as well as other signals (such as impedance signals for respiration, skin impedance, and perfusion). Furthermore, in some examples, sensor device 106 may additionally or alternatively include one or more optical sensors, accelerometers or other motion sensors, temperature sensors, chemical sensors, light sensors, pressure sensors, and / or acoustic sensors. Such sensors can sense a variety of signals that can enhance the ability of processing circuitry system 110 to detect, predict, or classify patient conditions.

[0059] 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.

[0060] 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 operate an application that enables the computing device to function as a security device. In some examples, external device 108 is the patient 102's smartphone and / or watch or other wearable computing device, which may be, for example, via Bluetooth. TM Communicates 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.

[0061] 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.

[0062] The processing circuitry 110 may refer to a processing circuitry located within either or both of the sensor device 106 and the external device 108. In some examples, the processing circuitry 110 may be entirely located within the housing of the sensor device 106. In other examples, the processing circuitry 110 may be entirely located within the housing of the external device 108. In still other examples, the processing circuitry 110 may be located within the sensor device 106, the external device 108, and... Figure 1A Any of the other devices or groups of devices not shown herein, or any combination thereof. Therefore, the techniques and capabilities attributed herein to processing circuit system 110 can also be attributed to sensor device 106, external device 108, and... Figure 1A Any combination of other devices not shown in the diagram.

[0063] Figure 1A The medical device system 100A is an example of a system configured to sense signals and generate detection, prediction, or classification of a patient's condition according to one or more techniques of this disclosure. In some examples, the sensed signals may include features representing cardiac function, such as cardiac depolarization and repolarization or cardiac contraction. The processing circuitry system 110 may apply information related to the aforementioned events (such as the time intervals separating one or more events) to a variety of purposes. The processing circuitry system 110 may perform signal processing techniques to extract information indicating one or more parameters of the cardiac signals. In some examples, the sensed electrical signals may include features representing brain function, such as the amplitude of frequencies in one or more frequency bands (such as the alpha, beta, or gamma bands). The processing circuitry system 110 may perform various signal processing 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, although with a slight timing delay.

[0064] In some examples, sensor device 106 includes one or more accelerometers or other motion sensors. The accelerometers of sensor device 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.

[0065] Sensor device 106 can measure other signals, such as impedance (e.g., via...). Figures 2A-2N , Figure 2P and Figure 2Q The electrodes depicted measure subcutaneous impedance; other signals may indicate respiration, skin impedance or perfusion, heart sound signals, cardiac impaction signals, pressure signals, etc. The processing circuitry system 110 can analyze any one or more parameters in this set to determine whether the patient 102 is experiencing a condition (such as a stroke or seizure) or has an over-threshold risk of experiencing such a condition. In some examples, pulsation signals optically or mechanically sensed from the scalp vascular system, such as via electrodes, optical sensors, accelerometers, pressure sensors, impedance sensors, or heart sound sensors, can provide alternatives to ECG or other cardiac electrical activity signals.

[0066] In some examples, one or more sensors of sensor device 106 (e.g., electrodes, motion sensors, optical sensors, temperature sensors, pressure 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 ​​over time, where sensor device 106 collects each parameter value of the sequence of parameter values ​​for each time interval of the time interval sequence. For example, sensor device 106 can perform parameter measurements to determine parameter values ​​of the sequence of parameter values ​​according to cyclic time intervals (e.g., daily, nightly, every other day, every twelve hours, hourly, secondly, or any other cyclic time interval). In this way, sensor device 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 sensor device 106 is implanted in patient 102 and configured to perform parameter measurements according to cyclic time intervals without missing time intervals or performing parameter measurements out of schedule.

[0067] Sensor device 106 may be referred to as a system or device. In one example, sensor device 106 may include: a plurality of electrodes carried by a housing of sensor device 106; a sensing circuit system configured to sense electrical signals from patient 10 via at least two of the plurality of electrodes; and a motion sensor (e.g., an accelerometer) configured to sense motion signals from the patient. Sensor device 106 may also include a processing circuit system 110. The housing of sensor device 106 carries the plurality of electrodes and contains or accommodates the sensing circuit system, the processing circuit system, the motion sensor, and any other sensors. In this way, sensor device 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, sensor device 106 may include one or more sensing leads extending from the sensor device and into the patient tissue. One or more such leads may be used in place of the electrodes of sensor device 106 (e.g., such as...). Figures 2I-2N , Figure 2P and Figure 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.

[0068] The signals sensed by sensing device 106 may include brain signals and / or cardiac signals. In some examples, multiple electrodes are configured to detect brain signals 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 housing of the sensor device 106 can be configured to be disposed in or near the back of the neck or skull of the patient 102. The housing of the sensor device 106 can be configured to be implanted into the patient 102, such as through subcutaneous implantation. In other examples, the housing of the sensor device 106 can be configured to be disposed on the outer surface of the patient 102's skin.

[0069] In some examples, sensor device 106 may include a single sensing circuitry configured to generate information from sensed electrical signals that includes both brain activity data (e.g., electroencephalogram (EEG) data) and cardiac activity data (e.g., ECG data or cardiac contraction data). In other examples, the processing circuitry of sensor device 106 may include separate hardware that generates different information from the sensed electrical signals. For example, IMD 106 may include: a first circuitry configured to generate brain activity data from electrical signals; and a second circuitry, distinct from the first circuitry and configured to generate cardiac activity data from 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, parameters determined from the brain activity data may include features, such as spectral characteristics, indicating signal strength in various frequency bands or at various frequencies.

[0070] In some examples, sensor device 106 includes one or more accelerometers or other motion sensors located within a housing. The accelerometers can be configured to generate motion data representing the movement of patient 102. Processing circuitry 110 can then be configured to generate detection, prediction, or classification of one or more conditions based on the motion signals, for example, in conditions having parameter values ​​determined from brain and heart signals. For example, body movement or lack of body movement can indicate the type of epileptic seizure experienced by patient 102. As another example, certain body movements or behaviors (e.g., movement patterns) can indicate a stroke. In one example, processing circuitry 110 can be configured to determine, based on motion data, that patient 102 has fallen or is close to falling. In response to determining that patient 102 has fallen, processing circuitry 110 can be configured to notify or modify algorithms used to detect or predict a stroke or another patient condition. In some examples, a stroke can cause a patient to fall. Therefore, by combining additional features extracted from sensed brain and heart signals, processing circuitry 110 can determine stroke metrics from fall indications indicating stroke detection. In other examples, sensor device 106 or processing circuitry system 110 may determine that a characteristic of the motion data exceeds a threshold. For example, the threshold could be an acceleration value indicating a fall. In the case of epileptic seizures, as another example, the frequency of motion exceeding a frequency threshold could indicate body movement caused by a seizure.

[0071] The processing circuitry 110 can extract various features, such as heart rate, heart rate variability, etc., from cardiac signals (e.g., ECG signals or signals representing cardiac mechanical activity) sensed by the sensor device 106. These cardiac parameters can indicate the patient's voluntary activity status and can inform the detection, prediction, and / or classification of various patient conditions. For example, the processing circuitry 110 can classify a seizure into one of several seizure types based on such parameters. Seizure types may include, for example, single seizures, stroke-induced seizures, epileptic seizures, non-epileptic seizures (such as VVS or psychogenic seizures), loss-of-consciousness seizures, tonic-clonic or convulsive seizures, atonic seizures, clonic seizures, tonic seizures, and myoclonic seizures. In some examples, the processing circuitry 110 can also determine the seizure type based on accelerometer data, temperature data, or any other parameters extracted from one or more sensors.

[0072] Figure 1B This is a conceptual diagram of a system 100B configured to detect the medical condition of patient 102, according to an example of this disclosure. System 100B can be integrated with... Figure 1ASystem 100B is substantially similar to system 100A. However, the sensor device 106 of system 100B can be configured to be implanted in a target region 120 located on the back of the head of patient 102, such as above the ear and / or above the temporal portion of the skull. The sensor device 106 implanted in the target region 120 can be configured to sense cardiac and brain signals, as well as other sensor signals described herein, in that region. In such an example, if the electrodes of sensor device 106 are implanted on the opposite side of the patient's head, the electrodes can detect signals corresponding to the T3 region (e.g., Figure 1C Electrical activity of brain activity in the T4 region (as shown in the diagram), or two or more sensor devices are bilaterally implanted in the temporal region. In some examples, sensor device 106 may need to employ different filters or other processing or signal modulation techniques than those at target region 104 due to different types of noise at target region 120 (such as muscle activity due to jaw movement or other types of electrical activity). In other examples, sensor device 106 may be configured to sense signals from the head of patient 102 that may be located in other regions outside target regions 104 and 120, as described herein.

