Patent Description:
This disclosure relates generally to the field of disease detection and, more specifically, to stroke detection.

A stroke results from the death of brain tissue due to disruptions of blood flow to the brain. An ischemic stroke happens when there is a blockage of blood flow to the brain, usually as the result of a blood clot. Hemorrhagic stroke happens when there is a rupture of a blood vessel in the brain, resulting in bleeding into the brain tissue and surrounding space.

There are many physiologic symptoms of stroke onset that vary depending on the location of the affected tissue. Early symptoms of an evolving stroke may be able to reduce or even resolve if the interruption of blood flow is resolved quickly, before the tissue has died. One category of symptoms is disrupted vision, including blurred, dimming often likened to a curtain falling) or even complete loss of vision. Stroke patients often also experience eye deviation or difficult with eye tracking.

Just as a stroke can affect the part of the brain that is associated with sight, it can also affect the parts of the brain that have to do with speech, comprehension and communication. Patients suffering from a stroke may exhibit slurred speech or garbled speech that renders them incomprehensible.

Another common symptom of stroke is weakness on one side of the body. This can manifest or partial or total paralysis of the side of the face, one arm, one leg, or the entire side of one's body.

Ischemic stroke is the most common type of stroke and is often painless when experienced, but hemorrhagic strokes are very painful, often being described as sudden onset of "the worst headache of one's life". Often, many people's headaches are accompanied with a feeling of dizziness, nausea, and vomiting. Smell and taste can also be impacted during the onset of a stroke.

Anything that affects the brain, from trauma to stroke, has the potential for cognitive disablement. A feeling of confusion, or a constant second-guessing of ones' actions, can sometimes appear days before a stroke occurs.

Another common symptom of a stroke is the sudden onset of fatigue.

Stroke symptoms can vary in duration and occur with or without pain, which can make stroke detection difficult. Further, strokes can occur during sleep, making detection even more difficult. If a stroke does occur while the person is sleeping, it may not wake a person up right away. As a result, when patients wake up symptomatic, it is unclear whether the stroke just started or whether it has already been occurring during sleep.

If a stroke is detected and patients seek care quickly, there are many evidence-based interventions that can dramatically reduce the death and disability resultant from the disease. In severe ischemic strokes, every minute of delay to flow restoration is equated to the loss of a week of Disability Adjusted Life Years (DALYs). Despite these treatments being available, fewer than <NUM>% of patients receive them. Even among patients that do receive intervention, outcomes are often suboptimal because of the delays to intervention. Stroke detection is difficult because stroke frequently doesn't hurt, mimics other health events, and is heterogeneous in its presentation. Improvements in detection of and care-seeking for stroke onset could dramatically reduce the death and disability associated with the disease.

Like stroke, COVID-<NUM> is proving to have heterogeneous symptoms, many of which resemble those of neurologic disorders. Recent publications have shown early evidence of encephalopathies, inflammatory CNS syndromes, ischemic strokes, and peripheral neurological disorders in patients being treated for COVID-<NUM>. (<NPL>) With most COVID-<NUM> patients being managed remotely, and a significant percentage of inpatients requiring invasive ventilation, monitoring for the obvious symptoms of neurological disruption may be difficult. As such, improvements in remote monitoring and care for COVID-<NUM> patients could dramatically reduce the death and disability associated with the disease. Document <CIT> discloses a wearable system for detecting an anomalous biologic event in a person according to the preambles of claims <NUM> and <NUM>.

One aspect of the present disclosure is directed to a wearable system for detecting an anomalous biologic event in a person according to claim <NUM>.

In some embodiments, the second blood volume signal includes a set of blood volume signals, such that the blood volume of the skin surface is measured repeatedly before, during, and after a heating cycle of the heat source. In some embodiments, the second blood volume signal includes a plurality of blood volume signals, such that the blood volume of the skin surface is measured continuously before, during, and after a heating cycle of the heat source.

In some embodiments, hardware processor is further configured to receive the second blood volume signal after the target temperature is reached, after a predetermined length of time has expired, or after one or more heating cycles have concluded.

In some embodiments, comparing the second blood volume signal to the baseline blood volume signal includes calculating a baseline ratio of alternating current (AC) to direct current (DC) for the baseline blood volume signal and a second ratio of AC to DC for the second blood volume signal and comparing the baseline ratio to the second ratio.

In some non-claimed embodiments, the environmental temperature sensor is positioned on the first side of the body of the wearable system.

In some non-claimed embodiments, the system further includes a remote computing device communicative coupled to the wearable system and comprising the environmental temperature sensor. In some non-claimed embodiments, the remote computing device includes one of: a laptop, cellular device, a workstation, a server, a desktop computer, a personal digital assistant, a second wearable system or device, or a netbook.

In some non-claimed embodiments, the heat source is positioned on the second surface of the body.

In some non-claimed embodiments, the hardware processor is further configured to receive baseline temperature signals from the skin temperature sensor and the environmental temperature sensor, determine the target temperature based on the baseline temperature signals, and determine whether the target temperature is below a maximum temperature value.

In some non-claimed embodiments, the hardware processor is further configured to cycle the heat source to maintain the target temperature.

In some non-claimed embodiments, the system further includes one or more electrodermal activity sensors positioned on the second surface.

In some non-claimed embodiments, the one or more electrodermal activity sensors are spaced apart from the heating element by about <NUM> inches to about <NUM> inches.

In some non-claimed embodiments, the system further includes one or more motion sensors configured to measure a motion of a body portion to which the wearable system is coupled.

In some non-claimed embodiments, the first and second surfaces define a cavity therebetween to provide airflow between the first and second surfaces.

In some non-claimed embodiments, the hardware processor resides on or within the first surface.

In some non-claimed embodiments, the cavity defined by the first and second surfaces physically separates the heat source from the hardware processor on or within the first surface.

In some non-claimed embodiments, the cavity defined by the first and second surfaces has sufficient volume to facilitate cooling of the heat source in between heating cycles.

In some non-claimed embodiments, the anomalous biologic event comprises a stroke event.

In some non-claimed embodiments, the wearable system is positioned on a left limb of a user and a second wearable system is positioned on a right limb of the user, wherein the second wearable system comprises a second heating element, a second skin temperature sensor, and a second blood volume sensor, wherein the hardware processor is further configured to compare right side blood volume signals to left side blood volume signals to determine whether the anomalous biologic event has occurred.

In some non-claimed embodiments, the hardware processor is further configured to synchronize the signals received from the left limb and the right limb in time; and compare the synchronized signals from the left limb and the right limb to determine whether the anomalous biologic event occurred. In some non-claimed embodiments, the comparison takes into account a baseline difference between the left limb and the right limb.

In some non-claimed embodiments, the system further includes a tensionable band coupled to the body. In some non-claimed embodiments, the tensionable band further includes a visual indicator to indicate when one or more of: the heating element, the skin temperature sensor, the blood volume sensor, or a combination thereof is sufficiently coupled to the skin surface to enable accurate sensor readings. In some non-claimed embodiments, one or more ends of the tensionable band are coupled to the body at a position that is centered with respect to one or more sensors positioned on the second surface.

In some non-claimed embodiments, the heat source is positioned concentrically about the blood volume sensor and the skin temperature sensor.

In some non-claimed embodiments, the blood volume sensor comprises a photoplethysmography sensor or an impedance plethysmographic sensor.

In some non-claimed embodiments, the skin temperature sensor comprises a thermocouple, a resistance temperature detector, a thermistor, or an infrared temperature sensor.

In some non-claimed embodiments, the system further includes a support structure coupled to the heat source and configured to couple the heat source to the second surface and at least partially expose the heat source to the cavity.

In some non-claimed embodiments, the blood volume sensor is further configured to measure one or more of: heart rate, heart rate variability, or oxygen saturation.

In some non-claimed embodiments, the target temperature is individualized to the user. In some embodiments, individualization of the target temperature includes receiving a user input related to perceived temperature of the skin surface. In some non-claimed embodiments, individualization of the target temperature is based on signals received from the blood volume sensor.

In some non-claimed embodiments, the heat source comprises one of: a heating element or an environmental temperature.

Another aspect of the present invention is directed to a wearable system for detecting an anomalous biologic event in a person according to claim <NUM>.

In some embodiments, the sensor is selected from the group consisting of: a stretch sensor, an electrodermal activity sensor, an electrocardiogram sensor, or a camera.

In some non-claimed embodiments, the parameter of interest includes one or more of a blood pressure, a heart rate, a heart rate variability, a gaze, a facial expression, a skin conductance response, a vasodilation response, or a dilation response.

The foregoing is a summary, and thus, necessarily limited in detail. The above-mentioned aspects, as well as other aspects, features, and advantages of the present technology are described below in connection with various embodiments, with reference made to the accompanying drawings.

The illustrated embodiments are merely examples and are not intended to limit the disclosure. The schematics are drawn to illustrate features and concepts and are not necessarily drawn to scale.

The foregoing is a summary, and thus, necessarily limited in detail. The above-mentioned aspects, as well as other aspects, features, and advantages of the present technology will now be described in connection with various embodiments. The inclusion of the following embodiments is not intended to limit the disclosure to these embodiments, but rather to enable any person skilled in the art to make and use the contemplated invention(s).

Described herein are systems, devices, and methods for multivariate detection of stroke. Multivariate may include using more than one, at least two, or a plurality of factors, markers, or other parameters to detect stroke. In some embodiments, multivariate may include using one parameter measured at multiple locations or positions or at multiple times (e.g., random or fixed intervals, on demand, automatically, etc.). In various embodiments, multivariate may include detecting a measured parameter symmetrically or asymmetrically. The measured parameter may include a functional parameter (e.g., gait, speech, facial changes, etc.); a biological parameter or marker (e.g., blood proteins, metabolites, etc.); a quantitative parameter (e.g., limb asymmetry, heart rate variability, etc.); a spatial (e.g., neck vs. chest; arm vs. leg; etc.) difference in one or multiple (e.g., <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, etc.) measured parameters; and/or a temporal difference in one or multiple measured parameters.

