WO2022266593A1 - Systèmes et procédés d'étalonnage de dispositif de pression artérielle - Google Patents

Systèmes et procédés d'étalonnage de dispositif de pression artérielle Download PDF

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Publication number
WO2022266593A1
WO2022266593A1 PCT/US2022/072866 US2022072866W WO2022266593A1 WO 2022266593 A1 WO2022266593 A1 WO 2022266593A1 US 2022072866 W US2022072866 W US 2022072866W WO 2022266593 A1 WO2022266593 A1 WO 2022266593A1
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WIPO (PCT)
Prior art keywords
blood pressure
ppg
height
machine learning
calibration factor
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PCT/US2022/072866
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English (en)
Inventor
Michael BURNAM
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Baropace, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Publication date
Application filed by Baropace, Inc. filed Critical Baropace, Inc.
Priority to EP22743692.0A priority Critical patent/EP4355208A1/fr
Priority to PCT/US2022/072989 priority patent/WO2022266655A1/fr
Priority to IL309106A priority patent/IL309106A/en
Priority to CA3222600A priority patent/CA3222600A1/fr
Publication of WO2022266593A1 publication Critical patent/WO2022266593A1/fr

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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0015—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system
    • A61B5/0022—Monitoring a patient using a global network, e.g. telephone networks, internet
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/021—Measuring pressure in heart or blood vessels
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/021—Measuring pressure in heart or blood vessels
    • A61B5/022—Measuring pressure in heart or blood vessels by applying pressure to close blood vessels, e.g. against the skin; Ophthalmodynamometers
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A61B5/02416—Detecting, measuring or recording pulse rate or heart rate using photoplethysmograph signals, e.g. generated by infrared radiation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6813—Specially adapted to be attached to a specific body part
    • A61B5/6824—Arm or wrist
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor

Definitions

  • Various embodiments of the present disclosure relate generally to periodic calibration of a blood pressure measuring device, and more particularly, to systems and methods for calibrating blood pressure based on calibration of the blood pressure device.
  • an exemplary embodiment of a method for calibrating a blood pressure measuring device may include: sensing a first blood pressure by a first device when the first device is at a first height; sensing a second blood pressure by the first device when the first device is at a second height, the second height being different than the first height; and generating the blood pressure calibration factor based on the first blood pressure, the first height, the second blood pressure, and the second height.
  • an exemplary embodiment of a system for calibrating a blood pressure measuring device may include: at least one memory storing instructions; and at least one processor executing the instructions to perform a process, the at least one processor configured to: receive a blood pressure sensed using the PPG device; receive a PPG device height when the blood pressure is sensed; and modify the blood pressure based on the PPG device height and a blood pressure calibration factor, wherein the blood pressure calibration factor is based on a first blood pressure sensed at a first device height and a second blood pressure sensed at a second device height.
  • an exemplary embodiment of a method for calibrating a blood pressure measuring device may include: sensing a first blood pressure using a PPG device, when the PPG device is at a first position; sensing a second blood pressure using the PPG device, when the PPG device is at a second position; determining a blood pressure calibration factor based on the first blood pressure, the first position, the second blood pressure, and the second position; sensing a third blood pressure using the PPG device when the PPG device is at a PPG device position; and modifying the third blood pressure based on the PPG device position and the blood pressure calibration factor.
  • FIG. 1 A depicts an exemplary environment for calibrating a blood pressure measuring device using a first blood pressure device as the source of calibration, according to one or more embodiments.
  • FIG. IB depicts an example diagram for calibrating a blood pressure measuring device using a first blood pressure device as the source of calibration, according to one or more embodiments.
  • FIG. 1C depicts another example diagram for calibrating a blood pressure measuring device using a first blood pressure device as the source of calibration, according to one or more embodiments.
  • FIG. ID depicts another example diagram of calibrating a blood pressure measuring device using a first blood pressure device as the source of calibration, according to one or more embodiments.
  • FIG. 2A depicts a flowchart of an exemplary method for calibrating a blood pressure measuring device using a first blood pressure device as the source of calibration, according to one or more embodiments.
  • FIG. 2B depicts another flowchart of an exemplary method for determining a calibration factor, according to one or more embodiments.
  • FIG. 3 A depicts an example diagram for calibrating a blood pressure measuring device using a photoplethysmography (PPG) device as the source of calibration, according to one or more embodiments.
  • PPG photoplethysmography
  • FIG. 3B depicts another example diagram for calibrating a blood pressure measuring device using a PPG device as the source of calibration, according to one or more embodiments.
  • FIG. 3C depicts another example diagram for calibrating a blood pressure measuring device using a photoplethysmography device as the source of calibration, according to one or more embodiments.
  • FIG. 4 depicts another flowchart of an exemplary method for calibrating a blood pressure measuring device using a PPG device as the source of calibration, according to one or more embodiments
  • FIG. 5 depicts an example of training a machine learning model, according to one or more embodiments.
  • FIG. 6 depicts an example of a computing device, according to one or more embodiments.
  • an embodiment or implementation described herein as “exemplary” is not to be construed as preferred or advantageous, for example, over other embodiments or implementations; rather, it is intended to reflect or indicate the embodiment(s) is/are “example” embodiment(s).
  • the same reference numbers will be used throughout the drawings to refer to the same or like parts.
  • relative terms such as “about,” “substantially,” “approximately,” etc. are used to indicate a possible variation of ⁇ 10% in a stated numeric value.
  • a blood pressure may be a sensed value, a blood pressure, a sensed value converted into one or more other formats (e.g., by a processor), or the like.
  • a blood pressure may indicate how much pressure a user’s blood exerts against the user’s artery walls when the user’s heart beats (e.g., a systolic blood pressure).
  • a blood pressure may indicate how much pressure a user’s blood exerts against the user’s artery walls when the user’s heart is resting between beats (e.g., diastolic blood pressure).
  • a first blood pressure may be sensed by a first device (e.g., a blood pressure device, a volumetric device, etc.) when the first device is at a first height or position.
  • the first device may be a gold standard devise as further described herein.
  • the first blood pressure may be a systolic or diastolic blood pressure.
  • a second blood pressure may be sensed by the first device when the first device is at a second height, the second height being different than the first height.
  • a blood pressure calibration factor may be determined based on the first blood pressure, the first height, the second blood pressure, and the second height.