[0073] Figure 1C This is a graph representing 10-20 times the order of EEG sensor measurements. (Example) Figure 1C As shown, electrodes carried by sensor device 106 can be used to target various locations on the head of patient 102. In the occipital region (such as in...) Figure 1A Within the target area 104, the sensor device 106 can sense signals at at least one of P3, P2, or P4. On the side of the head (such as in...) Figure 1B In the target area 120, the sensor device 106 can sense signals at at least one of F7, T3 or T5 and / or one or more of F8, T4 or T6.

[0074] Figure 2A A top view depicting a sensor device 210 (e.g., IMD) according to an example of this disclosure. Figure 2B Depicting Figure 2A The image shows a side view of the sensor device 210. In some examples, the sensor device 210 may include the components described above. Figure 1A and Figure 1B The sensor device 106 described and / or the following about Figures 3A-3D and Figure 4 The sensor device 310, 360B, 360B, 361, or 400 is described as having some or all of the features and being similar to those features, and may include features such as those combined with Figure 2AAdditional features described. In the example shown, sensor device 210 includes a housing 201 in which a plurality of electrodes 213A, 213B, 213C, and 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 more than four electrodes. Figure 2H As shown, any one of the electrodes can be segmented; that is, each electrode may include two conductive portions separated by an insulating material. In some examples, the first portion of each electrode may be configured to sense ECG signals or other cardiac signals, and the second portion may be configured to sense EEG signals.

[0075] In operation, electrode 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 sensor device 210 is implanted, the electrode makes direct contact with subcutaneous tissue). Housing 201 further encloses the electronic circuitry located within 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 examples, electrode 213 can be positioned along any surface of sensor device 210 (e.g., front surface, rear surface, left surface, right surface, upper surface, lower surface, or other surface), and this surface can take any suitable form.

[0076] 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.

[0077] The configuration of the shell 201 can facilitate placement on the patient's skin in a wearable or bandage-like form or for subcutaneous implantation. Therefore, a relatively thin shell 201 may be advantageous. Additionally, in some embodiments, the shell 201 may be flexible, such that the shell 201 can be at least partially bent to correspond to the anatomy of the patient's neck (e.g., the left portion 207 and right portion 209 of the shell 201 bend forward relative to the central portion 205 of the shell 201).

[0078] In some embodiments, 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.

[0079] 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 such a 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 both the brain and heart. 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).

[0080] In some examples, 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., titanium nitride or platinum-iridium) and can utilize one or more coatings, such as titanium nitride or fractal titanium nitride. In some examples, 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.

[0081] Figure 2C A top view depicting another exemplary 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...) Figures 2I-2N , Figure 2P and Figure 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.

[0082] In operation, electrode 213 is used to sense signals (e.g., EEG signals or other brain signals and / or ECG signals or other cardiac signals), which can be submuscular or subcutaneous. The sensed signals can be stored in the memory of the sensor device, and the signal data can be transmitted to another device (e.g., via a communication link). Figure 1A External device 108). The sensed 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 implanted or external location, such as EMG or neural signals and impedance signals. These signals may be time-coded or time-correlated and stored in such a form as described above regarding brain and heart signal data.

[0083] 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.

[0084] 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).

[0085] 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.

[0086] 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 raw signals including 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.

[0087] 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.

[0088] 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 some examples, the number and type of electrodes on electrode extension 265 and such extensions and electrodes on housing 251 may differ. Figure 2I The numbers and types shown.

[0089] 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.

[0090] Electrode extensions 265 are inherently flexible, allowing them to conform to the anatomy of the neck and / or skull. Additionally, the length and flexibility of one or more electrode extensions 265 allow electrodes on the extensions to be advantageously positioned near certain brain structures or locations, vascular structures, or other anatomical structures or locations, which can 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 can 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 can pass under the scalp to position one or more electrodes on the extensions at desired locations in the skull.

[0091] Figures 2J-2N and Figure 2P Example sensor devices 270J-270P, including electrode extensions 272J-272P, 276K, 276N, 284M-284P, 285M-285P, and 286M-286P, are depicted according to this disclosure. Sensor devices 270J-270P 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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-2N and Figure 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-2N and Figure 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.

[0096] 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.

[0097] Figure 2R An exemplary sensor device 290 including an optical sensor 291 is depicted. The optical sensor 291 can be used for any of the various purposes described herein. For example, the optical sensor 291 can be used to sense oxygen saturation, such as SpO2 or StO2. As another example, the signal sensed by the optical sensor can vary with the pulsatile flow of blood. Peak detection and / or other signal processing techniques can be used to identify the heartbeat in the optical signal. The processing circuitry can determine the heart rate, heart rate variability, and other parameters that can be derived from the time series of heartbeat detection based on the optical signal. According to any of the techniques described herein, the processing circuitry can use the optical signal as a substitute for an ECG signal.

[0098] In some examples, the processing circuitry can determine the pulse wave transit time (PTT) based on depolarization detected in the ECG signal and features detected in the optical signal. PTT can be negatively correlated with blood pressure and thus indicate blood pressure. In the techniques described herein, PTT can serve as a substitute for blood pressure.

[0099] exist Figure 2R In the example, optical sensor 291 includes light emitter 292 and photodetectors 294A and 294B (hereinafter referred to as "photodetector 294"). Figure 2R The number of light emitters and photodetectors shown is an example, and in other examples, the optical sensor may include a different number of light emitters and / or photodetectors. In addition to including optical sensor 291, sensor device 290 may be configured to be substantially similar to any other sensor device described herein, such as those described above. Figures 1A-1C , Figures 2A-2N , Figure 2P and Figure 2Q as well as Figure 4 The sensor devices described. For example, sensor device 290 may include, as described above. Figures 2A-2N , Figure 2P and Figure 2QThe description pertains to the configuration of the housing 201. Although not described in... Figure 2R As shown, sensor device 290 may include, as described above... Figures 2A-2N , Figure 2P and Figure 2Q The electrodes described by either of them. Furthermore, although not in Figure 2R As shown, but in some examples, sensor device 290 may include extensions or leads, as per [reference to...]. Figures 2I-2N , Figure 2P and Figure 2Q As described. In some examples, the optical sensor 291 may be located on an extension or lead extending from the housing 201, rather than on the housing 201, such as... Figure 2R As shown in the diagram. Positioning the optical sensor 291 on a lead or extension allows the optical sensor 291 to sense optical signals at one location, while the sensor device 290 senses ECG or EEG signals at another location via electrodes. This can be advantageous when the preferred locations for sensing signals differ. Exemplary locations for the optical sensor 291 include the temporal region, temple, occipital region, forehead, or top of the head.

[0100] In some examples, the optical sensor 291 may be positioned to emit light onto and receive light from a vascular bed on the skull. In some examples, the optical sensor 291 may be located on the main surface of the housing 201 facing the skull, which may help minimize interference from background light from outside the patient's body. In some examples, the optical sensor 291 may be located on a skull surface opposite another surface that includes one or more electrodes.

[0101] One or more light emitters 292 include a light source (such as one or more light-emitting diodes (LEDs)) that can emit light of one or more wavelengths within the visible (VIS) and / or near-infrared (NIR) spectrum. For example, one or more light emitters 292 can emit light of one or more of about 660 nanometers (nm), 720 nm, 760 nm, 800 nm, or any other suitable wavelength.

[0102] In some examples, techniques for determining blood oxygenation (e.g., StO2 or StO2) may include using one or more light emitters 292 to emit light of one or more VIS wavelengths (e.g., about 660 nm) and one or more NIR wavelengths (e.g., about 850 nm–890 nm). The combination of VIS and NIR wavelengths can help the processing circuitry system distinguish between oxyhemoglobin and deoxyhemoglobin because as hemoglobin becomes less oxygenated, the attenuation of VIS light increases and the attenuation of NIR light decreases. By comparing the amount of VIS light detected by photodetector 294 with the amount of NIR light detected by photodetector 294, the processing circuitry system can determine the relative amounts of oxyhemoglobin and deoxyhemoglobin in the patient's tissue.