In some embodiments, there may be an overlay of multivariate signals including two measurement data types, physiological or quantitative signals (e.g., skin electromagnetic potential, Doppler flow signal anomaly, hyperhydrosis, cutaneous blood flow, brain perfusion, heartrate variability, etc.), and/or clinical manifestations or functional parameters (e.g., limb asymmetry, speech slur, facial droop, retinal abnormality, etc.). Clinical manifestations occur following stroke onset, but a faint signal from a clinical manifestation measurement combined with a physiological signal measurement may detect or predict stroke likelihood prior to stroke onset. Parameters that may be measured before, during, or after a stroke include quantitative parameters, functional parameters, and/or blood/fluid parameters. Any of the parameters shown/described herein may be measured asymmetrically, as described elsewhere herein. Exemplary, non-limiting examples of quantitative parameters include: volumetric impedance spectroscopy, EEG asymmetry, brain perfusion, skin/body temperature (e.g., cold paretic limb, up to <NUM> colder or <NUM>% colder than non-paretic limb), hyperhidrosis (e.g., greater than <NUM>-<NUM>% increase on paretic limb), limb asymmetry, drift and pronation test, cutaneous blood flow, muscle tone, heartrate variability (e.g., decrease in spectral components by greater than 10X, lasting <NUM>-<NUM> days after stroke onset), facial surface EMG, cerebral blood flow (CBF), carotid artery stenosis, salivary cortisol, neuron specific enolase (NSE), salivary (NSE), etc. Exemplary, non-limiting examples of functional parameters include: speech changes, speech comprehension, text comprehension, consciousness, coordination/directions, facial muscle weakness, arm weakness, body weakness (e.g., grip), leg weakness, foot weakness, unilateral weakness, difficulty walking, vertigo, sudden vision problems, limited visual field, altered gaze, thunderclap headache, nuchal rigidity (nape of neck), respiration, blood pressure (e.g., increase up to <NUM>% in both systole (<NUM> mHg) and diastole (<NUM> mmHg)), etc. Exemplary, non-limiting examples of blood/fluid parameters include: CoaguCheck (Roche), HemoChron (ITC), iSTAT (Abbott), Cornell University, ReST (Valtari Bio Inc. ), SMARTChip (sarissa Biomedical), etc..

In some embodiments, multiple measurement locations (e.g., radial, brachial, etc. vessels) may be used to measure a difference in signal or data pattern among those locations compared to nominal, healthy location measurements or compared to an individual baseline as an input into a data processing module. For example, an individual baseline may be recorded over time and, when an adverse event occurs, a change (e.g., absolute or relative value) from baseline is determined unilaterally or bilaterally. In some embodiments, after the adverse event occurs, a new baseline may be established. Further for example, as shown in <FIG>, blood pressure pulse varies depending on the location in the body, demonstrating that a slightly different signal is measured depending on location. For example, if only one location is measured, then changes over time are observed. If multiple locations are monitored and/or measured, then changes over time and changes relative to one another and/or a baseline can be used to identify a pattern or an asymmetric signal occurrence. In some embodiments, an individualized baseline is further calculated based on a patient's health history (e.g., diabetes, heart-pacing, pre-existing stroke, etc.), demographics, lifestyle (e.g., smoker, active exerciser, drinks alcohol, etc.), etc..

In some embodiments, as shown in <FIG>, a system <NUM> for multivariate detection of stroke includes a hardware component (e.g., wearable device, sensor, computing device, remote sensing device, etc.) and a data processing module stored in the hardware or in communication with the hardware. The hardware component, for example one or more sensors, may be positioned on a user of the system, bilaterally on a user of the system, or throughout a location occupied by a user. Optionally (shown by dashed lines), a system for multivariate stroke detection may further include a third party device, for example a device including Amazon® Alexa® or an Amazon® Echo® device, as described in further detail elsewhere herein. For example, there may be bidirectional communication (e.g., via a wired connection or wireless communication) between the hardware component and the data processing module, the data processing module and the third party device, and/or the third party device and the hardware component.

In one exemplary, non-limiting embodiment of the system of <FIG>, a digital FAST (i.e., facial drooping, arm weakness, speech difficulties, time for help) test may be performed by the system of <FIG>. For example, the hardware component may include one or more cameras positioned throughout a location occupied by a user and configured to detect changes (e.g., using computer vision techniques) in facial expressions (e.g., drooping) as a result of stroke, as shown in <FIG> (i.e., the "F" part of a FAST test). Further, one or more sensors or other hardware component (e.g., camera, microphone, etc.) may be positioned throughout the location occupied by user. The one or more sensors are communicatively coupled to the data processing module such that parameters sensed by the sensors may be transmitted to the data processing module for digitization, filtering, process, and/or analysis. In the case of a digital FAST test, asymmetrical arm weakness may be sensed by the one or more sensors. To discern speech difficulties, a third party device configured to receive and assess speech quality may be communicatively coupled to the data processing module and/or hardware component. As such, a user may be prompted to speak by the third party device and the user's response may be sensed by the hardware component (e.g., one or more microphones) so that a quality of speech of the user may be determined. One or more of these detected parameters may be analyzed and optionally sent to a caregiver, approved family and/or friends, healthcare provider, physician, and/or emergency services.

In some embodiments, a system for multivariate stroke detection may further include an application downloaded and/or stored on a hardware component or downloaded and/or stored on a computing device (e.g., mobile computing device) communicatively coupled to the hardware component. The application may be configured to process sensor data, camera data, speech data, etc. and/or display data sensed or captured in real time, for example in a graphical representation, and/or allow zooming to view various features of the data.

In some embodiments, data may be transmitted to and/or from the device for detecting stroke to a central hub, mobile computing device, server, or other storage and/or computing device. Data transmission may include wireless communication (e.g., a nearfield communications (NFC) protocol, a low energy Bluetooth® protocol, other radiofrequency (RF) communication protocol, etc.) between sensor locations on the body and/or a central hub. In other embodiments, data transmission may include wire communication between sensor locations on the body and/or a central hub. In some embodiments, the central hub may be a monitor in a medical facility, home monitor, patients' mobile computing device, or other wireless device. Alternatively, one or more of the sensors on the body may act as the central hub. The hub device may wirelessly send signals to activate a medical care pathway and/or notify one or more individuals (e.g., family, friends, physician, EMS, etc.).

In some embodiments, data transmission, following multivariate analysis, to the central hub may alert the patient, the next of kin, and/or a third party to identify possible false positives or negatives.

In some embodiments, a device for stroke detection may be worn on an exterior or skin surface of the patient or implanted as hardware prior to and/or during stroke, including up to days before the event and during the event to provide continuous variable monitoring of various physiological parameters. The various embodiments described herein may either be a wearable device or an implantable device.

In some embodiments, a device for detecting stroke may include a wearable device, for example a patch, headband or sweatband, ring, watch (e.g., to measure movement as shown in <FIG>), adhesive strip, helmet, bracelet, anklet, sock (e.g., to measure heart rate, heart rate variability, temperature, gait, etc.), shoe insoles (e.g., to measure heart rate, heart rate variability, temperature, gait, etc.), clothing, belt, necklace, earring (e.g., over or in the ear to measure heart rate, heart rate variability, EEG asymmetry, etc.), hearing aid, earbuds, glasses or sunglasses or smart glasses (e.g., to measure EOG, EMG, EEG, gaze, facial muscle movement or drooping, etc.), smart tattoo (e.g., to measure EEG, ECG, etc.), bra, bra clip, chest strap, contacts (e.g., to measure tear composition, etc.), mouthguard or bite splint (e.g., to measure saliva neuron specific enolase, cortisol, temperature, motion, etc.), hat or cap (e.g., to measure various signals using ultrasound), wearable speaker (e.g., to measure heart rate, heart rate variability, motion, etc.), or otherwise a sensor integrated into any wearable clothing, accessory, or device. For example, a patch (e.g., wearable on the neck) may be used to estimate cerebral blood flow using doppler ultrasound, blood oxygen content, or other blood feature as an indicator of blood going into the brain (Carotid Artery) or leaving the brain (Jugular Vein); a patch or strip (e.g., wearable on the head) may be used to detect EEG or sEMG. Further for example, a wearable device for detecting stroke may include one or more transdermal sensors that are configured to measure changes in one or more gasses transfused through the skin (e.g., Nitric Oxide (NO) could either be measured directly, or through measurement of particular bi-products); one or more biomarkers that are in the blood that are diffused into the subcutaneous region or into the epidermis and can be measured externally. In some embodiments, a wearable device for detecting stroke may comprise a wristband or patch with a combination of micro-needles that are configured to measure the fluid sub-dermally or interstitial fluid (e.g., similar to continuous glucose monitors).

In some embodiments, a wearable device for detecting stroke may comprise a wearable array of indicators (e.g., chromogenic indicators) configured to measure a chemical, analyte, protein, etc. in a bodily fluid of an individual (e.g., blood, interstitial fluid, etc.). For example, the array may comprise a membrane with a printed array thereon that when exposed to one or more analytes, a subset of the indicator spots responds by changing color or properties. The color response of the indicators may be optically read, for example using a camera on a computing device or other image sensor and compared to a baseline reading or a reference or standard. A color difference map may be generated by superimposing and/or subtracting the two images (baseline and experimental or experimental and reference/standard). As an exemplary, non-limiting analyte, an increase in nitric oxide may be detected in blood or interstitial fluid of an individual after a stroke event and/or modification of one or more proteins by nitric oxide may be detected in blood or interstitial fluid of an individual after a stroke event and/or one or more intermediates or byproducts of nitric oxide may be detected in blood or interstitial fluid of an individual after a stroke event. For example, nitric oxide has been shown to modify proteins via: <NUM>) binding to metal centers; <NUM>) nitrosylation of thiol and amine groups; <NUM>) nitration of tyrosine, tryptophan, amine, carboxylic acid, and phenylalanine groups; and <NUM>) oxidation of thiols (both cysteine and methionine residues) and tyrosine. Such methods may bypass the need to measure an asymmetrical change in one or more parameters, as described elsewhere herein.