  • the blood pressure calibration factor may be a linear or non linear relationship, as further discussed herein.
  • a third blood pressure may be sensed using a second device (e.g., a device other than the first device, a blood pressure device, a PPG device, etc.), when the second device is at a second device height.
  • the third blood pressure may be modified based on the blood pressure calibration factor and the second device height.
  • the second device height may be applied to the blood pressure calibration factor to determine what amount the third blood pressure is to be modified by.
  • the blood pressure measuring device may be a photoplethysmography (PPG) device.
  • the PPG device may be calibrated by first measuring a user’s blood pressure using a gold standard blood pressure device, e.g., an arm cuff calibrated against a column of mercury, while the user holds their arm at different locations and/or heights relative to a reference point such as the user’s heart.
  • the gold standard blood pressure device and may be an invasive or intravascular device.
  • a calibration factor may be determined based on user’s blood pressure determined at the different locations and/or heights.
  • the user may use the PPG device to measure blood pressure for the user (e.g., by determining a pulse transmit time) while the user holds their arm at one or more locations and/or heights relative to the user’s heart.
  • the blood pressure measured by the PPG device may be calibrated (e.g., adjusted) based on the calibration factor determined based on the gold standard blood pressure device and further based on the respective locations and/or heights identified while measuring the blood pressure by the PPG device.
  • the calibrated values may be output by the PPG device or a processor that receives the measured PPG blood pressure and that applies the calibration factor to the measured PPG blood pressure. Accordingly, embodiments disclosed herein may apply integral trend analysis to eliminate errors introduced by changes in PPG device position(s).
  • the effect of gravity when measuring a user’s blood pressure may be corrected based on the user’s individual physiology and/or PPG device position(s), using a calibration factor for the user (e.g., as determined using a gold standard blood pressure measuring device.)
  • a gold standard blood pressure device may be attached to a user’s arm.
  • a baseline value of the user’s blood pressure may be based on the gold standard blood pressure device measurement when the gold standard blood pressure device is held level with the user’s heart.
  • a lower respective blood pressure may be measured when the user holds the arm with the gold standard blood pressure device above the user’s heart.
  • a higher respective blood pressure may be measured when the user holds the arm with the gold standard blood pressure below their heart.
  • multiple blood pressure including the baseline value while the user’s arm is level with the heart and one or more blood pressure when the user’s arm at different locations and/or heights relative to the heart may be recorded.
  • a calibration factor may be determined based on the baseline blood pressure and one or more blood pressure measured at various locations and/or heights relative to the heart.
  • the calibration factor may then apply the location and/or height of a blood pressure device (e.g., a PPG device) to adjust a measured blood pressure.
  • the calibration factor may be based on a linear or non-linear relationship between a blood pressure device location and/or height relative to the user’s heart, the ground, or other reference point.
  • a PPG device or an associated processor or device may utilize a machine learning model to correct for the effect of gravitational forces on the PPG device’s measured blood pressure.
  • a machine learning model e.g., via supervised, semi-supervised, or unsupervised learning, to leam associations between PPG device location and/or height and blood pressure measurements, the trained machine learning model may be used to correct for the effect of gravitational forces on blood pressure.
  • there may be numerous benefits to calibrating a PPG device for gravitational changes such as increased accuracy in medical diagnoses, more effective medical treatments, etc.
  • the term “based on” means “based at least in part on.”
  • the singular forms “a,” “an,” and “the” include plural referents unless the context dictates otherwise.
  • the term “exemplary” is used in the sense of “example” rather than “ideal.”
  • the terms “comprises,” “comprising,” “includes,” “including,” or other variations thereof, are intended to cover a non-exclusive inclusion such that a process, method, or product that comprises a list of elements does not necessarily include only those elements, but may include other elements not expressly listed or inherent to such a process, method, article, or apparatus.
  • first, second, third, etc. are, in some instances, used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another.
  • a first contact could be termed a second contact, and, similarly, a second contact could be termed a first contact, without departing from the scope of the various described embodiments.
  • the first contact and the second contact are both contacts, but they are not the same contact.
  • the terms “comprises,” “comprising,” “includes,” “including,” or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements, but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
  • the term “exemplary” is used in the sense of “example,” rather than “ideal.”
  • the terms “first,” “second,” and the like, herein do not denote any order, quantity, or importance, but rather are used to distinguish an element or a structure from another.
  • the terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of one or more of the referenced items.
  • the term “if’ is, optionally, construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context.
  • the phrase “if it is determined” or “if [a stated condition or event] is detected” is, optionally, construed to mean “upon determining” or “in response to determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event],” depending on the context.
  • Terms like “provider,” “medical provider,” or the like generally encompass an entity, person, or organization that may seek information, resolution of an issue, or engage in any other type of interaction with a user, e.g., to provide medical care, medical intervention or advice, or the like.
  • Terms like “user,” “patient,” or the like generally encompass any person (e.g., an individual, a medical provider, etc.) or entity who is using a device, calibrating a device, obtaining information, seeking resolution of an issue, or the like.
  • a gold standard device may be a device used to conduct a gold standard test for calibration.
  • a gold standard test may be a diagnostic test or benchmark that is the best available under reasonable conditions.
  • a gold standard device may be one that has been tested and has a reputation in the field as a reliable method.
  • a gold standard device may include, but is not limited to, a device that uses a column of mercury (e.g., in a cylinder, such as glass) to determine a blood pressure.
  • the gold standard device may detect a force of blood necessary to raise mercury column a known amount at sea level in the Earth’s gravitational field.
  • the column may oscillate with the systolic and diastolic blood pressure pumped from the heart a certain number of millimeters of the mercury column.
  • an inflated cuff around the artery and monitor the oscillations may be a gold standard device.
  • a predicate device calibrated using a device measuring blood pressure with mercury may be a gold standard device (e.g., a cuff device calibrated using a mercury manometer).
  • volumetric blood pressure device generally encompasses devices that obtain and usually record blood pressure at certain intervals, using direct or indirect means of determining pressure.
  • a volumetric blood pressure device may include devices such as a blood pressure cuff, which contains an air bladder which fills up with air and compresses the brachial artery to stop the flow of blood. When the air from the bladder is released, the blood flow restarts.
  • a physician or other medical provider records the systolic and diastolic BP by listening to the flow of blood through a stethoscope.