[0103] Techniques using optical signals to determine blood oxygenation or sense pulsatile blood flow can be based on the optical properties of blood-perfused tissues, which vary according to the relative amounts of oxyhemoglobin and deoxyhemoglobin in the tissue microcirculation. These optical properties are at least partly attributable to the different optical absorption spectra of oxyhemoglobin and deoxyhemoglobin. Therefore, the oxygen saturation level of a patient's tissue can affect the amount of light absorbed by the blood within the tissue and the amount of light reflected by the tissue. Each photodetector 294 can receive light reflected from the tissue from the light emitter 292 and generate an electrical signal indicating the intensity of the light detected by the photodetector 294. The processing circuitry can then evaluate the electrical signal from the photodetector 294 to determine an oxygen saturation value, detect a heartbeat, and / or determine a PTT value. In some examples, the light emitter 292 may additionally or alternatively emit light of other wavelengths (such as green or amber light) because the signal detected by the detector 294 can vary more significantly with pulsatile blood flow at such wavelengths, which can increase the ability to detect a pulse to identify a heartbeat and / or determine a PTT.

[0104] In some examples, the difference between the electrical signals generated by photodetectors 294A and 294B can enhance the accuracy of these determinations. For example, because tissue absorbs some of the light emitted by light emitter 292, the intensity of light reflected by the tissue attenuates as the distance (and amount of tissue) between light emitter 292 and photodetector 294 increases. Therefore, because photodetector 294B is farther from light emitter 292 than photodetector 294A, the intensity of light detected by photodetector 294B should be less than the intensity of light detected by photodetector 294A. Since detectors 294A and 294B are very close to each other, the difference in the intensity of light detected by photodetector 294A and photodetector 294B should be attributed solely to the difference in distance from light emitter 292.

[0105] In some examples, the optical sensor 291 includes a window 296 (e.g., glass or sapphire) formed as part of the housing 201. A light emitter 292 and a light detector 294 may be located below the window 296. The window 296 may be transparent or substantially transparent to light emitted and detected by the optical sensor 291 (e.g., the wavelength of the light). In some examples, all or a significant portion of one of the main surfaces of the housing 201 may be formed as the window 296.

[0106] In some examples, one or more portions of window 296 may be optically masked. In some examples, portions of the window other than those above emitter 292 and detector 294 may be optically masked. Optical masking can reduce or prevent light transmission, for example, to prevent internal reflections within window 296 that could confuse measurements. Optical masking may include materials configured to substantially absorb emitted light, such as titanium nitride, columnar titanium nitride, titanium, or another material that can be used to absorb light of selected wavelengths that can be emitted by light emitter 292.

[0107] Figures 3A-3C Other 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 sensor devices 106, 210, 220, 230, and 400 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.

[0108] exist Figure 3AIn 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, wherein 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 the 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, the device shown in Figure 3 includes radial asymmetry along the longitudinal axis (specifically, a rectangular shape), 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 may range from 30 mm to 55 mm, 35 mm to 55 mm, and 40 mm to 55 mm, and may be any range from 25 mm to 60 mm or a single spacing. In some examples, the length L may be from 30 mm to approximately 70 mm. In other examples, the length L can be in the range of 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 be in the range of 3 mm to 10 mm, and can be any single width or width range between 3 mm and 10 mm. The depth D of the sensor device 310 can be in the range of 2 mm to 9 mm. In other examples, the depth D of the sensor device 310 can be in the range of 2 mm to 5 mm, and can be any single depth or depth range between 2 mm and 9 mm. In addition, the sensor device 310 according to the 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 may have a volume of 3cc or less, 2cc or less, 1cc or less, 0.9cc or less, 0.8cc or less, 0.7cc or less, 0.6cc or less, 0.5cc or less, 0.4cc or less, any volume between 3cc and 0.4cc, or any volume less than 0.4cc. Furthermore, 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.

[0109] 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.

[0110] The proximal electrode 313A and distal electrode 313B are used to sense signals (e.g., EEG signals, ECG signals, other brain signals and / or cardiac signals or impedance), which may be submuscular or subcutaneous. The 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 may be another implantable device or an 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 EMG or neural signals.

[0111] 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 end surface 330 onto the second main surface 320, such that the electrode 313B has a three-dimensional curved configuration. In the example shown in FIG3, 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 can utilize the three-dimensional curved configuration of the distal electrode 313B to provide a three-dimensional proximal electrode (not shown in this example). Similarly, in other examples, the distal electrode 313B can utilize a substantially flat, 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 the one shown in Figure 3, 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., titanium nitride or platinum-iridium) and may utilize one or more coatings, such as titanium nitride or fractal titanium nitride. Although Figure 3AThe 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.

[0112] 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.

[0113] 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 360 ​​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.

[0114] Figure 3D Another exemplary sensing device 361, including an optical sensor 363, is depicted according to an example of this disclosure. The optical sensor 363 can be configured and provided as described above regarding optical sensor 291 and... Figure 2R The functionality described. Optical sensor 363 includes one or more light emitters 365 and light detectors 367A and 367B (hereinafter referred to as "light detector 367"), which can be configured to be substantially similar in function to, and substantially similar in purpose to, the light emitters 365 and light detectors 367A and 367B (hereinafter referred to as "light detector 367"). Figure 2R The light emitter 292 and light detector 294 are described. In addition to including the optical sensor 363, the sensor device 361 can be configured to be substantially similar to any other sensor device described herein, such as those described above. Figures 1A-1C , Figures 3A-3C and Figure 4 The sensor devices described. For example, sensor device 363 may include, as described above... Figures 3A-3C The description refers to the configuration of housing 375. Although not in... Figure 3D As shown, sensor device 363 may include, as per the description of... Figures 3A-3C The electrodes described in either of the above. In some examples, the surface 377 of the housing 375 (e.g., the main surface or a portion thereof) may be configured as a window that is transparent or substantially transparent to light emitted and detected by the optical sensor 363 (e.g., the wavelength of the light).

[0115] like Figure 3D As shown, sensor device 361 includes an antenna 379 disposed on surface 377 of housing 375. In some examples, antenna 379 may include a substrate and a metallization layer formed on the substrate. The substrate may include, for example, a biocompatible polymer such as polyamide or polyimide, quartz glass, silicon, sapphire, etc. The metallization layer may include, for example, aluminum, copper, silver, or other conductive metals. Antenna 379 may include other materials, such as, for example, ceramic or other dielectric materials (e.g., as in a dielectric resonator antenna). In some examples, antenna 379 (e.g., metallization layer, etc.) may be formed directly on the outer surface 377 of housing 375.

[0116] Regardless of the material, antenna 379 may include an opaque or substantially opaque material. For example, an opaque (e.g., or substantially opaque) material may block the transmission of at least a portion of radiation of a selected wavelength (e.g., between about 75% and about 100% of visible light).

[0117] In an example where antenna 379 includes an opaque material, components of optical sensor 363 can be arranged relative to portions of antenna 379 to reduce or prevent optical interference between components. For example, as Figure 3D As shown, a light emitter 365 is positioned on the outer periphery of an antenna 379, while a photodetector 367 is positioned within an aperture defined by the antenna 379. In this manner, the antenna 379 can define an optical boundary of an opaque material that reduces or prevents light from directly transmitting from the emitter 365 to the detector 367. Instead, light emitted from the light emitter 365 must pass through tissue. In some examples, one or more optical masks 371A and 371B may be applied to further prevent optical interference.

[0118] Figure 4 This is a block diagram of an exemplary configuration of a sensor device 400 configured to sense signals used to generate at least one of detection, prediction, or classification of a patient's condition. The sensor device 400 may be an example of any of sensor devices 106, 210, 220, 230, 240, 250, 270, 310, 360A, or 360B. In the example shown, the sensor device 400 includes electrodes 418A-418C (collectively referred to as "electrodes 418"), an antenna 405, a processing circuit system 402, a sensing circuit system 406, a communication circuit system 404, a storage device 410, a switching circuit system 408, a sensor 414 (including one or more motion sensors 416), and a power supply 412.

[0119] 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 1BExamples 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.

[0120] Sensing circuitry 406 and communication circuitry 404 can be selectively coupled to electrodes 418 via switching circuitry 408, such as that controlled by processing circuitry 402. Sensing circuitry 406 can monitor signals from electrodes 418A-418C to monitor brain and heart activity (e.g., generating EEG and ECG or other cardiac signals), from which processing circuitry 402 (or the processing circuitry of another device) can determine the values ​​of parameters over time used to generate detection, prediction, or classification. Sensing circuitry 406 can also sense physiological characteristics (such as subcutaneous tissue impedance) indicating at least some aspect of the patient 102's breathing pattern or perfusion. 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 optical sensors 291, 363, pressure sensors, or acoustic sensors) that may be positioned on or within sensor device 400.