In some embodiments, a system for stroke detection may include one or more Doppler radar sensors, microphones, and cameras throughout a home to detect visual signs of stroke, equivalent to a "FAST" test using computer vision or similar techniques, as shown in <FIG>. For example, a machine learning model may be trained on a training data set of images of stroke patients to identify asymmetrical facial features, such as facial drooping. As can be seen in <FIG>, the system is able to identify drooping in a mouth, nose, and eye positioning of the patient. Facial capillary asymmetries via high frame-rate Eulerian video processing techniques may also be detected by the systems described herein. The system may further employ confirmation biometrics such as HR/HRV, respiratory rate (e.g., via Doppler radar), and/or bilateral temperature via infrared camera (i.e., FLIR).

In some embodiments, a device for detecting stroke may include a device positionable in a room, office, home, vehicle, or other location; or in or on a bed or other furniture (e.g., bedside monitors; monitors within mattresses, bedding, etc.). For example, a smart speaker (e.g., to prompt a user to respond to a question to analyze speech quality), microphone, camera, and/or mirror may be positionable in a location to detect changes in a user's speech, activities, movement, gait, facial appearance, heart rate, and/or heart rate variability. The device may comprise a data processing module to differentiate changes in the measured parameters as compared to that from healthy learned patient data or individualized baseline data. This can be also be referred to as reference data. The healthy learned patient data may be unique to a particular user or an aggregate value that is predetermined from previous studies. The healthy learned patient data or individualized patient data can be stored as a one or more parameters or a signature.

In some embodiments, as shown in <FIG>, the device may be a ring or a pair of rings to be worn one on each hand or each foot to measure temperature; volumetric impedance spectroscopy; hyperhidrosis; heart rate or heart rate variability through, for example, a PPG sensor to monitor rate of blood flow; and/or motion (e.g., by including an accelerometer and/or gyroscope therein) to measure, for example, limb asymmetry or changes in gait. Temperature measurement devices may include, but are not limited to, infrared sensors, thermometers, thermistors, or thermal flux transducer. Hyperhydrosis measurement devices may include, but are not limited to, detection of analytes including ions, metabolites, acids, hormones, and small proteins through potentiometry, chronoamperometry, cyclic voltammetry, square wave stripping voltammetry, or detection of changes in conductivity. Sensor measurement devices may include, but are not limited to, a photoplethysmographic (PPG) device, a skin conductance sensor measuring skin conductance/galvanic skin response (GSR) or electrodermal activity (EDA), or a skin temperature measurement device (e.g., contact devices and non-contact devices, like IR imaging camera).

In some embodiments, the ring may incorporate a stretchable or expandable element or stretch sensor to allow the ring to expand or stretch when the finger swells. This element may include, but is not limited to, elastomer film polymers of various degree of bonding to allow for different pliable elements or measuring the reflectivity of polarized light. This element may comprise a plastic segment of the ring that can be loosened/tightened, or by building a slidable element that can be pulled apart. Non-limiting examples of a stretch sensor include, but are not limited to, a strain gauge or an electrical component configured to change inductance, resistance, or capacitance when stretched.

In some embodiments, the device may be a strip that measures brain waves through electroencephalogram (EEG) and/or muscle contractions through surface electromyography (sEMG). The measurement of EEG may be compared to a baseline value to detect a change or asymmetry of the EEG. In some embodiments, EMG measures facial muscle changes compared to a baseline measurement to identify muscle weakness and tone.

In some embodiments, as shown in <FIG>, the device may be a wearable eyeglass device that measures electrooculography (EOG), EMG, EEG, gaze, and facial muscle symmetry. The measurement of EOG identifies a change in the corneo-retinal standing potential between the front and back of the eye that may detect a change in gaze and size of visual field and may be compared to either the other eye or a previous baseline value.

In some embodiments, as shown in <FIG>, a device for stroke detection may include a wearable device for measuring changes in motion (e.g., in three axes), for example asymmetrical motion to detect tremors. In some embodiments, a device for stroke detection may include a wearable device for measuring changes in motion (e.g., in three axes), for example asymmetrical changes in motion to detect tremors. Such device may include an accelerometer, gyroscope, inclinometer, compass, or other device for measuring acceleration, distance, and/or movement. For example, as shown in <FIG>, as the wearable device is moved so does a plane of action. The accelerometer may track a change of plane and accordingly adjust the movement in three dimensions. Further, as shown in <FIG>, an accelerometer may track azimuth, roll and pitch.

In some embodiments, a device for detecting stroke may be configured to detect asymmetrical responses, outputs, or signals. For example, one or more devices (e.g., ring, watch, etc.) described herein may be used to measure symmetrical and asymmetrical limb movement. <FIG> show various symmetrical and asymmetrical movements that may be measured by one or more embodiments described herein. For example, <FIG>, <FIG>, <FIG>, <FIG>, <FIG>, and <FIG> show various embodiments of symmetrical movements (e.g., up and down movement, left and right movement, rotational movement, etc.) between two limbs measurable by various devices described herein. <FIG><FIG><FIG><FIG><FIG>and <FIG>show various embodiments of asymmetrical movements (e.g., up and down movement, left and right movement, rotational movement, etc.) of limbs measurable by various devices described herein.

In some embodiments, as shown in <FIG>, a device or system for detecting stroke may be configured to stimulate a response and measure the response on each side (e.g., to detect asymmetrical responses) of the body of the user to determine whether the response or the difference in response between the two sides indicates a stroke event. For example, a thermal (i.e., hot or cold) stimulus may be applied to a section of skin on a body of a user (shown in top panel) and the body's response to the thermal stimulus may be monitored over time (shown in bottom panel) to determine whether homeostasis is reached and/or a difference in response or return rate exists between the two sides of the body (in other words, determine whether an asymmetrical response exists). Further examples include stimulating the muscular or nervous system using electrical signals and monitoring the response over time and/or between sides using electromyogram (EMG), bioimpedance, or electroneurogram (ENG), respectively. These "stimulators/transmitters" and "receivers/detectors" could be in the same region or could be separated to measure across regions of the body.

As discussed above, if a stroke is detected and patients seek care quickly, it can dramatically reduce death and disability. Continuous monitoring for a stroke event may improve the response time. However, continuous monitoring of anomalous biologic events such as stroke events using existing monitors can be challenging. These monitors are cumbersome and may be difficult for users to wear over an extended period of time. In contrast, the inventors realized that wearable devices, such as watches with integrated sensors and electronics may improve continuous monitoring of stroke events. An impaired vasodilation response may be indicative of a stroke, heart failure, hypertension, diabetes, menopause, or other conditions.

Applying heat stress to a portion of the skin may enable detection of vasodilation response. Accordingly, systems and methods described below enable detection of impaired vasodilation in a form factor that improves continuous anomalous cardiac event monitoring. In some embodiments, as shown in <FIG>, a system or device <NUM> for detecting an anomalous biologic event may function to heat a skin surface and measure a vasodilation response of the skin surface. The system or device <NUM> may further function to measure one or more additional parameters, biologic signals, etc. as will be described in greater detail elsewhere herein.

In one example, a system or device <NUM> for detecting an anomalous biologic event may include a body <NUM> having a first surface <NUM> opposite a second surface <NUM> in contact with a skin surface of a person. The first <NUM> and second <NUM> surfaces may be coupled via one or more or a plurality of sidewalls <NUM>. For example, one or more sidewalls <NUM> may extend from a perimeter of the first surface <NUM> and couple to a perimeter of the second surface <NUM>. The first <NUM> and/or second <NUM> surface may include one or more sensors positioned thereon. For example, one or more sensors on the first surface <NUM> may measure an environment of the user wearing or using the wearable system, and one or more sensors on the second surface <NUM> may measure one or more properties, features, or characteristics of the skin surface of the user and thus the user itself. Alternatively, the first surface <NUM> may include one or more sensors or imagers or cameras for assessing a facial region of a user, for example, via a FAST test.

A wearable device <NUM> may be secured to a user, for example a limb of a user or a skin surface of a user, via a coupling element <NUM>, for example a tensionable band, which will be described in greater detail elsewhere herein. The coupling element <NUM> may be adjustable such that the wearable device may be cinched or tensioned to promote greater contact and thus coupling between the wearable device and the skin surface or tension released to reduce contact or coupling between the wearable device and the skin surface. As shown in <FIG>, a coupling element <NUM> may be coupled to a body <NUM> of a wearable device via one or more connectors 422a, 422b, 422c, 422d. For example, a coupling element <NUM> may couple to a body <NUM> of a wearable device via a connector <NUM> that includes one or more pin joints, a snap fit connection to the coupling element <NUM>, a slide and fit connection to the coupling element <NUM>, etc. When the tensionable band <NUM> is coupled to the body <NUM> via connectors <NUM>, the tensionable band is centered with respect to one or more sensors positioned on the second surface, so that there is sufficient coupling between the sensors and the skin surface.

A wearable device <NUM> may include a heat source <NUM> in communication with the skin surface. The heat source <NUM> is configured to heat the skin surface to a target temperature or a pre-determined temperature. The heat source <NUM> may be a heating element; an environmental heat source, for example a thin film resistance flexible heater; polyimide heater; etc. In some embodiments, a heat source <NUM> is positioned on a second surface <NUM> of the body <NUM>, so that there is coupling or contact between the heat source <NUM> and a skin surface. Alternatively, a heat source <NUM> or one or more sensors <NUM>, <NUM> may be positioned on a coupling element <NUM> of the system <NUM>, as shown in <FIG>, such that the body <NUM> is separate from the sensor module <NUM> that includes the heat source <NUM> and the one or more sensors <NUM>, <NUM>. Alternatively, the heat source and/or one or more sensors may be distributed between the coupling element, body, and sensor module depending on which sensors are incorporated into the system and their specific requirements or parameters.