  • a volumetric blood pressure device may be calibrated against a column of mercury and, thus, may be a gold standard device.
  • a gold standard device may be implemented by the placement of a catheter into a peripheral artery, most commonly the dorsal metatarsal or femoral artery in smaller patients, although any accessible artery could be used.
  • the catheter may be connected to a pressure transducer with non-compliant tubing filled with heparinized saline to allow continuous measurement, which can be observed on a monitoring device.
  • a pressure transducer with non-compliant tubing filled with heparinized saline to allow continuous measurement, which can be observed on a monitoring device.
  • Such a device may provide continuous values and a pressure waveform that can be observed. The waveform is helpful in assessing pulse quality and pulse deficits caused by an abnormal heart rhythm.
  • the transducer Once connected to the patient the transducer may be zeroed to ambient air at the level of the right atrium. This may ensure the readings produced are accurate. Regular flushing of the line may be conducted to ensure patency and accuracy of readings.
  • Arterial catheters require secure taping
  • a gold standard device may be a sphygmomanometer. Such devices may be calibrated over a period of months using a column of mercury as a standard.
  • a gold standard device may be an oscillometric device that can include the use of an inflatable cuff around a limb or tail base, which is attached to a monitor. Measurement may be automatic and may allow for detection of oscillations produced by the artery wall as the cuff deflates.
  • an air-filled occluding cuff can be used that enables blood pressure to be measured either manually or automatically.
  • Manual measurement of blood pressure by an occluding cuff can be done either by palpation or auscultation.
  • an inflatable cuff may be wrapped around the upper arm of a patient.
  • the manometer connected to the cuff by a tube shows the pressure applied.
  • a medical provider feel for a radial pulse inflates the cuff until the brachial artery collapses, such there is no blood flow any more.
  • the pressure at which a pulse can be detected again while deflating the cuff may correspond to the systolic arterial pressure of the patient. This method may provide a systolic arterial pressure.
  • An auscultatory method may be performed in a similar way, after inflation of the cuff to a pressure above the systolic pressure, sounds can be detected by a stethoscope applied distal of the upper arm cuff during slow deflation.
  • the onset of the sounds may correspond to a patients’ systolic arterial pressure, the last sound at decreasing cuff pressure equals the patients’ diastolic arterial pressure.
  • An advantage of this technique is that it may provide the diastolic arterial pressure value.
  • An automated method to measure blood pressure with the help of an occluding cuff may employ an oscillometric technique.
  • a cuff may be inflated to a preset value automatically. Then, the pressure is gradually being reduced.
  • the pressure wave causes oscillations in the vessel, which can be detected by the cuff.
  • Mean arterial pressure corresponds to the maximum of oscillations.
  • An algorithm applied to the change of oscillations may set systolic and diastolic arterial pressure values.
  • the advantages of oscillometry may include the presence of reasonably accurate mean arterial pressure (in normal blood pressure ranges) and the possibility of having an automated tool to determine a patient’s blood pressure at a preset interval.
  • the disadvantages may include the overestimation of low and underestimation of high values and the possibility to falsify measurements (e.g., by movement (detected as oscillations) or the patient’s arm resting on the bed).
  • Continuous non-invasive measurement principles are based on either one of two different techniques, namely arterial applanation tonometry or the volume clamp method.
  • Arterial applanation tonometry may be used to assess mean arterial pressure in the radial artery and allows the calculation of diastolic and systolic arterial pressure (e.g., using population-based algorithms).
  • the technique is used in cardiology to assess central vascular pressures.
  • the pulse wave obtained by applanation tonometry can be analyzed and bears more information than systolic and diastolic pressure alone.
  • the second technique for non-invasive continuous blood pressure measurement is a volume clamp method (or vascular unloading technology).
  • Nlood pressure may measured at the finger with an inflatable cuff combined with a photodiode.
  • the diameter of the artery in the finger is measured by the photodiode; the pressure in the cuff is adjusted to keep the diameter of the artery constant. From the pressure changes in the cuff, a blood pressure curve can be calculated and transferred to correspond to brachial artery blood pressure.
  • Oscillometric measurement may be used to monitor systolic, mean and diastolic pressures, unlike the Doppler method which may detect systolic pressure. Single measurements by this method may underestimate arterial pressure by 5-20 mmHg, meaning oscillometric measurement of blood pressure can only be used to observe trends and accuracy may be reduced in patients under 5 kg. Oscillometric monitors have been reported as failing to produce results more often than the Doppler method.
  • Doppler ultrasonography with the use of an ultrasound probe produces an audible sound of blood flow through an artery. Inflation until no sound may be audible followed by slow deflation of the cuff until sound returns allows determination of systolic pressure.
  • the Doppler method requires more skill due to manual operation and the requirement for detection of the pulse. According to the Doppler method, a spirit swab or clipping the area directly over the artery if required and tolerated, may be implemented, followed by ample ultrasound gel applied to the probe to improve contact. Correct placement can be confirmed by the sound of blood flow, which is typically a “whoosh” sound.
  • the term “photoplethysmography device” (“PPG” device) may be a light based device and/or may use different techniques to measure the changes in blood flow or volume (e.g., light, pulse transit time measurements, ultrasound, magnetic resonance imaging, indicator dilution methods, intravenous injection of contrast for X-ray imaging, thermography, estimates of capillary filling, etc.).
  • the PPG device may be a wearable device, e.g., a watch, a band, a strap, etc., or a non-wearable device.
  • a PPG device may operate by measuring the “pulse transit time,” which is converted to a respective blood pressure.
  • the pulse transit time measures the time it takes for blood to move from a first part of an artery to a second part of an artery.
  • a PPG device may use a non-invasive optical method for measuring blood volume changes per pulse.
  • a PPG waveform output by a PPG device may represent the mechanical activity of the heart.
  • Blood pressure may be determined by analysis of the PPG waveform.
  • a PPG measurement may be subject to imprecision from a number of factors, including but not limited to, calibration issues, effects on blood pressure based on arm positions (e.g., from the variable contribution of gravity), and/or local vasospasm effects on blood flow such as cold temperature, etc.
  • the blood pressure measurement itself, although improved by the use of light-emitting diodes, suffers inherent drift with prolonged use.
  • algorithm refers to a sequence of defined computer- implementable instructions, typically to solve a class of problems or to perform a computation.