[0121] In some examples, the subcutaneous impedance signal collected by sensor device 400 can indicate the respiratory rate and / or respiratory intensity of patient 102. In some examples, the respiratory component may be sensed additionally (using hybrid sensor technology) or alternatively in other signals (such as motion sensor signals, optical signals), or as a component of the cardiac signal sensed via electrode 418 (e.g., baseline offset). In some examples, sensing circuitry system 406 may include one or more filters and amplifiers for filtering and amplifying signals received from electrode 418 and / or one or more of sensors 414.

[0122] 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. 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 an internal or external antenna (e.g., 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 conducted organization communication (TCC). Furthermore, 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). (Network) communicates with networked computing devices.

[0123] Clinicians or other users can retrieve data from sensor device 400 using external device 108 or by using another local or networked computing device configured to communicate with processing circuitry system 402 via communication circuitry system 404. Clinicians can also use external device 108 or another local or networked computing device to program parameters of sensor device 400.

[0124] 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 sensor device 400 and processing circuitry system 402 to perform various functions accorded to sensor device 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 (such as signals) generated by sensing circuitry system 406 or data (such as indications of parameter values ​​or condition detection, prediction, or classification) generated by processing circuitry system 402.

[0125] Power source 412 is configured to deliver operating power to components of sensor device 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 different 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.

[0126] As described herein, sensor device 400 may be configured to sense signals, for example, via electrodes 418 and sensor 414, for detecting, predicting, and / or classifying one or more patient conditions, such as stroke or epileptic seizure. In some examples, processing circuitry 402 may be configured to calculate parameter values ​​associated with one or more signals received from electrodes 418 and / or from sensor 414. In some examples, processing circuitry 402 may be configured to algorithmically determine, based on the parameter values, the presence or absence of a patient condition, whether the patient has an excess risk of the condition, or whether the condition is most likely of a certain type or has a certain cause.

[0127] 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. As described herein, 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.

[0128] 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.

[0129] 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 291, 363, pressure sensors, or motion sensors 416.

[0130] For example, sensing circuitry 406 and / or processing circuitry 402 can detect cardiac pulses via optical sensors 291, 363. Processing circuitry 402 can determine heart rate or heart rate variability based on the detection of cardiac pulses via optical sensors 291, 363, 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). Signals from optical sensors 291, 363 can be additionally or alternatively used for other purposes, such as for determining blood oxygenation, local tissue perfusion, or blood pressure surrogate (e.g., PTT), any of which can be used for stroke detection or prediction and / or differentiation between ischemic and hemorrhagic strokes.

[0131] 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 1CExemplary locations for positioning electrodes used to sense cardiac signals include P3, PQ3, PQ7, F3, F2, AF3, or C2. In some examples, one or more sensors 414 (such as optical sensors 291, 363) may be positioned on the extension.

[0132] 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.

[0133] Signals from optical sensors 291 and 363 (e.g., photoplethysmography pulse wave signals) can be used as a time base for ensemble averaging or as another means of improving the signal-to-noise ratio of cardiac signals. Therefore, optical sensor signals can be considered as a substitute for cardiac signals and / or used to derive 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. Cardiac waveforms aligned with the trigger points can be stored and averaged to generate the ensemble signal.

[0134] In some examples, the processing circuitry 402 may incorporate patient movement information as part of the detection, prediction, and / or classification of a condition. 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 or modify a stroke detection or prediction algorithm when a fall (or near fall) is detected using an accelerometer. In addition to fall detection, motion sensor 416 may also be used to determine potential physical trauma caused by 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 detecting, predicting, or classifying epileptic seizures. For example, sensor 414 can detect the frequency of head movements indicative of a seizure, or detect other patient movements (or their absence) indicative of a seizure originating from epilepsy or not from epilepsy.

[0135] Figure 5 This is a block diagram of an exemplary configuration of an external device 500 configured to communicate with any of the sensor devices described herein (e.g., sensor device 106 or sensor device 400). 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.

[0136] 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. Processing circuitry system 502 may be processing circuitry system 110 (… Figure 1A and Figure 1B Examples or components of ).

[0137] 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 the sensor device 400 or another device, and send uplink telemetry to the sensor device or another device.

[0138] 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.

[0139] The data exchanged between the external device 500 and the sensor device 400 may include operating parameters. The external device 500 may transmit data including computer-readable instructions that, when implemented by the sensor device 400, can control the sensor device 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 sensor device 400 requesting the sensor device 400 to output the collected data (e.g., data corresponding to one or more of a sensed signal, parameter values ​​determined based on the signal, or indications that a condition has been detected, predicted, or classified) to the external device 500. Furthermore, the external device 500 may receive the collected data from the sensor device 400 and store the collected data in the storage device 510.

[0140] 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.

[0141] 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.

[0142] In some examples, external device 500 may provide alerts to a patient or another entity (e.g., a call center) based on condition detection, prediction, or classification provided by sensor device 400. In some examples, user interface 506 may provide an interface for presenting alerts for condition detection, prediction, or classification (e.g., stroke) and provide input to the user (e.g., 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 may output user prompts that can be synchronized with data collection via sensor device 400. For example, external device 500 may instruct the user to raise an arm, make a facial expression, etc., and sensor device 400 may record physiological data as the user performs the requested action. Furthermore, external device 500 itself may 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.

[0143] 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 a sensor device 106, an external device 108, and a processing circuitry system 110 via the network 602. In this example, the sensor device 106 can use a communication circuitry system to communicate with the external device 108 via a first wireless connection and with the access point 600 via a second wireless connection. 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.

[0144] Access point 600 may include a device connected to network 602 via any of a variety of wired or wireless network connections. In some examples, access point 600 may be a user device that can be co-located with the patient, such as a tablet or smartphone. As discussed above, sensor device 106 may be configured to transmit data (such as signals, parameter values ​​determined from signals, or condition / classification indications) to external device 108. In addition, access point 600 may, for example, periodically or in response to commands from the patient or network 602, query sensor device 106 to retrieve such data or other operational or patient data from sensor device 106. Access point 600 may then transmit the retrieved data to server 604 via network 602.

[0145] In some cases, server 604 can be configured to provide a secure storage site for data already collected from sensor device 106 and / or external device 108. In some cases, server 604 can aggregate the data into 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, developed by Medtronic, a company based in Dublin, Ireland. The network provides general network technologies and functions for implementation.

[0146] 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, processing circuitry system 606 may perform one or more techniques described herein based on sensed signals and / or parameter values ​​received from sensor device 106. For example, processing circuitry system 606 may perform one or more techniques described herein to detect, predict, and / or classify one or more patient conditions.

[0147] Server 604 may include memory 608. Memory 608 includes computer-readable instructions that, when executed by processing circuitry 606, cause server 604 and processing circuitry 606 to perform various functions accorded to server 604 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.

[0148] 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 be programmed to receive alerts and / or query the sensor device 106. For example, the clinician may access data corresponding to any or any combination of sensed physiological signals, parameters, or indications of detected, predicted, or classified conditions collected by the sensor device 106. 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 state of the patient's condition determined by sensor device 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 the instructions for medical interventions to another computing device 610A-610N (e.g., device 610B or external device 108) located on patient 102 or a caregiver of patient 102. 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 state of patient 102's medical condition determined by sensor device 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.

[0149] Figure 7 This is a flowchart illustrating an exemplary technique for generating at least one of the detection, prediction, or classification of a patient's condition based on information from multiple sensors, such as sensor devices 106, 210, 220, 310, 400 located on the patient's neck, lower occiput, or otherwise positioned above the patient's shoulders. Figure 7 The exemplary techniques are described as being performed by sensor device 400 and processing circuitry system 110, but can be performed by any sensor device described herein, for example, the sensor device may be related to Figure 4 The sensor device 400 is configured as shown herein. As described herein, the processing circuitry system 110 may include the processing circuitry system of any one or more devices described herein, such as the processing circuitry system 402 of the sensor device 400, the processing circuitry system 502 of the external device 500, or the processing circuitry system 606 or the server 604.