In some embodiments, as shown in <FIG>, a heat source <NUM> may comprise a thermal stimulator comprising a single printed layer of resistive ink on polyimide film <NUM>. Heat traces <NUM> and traces to one or more sensors <NUM> (e.g., blood volume sensor, infrared sensor, temperature sensor, etc.) could also be likewise printed on the polyimide film <NUM>, as shown in <FIG>.

In a still further embodiment, the sensor module <NUM> may be positionable in an in-ear device (e.g., ear lobe clip, ear bud, hearing aid, etc.), as shown in <FIG>. The sensor module may be configured to measure one or more parameters, depending on which sensors are present, for example blood pressure, temperature, and/or oxygen saturation.

Further, the heat source <NUM> may be communicatively coupled to a hardware processor such that the hardware processor outputs a heating signal to the heat source <NUM> to activate the heat source to initiate a heating cycle. For example, a heating cycle may include receiving baseline temperature signals from a skin temperature sensor and an environmental temperature sensor, determining the target temperature based on the baseline temperature signals, and determining whether the target temperature is below a maximum temperature value.

In some embodiments, a target temperature may be equal to a baseline skin temperature as measured by the skin temperature sensor plus about <NUM> to about <NUM> degrees, for example about <NUM> to about <NUM> degrees, about <NUM> to about <NUM> degrees, about <NUM> to about <NUM> degrees, about <NUM> to about <NUM> degrees, about <NUM> to about <NUM> degrees, etc. In one embodiment, the target temperature is equal to the baseline skin temperature as measured by the skin temperature sensor plus about <NUM> to about <NUM> degrees. In another embodiment, the target temperature is equal to the baseline skin temperature as measured by the skin temperature sensor plus about <NUM> to about <NUM> degrees. In another embodiment, the target temperature is equal to the baseline skin temperature as measured by the skin temperature sensor plus about <NUM> degrees. If the target temperature is greater than a maximum temperature value, the system pauses or delays until the baseline skin temperature drops below a minimum threshold or recalculates the target temperature so that it is less than the maximum temperature value. If the target temperature is less than a maximum temperature sensor, the system proceeds to activate the heat source to heat the skin surface to the target temperature.

In some embodiments, the heat source cycles between the target temperature and a deactivated or off state or between the target temperature and a temperature that is lower than the target temperature but greater than the skin baseline temperature, for example to maintain the target temperature, hereinafter referred to as a dwell time.

In some embodiments, a duration of a heating cycle and a target temperature are interconnected and based on user preference or user perception of heat on the skin surface or a vasodilation response of the user. For example, a higher target temperature may be used for a shorter time period or a lower target temperature may be used for a longer time period.

Further, the system or device <NUM> may be configured to receive one or more user inputs related to a perceived heat sensation on the skin surface and/or to a sensitivity of a vasodilation response of the user. For example, a user may input that the target temperature felt too hot or too cold, for example via a user input element (e.g., button), such that the system responds by reducing the target temperature but elongating an amount of time that the skin is heated. Additionally, or alternatively, based on user preference, preset configurations (e.g., during manufacturing), or as a result of sensed data (e.g., based on sensor data), the heat source may reach the target temperature via one of a plurality of ramping functions, for example slow ramping, larger step functions, etc. Alternatively, the heat source may reach the target temperature through a plurality of micro-stimulations. Further, for example, a target temperature may be individualized for the user based on the sensitivity of the vasodilation response of the user.

In some embodiments, a device or system <NUM> for detecting an anomalous biologic event includes a support structure <NUM> coupled to the heat source <NUM> and configured to couple the heat source <NUM> to the second surface <NUM>. For example, as shown in <FIG>, the support structure <NUM> includes arm <NUM> that extends towards or to a center of the heat source <NUM> to support the heat source <NUM> and one or more spokes <NUM> that extend from the arm <NUM> to a perimeter of the heat source <NUM>. The spokes <NUM> may be substantially equally spaced from adjacent spokes <NUM>. The spokes <NUM> may also be circumferentially arranged about pin or joint <NUM>. Spokes <NUM> of support structure <NUM> further define air flow apertures <NUM> to allow air to interact with the heat source <NUM> to cool the heat source <NUM>. Spokes <NUM> further define air flow apertures <NUM> to at least partially expose the heat source to a cavity defined by the first and second surfaces as described elsewhere herein. Alternatively, or additionally, heat source <NUM> may be cooled by one or more vents, a blower for passing airflow over the heat source <NUM>, coolant, or another mechanism known to one of skill in the art.

In some embodiments, support structure <NUM> exerts pressure on the heat source <NUM> to increase contact or coupling between the heat source <NUM> and the skin surface. In one embodiment, the tensionable band includes a strain gauge that determines the tensile stress the band is subjected to. The strain gauge output or signal could then be visualized or displayed to a user so the user knows if the band is tensioned to an appropriate level for the heat source and/or sensor(s). Alternatively, a spring constant (k) of the material may be used to calculate the force (F=kx), so depending on how much the material is stretched (put in tension), the band could indicate that force based on the displacement. As such, the support structure <NUM> may comprise a flexible material, for example a flexible plastic. In other embodiments, the support structure <NUM> comprises a rigid material.

Further, as shown in <FIG>, a device or system <NUM> for detection of an anomalous biologic event further includes a skin temperature sensor <NUM> and a blood volume sensor <NUM>. The blood volume sensor <NUM> can be integrated into a form factor such as the device or system <NUM> that improves continuous anomalous cardiac event monitoring. The blood volume sensor <NUM> can measure parameters that can provide vasodilation response. Furthermore, the skin temperature sensor <NUM> can also be integrated into the device or system <NUM>. The skin temperature sensor <NUM> is positioned on the second surface <NUM> and configured to measure a temperature of the skin surface in contact with the heat source <NUM>. The blood volume sensor <NUM> is positioned on the second surface <NUM> and configured to measure a blood volume of the skin surface. The blood volume sensor may be a photoplethysmography sensor or an impedance plethysmographic sensor. The blood volume sensor may employ light at <NUM> (green), <NUM> (red), <NUM> (blue) wavelength, or a combination thereof. Different wavelengths may be more appropriate for different applications, for example green (<NUM>) light may be more accurate for heart rate measurements (e.g., heart rate variability, heart rate, etc.). In addition to, or alternatively, the blood volume sensor may be further configured to measure one or more of: heart rate, heart rate variability, or oxygen saturation.

A system or device <NUM> for detection of an anomalous biologic event may include an environmental temperature sensor configured to measure a temperature of the environment around the wearable system <NUM>. For example, the environmental temperature sensor may be positioned on the first side <NUM> of the body <NUM> of the wearable system, opposite the second side <NUM> that includes the heat source <NUM>. Alternatively, the system or device <NUM> may be communicatively coupled to an environmental temperature sensor on or in a remote computing device. For example, the remote computing device may include a laptop, a cellular device, a workstation, a server, a desktop computer, a personal digital assistant, a second wearable system or device, a netbook, or the like.

The skin temperature sensor and/or environmental temperature sensor may include a thermocouple, a resistance temperature detector, a thermistor, or an infrared temperature sensor. The type of temperature sensor selected may depend on error rate, coupling to skin surface efficiency, among other features.

In some embodiments, the heat source <NUM> is positioned concentrically about one or both of the blood volume sensor <NUM> and the skin temperature sensor <NUM>, as shown in <FIG>. Although, a location or position of the blood volume sensor <NUM> and the skin temperature sensor <NUM> that enables coupling to a skin surface is envisioned.

A hardware processor (within the wearable system or communicatively coupled to the wearable system) communicatively coupled to the skin temperature sensor <NUM> and the environmental temperature sensor may be configured to perform a method comprising: receiving a first temperature signal using the skin temperature sensor and a second temperature signal using the environmental temperature sensor; and calculating a temperature differential between the skin temperature and the environment temperature. For example, if the temperature differential is below a set threshold, a difference between the target temperature and the maximum temperature value may be increased. In contrast, if the temperature differential is above a set threshold, a difference between the target temperature and the maximum temperature value may be reduced. The environmental temperature sensor may also be used in analysis of determining erroneous results, such as false positive indications of abnormalities. By comparing signals before and after stimulus and/or by comparing left versus right limb, externalities such ambient temperature response may be reduced in the analysis of abnormalities.

Further, the hardware processor may be coupled to the heat source <NUM> and the blood volume sensor <NUM>. In some instances, the system <NUM> describe above can enable non-invasive monitoring of vasodilation and/or vasoconstriction. Human body regulates stable equilibrium through the process of homeostasis. For example, if a stimulus is applied to a body of patient, one or more homeostatic processes will attempt to counteract the effect of stimulus. For example, with respect to an induced thermal stimulus that increases or decreases temperature at a tissue site, the body will attempt to reverse the temperature change through blood flow (vasodilation or vasocontraction). Accordingly, the system <NUM> can induce and measure the vasodilatory response. As discussed above, stroke and other abnormalities can impair the vasodilatory response. Therefore, in some instances, it may be advantageous to monitor the change in the vasodilatory response to determine abnormalities, such as stroke. A blood volume sensor, such as optical sensors, can enable monitoring of the blood flow and correspondingly the vasodilatory response. In some instances, one or more temperature sensors (through a thermistor or optical radiation-based detectors) can also enable determination of the vasodilatory response by monitoring how quickly the temperature of the skin returns to equilibrium following the stimulus. In some examples, the vasodilatory response is correlated with a rate of change or slope in the measured parameter, such as blood volume parameters, temperature, and others discussed herein. In additional examples, the vasodilatory response can be correlated with a steepness of the rate of change. This can be calculated using a second derivative.