  • a “machine learning model” generally encompasses instructions, systems, data, and/or a model configured to receive input, and apply one or more of a weight, bias, classification, or analysis on the input to generate an output.
  • the output may include, for example, a classification of the input, an analysis based on the input, a design, process, prediction, or recommendation associated with the input, or any other suitable type of output.
  • a machine learning model is generally trained using training data, e.g., experiential data and/or samples of input data, which are fed into the model in order to establish, tune, or modify one or more aspects of the model, e.g., the weights, layers, biases, criteria for forming classifications or clusters, or the like.
  • aspects of a machine learning model may operate on an input linearly, in parallel, via a network (e.g., a neural network), or via any suitable configuration.
  • the execution of the machine learning model may include deployment of one or more machine learning techniques, such as linear regression, logistical regression, random forest, gradient boosted machine (GBM), deep learning, and/or a deep neural network.
  • Supervised and/or semi-supervised training may be employed.
  • supervised learning may include providing training data and labels corresponding to the training data, e.g., as ground truth.
  • Semi-supervised approaches may include heuristic, generative, low-density, Laplacian or other like models.
  • K-means clustering or K-Nearest Neighbors may also be used, which may be supervised or semi-supervised. Combinations of K-Nearest Neighbors and a semi- supervised cluster technique may also be used.
  • a user 105 and/or a medical provider 110 may operate a gold standard blood pressure device 115 and/or a PPG device 120, the results from gold standard blood pressure device 115 and/or PPG device 120 being transmitted via a network 125 to a data storage system 130.
  • User 105 may wear gold standard blood pressure device 115 and PPG device 120 on the same arm simultaneously, on opposite arms simultaneously, or user 105 may wear only one of either gold standard blood pressure device 115 or PPG device 120 at a time.
  • Gold standard blood pressure device 115 and/or PPG device 120 may operate continuously, at intervals, or at the determination of user 105, medical provider 110, and/or a user.
  • Gold standard pressure device 115 and/or PPG device 120 may include one or more sensors either internal or external to the respective device.
  • the one or more sensors may detective a location or height of the gold standard blood pressure device 115 and/or PPG device 120 relative to a reference point (e.g., a heart, a ground level, etc.)
  • the one or more sensors may include a motion sensor, an accelerometer, an electromechanical sensor, a stadiometer, an active ultrasonic sensor, a passive infrared sensor, a vibration motion sensor, a dual technology or hybrid sensor, a Doppler radar sensor, a tomographic sensor, a gesture detector, a displacement sensor, and/or any other suitable sensor.
  • the one or more sensors may operate continuously, at intervals, or at the instruction or input of user 105, medical provider 110, and/or another user.
  • the one or more sensors may operate while the user moves the arm that has gold standard blood pressure device 115 and/or PPG device 120 attached and not operate (e.g., enter a sleep mode) when the user’s arm is not moving.
  • the one or more sensors may operate in response to the activation of gold standard blood pressure device 115 and/or PPG device 120.
  • the one or more sensors may remain idle until gold standard blood pressure device 115 and/or PPG device 120 begins measuring a blood pressure, at which point the one or more sensors may activate.
  • the one or more sensors may determine a gold standard blood pressure device 115 and/or PPG device 120 height and/or positioning for a given blood pressure measurement based on the average position of gold standard blood pressure device 115 and/or PPG device 120 during a time period (e.g., approximately three seconds) that a current blood pressure is being measured.
  • the one or more sensors may determine gold standard blood pressure device 115 and/or PPG device 120 height and/or positioning at a predetermined time over a duration of measuring the current blood pressure (e.g., at the end of a measurement time period, middle of the measurement time period, etc.).
  • one or more forms of noise found in one or more signals may be filtered (e.g., reduced, modified, and/or removed).
  • the one or more signals may be generated using gold standard blood pressure device 115 and/or PPG device 120.
  • a noise reduction algorithm may be used to filter noise.
  • the type of noise reduction algorithm may depend on the type of noise in the data, the type of data, or the like.
  • the noise reduction algorithm may be automatically selected and/or applied or may be selected and/or applied based on user input.
  • a type of noise may include, but is not limited to, high frequency noise, movement noise, and/or any other form of noise.
  • a plurality of noise reduction algorithms may be used to filter noise for a given signal.
  • an amplification may be applied to amplify a signal generated at gold standard blood pressure device 115 and/or PPG device 120. Such signal amplification may be performed prior to, in conjunction with, or post filtering the signal for noise.
  • a machine learning model may be used to output a calibration factor, to modify the calibration factor, and/or to apply the calibration factor.
  • the machine learning model may analyze data received from user 105, provider 110, gold standard blood pressure device 115, PPG device 120, data storage system 130, and/or any other person, entity, or device. For example, data from gold standard blood pressure device 115 (e.g., mercury column calibrated data) and/or device from PPG device 120 (PPG device data) may be input into the machine learning model.
  • the trained machine learning model may correlate the PPG device 120 height with the current blood pressure. Using this correlation, the machine learning model may use the calibration factor to calibrate the current blood pressure.
  • the machine learning model may output a calibrated current blood pressure.
  • the machine learning model may receive additional data, e.g., medical, demographic, or personal information for user 105, etc.
  • provider 110 may specify a particular condition or characteristic of interest for user 105 that could affect the blood pressure for the user.
  • the machine learning model may analyze the gold standard data, the PPG device data, and/or any additional data.
  • the machine learning model output may be individualized to a user 105.
  • the individualization may be based on data from a user’s or a plurality of users’ user height, a user medical conditions, or a user PPG device height relative to a user heart. Training the machine learning model for individualization is discussed in further detail below.
  • machine learning model output by the machine learning model may be transmitted to user 105, provider 110, gold standard blood pressure device 115, PPG device 120, and/or data storage system 130.
  • the machine learning data may include medical, diagnostic, or other health-related information or results.
  • a processor or storage component, gold standard blood pressure device 115, and/or PPG device 120 may generate, store, train, or use the machine learning model and/or may include instructions associated with the machine learning model, e.g., instructions for generating the machine learning model, training the machine learning model, using the machine learning model, etc.
  • blood pressure measured using gold standard blood pressure device 115 may be transmitted, via a Bluetooth protocol, to processor associated with PPG device 120.
  • the processor may receive the measured blood pressure to determine a calibration factor (e.g., output by a machine learning model accessible by the processor).