[0150] Sensor device 400 includes one or more sensors, such as electrode 418 and sensor 414. According to... Figure 7 As illustrated, the sensing circuitry system 406 of the sensor device 400 uses one or more sensors (700, 702, 704) to sense one or more signals. For example, the sensing circuitry system 406 may sense one or more signals via electrodes 418. Signals may include brain signals (e.g., EEG signals) and cardiac or heart signals (e.g., ECG signals or cardiac mechanical signals). In some examples, the sensing circuitry system 406 uses the techniques described in co-assigned U.S. Provisional Patent Application 63 / 071,908 (Attorney General File No. A0004949US01 / 1213-128USP1), filed August 28, 2020, entitled “DETERMINING COMPOSITE SIGNALS FROM AT LEAST THREE ELECTRODES”, the entire contents of which are incorporated herein by reference. The sensed signals may also include motion signals sensed by motion sensor 416 (e.g., one or more accelerometers). The sensed signals may also include respiratory signals, skin impedance signals and / or perfusion signals (e.g., sensed via impedance using electrode 418), blood pressure signals (e.g., PTT or other alternative blood pressure signals sensed via photoplethysmography using optical sensors 291, 363), heart sound signals (e.g., sensed using motion sensor 416 or an acoustic sensor), or impactogram signals (e.g., sensed using ECG and motion sensor signals).

[0151] Signals, or parameters derived therefrom, can be used to detect, predict, or classify any of a variety of patient conditions. For example, brain and cardiac signals can be used to detect or predict stroke and / or epileptic seizures. Patient movement and posture can further enhance the ability of the processing circuitry system 110 to detect, predict, and classify patient conditions. For example, posture has significant effects on cardiovascular stress and the autonomic nervous system, which can lead to certain conditions. Movement, respiration, and other sensor signals can capture clinical symptoms that may be present during stroke, epileptic seizures, and other neurological and / or cardiac events, and differentiate between different conditions or the type or origin of a specific condition. Additional parameters and signals can improve the sensitivity and specificity of the processing circuitry system 110 in detection, prediction, and / or classification.

[0152] Figure 7 Exemplary techniques also include preprocessing and parameter value extraction, which can be performed by sensing circuitry system 406 and / or processing circuitry system 110 (706). Preprocessing may include any of a variety of analog and / or digital filtering or other signal processing techniques to allow easy extraction of desired feature or parameter values ​​from the signal.

[0153] according to Figure 7 For example, processing circuitry system 110 applies one or more detection, prediction, and / or classification algorithms to parameter values ​​and / or signals to detect, predict, and / or classify one or more patient conditions (708). In some examples, the result of the algorithm is a probability (e.g., the risk of the condition), and processing circuitry system 110 can determine whether the probability meets (e.g., is greater than or equal to) a threshold (710). If the threshold is not met (No in 710), processing circuitry system 110 may not provide an alert and / or may indicate an uncertain classification of the patient condition (712). If the threshold is met (Yes in 710), processing circuitry system 110 may, for example, provide an alert and / or classification of the patient condition to or via external device 108 or another computing device described herein (714).

[0154] The algorithms applied by the processing circuit system 110 can be derived from algorithms applied to a database of patient data (e.g., parameter values ​​and signal values) with conditions or classifications. The determinations made by such algorithms can be binary or probabilistic. Classification algorithms may include etiological classifiers that can (probabilistically or deterministically) determine the origin or type of the condition (e.g., ischemic stroke or hemorrhagic stroke, or which hemisphere the stroke originated from).

[0155] Figure 8 This is a flowchart illustrating exemplary operations in a technique for generating at least one of detection, prediction, or classification of a patient's condition based on information from multiple sensors. Figure 8 The exemplary techniques are described as being performed by sensor device 400 and processing circuitry system 110, but can be performed by any sensor device described herein, for example, the sensor device may be related to Figure 4 The sensor device 400 is configured as shown herein. As described herein, the processing circuitry system 110 may include the processing circuitry system of any one or more devices described herein, such as the processing circuitry system 402 of the sensor device 400, the processing circuitry system 502 of the external device 500, or the processing circuitry system 606 or the server 604.

[0156] Figure 8 The exemplary techniques can typically correspond to the techniques related to... Figure 7 The exemplary technology described includes preprocessing and parameter value extraction (706). Figure 8 An exemplary method is described, in which processing circuitry system 110 extracts parameter values ​​(802) from one or more signals sensed by sensor device 400. Exemplary parameter values ​​that can be extracted from brain and heart signals are discussed in further detail herein. Extraction can produce a time series of values ​​for each of the one or more parameters. The detection, prediction, and classification algorithms used by processing circuitry system 110 can consider a single (e.g., current) value of a given parameter, or multiple values ​​of the parameter over time.

[0157] The processing circuitry system 804 can also normalize one or more parameter values ​​relative to the patient's baseline values ​​(804). The baseline values ​​can be derived from values ​​of parameters previously determined for the patient, such as the average of values ​​of a quantity over a period of time prior to the current value, or from a past baseline or learning period. Normalization can include any comparison or mathematical operation, such as difference operations, using the determined values ​​and the baseline values. The processing circuitry system 110 can apply the normalized parameter values ​​to algorithms used for detecting, predicting, or classifying patient conditions (806). Normalization of parameter values ​​allows the processing circuitry system 110 to consider patient-to-patient variations in parameter values ​​that do not necessarily indicate the probability or risk of a particular condition or classification. This, in turn, can enhance the sensitivity and specificity of the processing circuitry system in detecting, predicting, and / or classifying conditions.

[0158] Figure 9 This is a flowchart illustrating an exemplary technique for determining the values ​​of one or more parameters based on electroencephalogram (EEG) signals. Figure 9 The exemplary techniques are described as being performed by sensor device 400 and processing circuitry system 110, but can be performed by any sensor device described herein, for example, the sensor device may be related to Figure 4The sensor device 400 is configured as shown herein. As described herein, the processing circuitry system 110 may include the processing circuitry system of any one or more devices described herein, such as the processing circuitry system 402 of the sensor device 400, the processing circuitry system 502 of the external device 500, or the processing circuitry system 606 or the server 604.

[0159] according to Figure 9 For example, the sensing circuitry 406 of sensor device 400 isolates EEG or other electroencephalogram (EEG) signals from one or more electrical signals sensed via electrodes 418 (902). Exemplary techniques for isolating EEG signals are described in previously incorporated U.S. Provisional Patent Application No. 63 / 071,908 (Attorney's File No. A0004949US01 / 1213-128USP1), filed August 28, 2020, entitled “DETERMINING COMPOSITE SIGNALS FROM AT LEAST THREEELECTRODES,” and U.S. Provisional Patent Application No. 62 / 997,503, filed February 17, 2020, the entire contents of which are incorporated herein by reference.

[0160] according to Figure 9 For example, the processing circuitry system 110 also determines the power of the EEG signal within certain selected frequency bands (904). In some examples, the processing circuitry system 110 may optionally compare the foreground power of one or more frequency bands with the background power (e.g., current power versus previous power) and / or compare the power of two or more frequency bands with each other. The power within a frequency band or the result of such comparisons may be parameter values ​​for one or more algorithms used to detect, predict, and / or classify patient conditions.

[0161] Techniques for determining power within certain frequency bands as parameter values ​​for determining patient conditions (such as stroke) are described in previously incorporated U.S. Provisional Patent Application 63 / 071,908 (Attorney-in-Chief File No. A0004949US01 / 1213-128USP1), filed August 28, 2020, entitled “DETERMINING COMPOSITE SIGNALS FROM AT LEAST THREEELECTRODES”. Techniques for determining power within certain frequency bands and comparing foreground and background power to detect seizures or other neurological conditions are described in U.S. Patent 8,068,903 to Virag et al., issued November 29, 2011, the entire contents of which are incorporated herein by reference.

[0162] Figure 10 This is a flowchart illustrating an exemplary technique for determining the values ​​of one or more parameters based on an electrocardiogram signal. Figure 10 The exemplary techniques are described as being performed by sensor device 400 and processing circuitry system 110, but can be performed by any sensor device described herein, for example, the sensor device may be related to Figure 4 The sensor device 400 is configured as shown herein. As described herein, the processing circuitry system 110 may include the processing circuitry system of any one or more devices described herein, such as the processing circuitry system 402 of the sensor device 400, the processing circuitry system 502 of the external device 500, or the processing circuitry system 606 or the server 604.