In some instances, it can be advantageous to use a combination of a heat source <NUM> and the blood volume sensor <NUM> to improve cardiac monitoring. The heat source <NUM> and the blood volume sensors <NUM> can be integrated into a form factor that a user can wear for continuous monitoring. The measurements can be repeated non-invasively without significant discomfort to the patients. Furthermore, as shown in <FIG>and <FIG>, the response time between the application of heat and the change in blood volume is relatively small. This can enable a relatively fast determination of the anomalous biologic event. Therefore, it can be advantageous to integrate a heat source and a blood volume sensor in any wearable system disclosed herein to improve continuous cardiac monitoring. In some instances, a Peltier cooler can be used as a thermal source instead of or in addition to the heat source <NUM>.

Furthermore, in some instances, the stimulus can be an electrical stimulus in addition to or instead of the thermal stimulus. For example, the system <NUM> may include a plurality of electrodes for inducing and/or measuring electrical activity across a tissue site. Electrical activity can include bioimpedance for detecting high or low muscle tone, which can occur with hemiplegia. The system <NUM> can include at least two electrodes. In some instances, the system <NUM> can include at least four electrodes. For example, the system <NUM> can include two pairs of electrodes for measurement of bioimpedance. These four electrodes may positioned on the second surface <NUM>. The electrodes may also be positioned on the strap <NUM> or an external accessory that can attach the system <NUM>. Bioimpedance can measure muscles both inter and trans cellularly which could be used to detect hemiparesis and could be used for both detection as well as rehabilitation. The EDA electrodes can also be mounted anywhere along the second surface facing the skin to the strap <NUM>. Furthermore, the system <NUM> can also include six or more electrodes. The electrodes can be integrated on the system <NUM> such that they are in contact with the skin tissue of the user.

As discussed above, an optical sensors, such as the blood volume sensor <NUM>, can interrogate a target tissue to determine parameters that correlate with the vasodilatory response. Other sensors can also be used to extract parameters for determination of the vasodilatory response. For example, the system <NUM> can use minimally invasive and/or invasive sensors to determine hemodynamic parameters, such as cardiac output, to provide an indication of the vasodilation response. The system <NUM> can also include on or more electrical based sensors, such as bioimpedance sensors, EDA sensors, ECG sensors, EEG sensors, EMG sensors, and the like. Electrical sensors may enable measurement of hydration, skin conductance, bioimpedance, and other electrical parameters that relate to hemodynamic function or measure electrical signaling of neural activity and its effect. Furthermore, the system <NUM> can include one or more ultrasound sensors to obtain hemodynamic parameters. Temperature sensors can also enable determination of the vasodilation response. Accordingly, the system <NUM> can include a combination of some or all of the sensors discussed above to extract one or more parameters that correlate with hemodynamic function or maintenance of homeostasis.

The following table illustrates example physiological phenomena and corresponding parameters that can be monitored:.

Patients are often monitored in neuro ICU after a stroke. This can be expensive as a nurse needs to conduct periodic checks on the patient. Accordingly, the system <NUM> can enable improved monitoring without requiring the patient to be in the neuro ICU and/or without requiring a caregiver to conduct periodic checks. While the system <NUM> is described as a wearable system, in some examples, some or all of the components of the system <NUM> may be positioned in proximity to the user but not directly attached or worn by the user. For example, when a user needs to be monitored in a hospital environment, some or all of the components of the system <NUM> can be positioned in proximity to the user's hospital bed. For example, the thermal stimulus source can include a laser.

As such, the hardware processor may be configured to perform the method, as shown in <FIG>, which includes: receiving a baseline blood volume signal from the blood volume sensor S5202, outputting a heating signal to the heat source to initiate a heating cycle S5204, receiving a second blood volume signal from the blood volume sensor S5206, comparing the second blood volume signal to the baseline blood volume signal S5208, and determining whether an anomalous biologic event has occurred based on the comparison S5210. The steps of the method may be repeated at least once, one or more times, a plurality of times, on a loop, according to physician, caregiver, or user preferences, or otherwise.

In some embodiments, the second blood volume signal is a set of blood volume signals, such that the blood volume of the skin surface is measured repeatedly before, during, and/or after a heating cycle of the heat source. The blood volume of the skin surface may be measured at a pre-set interval, for example every about <NUM> to about <NUM> sec, about <NUM> sec to about <NUM> sec, about <NUM> sec to about <NUM> sec, etc. Alternatively, the blood volume of the skin surface is measured randomly or only upon detection of a change in temperature of the skin surface or upon detection of a change in vasodilation by the blood volume sensor. A measurement frequency may be individualized for a user, for example if a vasodilation response of a user in response to heat is very sensitive, a reduced frequency of blood volume measurements may be needed. In contrast, if a vasodilation response of a user in response to heat is less sensitive, an increased frequency of blood volume measurements may be needed.

In some embodiments, the second blood volume signal is a plurality of blood volume signals, such that the blood volume of the skin surface is measured continuously before, during, and/or after a heating cycle of the heat source.

In some embodiments, block S5206 includes receiving the second blood volume signal after the target temperature is reached, after a predetermined length of time has expired, after a dwell time (i.e., cycling heat source on and off during a heat cycle or cycling heat source between target temperature and lower temperature during a heat cycle) has expired, or after one or more heating cycles have concluded. A frequency of sampling and/or sampling relative to a heat cycle (before, during, or after the heat cycle) may be based on a user's biology, such that the sampling is individualized.

In some embodiments, block S5208 includes calculating a baseline ratio of alternating current (AC) to direct current (DC) for the baseline blood volume signal and a second ratio of AC to DC for the second blood volume signal and comparing the baseline ratio to the second ratio, as shown in <FIG>. The methodology and rationale for the AC to DC ratio is described in <NPL>. The top left panel of <FIG> shows raw PPG amplitude data and the respective DC and AC components of the signal. Taking the ratio of AC to DC of the raw signal yields the top right panel. During a two-heating cycle experiment, PPG data in the lower left panel was collected. The AC and DC components of the signal are represented in separate, stacked graphs. When the AC to DC ratio is calculated for this two-heating cycle experiment, a normalized PPG signal is achieved, which is shown in the lower right panel. The same PPG data is shown in <FIG> overlaid with heat cycle data. As shown, the temperature of the skin surface reaches the target temperature (i.e., about 42C) in each heat cycle, shown by the shaded portions of the graph. The perfusion index or normalized PPG signal similarly spikes during each heat cycle in response to the application of heat. <FIG> shows the same data as <FIG> with additional definition of baseline, vasodilation, and post vasodilation windows. The heat cycle was off for <NUM>, on for <NUM>, off for <NUM>, on for <NUM>, and off for <NUM>. The time windows selected for comparison were: a baseline time window (e.g., minimum <NUM> minutes before "heat source first on"), a vasodilation time window (e.g., maximum <NUM> minutes of "heat source on"), a first post vasodilation time window (e.g., minimum <NUM> minutes after "heat source first on"), and a second post vasodilation (e.g., minimum <NUM> minutes after "heat source second on"). As shown in <FIG>, application of heat elicits a vasodilation response that is reproducible over multiple cycles.

As discussed above, tracking a vasodilation response can be used in monitoring abnormalities, such as stroke. However, the vasodilation response in a user can be affected by several sources that are unrelated to the stroke or the abnormality that is being monitored. Accordingly, using the system <NUM> in only one tissue site may result in false positives. It was observed by the inventors that by monitoring multiple tissue sites, the monitoring results may more closely track the abnormalities and reduce erroneous results. <FIG> illustrates a first system <NUM> and a second system <NUM> placed approximately symmetrically on the right and left limbs. Accordingly, if a stimulus is applied approximately in synchronization between the first system <NUM> and the second system <NUM>, the degree of symmetry or asymmetry in the measurements responsive to the approximately simultaneous stimulation can be used in the determination of stroke and reduction of erroneous results. While the disclosure herein provides stroke as an example of abnormalities, the system <NUM> and the methods described herein can also be used to monitor other abnormalities. For instance, other abnormalities or physiological deviation can include menopause, diabetes, and peripheral blood circulation disorders that can affect peripheral blood circulation. In some instances, menopause, diabetes, and other disorders may affect all parts of the body or may affect certain parameters uniformly. For example, vasodilation response may be impaired uniformly in conditions like menopause compared to a stroke where there is a high likelihood of asymmetry. Accordingly, a stroke can be differentiated from these other abnormalities and vice versa based on the asymmetry observed in the vasodilation response and other multilateral measurements. In another example, the vasodilation response may be affected, but the electrical measurements described herein using EDA and bioimpedance may remain the same. Accordingly, the asymmetry in measurements may also be used to determine abnormalities.

In some embodiments, as shown in <FIG>, a method <NUM> of detecting an anomalous biologic event includes: applying a high temperature stimulus (e.g., shown in <FIG>) S4810; receiving one or more signals indicative of a blood volume, blood flow, or blood perfusion in a tissue of the user in response to the high temperature stimulus S4820; extracting one or more features of the one or more signals S4830; comparing the one or more features for a right side and a left side of the user (e.g., right and left limbs, as shown in <FIG>) S4840; and calculating an acute stroke classification score S4850. Furthermore, the method <NUM> can optionally compare baseline measurements prior to the application of the stimulus and after the application of stimulus, as discussed in more detail with respect to <FIG> for both left and right limbs. During multiple tissue site monitoring, such as the left and right limb monitoring as shown in <FIG>, the system <NUM> may include all the same components as the system <NUM> described above. In other cases, the system <NUM> may include less components than system <NUM>. For example, both systems may not require a display. Additionally, one of the systems may include computational capabilities while the other one collects the data and transmits to the paired system for computation. Therefore, one of the systems <NUM> and <NUM> may not include a hardware processor. Accordingly, the system <NUM> and <NUM> may operate in a master-slave configuration. The systems <NUM> and <NUM> may be paired wirelessly via Bluetooth or other wireless protocol. In some instances, the systems <NUM> and <NUM> may be paired with an external computing system, such a patient monitor, a hub, or a smartphone.