  • the processor or one or more other processors may apply the calibration factor to current blood pressure measured by the PPG device, based on corresponding heights and/or locations of the PPG device during respective measurements.
  • a system or device other than gold standard blood pressure device 115 or PPG device 120 may be used to generate and/or train the machine learning model.
  • such a system may include instructions for generating the machine learning model, the training data and ground truth, and/or instructions for training the machine learning model.
  • a resulting trained machine learning model may then be provided to PPG device 120 or a component associated with PPG device 120 such that the trained machine learning model can output a calibration factor, modify the calibration factor, and/or apply the calibration factor.
  • a machine learning model includes a set of variables, e.g., layers, nodes, neurons, filters, weights, biases, etc., that are tuned, e.g., weighted or biased, to different values via the application of training data.
  • variables e.g., layers, nodes, neurons, filters, weights, biases, etc.
  • training may proceed by feeding a sample of training data into a model with variables set at initialized values, e.g., at random, based on Gaussian noise, a pre-trained model, or the like.
  • the output may be compared with the ground truth to determine an error, which may then be back-propagated through the model to adjust the values of the variable.
  • Training may be conducted in any suitable manner, e.g., in batches, and may include any suitable training methodology, e.g., stochastic or non-stochastic gradient descent, gradient boosting, random forest, adaptive moment estimation (“ADAM”), etc. Training may be conducted with or without sample and/or class weighting. In some embodiments, a portion of the training data may be withheld during training and/or used to validate the trained machine-learning model, e.g., compare the output of the trained model with the ground truth for that portion of the training data.
  • any suitable training methodology e.g., stochastic or non-stochastic gradient descent, gradient boosting, random forest, adaptive moment estimation (“ADAM”), etc.
  • ADAM adaptive moment estimation
  • Training may be conducted with or without sample and/or class weighting.
  • a portion of the training data may be withheld during training and/or used to validate the trained machine-learning model, e.g., compare the output of the trained model with the ground truth for that portion of the training data.
  • the training of the machine learning model may be configured to cause the machine learning model to learn associations between (i) gold standard data and/or PPG device data and (ii) gravitational effects based on device positioning, such that the trained machine learning model is configured to determine an output (e.g., corrected PPG device data) in response to the input data based on the learned associations.
  • the machine learning model may receive PPG device data points (e.g., blood pressure) associated with a particular arm positioning, which the machine learning model may be trained to correct based on the calibration factor applied to the arm positioning.
  • the variables of a machine learning model may be interrelated in any suitable arrangement in order to generate the output.
  • the machine learning model may include architecture that is configured to identify, isolate, and/or extract features, geometry, and or structure in input data.
  • the machine learning model may include one or more convolutional neural networks (“CNN”) configured to identify features in the signal-processed data, and may include further architecture, e.g., a connected layer, neural network, etc., configured to determine a relationship between the identified features in order to determine a location in the signal-processed data.
  • CNN convolutional neural networks
  • the network 125 may connect one or more components of the environment 100 via a wired connection, e.g., a USB connection between gold standard blood pressure device 115 and PPG device 120.
  • the network 125 may connect one or more aspects of the environment 100 via an electronic network connection, for example a Bluetooth connection, a wide area network (“WAN”), a local area network (“LAN”), personal area network (“PAN”), or the like.
  • the electronic network connection includes the Internet, and information and data provided between various systems occurs online. “Online” may mean connecting to or accessing source data or information from a location remote from other devices or networks coupled to the Internet.
  • “online” may refer to connecting or accessing an electronic network (wired or wireless) via a mobile communications network or device.
  • the Internet is a worldwide system of computer networks — a network of networks in which a party at one computer or other device connected to the network can obtain information from any other computer and communicate with parties of other computers or devices.
  • the most widely used part of the Internet is the World Wide Web (often-abbreviated “WWW” or called “the Web”).
  • a “website page,” a “portal,” or the like generally encompasses a location, data store, or the like that is, for example, hosted and/or operated by a computer system so as to be accessible online, and that may include data configured to cause a program such as a web browser to perform operations such as send, receive, or process data, generate a visual display and/or an interactive interface, or the like.
  • environment 100 may be a closed loop such that no external network connection may be necessary to implement the techniques disclosed herein.
  • the closed loop maybe used to provide a real-time automatic method that is self-contained and not dependent upon linkage to a remote server containing additional software, often referred to as “edge computing.”
  • the method is also suitable for transmission to the cloud to allow for an interface with conventional electronic health records and other data analysis and reporting processes.
  • the gold standard blood pressure device 115 may transmit blood pressure and/or a calibration factor to PPG device 120 over a Bluetooth connection.
  • PPG device 120 may be associated with a processor that may apply the received blood pressure and/or calibration factor from the gold standard blood pressure device 115 to generate calibrated blood pressure outputs based on blood pressure and respective device heights and/or locations of the PPG device 120 (e.g., using a machine learning model).
  • the connections within the environment 100 can be wireless, wired, or be any other suitable connection, or any combination thereof.
  • the data storage system 130 may store the data from and/or provide data to various aspects of the environment 100.
  • Data storage system 130 may include a server system, an electronic medical data system, computer-readable memory such as a hard drive, flash drive, disk, etc.
  • data storage system 130 includes and/or interacts with an application programming interface for exchanging data to other systems, e.g., one or more of the other components of the environment 100.
  • Data storage system 130 may include and/or act as a repository or source for data from gold standard blood pressure device 115, data from PPG device 120, medical history and/or diagnoses for user 105, machine learning data, and/or other forms of data.
  • Data storage system 130 may be external to or may be a part of gold standard blood pressure device 115 or PPG device 120.
  • user 105 or medical provider 110 may operate gold standard blood pressure device 115 and/or PPG device 120 while user 105 holds their arm(s) at different positions to calibrate PPG device 120 for use by user 105.
  • a first set of data e.g., blood pressure for calibration
  • the user may remove the gold standard blood pressure device 115 and wear the PPG device 120 on the same arm or the other arm as the gold standard blood pressure device 115.
  • user 105 may first wear gold standard blood pressure device 115 such that gold standard blood pressure device 115 is level with the user’s heart in order to determine a first calibration blood pressure as shown in FIG. IB.
  • the first calibration blood pressure may be determined by, for example, pressing a button or otherwise activating gold standard blood pressure device 115.