[0163] according to Figure 10 For example, the sensing circuitry 406 of sensor device 400 isolates ECG or other cardiac signals from one or more electrical signals sensed via electrodes 418 or other sensors, or generates an integrated signal or enhances the ECG signal from said one or more electrical signals (1002). Exemplary techniques for isolating ECG signals are described in previously incorporated U.S. Provisional Patent Application 63 / 071,908 (Attorney's File No. A0004949US01 / 1213-128USP1), filed August 28, 2020, entitled "DETERMINING COMPOSITE SIGNALS FROM AT LEAST THREE ELECTRODES".

[0164] The sensing circuitry 406 and / or processing circuitry 110 can further identify the heartbeat within the cardiac signal (1004). Known techniques can be used to identify the heartbeat, such as identifying the occurrence and timing of other features of the R wave or QRS complex, or detecting peaks as signals indicative of the heart's mechanical activity. The processing circuitry 110 can then determine the heartbeat interval (1006), such as the duration between consecutive heartbeats. The processing circuitry 110 can then determine values ​​for multiple parameters based on the determined heartbeat interval.

[0165] For example, the processing circuitry 110 can determine a heart rate variability (HRV) value (1008) based on the heart rate interval. In some examples, the processing circuitry 110 can determine HRV based on a Lorenz scatter plot of the heart rate interval. HRV can change (increase or decrease) during or before certain patient conditions. These changes may be due to changes in the patient's autonomic function or state. HRV values ​​can be examples of parameter values.

[0166] In some examples, the processing circuitry 110 can transform HRV values ​​to the frequency domain and determine the relatively low frequency (LF) and relatively high frequency (HF) components of the HRV values ​​(1010). The LF and HF values ​​of HRV can be examples of parameter values. In some examples, the processing circuitry 110 can determine the ratio between the LF and HF components of HRV or otherwise compare the LF and HF components of HRV, which can indicate the patient's sympathetic-vagal balance (1012). The values ​​obtained from such comparisons can be another example of parameter values ​​over time. In some examples, the processing circuitry 110 can assess HRV by using parametric spectrum estimation using, for example, a sliding analysis window of 60 to 240 seconds (e.g., shifted in 5- to 20-second increments). The processing circuitry 110 can calculate the sympathetic-vagal balance as the ratio of the LF component (e.g., 0.04 Hz to 0.15 Hz, primarily sympathetic activity) to the HF component (e.g., 0.15 Hz to 0.4 Hz, primarily parasympathetic activity).

[0167] As another example, the processing circuitry 110 can determine a power density function (PDF) estimate of the heartbeat interval (1014). The processing circuitry 110 can then identify marginal intervals in the heartbeat intervals used in the PDF estimate (1016) and determine the degree of marginalization (1018). A numerical representation of the degree of marginalization is an example of a parameter value.

[0168] Marginal intervals (e.g., RR intervals) reflect cardiac hyperexcitability and can be analyzed using statistical assessments of the percentage of heartbeat intervals outside the confidence interval (e.g., a window size of 40 heartbeats) or other portions. Processing circuitry system 110 can determine or estimate the statistical distribution of these heartbeat intervals over a period of time (such as the last six minutes) and determine the number of ectopic and marginal events. For example, the statistical distribution can be estimated as detrended after removing the third-order polynomial tendency.

[0169] Marginalization can increase prior to syncope and epileptic seizures, for example, in the timeframe of 30 minutes to 2 hours before a syncope / epileptic seizure. Marginalization reflects several uncoordinated chronotropic responses. Under normal baseline conditions, marginalization is very low. Higher marginalization is observed in syncope events consisting primarily of ectopic heartbeats. On the other hand, epileptic seizures are often preceded by fairly high marginalization or are accompanied by fairly high marginalization, particularly sudden tachycardia, with bradycardia being only rare. Some clinical studies have reported lockstepping, defined as the intermittent synchronization of cardiac sympathetic and parasympathetic discharges with epileptiform discharges (fluctuations in voluntary cardiac activity). Therefore, the processing circuitry system 110 can use parameter values ​​indicating marginalization for various purposes, including detecting or predicting seizures and / or syncope, and classifying events as either seizures or syncope. The technique of using borderline to distinguish between epileptic seizures and neurogenic or cardiac syncope is described in U.S. Patent 8,738,121 to Virag et al., issued May 27, 2014, the entire contents of which are incorporated herein by reference.

[0170] In some examples, the processing circuitry 110 may additionally or alternatively identify a significant decrease in heart rate intervals, such as a maximum decrease during a predetermined time period (e.g., 60 seconds). An increase in the magnitude of such a decrease may precede certain patient conditions (such as syncope or epileptic seizures). Such a decrease over time may be the value over time of a parameter used by the processing circuitry 110 to detect, predict, or classify such conditions. In some examples, the processing circuitry 110 may additionally or alternatively identify tachyarrhythmias based on heart rate intervals. The number of tachyarrhythmias, duration, etc., may be the value over time of a parameter used by the processing circuitry 110 to detect, predict, or classify such conditions.

[0171] Reliable automated detection / prediction of epileptic seizures is a necessary first step in preventative treatment (pacing or medication). Over the past decade, numerous studies have addressed the problem of seizure detection / prediction, and a large number of methods have been proposed. First-rate algorithms based on linear analysis of EEG (Fourier transform, coherence function, multidimensional autoregressive modeling) only allow detection a few seconds (1-6 seconds) before visible symptoms. However, this prediction time is insufficient for designing closed-loop treatment systems. For linear or nonlinear techniques based on wavelet transform, neural networks, or correlation integrals of EEG, prediction times of several minutes have been reported. However, these more sophisticated methods face challenges such as optimal feature selection and optimal signal selection in multifocal epilepsy. They exhibit high variability in results and a large number of false alarms.

[0172] Problems associated with existing seizure prediction algorithms may be related to a lack of information about the patient's physiological state. Additional information included in cardiovascular signals (such as parameter values ​​derived from ECG signals as described above) could increase robustness. Such parameters could include information about the regulation of the autonomic nervous system. Blood pressure signals, or signals indicating blood pressure signals or alternatives (e.g., PTT), can additionally or alternatively provide information about the regulation of the autonomic nervous system. Regulation of the autonomic nervous system may precede syncope and epileptic seizures. Vagal pacing has been used to prevent epileptic seizures, and it is highly likely that changes in the patient's nervous system may be necessary to allow the onset of spontaneous focal seizures.

[0173] Algorithms that utilize both brain and cardiac signals to detect, predict, and / or classify epileptic seizures or other analogues of them, such as syncope and psychogenic seizures, may be more reliable than existing algorithms that use only one of these signal types. The processing circuitry system 110 can evaluate the EEG features described above to identify early manifestations of synchronicity / coherence, while cardiac features can reveal cardiac origins.

[0174] The use of combinations of brain signals, cardiac signals, and other signals, as described herein, can allow for the classification of conditions that are considered epileptic seizures, for example, to distinguish epileptic seizures from other conditions that can mimic epileptic seizures. For instance, immediate analysis of cardiac-derived parameters prior to and during syncope can be used to define different patterns or “characteristics” that can be used to differentiate between syncope, epilepsy, and psychogenic seizures (the three most common causes of syncope).

[0175] There are recognized difficulties in diagnosing the cause of syncope. Routine EEG recordings are often useless. Only a small number of patients are hospitalized for long-term monitoring, and recording long-term EEGs for more than 48 hours without hospitalization is impractical. Considering cardiac parameters and other parameters to perform such a classification of suspected seizures or syncope according to the techniques described in this article can avoid unnecessary hospitalization for long-term epileptic monitoring unit (EMU) monitoring.

[0176] Figure 11 This is a flowchart illustrating an exemplary technique for updating baseline and condition reference values ​​for one or more parameters. Figure 11 The exemplary techniques are described as being performed by the processing circuitry system 110. As described herein, the processing circuitry system 110 may include the processing circuitry system of any one or more devices described herein, such as the processing circuitry system 402 of the sensor device 400, the processing circuitry system 502 of the external device 500, or the processing circuitry system 606 or the server 604.

[0177] according to Figure 11As an example, as part of an algorithm for detecting, predicting, and / or classifying patient conditions, the processing circuitry system 110 compares determined parameter values ​​(e.g., parameter values ​​determined from electroencephalogram (EEG) and electrocardiogram (ECG) signals) with at least two distinct sets of reference values ​​(1100). One set of reference values ​​is a set of baseline reference values, and the other set is a set of reference values ​​used for the condition to be detected or predicted, or for classification. The reference values ​​in both sets may be initially determined based on a database of parameter values ​​from individuals other than the patient that are known to represent the condition / classification or their absence (baseline), for example, using machine learning and / or neural network techniques. In some examples, the reference set and the current set of parameter values ​​may be feature vectors.