In some embodiments of block S4830, the one or more features include, but are not limited to, an amplitude or a systolic or diastolic wave, a waveform shape, a waveform complexity, a perfusion index (i.e., a relationship between the pulsatile (AC) and the non-pulsatile (DC) components of PPG signal), DC offset, a stiffness index (i.e., time between peaks of forward and backward waves along the vascular tree; h / ΔT, where h is a patient's height), a reflection index (i.e., a ratio between the heights of the backward and the forward waves; B / A x <NUM>), a notch position (i.e., position of the dichrotic notch; e.g., with vasoconstriction, the position moves toward the left into the systolic wave), a peak to peak phase shift, slope onset of temperature signal and/or blood volume signal, slope decay of temperature signal and/or blood volume signal, midpoint of rising slop of temperature signal and/or blood volume signal, a vasodilation response as an indicator of a collateral state of the brain and/or heart, etc..

In any of the embodiments described herein, a wearable system or device for detecting anomalous biologic events may include one or more electrodermal activity sensors positioned on the second surface and/or a tensionable band of the system. For example, as shown in <FIG>, electrodermal sensors <NUM>, <NUM> are positioned on the second surface <NUM> of the wearable system <NUM>. Electrodermal sensors <NUM>, <NUM> may be spaced apart from one another by distance <NUM>, which equals about <NUM> to about <NUM>, about <NUM> to about <NUM>, about <NUM> to about <NUM>, about <NUM> to about <NUM>, about <NUM> to about <NUM>, about <NUM> to about <NUM>, about <NUM> to about <NUM>, about <NUM> to about <NUM>, about <NUM> to about <NUM>, about <NUM> to about <NUM>, measured from a center point of each sensor. Further, electrodermal sensors <NUM>, <NUM> may be spaced apart from the heat source by distance <NUM>, which equals about <NUM> to about <NUM>, about <NUM> to about <NUM>, about <NUM> to about <NUM>, about <NUM> to about <NUM>, about <NUM> to about <NUM>, about <NUM> to about <NUM>, about <NUM> to about <NUM>, about <NUM> to about <NUM>, about <NUM> to about <NUM>, measured from a center point of the sensor and a center point of the heat source.

As shown in <FIG> as one example, electrodermal activity (EDA) of a skin surface of a user may be measured overtime. Left side and right side electrodermal activity may be measured over time and compared. <FIG> shows left and right side electrodermal activity including events (shown as triangles) potentially indicative of an anomalous biologic event. A signal collected by an electrodermal activity sensor may be processed to extract one or more features. For example, as shown in <FIG>, one or more features may include a rise time (i.e., start of the SCR to the apex), an amplitude (i.e., EDA at apex minus an EDA at start of the SCR), a skin conductance response (SCR) width (i.e., between the <NUM>% of the amplitude on the incline side and <NUM>% of the amplitude on the decline side), a decay time (i.e., time from apex to <NUM>% of the amplitude), an area under the curve (i.e., SCRwidth multiplied by amplitude ), Maximum derivative of SCR, and/or an apex value.

<FIG> shows a method <NUM> of analyzing EDA data, and <FIG> show representative EDA data. A method <NUM> for analyzing EDA data includes: receiving signals from one or more EDA sensors (e.g., as shown in <FIG>) S6310; detecting and/or removing one or more artifacts (e.g., as shown in <FIG>) S6320; calculating or extracting one or more skin conductance response (SCR) features (e.g., as shown in <FIG>) S6330; calculating a mean or average of one or more features S6340; and calculating an SCR for a period of time S6350. For example, SCR amplitude is shown graphically in <FIG> for one-minute intervals. As shown, for this individual, SCR amplitude varies over time and asymmetrically (i.e., comparing right vs. left response). Further, if the SCRs per minute are compared for left and right responses, as shown in <FIG>, the SCR per minute varies over time and asymmetrically.

In any of the embodiments described herein, a wearable system or device for detecting anomalous biologic events may include one or more motion sensors <NUM> configured to measure a motion of a body portion to which the wearable system is coupled, as shown in <FIG>. For example, the one or more motion sensors may measure an acceleration in six or nine degrees of freedom. As described elsewhere herein, a wearable system or device for detecting stroke may, in combination with measuring a vasodilation response in response to application of heat, may measure asymmetrical movement or tremors of the right and left limbs. One or more motion sensors may be positioned anywhere on the wearable device. For example, in one embodiment, a motion sensor is positioned in or on the first surface. In another embodiment, a motion sensor is positioned in or on the second surface. In another embodiment, a motion sensor is positioned in between the first and second surfaces. In another embodiment, a motion sensor is positioned on a sidewall of the body of the wearable device. In another embodiment, a motion sensor is positioned adjacent to a vasodilation sensor or temperature sensor of the system, for example concentrically surrounded by the heat source, as shown in <FIG>.

The heat source of the wearable device or system <NUM> may be cooled in between heating cycles to ensure a return to baseline or substantially baseline of the vasodilation response of the skin surface in between heating cycles. As such, the heat source may be cooled by an airflow system (e.g., fan), a vacuum or vibrating mechanism configured to displace or pull or move environmental air across the heat source (e.g., solenoid and diaphragm, oscillating piezo element), etc. In one embodiment, as shown in <FIG>, a wearable system or device for detecting an anomalous biologic event includes first <NUM> and second <NUM> surfaces that together define a cavity <NUM> therebetween to provide airflow between the first <NUM> and second <NUM> surfaces. The cavity <NUM> defined by the first <NUM> and second <NUM> surfaces physically separates the heat source <NUM> from the hardware processor <NUM> positioned on or within the first surface <NUM>. The hardware processor <NUM> can include microcontrollers, digital signal processors, application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The cavity <NUM> functions to expose the heat source <NUM> to ambient or environmental or surrounding air to cool the heat source <NUM> to a temperature that approaches, substantially equals, or equals a temperature of the air in the environment or an ambient temperature. The cavity <NUM> may be an empty space, an interstitial space, a space that houses one or more components, etc. In some embodiments, cavity <NUM> formed by the first <NUM> and second <NUM> surfaces is open to ambient air or environmental air such that the sidewalls <NUM> that couple together the first <NUM> and the second <NUM> surfaces are opposite one another so that the cavity <NUM> is open to the environmental air on opposing sides, as shown in <FIG>. Alternatively, the sidewalls <NUM> are connected to one another and adjacent to one another so that the cavity is open to the environmental air on adjacent or connected sides.

For example, in some embodiments, the cavity <NUM> defined by the first <NUM> and second <NUM> surfaces has sufficient volume to facilitate cooling of the heat source <NUM> in between heating cycles. Alternatively, or additionally, the cavity <NUM> may further include an airflow system, vacuum or vibrating mechanism, or other airflow mechanism to promote airflow through the cavity <NUM> to reduce a temperature or cool the heat source <NUM>.

In some embodiments of a wearable system or device, the device includes a port <NUM> for electrically coupling the device to a power source, for example to charge a battery <NUM> in the device. Additionally, or alternatively, port <NUM> electrically couples the wearable device to an external or remote computing device (e.g., laptop, desktop, server, workstation, etc.) to download data from the device or upload system parameters or install updates to the wearable device. The wearable device may further include one or more user input elements <NUM> to power on and off the device; to input user specific reactions, features, or characteristics, to customize an interface or functionality of the user device, etc..

In some embodiments, as shown in <FIG>, a wearable system for detecting an anomalous biologic event includes a first system or device <NUM> positioned on a left limb of a user and a second system or device <NUM> positioned on a right limb of the user. The first and second devices <NUM>, <NUM> may measure similar parameters or features so that the parameters or features are comparable over time and/or on an event-by-event basis to detect asymmetrical biologic responses. For example, a hardware processor as part of the system or communicatively coupled to the devices (e.g., laptop <NUM> or mobile computing device <NUM>) may be configured to compare right side blood volume signals (e.g., in response to an application of heat) to left side blood volume signals (e.g., in response to application of heat) to determine whether the anomalous biologic event has occurred. The right and left side blood volume signals may be compared to a baseline right and left side blood volume signals, respectively, to account for any asymmetrical baseline differences that may exist between the left and right sides. Further, a method performed by the hardware processor may include synchronizing the signals received from the left limb and the right limb in time; and comparing the synchronized signals from the left limb and the right limb to determine whether the anomalous biologic event occurred.

Turning now to <FIG>, which shows a coupling element <NUM>, configured to couple a wearable system for detecting an anomalous event to a limb or body portion of a user. For example, the coupling element may be a tensionable band for coupling a detection system or device to a limb or body portion of a user. The tensionable band is formed of or comprises a stretchable material (e.g., silicone, rubber, Lycra, Spandex, Elastane, neoprene, leather, fabric, etc.). Alternatively, a portion or section <NUM> of the coupling element may be stretchable, such that the stretchable portion or section <NUM> can be extended or retracted by applying varying amounts of tension to the coupling element. Accordingly, the coupling element may be adjustable so that the coupling element fits a variety of body portion shapes and sizes. For example, the coupling element may have an adjustable circumference. The coupling element may further include a visual indicator <NUM> to indicate when one or more of: the heating element, the skin temperature sensor, the blood volume sensor, or a combination thereof is sufficiently coupled to the skin surface to enable accurate sensor readings.