  • a second calibration blood pressure may be determined while user 105 holds their arm at a second position (e.g., above their heart).
  • a third calibration blood pressure may be determined while user 105 holds their arm at a third position (e.g., below their heart).
  • the first, second, and third calibration blood pressure may be stored (e.g., in a memory or at data storage system 130).
  • Calibration blood pressure may be determined using gold standard blood pressure device 115 as few or as many times as necessary. The number of times may be based on the quality of the calibration blood pressure. The quality of the calibration blood pressure may be determined using a process to analyze the calibration blood pressure after one or more filters are applied to the values (e.g., a noise filter, an amplifier, etc.). Although a first, second, and third position are shown in FIGs. 1B-1D, any number of calibration blood pressure greater than two may be collected at respective heights and/or locations. As discussed herein, user 105 may attach gold standard blood pressure device 115 and/or PPG device 120 to the same arm, to opposite arms, or may wear one of the devices at a time to calibrate and/or operate PPG device 120.
  • the calibration blood pressure may be used to generate a calibration factor.
  • the calibration factor may be generated at gold standard blood pressure device 115, PPG device 120, or an external component (e.g., a processor, a cloud component, etc.).
  • the calibration factor may be based on a linear or non-linear relationship between the plurality of calibration blood pressure and the corresponding positions of the gold standard blood pressure device 115 relative to a reference point (e.g., the user’s heart, a ground level, etc.).
  • the calibration factor may be used to correct PPG device data for the influence of gravity on blood pressure when the arm of user 105 is held at different heights or positions. For example, user 105 may wear PPG device 120 and one or more current blood pressure may be generated using PPG device 120.
  • the calibration factor may be applied to the one or more current blood pressure based on the respective location and/or height of the PPG device during detection of the one or more current blood pressure.
  • the current blood pressure may be adjusted to generate respective one or more calibrated current blood pressure that are calibrated based on the calibration factor and the respective heights and/or locations of the PPG device.
  • the calibrated current blood pressure may be provided as an output from the PPG device 120.
  • the calibrated current blood pressure may be provided via a display or to one or more components of environment 100 or an external component (e.g., via a Bluetooth or other network 125 connection).
  • FIG. 2 A illustrates an exemplary process for calibrating PPG device 120 blood pressure based on calibration using gold standard blood pressure device 115.
  • PPG device 120 receives a calibration factor.
  • the calibration factor may be determined by using a first device (e.g., gold standard blood pressure device 115) to determine a first calibration blood pressure when the first device is at a first height and a second calibration blood pressure when the first device is at a second height.
  • the calibration blood pressure may be provided from the first device to PPG device 120 and/or another component via a wired or wireless connection, as disclosed herein.
  • the calibration blood pressure may be provided, for example, using Bluetooth Low Energy (BLE) or other communication means.
  • BLE Bluetooth Low Energy
  • the respective calibration blood pressure and heights may be used to generate a calibration factor (e.g., a linear or non-linear relationship) associating differences in blood pressure based on height, as discussed above.
  • the calibration factor may be generated using a machine learning model or may be used to train a machine learning model.
  • the calibration factor may be determined by an algorithm configured to receive the calibration blood pressure and respective heights to generate the calibration factor. Accordingly, the algorithm may perform calculations using pre-programmed decision analysis using predefined mathematical relationships.
  • the calibration factor may be the linear or non-linear relationship or an analyzed version of the linear or non-linear relationship.
  • the calibration factor may be a variable algorithm that is the best fit to the calibration blood pressure as a function of height.
  • the calibration factor may be provided to PPG device 120 such that the calibration factor is stored at PPG device 120 and/or a component (e.g., data storage system 130) accessible by PPG device 120.
  • PPG device 120 may detect a current blood pressure for user 105.
  • the current blood pressure may be stored in memory or at a component (e.g., data storage system 130) accessible by PPG device 120.
  • one or more sensors may be used to determine the height and/or position of PPG device 120 relative to a reference point, e.g.,
  • the PPG device 120 s position relative to user 105’s heart.
  • the PPG device 120 location and/or height may be stored in memory or at a component (e.g., data storage system 130) accessible by PPG device 120.
  • the PPG device 120 location and/or height may be stored or accessible by the same storage or component as the current blood pressure.
  • the calibration factor may be stored or accessible by the same storage or component as the current blood pressure and the PPG device 120 location and/or height.
  • the current blood pressure from step 204 may be calibrated using the calibration factor from step 202 and the PPG device 120 location and/or height corresponding to the current blood pressure.
  • the calibration factor may be applied to the PPG device 120 location and/or height corresponding to the current blood pressure.
  • the output of the application may be an adjustment factor by which the current blood pressure is to be adjusted.
  • the calibration factor may be in the form of a trained machine learning model (e.g., a non-linear calibration factor).
  • the machine learning model may be trained based on the calibration blood pressure and respective device height and/or locations of the first device.
  • the machine learning model may be configured to output calibrated blood pressure based on current blood pressure and device height and/or locations of PPG device 120.
  • the calibrated blood pressure may be output at step 210.
  • a first blood pressure may be sensed by a first device (e.g., a blood pressure device, a gold standard device, etc.) when the first device is at a first height or position at 252.
  • the first blood pressure may be a systolic or diastolic blood pressure.
  • a second blood pressure may be sensed by the first device when the first device is at a second height, the second height being different than the first height.
  • a blood pressure calibration factor may be determined based on the first blood pressure, the first height, the second blood pressure, and the second height.
  • the blood pressure calibration factor may be a linear or non-linear relationship, as further discussed herein.
  • PPG device 120 may be configured to output calibrated blood pressure without using the first device (e.g., the gold standard blood pressure device 115).
  • Environment 300 of FIG. 3 A, environment 340 of FIG. 3B, and environment 360 of FIG. 3C, show PPG device 120 while user 105 holds their arm(s) at different positions, to calibrate PPG device 120.
  • PPG device 120 may be used to determine calibration blood pressure during a calibration period. Alternatively, as further discussed herein, PPG device 120 may calibrate blood pressure without first calibrating PPG device 120. According to an implementation of using a calibration period, user 105 may operate PPG device 120 while user 105 holds PPG device 120 level with the user’s heart in order to determine a first calibration blood pressure, as shown in FIG. 3A. A calibration blood pressure may be determined by pressing a buton or otherwise activating PPG device 120. As shown in FIG. 3B, the user may operate PPG device 120 at a second time to determine a second calibration blood pressure at a second position of PPG device 120 (e.g., above the user’s heart). As shown in FIG. 3C, the user may operate PPG device 120 at a third time to determine a third calibration blood pressure at a third position of PPG device 120 (e.g., below the user’s heart).