[0178] Based on a comparison of the patient's current parameter values ​​with two sets of reference values, the processing circuitry 110 determines whether a condition is occurring or has occurred, or whether the classification is correct (1102). If the processing circuitry 110 determines that a condition is not occurring or has not occurred, or that the classification is incorrect, the processing circuitry 110 updates the baseline reference based on the current parameter values ​​used in the comparison (1104). If the processing circuitry 110 determines that a condition is occurring or has occurred, or that the classification is correct, the processing circuitry 110 updates the condition / classification reference based on the current parameter values ​​used in the comparison (1106).

[0179] In some examples, the processing circuit system 110 can determine similarity and dissimilarity indices regarding both the condition / classification and the baseline reference, and based on these indices, determine whether the condition is occurring or has already occurred, or whether the classification is correct. In some examples, the indices can be discriminant distance (heteroscedastic linear discriminant analysis (LDA)) indices. Such algorithms can be referred to as discriminant indices algorithms. The processing circuit system 110 can apply different weights to different parameters and can update the weights based on reliability, which can be obtained through statistical analysis of the reference. This allows the algorithm employed by the processing circuit system 110 to consider changing physiological states of the patient, such as sleep, stress, and physical activity. Periodic updates to the reference group can also allow the algorithm to consider such changing conditions.

[0180] Figure 12 This is a flowchart illustrating an exemplary technique for determining whether a detected or predicted patient condition is cardiac or neurogenic. Figure 12 The exemplary techniques are described as being performed by the processing circuitry system 110. As described herein, the processing circuitry system 110 may include the processing circuitry system of any one or more devices described herein, such as the processing circuitry system 402 of the sensor device 400, the processing circuitry system 502 of the external device 500, or the processing circuitry system 606 or the server 604.

[0181] according to Figure 12 For example, processing circuitry system 110 uses any of the exemplary techniques described herein to detect or predict patient conditions, such as epileptic seizures (1200). Processing circuitry system 110 also compares the time progression of parameter values ​​derived from different signals sensed by sensor device 400 and used by the processing circuitry system to detect or predict the condition (1202). For example, processing circuitry system 110 may compare parameter values ​​from brain signals with parameter values ​​from heart signals. Based on the relative timing of the parameter values ​​indicating the condition, processing circuitry system 110 may determine whether the condition is cardiac or neurogenic.

[0182] In some examples, the processing circuitry system 110 can similarly use timing comparisons between different signals to identify which hemisphere of the brain the stroke or epileptic seizure originated from. For example, a single sensor device or bilateral sensor device with one or more extensions can position electrodes or other sensors to sense signals from each hemisphere of the brain, such as signals from the corresponding temporal location. The processing circuitry system 110 can compare parameter values ​​from the hemispheres to determine which hemisphere the condition originated from and / or the extent of the electrocardiographic spread.

[0183] In some examples, the processing circuitry system 110 can use time-based assessments of brain and cardiac signals to differentiate between ischemic and hemorrhagic strokes. For example, the processing circuitry system 110 can determine whether an increase in heart rate variability determined from cardiac signals precedes or follows changes in stroke-associated brain signals. Changes in brain signals may include the frequency and / or amplitude of the signal, or the power in one or more frequency bands. Indicators of heart rate variability include heart rate variability, intra-interval variability (such as QT interval variability), morphological variability of heartbeat characteristics, ST segment elevation, or T wave alternation. Changes in heart rate variability following changes in brain signals may suggest a hemorrhagic stroke, while changes in brain signals following changes in heart rate variability may suggest an ischemic stroke.

[0184] Figure 13 This is a flowchart illustrating an exemplary technique for adjusting the operating point of a stroke detection / prediction algorithm based on the detection of falls or near falls. Figure 13 The exemplary techniques are described as being performed by the processing circuitry system 110. As described herein, the processing circuitry system 110 may include the processing circuitry system of any one or more devices described herein, such as the processing circuitry system 402 of the sensor device 400, the processing circuitry system 502 of the external device 500, or the processing circuitry system 606 or the server 604.

[0185] according to Figure 13For example, the processing circuitry 110 applies a stroke detection / prediction algorithm (1300) at a first operating point. The operating point of this algorithm may have associated sensitivity and specificity, which may be affected by the values ​​of one or more thresholds, such as probability thresholds used to determine whether a stroke is occurring or sufficiently likely to occur. The processing circuitry 110 also monitors one or more motion signals (1302) from one or more motion sensors 416 of the sensor device 400.

[0186] Based on one or more motion signals, the processing circuitry 110 determines whether the patient has fallen (1304). If the processing circuitry 110 determines that the patient has fallen (yes in 1304), the processing circuitry 110 applies the algorithm at a second operating point (1306). The second operating point may have higher sensitivity and lower specificity than the first operating point. The processing circuitry 110 can adjust the operating point by adjusting parameters of the algorithm (such as lowering the probability threshold for stroke).

[0187] If the processing circuitry 110 does not determine that the patient has fallen (No in 1304), the processing circuitry 110 may determine whether the patient has experienced a near fall based on one or more motion signals (1308). If the processing circuitry 110 determines that the patient has experienced a near fall (Yes in 1308), the processing circuitry 110 applies the algorithm at a third operating point (1310). The third operating point may be between the first and second operating points in terms of sensitivity and specificity. If the processing circuitry 110 does not determine that the patient has experienced a near fall (No in 1308), the processing circuitry 110 applies the algorithm at the first operating point.

[0188] When the processing circuitry 110 adjusts the operating point to a second or third operating point, it can maintain that adjustment for a period of time (such as a predetermined number of days). A stroke leading to hospitalization is often preceded by a fall or near-fall, which may be caused by a less severe stroke. Increased sensitivity at the second and third operating points can enhance the processing circuitry 110's ability to identify a stroke in the period following a fall or near-fall.

[0189] The processing circuitry system 110 can also use one or more motion signals from one or more motion sensors 416 of the sensor device 400 to detect or predict conditions other than stroke. For example, although vasovagal syncope (VVS) and various other conditions can result in cardiac parameter values ​​indicating changes in sympathetic-vagal balance, VVS occurs under orthostatic constraints. Therefore, syncope typically occurs after a change in posture (such as from supine to upright). The processing circuitry system 110 can use cardiac signals (such as ECG), blood pressure signals, and / or other cardiac mechanical signals, as well as motion sensor signals, to detect, predict, and / or classify syncope.

[0190] In some examples, one sensor device 400 may be placed above the shoulder as described herein, while another sensor device is placed on the chest for cardiac monitoring. Such systems, or systems with sensor devices only on or near the head, can be configured to assess the cause of sudden unexplained death (SUDEP) in epilepsy, which is typically attributed to neurological and / or cardiac conditions. Individual onboard detection algorithms for each sensor uniquely capture abnormalities (i.e., EEG / ECG), and when combined, can determine which occurs first for a given symptom (i.e., brain or heart).

[0191] Figure 14 This is a flowchart illustrating an exemplary technique for determining and implementing a patient's preferred treatment pathway based on the generated detection, prediction, or classification of the patient's condition.

[0192] according to Figure 14 For example, external device 500 (which may be an example of external device 108, such as the patient's smartphone) can receive an indication (1400) from sensor device 400 that an emergency situation (such as a large vessel blockage (LVO) stroke) has been detected or predicted. In response to receiving the indication of an emergency situation, external device 500 may, for example, use GPS or cellular triangulation to determine the patient's location (1402). External device 500 may transmit the indication of the emergency situation and location to server 604 via network 602 (1404).

[0193] Server 604 can determine the patient's priority treatment route based on the emergency situation and the patient's location (1406). Certain patient conditions (such as LVO stroke) are best treated at specific hospitals or medical centers by specific doctors. Based on the patient's condition and location, server 604 can select the preferred treatment route that includes such centers and doctors, and notify on-site emergency responders to transport the patient to the center (1408). Server 604 can alert the medical center to the patient's condition (1410), which can then begin preparing appropriate treatment and equipment for the patient's arrival in response to the notification.