Referring to <FIG>, a system for detecting stroke may include collect data from one or more sources, for example a contact-based source, a non-contact-based source, and a source that stimulates a response and then measures the response output. As shown in <FIG>, the system may include a main station or docking station and/or measurement station for one or more measurement devices. For example, a heart rate monitor, devices for measuring asymmetrical responses or effects (e.g., watches worn on each wrist), etc. may be included in the system. The system may be portable such that is may be positioned in a mobile stroke detection unit for rapid detection of stroke or positionable in homes of high-risk patients.

For example, as shown in <FIG>, a method of detecting tremors (i.e., asymmetrical wrist movement) includes: measuring an acceleration in x, y, and/or z planes of two limbs (e.g., two arms or two legs) of an individual; measuring a distance in x, y, and/or z planes of the two limb of the individual; and calculating a movement of each limb, relative to the other limb, of the individual. In some embodiments, symmetrical movement is indicative of healthy, non-stroke movement, and asymmetrical movement is indicative of a tremor or a stroke event. Exemplary acceleration data (XYZ) is shown in <FIG>; distance data (XYZ) in <FIG>; and distance (MM/S; movement) data in <FIG>. In some embodiments, a specific pattern of time series movements is unique to an individual and classified as a tremor based on data collected over time. For example, tremor data may be collected for a number of hours, including wake cycles and sleep cycles. The statistical modeling of a tremor then becomes a signature for each patient. This signature also allows a baseline to be set for each patient. Again, this baseline behavior may be unique to an individual, and even to the 'awake' and 'sleep cycles' of the individual.

As shown in <FIG>, an application downloaded and/or stored on a hardware component of a stroke detection system or a computing device collates and analyzes acceleration and distance data sensed by a sensor, for example an accelerometer. The comparison of two data sets (i.e., Test Run <NUM> and Test Run <NUM>) derived from devices located on the two limbs (e.g., wrists) of the user is shown in <FIG>. For example, an application on a computing device may be configured to compare two acceleration data sets (<FIG><FIG> two distance data sets (<FIG><FIG>and two movement data sets (<FIG><FIG>from devices positioned on two wrists of a user. As shown in <FIG>, an application on a computing device may further include a zoom feature, for example, for viewing a subset of the total data collected during a period of time (e.g., overnight, during a tremor instance, etc.).

In some embodiments of a device for detecting tremors or asymmetrical motion, the device may include a feedback mechanism (e.g., visual, haptic, or audio) when a threshold has been reached or surpassed or various comparison criteria have been met, for example when a current movement pattern matches a previously identified tremor pattern for the individual. In some embodiments, a mobile computing device communicatively coupled to a movement sensor or wearable device generates a vibration signal in the wearable device, sensor, and/or computing device if the comparison between the two signals exceeds a predefined threshold.

To determine which embodiments would be best for stroke detection, several factors may be considered: alert <NUM> capability; passive monitoring; detection when patient is alone; and detection when patient is sleeping. Additional factors may include, but not be limited to: fully mobile; patient specific algorithm; active patient engagement after a passive alert; detection for the cognitively impaired patient; detection for prior stroke patient; detection of all strokes including posterior; diagnose type of stroke; passive monitor that wakes the patient up; and commence stroke treatment. For example, if a possible stroke event is detected, a wearable system may initiate a tactile, auditory, and/or visual alert to determine whether the user is conscious, unconscious, experiencing other stroke symptoms, etc. If the patient does not respond in a predetermined time window, a caregiver, emergency services, physician, etc. may be alerted to the stroke event. The wearable system can be linked to a clinician computing system. The alert can be transmitted directly to the clinician computing system that may prompt a telemedicine assessments. The clinician may work up an NIH Stroke Score assessment in response to the alert and/or data received from the wearable system. In some instances, the wearable system can by itself or in conjunction with a personal computing system enable self-assessment by walking the person and/or available witnesses through a FAST (Facial drooping, Arm weakness, Speech difficulties and Time) assessment.

In some instances, the wearable system can transmit a signal to the user's home automation system or to at least one electronically enabled door lock to unlock at least one door and / or disable the user's home alarm system in response to an alert for the stroke event. The wearable system can also initiate transmission of a floor plan access pathway leading from an access point of entry to the location of the patient, in the home or facility where the user has had indicium of a potential stroke. The location of the patient can be determined based on a local area network or differential GPS. In some embodiments, a stroke detection device or system may trigger an audible alarm to alert a patient or caretaker, for example while sleeping, that a stroke event has occurred. The audible alarm can also enable emergency services to locate patient when they enter home. All of these measures can help to reduce the time it takes for the emergency services or caregivers to reach the patient.

The home automation system can also include smart displays and smart speakers. These smart displays and speakers can be used to convey information to emergency medical response personnel, such as the identification of which medications the patient should be taking and, if available, information about whether they are compliant with prescribed regimens. Information such as the identity of physicians, medical history, allergies, and the existence of medical care power of attorney or advance directives associated with the patient may also be conveyed.

Furthermore, when alerting emergency services or physicians, data including medical history may be transmitted directly to emergency services or physician computing systems, either directly from the wearable system or from a remote memory, initiated by a signal from the wearable system. In addition to alerts, the wearable system can also instruct a user to undertake or automatically activate certain stroke treatments. Stroke treatments can include inducing hypothermia to provide a neuro-protectant for the patient. The wearable system can trigger inhalation of cooling gases, activation of a cooling helmet, activation of an ultrasonic helmet to break up cloths, or ingestion or triggering administration of a drug patch or pill. The trigger can be instructions to the patient or medical responder, or automatic activation. In some instances, for Ischemic strokes, the wearable system can trigger mechanisms to increasing blood pressure and vasodilate blood vessels (through some of the mechanisms discussed above).

Treatments responsive to the detection of a potential stroke can be initiated by the patient if they are conscious and able, or by the medical response personnel via the home automation system. Patients in a particular high risk category may have previously been fitted with a wearable treatment device which can be activated automatically in response to a signal indicating the detection of a potential stroke, or activated by medical personnel following clinical examination which was initiated by an alert from the wearable system.

In some embodiments, a stroke detection device or system may trigger an audible alarm to alert a patient or caretaker, for example while sleeping, that a stroke event has occurred. The audible alarm can also enable emergency services to locate patient when they enter home.

In any of the embodiments described herein, a stroke detection device or system may record an onset of a stroke event and/or provide a "last known well" indicator to help inform treatment decisions.

In some embodiments, a system for detecting stroke includes a data processing module. The data processing module may be configured to extract a pattern. The pattern may suggest any ischemic or hemorrhagic episode very early, possibly imminently prior to an actual stroke event. In some embodiments, the pattern may be empirically determined, for example based on a population wide analysis, cohort analysis, and/or individual analysis of signals, which are analyzed for parameters and/or patterns indicative of stroke onset. In some embodiments, signal processing may employ signal processing tools, for example filtering, extracting, digitizing, data de-convolution, machine learning, and/or other methods known in the art. Specifically, the signal processing may use higher order statistics to ascertain hidden patterns in data. Use of higher order statistics, known as cumulants, and their Fourier spectra, often termed poly spectra, not only reveal the amplitude information in the higher order (such as those carried by power spectra or auto correlation) but may also include phase information. Phase information can reveal salient features of the data, otherwise unattainable from simple harmonic analysis. Another important feature of the polyspectra is the fact that they are blind to Gaussian processes. As a result, they can automatically handle Gaussians processes and thus improve signal to noise ratio, allowing novel detection. In some embodiments, a number of spectrums and their manipulations may be selected in order to identify hidden patterns in the sensed signals, for example BP(t), ECG(t) etc..

For example, as shown in <FIG>, a wearable system may collect electrocardiogram (ECG) data, pre-process the data, identify peaks in the data, and apply a decision logic to the data. <FIG> shows electrocardiogram data collected over time. <FIG> shows extracted R-R intervals from the electrocardiogram data (i.e., time between beats shown in milliseconds). The method <NUM> shown in <FIG> may be used to calculate a heartbeat and/or a heart rate variability (i.e., specific changes in time between successive heart beats) of an individual. As shown in <FIG>, ECG data is input into the method <NUM>, which detects QRS complexes (i.e., ventricular depolarization and the main spike in an ECG signal) in electrocardiographic signals. Preprocessing at block S5310 includes apply signal processing techniques for QRS feature extraction. For example, preprocessing may be applied to reduce the influence of muscle noise, powerline interference, baseline wander, and/or T-wave interference. Peak Detection at block S5320 includes QRS peak detection with adaptive threshold, for example. Each potential peak is compared to a baseline value. A baseline skin temperature is established by measuring unstimulated skin for a period of time. Once the baseline is determined, the stimulus (e.g., application of heat) can either reach a time limit or a temperature limit. The temperature limit can be absolute or relative to the baseline skin temperature. The baseline value is updated according to the amplitude of the detected peak. Decision Logic at block S5330 classifies the current peak as QRS, T-wave, or error beat, using the peak slope and/or peak-to-peak interval.

As shown in <FIG>, electrocardiogram data may be processed via several methods to extract various features, calculate one or more features (e.g., heart rate variability, heart rate, total power, etc.), etc. For example, a time domain analysis (<FIG>), a geometrical analysis (<FIG>), a frequency domain analysis (<FIG>), and/or a nonlinear analysis (<FIG>) analysis may be used.