  • the calibration blood pressure and respective PPG device 120 positions may be used to generate a calibration factor.
  • the calibration factor may be generated at PPG device 120, or different device component configured to receive the calibration blood pressure and respective PPG device 120 positions, in accordance with the techniques disclosed herein.
  • the calibration factor may be used to modify current blood pressure collected using PPG device 120 after the calibration period.
  • the calibration factor may include a relationship between PPG device 120 positions and adjustments to corresponding current blood pressure.
  • the calibration factor-based relationship may be a percentage increase or decrease in a blood pressure, based on the position of PPG device 120 at the time of determining the reading.
  • a distance of two feet above a user’s heart may correspond to a three percent increase in the corresponding current blood pressure, according to the user’s calibration factor for PPG device 120.
  • the user may use PPG device 120 while exercising.
  • a current blood pressure while exercising with the PPG device 120 two feet above the user’s heart may be multiplied by 1.03 to generate a calibrated blood pressure.
  • the absolute blood pressure value change based on a three percent increase while exercising may be a greater than the absolute blood pressure value change of a similar three percent increase (based on PPG device 120 being two feet above the user’s heart), while the user is resting.
  • the calibration factor may be used to apply the relationship based on PPG device 120 position.
  • PPG device 120 may be calibrated at a first time (e.g., at calibration conditions such as during rest). A calibration factor may be determined based on calibration blood pressure determined during the first time. Subsequently, the calibration factor may be applied to current blood pressure determined by PPG device 120, to adjust the current blood pressure to calibrated blood pressure.
  • FIG. 4 depicts an exemplary process for calibrating PPG device 120 blood pressure based on the calibration factor discussed above.
  • PPG device 120 may determine a calibration factor.
  • the calibration factor may be determined by first: measuring a baseline blood pressure of a user based on PPG device 120 being level with the user’s heart, measuring a first blood pressure of the user based on PPG device 120 being at a first position relative to the user’s heart, and measuring a second blood pressure of the user based on PPG device 120 being at a second position relative to the user’s heart.
  • the calibration factor may then be determined by linearly or non-bnearly associating the baseline blood pressure, the first blood pressure, the second blood pressure, and the respective positions of PPG device 120.
  • the calibration blood pressure may be recorded when the user is in a resting state.
  • PPG device 120 may determine a current blood pressure of user 105 while the height and/or position of PPG device 120 relative to the ground and/or heart of user 105 is also determined.
  • the current blood pressure from step 404 may be modified based on the calibration factor from step 402 to generate a calibrated current blood pressure.
  • PPG device 120 may output the calibrated current blood pressure.
  • PPG device 120 may store the calibrated current blood pressure at data storage system 130 and/or at another component, and/or may transmit the calibrated current blood pressure to another entity or device, e.g., a medical provider’s user portal.
  • PPG device 120 may include or have access to a calibration factor.
  • the calibration factor may be stored at PPG device 120 or a component accessible by PPG device 120 during a manufacturing process or during a system update. Accordingly, PPG device 120 may determine one or more current blood pressure of a user. The height and/or position of PPG device 120 may be determined when PPG device 120 determines the respective one or more current blood pressure. The one or more current blood pressure may be modified based on the calibration factor and the corresponding height and/or positions of PPG device 120, in accordance with the techniques disclosed herein. According to this implementation, a user may not calibrate PPG device 120. Additionally, as disclosed herein, a machine learning model may update the calibration factor for PPG device 120 based on user data and/or data from one or more other users that is provided to the machine learning model.
  • a machine learning model may generate a calibration factor based on historical data and/or user data.
  • the machine learning model or a second machine learning model may update the calibration factor based additional user data (e.g., user blood pressure) or data from other users (e.g., a cohort of other users that may have overlapping attributes with a given user).
  • a calibration factor may be a linear or non-linear relationship of blood pressure and PPG device 120 height and/or position relative to a reference point.
  • a machine learning model may update the calibration factor over time, based on user data (e.g., user blood pressure values, user medical condition(s), user history, climate changes, etc.).
  • the machine learning model may receive the user data and may apply weights, layers, biases, etc., to determine adjustments to the calibration factor for the given user.
  • the updates may be based on inherent changes to the user’s anatomy or medical condition(s) such that, over time, an original calibration factor may not be as applicable to the user as an updated calibration factor.
  • FIG. 5 depicts a flow diagram for training a machine learning model to generate a calibration factor and/or update a calibration factor, according to one or more embodiments.
  • One or more of training data 512, stage inputs 514, known outcomes 518, comparison results 516, training algorithm 520, and training component 530 may communicate by any suitable means.
  • One or more implementations disclosed herein may be implemented by using a trained machine learning model.
  • a machine learning model, as disclosed herein may be trained using environment 100 of FIG. 1A, environment 140 of FIG. IB, environment 150 of FIG. 1C, environment 150 of FIG. ID, environment 200 of FIG. 2A, environment 250 of FIG. 2B, environment 300 of FIG. 3A, environment 340 of FIG. 3B, environment 360 of FIG. 3C, and/or environment 400 of FIG. 4.
  • training data 512 may include one or more of stage inputs 514 and known outcomes 518 related to a machine learning model to be trained.
  • Training data 512 may include historical user blood pressure, historical user medical diagnoses, or historical PPG device 120 heights.
  • Historical blood pressure may include readings from a gold standard device, a PPG device, a data storage system, or any other suitable source.
  • Historical user medical diagnoses may include medical records, medical conditions, and/or any other relevant medical information.
  • Historical PPG device heights may include PPG device heights from a gold standard device, PPG device, data storage system, and/or any other suitable device or system.
  • Training data 512 may include data from a user and/or from a plurality of users. The data from the user or plurality of users may include user height, user medical condition, or user PPG device height relative to a user heart.
  • the stage inputs 514 may be from any applicable source including a component or set shown in FIGS. 1A, IB, 1C, ID, 2, 3A, 3B, 3C, and/or 4.
  • Known outcomes 518 may be included for machine learning models generated based on supervised or semi-supervised training. An unsupervised machine learning model might not be trained using known outcomes 518.