[0194] The technology disclosed herein allows for corresponding machine learning classifier routines for three or more sensor types (e.g., brain, heart, and motion) to assess and determine the probability of a condition (e.g., LVO). In this exemplary classifier, if all three hybrid sensors trigger "yes," the probability of LVO is greater than 95%, for example, 96.5%. Conversely, if all three hybrid sensors trigger "no," the probability of LVO is less than 5%. Any combination of three hybrid sensor classifiers will result in a probability of LVO or other stroke between 5% and 95% (e.g., a "yes" triggered by the brain, a "no" triggered by the heart, and a "yes" triggered by the accelerometer producing an 80% probability). It should be noted that as more events are detected, individual data can be combined with population data to further enhance the sensitivity and specificity of the classifier routine. Because the technology of this invention can lead to sensitive and specific detection / prediction of conditions, it may be appropriate for emergency response systems and specialized medical centers to activate resources to treat patients in response to the determination of the algorithm. In some examples, the sensor device may, for example, use tissue conduction communication (TCC) to communicate with other accompanying devices and sensors (e.g., implantable cardioverter defibrillators (ICDs)) to improve accuracy before deciding to activate an emergency response pathway. In some examples, server 604 may decide to activate an emergency response pathway after both a risk score indicating the condition and condition detection using conventional algorithms (e.g., real-time detection). For example, if a patient's VT / VF risk score is above a threshold and the sensor device detects a VT / VF episode, it has a higher chance of being a real event than real-time detection alone. In some examples, the system according to this disclosure may use an accelerometer in conjunction with an ECG to detect cardiac mechanical signals to detect and differentiate cardiac arrest, VT / VF, or pulseless electrical activity (PEA). The system described herein can be configured to... Figure 14 The techniques illustrated are used for various emergency situations, such as myocardial infarction based on, for example, ST-segment elevation, syncope, Brady pause (e.g., a patient falls and exhibits apathy after the event, which can infer an extreme emergency situation; if a patient falls and exhibits posture / activity of standing and walking after the event, it can infer a lesser emergency situation), fall detection alarms (e.g., graded falls based on acceleration vectors during the fall and post-fall recovery indicators), traumatic brain injury (e.g., concussion detection using accelerometers), respiratory distress based on impedance signals, motion signals, and ECG signals, and QT interval prolongation (e.g., time-averaged triggering. Multi-vector methods allow ECG fidelity beyond just R waves).

[0195] As discussed above, the processing circuitry can determine surrogate parameters for blood pressure as used in the analyses described herein. Hypertension is considered a significant risk factor for stroke and epilepsy. Elevated blood pressure is common in patients with stroke and epileptic seizures and can be a predictor of stroke and / or seizures. Lowering blood pressure is considered a first-order risk mitigator for managing patients at risk of such conditions.

[0196] PTT is negatively correlated with blood pressure and can be used as a substitute for blood pressure in the analysis described herein. Signals indicating cardiac pulse pressure waves can be acquired by a sensor device implanted above the shoulder (e.g., cranial side). Assuming priority is given to cerebral blood flow, pulse pressure signals acquired above the shoulder can have higher fidelity, e.g., less variability, than pulse pressure signals acquired from other body locations.

[0197] In some examples, the processing circuitry can determine the patient's PTT value based on ECG signals sensed from electrodes and signals simultaneously sensed by optical sensors 291 and 363. The processing circuitry can identify the R wave within the cardiac cycle and correlate a first time (T1) with the occurrence of the R wave. Next, the processing circuitry can identify fluctuations in light detected by photodetectors 40A and 40B that occur after T1 and correlate a second time (T2) with these fluctuations, which may represent a portion of the ejected blood passing through the vascular system near photodetectors 294 and 367 during the observed cardiac cycle. The processing circuitry of IMD 10 can then determine the PTT value by subtracting T2 from T1. The processing circuitry can determine T2 by identifying fluctuations in the intensity and / or wavelength of light detected by photodetectors 294 and 367 that occur after T1 and correlate a second time (T2) with these fluctuations, which may represent a portion of the ejected blood passing through the vascular system near the photodetectors during the cardiac cycle. To generate such a signal, light emitters 292 and 365 can emit light of one or more wavelengths in the NIR, visible, green, or amber spectrum into the tissue. A portion of the emitted light is absorbed by the tissue, and a portion of the emitted light is reflected by the tissue and received by a photodetector.

[0198] 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, 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.

[0199] 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.

[0200] 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: Sensor device, the sensor device comprising: A housing configured to be positioned above the patient's shoulder; Multiple electrodes are configured to be positioned above the patient's shoulder; The motion sensor inside the housing; and The sensing circuit system within the housing is configured to: The patient's brain and heart signals are sensed via the plurality of electrodes positioned above the patient's shoulder; and The motion signal of the patient is sensed via the motion sensor disposed above the patient's shoulder; and Processing circuitry system, the processing circuitry system being configured to: Determine the values ​​of one or more parameters from the brain signals over time; Determine the values ​​of one or more parameters from the cardiac signal over time; At least one of the following is generated: a detection, prediction, or classification of the patient's condition, based on the values ​​of one or more parameters from the brain signals over time, the values ​​of one or more parameters from the heart signals over time, and the motion signals; wherein the condition includes stroke or epileptic seizure; and An indication of at least one of the detection, prediction, or classification is output to a computing device.

2. The medical system of claim 1, wherein the housing is configured to be disposed at one of the patient's neck or the posterior part of the skull, or at the temporal region of the patient.

3. The medical system of claim 1 or 2, wherein the value of the one or more parameters from the cardiac signal over time indicates the patient's voluntary activity.

4. The system according to claim 1 or 2, wherein the processing circuitry is configured to: Compare the time progression of the values ​​of one or more parameters from the brain signal over time with the time progression of the values ​​of one or more parameters from the heart signal over time; and The classification of the condition, either neurogenic or cardiac, is generated based on the comparison.

5. The medical system of claim 1 or 2, wherein the condition includes stroke, and the processing circuitry is configured to apply a stroke detection or prediction algorithm to the values ​​of the one or more parameters from the brain signal over time and the values ​​of the one or more parameters from the heart signal over time to generate at least one of the detections or predictions of stroke in the patient, wherein the processing circuitry is further configured to: Detecting the patient's fall based on the motion signal; and In response to detecting the fall, the operation point of the algorithm is changed from a first value to a second value, the second value having higher sensitivity than the first value.

6. The medical system of claim 5, wherein the processing circuitry is configured to: Detecting the patient's impending fall based on the motion signals; and In response to detecting the near fall, the operation point of the algorithm is changed from the first value to a third value, the third value having higher sensitivity than the first value and lower sensitivity than the second value.

7. The medical system according to claim 1 or 2, The sensing circuitry is configured to sense at least one of the following: The patient's respiratory signals; The impedance through the electrode; Cardiac impact signal via the motion sensor; The processing circuitry is configured to generate at least one of the detection, prediction, or classification of the patient's condition based on at least one of the respiratory signal, the impedance, or the cardiac impaction signal.

8. The medical system of claim 1 or 2, further comprising an optical sensor configured for implantation above the shoulder, wherein the processing circuitry is configured to: The pulse wave transit time (PTT) is determined based on the signal from the optical sensor; and At least one of the detections, predictions, or classifications used to generate the patient's condition based on the PTT.

9. The medical system of claim 1, wherein the processing circuitry is configured to perform at least one of the following: The classification of the condition is generated as either epilepsy or non-epilepsy; or Generate a condition classification, namely, one of epilepsy, psychogenic attack, or syncope.

10. The medical system of claim 1 or 2, wherein the brain signal comprises an electroencephalogram (EEG) signal, and the heart signal comprises at least one of an electrocardiogram (ECG) signal or a signal indicating cardiac contraction.

11. The medical system according to claim 1 or 2, wherein the plurality of electrodes includes at least one electrode disposed on the housing.

12. The medical system of claim 1 or 2, wherein the plurality of electrodes comprises at least one electrode carried by an electrode extension coupled to the housing.

13. The medical system of claim 12, wherein the electrode extension includes a first electrode extension extending from the housing along a first direction, the sensor device includes a second electrode extension including at least one of the plurality of electrodes extending from the housing along a second direction, and the second direction is opposite to the first direction.

14. The medical system of claim 1, wherein the processing circuitry is configured to generate at least one of the detection, prediction, or classification of the patient's condition based on at least one of the following: The patient's posture was determined based on the motion signals; The patient's postural changes were detected based on the motion signals; Falls detected based on the motion signals; Trauma detected based on the motion signals; or The head movement frequency detected based on the motion signal.

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