As shown in <FIG>, ECG data (e.g., <FIG>) is fed into method <NUM>. The method includes: receiving ECG data of a user using an ECG; detecting beats in the ECG data (e.g., detect R-peaks in the ECG data) S5810; identifying and correcting irregular beats (e.g., missed, extra, and ectopic beats; uses neighboring beats to correct each beat) S5820; identifying intervals between normal R-peaks (i.e., NN Interval Time Series (NNIs) S5830; preprocessing the data (e.g., corrects outliers of NNIs) S5840; and performing one or more analyses S5850. For example, a time domain analysis, as shown in <FIG> may be used to calculate heart rate (e.g., <NUM> divided by the mean of NNIs); the standard deviation of NNIs (SDNN); the root mean square of successive differences (RMSSD); and the percentage of adjacent NNIs that differ from each other by more than <NUM> (pNN50). Further, for example, a frequency domain analysis, as shown in <FIG>, may be used to calculate a relative power (e.g., relative power of each frequency band (VLF/Total, LF/Total, HF/Total)); a normalized power (e.g., normalized powers of the LF and HF frequency bands (LF/(LF+HF), HF/(LF+HF)); an LF/HF Ratio (e.g., LF power / HF power); and/or a total power (e.g., total power over all frequency bands). Further, for example, a geometrical analysis, as shown in <FIG>, may be used to calculate a baseline width of the interpolated triangle (TINN); and/or the ratio between the total number of NNI and the maximum of the NNI histogram distribution (i.e., triangular index). Further, for example, as shown in <FIG>, a nonlinear analysis may be used to perform a Poincare Analysis (i.e., analyze Poincare plot of NNIs - SD1, SD2, SD Ratio, Ellipse Area); a DFA (Detrended Fluctuation Analysis (i.e., short and long-term fluctuations of NNIs); and/or an Entropy Analysis (i.e., computes approximate entropy, sample entropy, and fuzzy entropy of NNIs).

In some embodiments, the data processing module may use the continuously monitored or intermittently monitored physiological signals to differentiate changes from healthy "learned" or individualized baseline data. For example, the module may continuously learn the signals coming from an individual patient rather than using a statistical average taken from many patients. A custom reference signal may significantly improve minute changes in the physiological signals for an individual patient. In some embodiments, the physiological parameters may be processed as a function of time that includes the shape of the curve changes, including hidden harmonics, changes in higher order derivatives, etc..

<FIG> shows one embodiment of various components of a data processing module. The core engine for one embodiment of the data processing module may include one or more of the following parameters: fast processing, support for sophisticated analytics, real time stream processing, integration with both NoSQL and RDBMS, and integration with Hadoop.

The data processing module may employ various machine learning methods to identify patterns, extract patterns, identify parameters indicative of stroke onset, etc. Machine learning can be broadly defined as the application of any computer-enabled algorithm that can be applied against a data set to find a pattern in the data. A machine-learning algorithm is used to determine the relationship between a system's inputs and outputs using a learning data set that is representative of all the behavior found in the system. This learning can be supervised or unsupervised. For example, a simple neural network called a Multilayer Perceptron (MLP), as shown in <FIG>, may be used to model various parameters or patterns of an individual, for example while sleeping. Each node is a neuron that uses a nonlinear activation function. Such a simple neural network may be used to distinguish data that are not linearly separable. In some embodiments, as shown in <FIG>, a deep learning network may be used. A deep learning network may comprise a Leverage Recurrent Neural Networks (RNN) implementation, as shown in <FIG>. The system creates layers of interconnected networks, where each layer corresponds to a time slice. RNN are proven highly effective in handling time series data, assumes training inputs are time dependent, capable of accurately modeling / predicting changes through time, capable of generating an actual output value for a data point versus giving just a range, and each time slice is its own feed forward network - specified by a user.

In some embodiments, a system for providing comprehensive stroke care comprises one or more of: educational resources tailored to the patient based on demographics, type of stroke, co-morbidities, medications, etc; management tools to assist with the dramatic changes in lifestyle, such as reminders (e.g., medications, rehabilitation appointments, etc.), collaborative care resources (e.g., for spouse, doctor, physical therapist, caretaker, etc.), activity tracking with continuous data collection via a wearable, fitness tracking and guided meditation, stroke risk level assessment, etc.; community with others as part of the first national stroke survivor network where stroke survivors can give and receive support and encouragement connecting both patients and caregivers, "check in" with others in your group to make sure they are making progress towards their goals and are doing well mentally, share stories and relate to others, receive telemedicine/rehab resources with a speech therapist or mental health counselor; patient rehab and monitoring, or other enabling technologies; set recovery goals and track progress, cognitive evaluation tools, etc.; stroke Detection to alert caretakers via call/message, communication tools for patients with aphasia, etc..

Various functional symptoms, quantitative markers, and blood/fluid products were scored for their ability to detect stroke. The scoring criteria were the following: should be grounded in scientific rationale, should be highly sensitive (><NUM>%), should only have very few false positives (<<NUM>%), and stroke detection should be passive (automatic). Each of these parameters were scored from <NUM> to <NUM>, except for passive detection which was scored on a scale of <NUM> (active detection) to <NUM> (passive detection). The score was then multiplied by a weight factor, shown in Table <NUM> below, and all the weighted factors summed to yield a total score.

As shown below in Tables <NUM> and <NUM>, the functional symptoms with the highest total score were facial muscle weakness, unilateral weakness, limited visual field, gaze altered, and speech change. Of these functional symptoms, only facial muscle weakness, unilateral weakness, and speech change can be detected passively.

As shown in Tables <NUM> and <NUM>, the quantitative markers with the highest total score were cerebral blood flow, EEG asymmetry, carotid artery stenosis, volumetric impedance spectroscopy, and limb asymmetry. Of these quantitative markers, all were considered to be detectable passively.

As shown in Tables <NUM> and <NUM>, the products with the highest total score were Cornell University's products, SMARTChip, and ReST. Of these, none were considered to be passive detection.

Taken together, a multivariate system for stroke detection may include detecting one or more of: cerebral blood flow, EEG asymmetry, carotid artery stenosis, volumetric impedance spectroscopy, limb asymmetry, facial muscle weakness, unilateral weakness, and speech change. In some embodiments, these various parameters may be measured at a variety of locations and/or times to determine stroke onset, occurrence, or after affects.

Symmetrical and asymmetrical acceleration and distance were measured using an Apple® Watch and displayed in a graphic representation (<FIG><FIG>in an application on a computing device. For this example, the implementation also measures the resolution of the Apple® Watch accelerometer sensor and existing API capabilities.

For this example, the device was worn on a user's wrist. Any acceleration of the wrist was recoded and saved in the onboard database, including acceleration in x-, y- and z-axes. The computing device has a "sync" function that allows the data to be transferred to a computing device for analysis. Tables <NUM>-<NUM> show acceleration data, distance data, and calculated movement data (i.e., distance traveled), respectively, acquired using an Apple® Watch worn on each wrist of a user. Data values were recorded at various time points, as shown in <FIG><FIG>.

Taken together, a system for stroke detection may include detecting one or more of: acceleration in x-, y- and/or z-axes; and /or distance in x-, y- and/or z-axes; and, in some embodiments, calculating a distance traveled (i.e., movement) to determine asymmetrical limb movement, gait, etc. possibly indicative of a stroke event.

The systems and methods of the preferred embodiment and variations thereof can be embodied and/or implemented at least in part as a machine configured to receive a computer-readable medium storing computer-readable instruction. The instructions are preferably executed by computer-executable components preferably integrated with the system and one or more portions of the hardware processor on the device for detecting stroke and/or computing device. The computer-readable medium can be stored on any suitable computer-readable media such as RAMs, ROMs, flash memory, EEPROMs, optical devices (e.g., CD or DVD), hard drives, floppy drives, or any suitable device. The computer-executable component is preferably a general or application-specific hardware processor, but any suitable dedicated hardware or hardware/firmware combination can alternatively or additionally execute the instructions.

As used in the description and claims, the singular form "a", "an" and "the" include both singular and plural references unless the context clearly dictates otherwise. For example, the term "signal" may include, and is contemplated to include, a plurality of signals. At times, the claims and disclosure may include terms such as "a plurality," "one or more," or "at least one;" however, the absence of such terms is not intended to mean, and should not be interpreted to mean, that a plurality is not conceived.

The term "about" or "approximately," when used before a numerical designation or range (e.g., to define a length or pressure), indicates approximations which may vary by ( + ) or ( - ) <NUM>%, <NUM>% or <NUM>%. All numerical ranges provided herein are inclusive of the stated start and end numbers. The term "substantially" indicates mostly (i.e., greater than <NUM>%) or essentially all of a device, substance, or composition.

As used herein, the term "comprising" or "comprises" is intended to mean that the devices, systems, and methods include the recited elements, and may additionally include any other elements. "Consisting essentially of" shall mean that the devices, systems, and methods include the recited elements and exclude other elements of essential significance to the combination for the stated purpose. Thus, a system or method consisting essentially of the elements as defined herein would not exclude other materials, features, or steps that do not materially affect the basic and novel characteristic(s) of the claimed disclosure. "Consisting of' shall mean that the devices, systems, and methods include the recited elements and exclude anything more than a trivial or inconsequential element or step. Embodiments defined by each of these transitional terms are within the scope of this disclosure.

Claim 1:
A wearable system (<NUM>) for detecting an anomalous biologic event in a person, comprising:
a body (<NUM>) having a first surface (<NUM>) opposite a second surface (<NUM>) in contact with a skin surface of a person;
a stimulus source in communication with the skin surface, wherein the stimulus source is configured to apply a stimulus to the skin surface;
a blood volume sensor (<NUM>) positioned on the second surface (<NUM>) and configured to measure a blood volume of the skin surface; and
a hardware processor (<NUM>) communicatively coupled to the stimulus source and the blood volume sensor (<NUM>), wherein the hardware processor (<NUM>) is configured to:
receive a baseline blood volume signal from the blood volume sensor (<NUM>),
output a stimulus signal to the stimulus source to initiate a stimulus cycle,
receive a second blood volume signal from the blood volume sensor (<NUM>) in response to the initiation of the stimulus cycle,
compare the second blood volume signal to the baseline blood volume signal, and
determine whether an anomalous biologic event has occurred based on the comparison;
and characterized in that the stimulus source is positioned concentrically about the blood volume sensor (<NUM>).