  • Known outcomes 518 may include known or desired outputs for future inputs similar to or in the same category as stage inputs 514 that do not have corresponding known outputs.
  • Training data 512 and a training algorithm 520 may be provided to a training component 530 that may apply training data 512 to training algorithm 520 to generate a trained machine learning model.
  • training component 530 may be provided comparison results 516 that compare a previous output of the corresponding machine learning model to apply the previous result to re-train the machine learning model. Comparison results 516 may be used by training component 530 to update the corresponding machine learning model.
  • Training algorithm 520 may utilize machine learning networks and/or models including, but not limited to a deep learning network such as Deep Neural Networks (“DNN”), Convolutional Neural Networks (“CNN”), Fully Convolutional Networks (“FCN”) and Recurrent Neural Networks (“RCN”), probabilistic models such as Bayesian Networks and Graphical Models, and/or discriminative models such as Decision Forests and maximum margin methods, or the like.
  • DNN Deep Neural Networks
  • CNN Convolutional Neural Networks
  • FCN Fully Convolutional Networks
  • RCN Recurrent Neural Networks
  • the output of the flow diagram 510 may be a trained machine learning model.
  • FIG. 6 is a simplified functional block diagram of a computer 600 that may be configured as a device for executing the methods of FIGs. 2 and/or 4, according to exemplary embodiments of the present disclosure.
  • any process or operation discussed in this disclosure that is understood to be computer-implementable such as the environments and/or processes illustrated in FIGS. 1A, IB, 1C, ID, 2, 3A, 3B, 3C, and/or 4, may be implemented or performed by one or more processors of a computer system, such any of the systems or devices in the environment 100 of FIG. 1A, as described above.
  • a process or process step performed by one or more processors may also be referred to as an operation.
  • the one or more processors may be configured to perform such processes by having access to instructions (e.g., software or computer-readable code) that, when executed by the one or more processors, cause the one or more processors to perform the processes.
  • the instructions may be stored in a memory of the computer system.
  • a processor may be a central processing unit (“CPU”), a graphics processing unit (“GPU”), or any suitable types of processing unit.
  • a computer system such as a system or device implementing a process or operation in the examples above, may include one or more computing devices, such as one or more of the systems or devices in FIGS. 1A, IB, 1C, ID, 3A, 3B, and/or 3C.
  • One or more processors of a computer system may be included in a single computing device or distributed among a plurality of computing devices.
  • a memory of the computer system may include the respective memory of each computing device of the plurality of computing devices.
  • a processor 602 may communicate by any suitable means.
  • computer 600 may be configured as PPG device 120 and/or another system according to exemplary embodiments of this disclosure.
  • any of the systems herein may be a computer 600 including, for example, data communication interface 620 for packet data communication.
  • Computer 600 also may include a central processing unit (“CPU”) 602, in the form of one or more processors, for executing program instructions.
  • CPU central processing unit
  • Computer 600 may include internal communication bus 608, and storage unit 606 (such as Read-Only Memory (“ROM”), Hard Disk Drive (“HDD”), Solid-State Drive (“SSD”), etc.) that may store data on computer readable medium 622, although computer 600 may receive programming and data via network communications.
  • Computer 600 may also have memory 604 (such as Random- Access Memory (“RAM”)) storing instructions 624 for executing techniques presented herein, although instructions 624 may be stored temporarily or permanently within other modules of computer 600 (e.g., processor 602 and/or computer readable medium 622).
  • Computer 600 also may include input and output ports 612 and/or display 610 to connect with input and output devices such as keyboards, mice, touchscreens, monitors, displays, etc.
  • the various system functions may be implemented in a distributed fashion on a number of similar platforms, to distribute the processing load. Alternatively, the systems may be implemented by appropriate programming of one computer hardware platform.
  • Storage type media include any or all of the tangible memory of the computers, processors or the like, or associated modules thereof, such as various semiconductor memories, tape drives, disk drives and the like, which may provide non-transitory storage at any time for the software programming. All or portions of the software may at times be communicated through the Internet or various other telecommunication networks.
  • Such communications may enable loading of the software from one computer or processor into another, for example, from a management server or host computer of the mobile communication network into the computer platform of a server and/or from a server to the mobile device.
  • another type of media that may bear the software elements includes optical, electrical and electromagnetic waves, such as used across physical interfaces between local devices, through wired and optical landline networks and over various air-links.
  • the physical elements that carry such waves, such as wired or wireless links, optical links, or the like, also may be considered as media bearing the software.
  • terms such as computer or machine “readable medium” refer to any medium that participates in providing instructions to a processor for execution.
  • the disclosed methods, devices, and systems are described with exemplary reference to transmitting data, it should be appreciated that the disclosed embodiments may be applicable to any environment, such as a desktop or laptop computer, an automobile entertainment system, a home entertainment system, etc. Also, the disclosed embodiments may be applicable to any type of Internet protocol.

Abstract

La divulgation concerne des techniques et des systèmes d'étalonnage d'un dispositif de mesure de la pression artérielle. Ils consistent à détecter une première pression artérielle à l'aide d'un premier dispositif lorsque le premier dispositif est à une première hauteur, détecter une seconde pression artérielle à l'aide du premier dispositif lorsque le premier dispositif est à une seconde hauteur, la seconde hauteur étant différente de la première hauteur, et générer le facteur d'étalonnage de pression artérielle sur la base de la première pression artérielle, de la première hauteur, de la seconde pression artérielle et de la seconde hauteur.
PCT/US2022/072866 2021-06-17 2022-06-10 Systèmes et procédés d'étalonnage de dispositif de pression artérielle WO2022266593A1 (fr)

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EP22743692.0A EP4355208A1 (fr) 2021-06-17 2022-06-16 Systèmes et méthodes d'étalonnage de dispositif de mesure de la pression sanguine
PCT/US2022/072989 WO2022266655A1 (fr) 2021-06-17 2022-06-16 Systèmes et méthodes d'étalonnage de dispositif de mesure de la pression sanguine
IL309106A IL309106A (en) 2021-06-17 2022-06-16 Systems and methods for calibrating blood pressure devices
CA3222600A CA3222600A1 (fr) 2021-06-17 2022-06-16 Systemes et methodes d'etalonnage de dispositif de mesure de la pression sanguine

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