WO2016065476A1 - A wearable device and method for non-invasive monitoring continuous blood pressure and other physiological parameters with reduced movement artifacts - Google Patents

A wearable device and method for non-invasive monitoring continuous blood pressure and other physiological parameters with reduced movement artifacts Download PDF

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Publication number
WO2016065476A1
WO2016065476A1 PCT/CA2015/051104 CA2015051104W WO2016065476A1 WO 2016065476 A1 WO2016065476 A1 WO 2016065476A1 CA 2015051104 W CA2015051104 W CA 2015051104W WO 2016065476 A1 WO2016065476 A1 WO 2016065476A1
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Prior art keywords
blood pressure
waveform
pressure
values
artifact
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PCT/CA2015/051104
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French (fr)
Inventor
Miki Raviv
Nir Dotan
Liron ILOUZ
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2352409 Ontario Inc.
Guardlyff S.A.
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Application filed by 2352409 Ontario Inc., Guardlyff S.A. filed Critical 2352409 Ontario Inc.
Publication of WO2016065476A1 publication Critical patent/WO2016065476A1/en
Priority to PCT/US2016/056958 priority Critical patent/WO2017074713A1/en

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    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271Specific aspects of physiological measurement analysis
    • A61B5/7285Specific aspects of physiological measurement analysis for synchronising or triggering a physiological measurement or image acquisition with a physiological event or waveform, e.g. an ECG signal
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/02Detecting, 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
    • A61B5/021Measuring pressure in heart or blood vessels
    • A61B5/022Measuring pressure in heart or blood vessels by applying pressure to close blood vessels, e.g. against the skin; Ophthalmodynamometers
    • A61B5/02233Occluders specially adapted therefor
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/103Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1118Determining activity level
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/68Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801Arrangements 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
    • A61B5/6802Sensor mounted on worn items
    • A61B5/681Wristwatch-type devices
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A61B5/7207Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts
    • A61B5/721Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts using a separate sensor to detect motion or using motion information derived from signals other than the physiological signal to be measured

Definitions

  • the present invention pertains to a device and method for non-invasive monitoring continuous blood pressure and other physiological parameters and more particularly to a wearable device and method for monitoring physiological parameters with reduced movement artifacts.
  • body physiological parameters e.g. blood pressure, heart rate (HR) pulse, body temperature, blood glucose level, movement patterns
  • HR heart rate
  • Blood pressure can be estimated by measuring the mechanical pressure applied by the blood volume fluctuations in arteries under the tissue. Sensitive pressure sensors and small signals converters attached to the skin sensing the mechanical movements of the tissue. The mechanical changes reflected from the skin are coupled with the blood density in the artery and is affected by the mechanical characteristics of it
  • the pressure sensor converts that pressure to electrical voltage that is proportional to it.
  • the measured voltage waveform is characterized by a set of features that were correlated with the actual blood pressure.
  • a micro controller extracts those features from the pressure waveform and applies a linear filter on it in order to estimate the real blood pressure values.
  • the coefficients of the filter are pre-determined using machine learning methods.
  • the major problem of the pressure sensing method is its high sensitivity to physical motion. Any measurement that involves measurement of blood flow features within living bodies, particularly by a wearable device, is prone to movement artifact.
  • the two main sources are: 1, the relative movement of the device or sensor relative to the body, and 2, changes in the tissue due to the body movement, e.g. pressure wave in the blood circulation system that affects volume and shape of blood vessels during the movement. This pressure wave is measured as undesirable artifact in the pressure signal, which is sometimes much more significant than the typical pulse fluctuations making the latter unnoticeable.
  • the present invention relies on direct pressure sensing of the radial artery at the wrist.
  • the best mode of implementation describes a calibration step that is applied when wearing the wristband.
  • This invention provides a new method and device for pressure sensor based blood pressure monitoring while avoiding motion artifact problems.
  • the method is based on identifying the proper time slots in between for the static BP meter to be triggered.
  • An object of the present invention is to provide a wearable device and method for non-invasive monitoring continuous (or intermittent) blood pressure and other physiological parameters with reduced movement artifacts.
  • This invention provides a new method and device for pressure sensor based blood pressure monitoring while avoiding motion artifact problems.
  • Pressure sensing is obtained by locating at least one sensitive pressure sensor upon the radial artery.
  • the calibration of the wristband as provided in the best mode of implementation is pursued in order to provide the best signal to noise values from the pressure sensor/s.
  • Additional sensors can be located on the ulnar artery to increase the precision that can be extracted from the pressure sensors.
  • the method is based on identifying the proper time slots in between for the static BP meter to be triggered ("Avoidance Method”).
  • the method includes a real time movement cancellation on the basis of other movement-correlated data (“Artifact Correlated Data”), such as, but not limited to, single or multiple inertial sensors based movement characteristics, optical-based detection inputs, and/or frequency analysis algorithms ("Cancellation Method").
  • the Avoidance Method is based on identification of a time period where the user does not significantly move the body part the device is attached to, for example, the wrist. This classification is based on measurements of the dynamic motion values of the user's body part to which the device is attached. If the rotation and/or acceleration values show low variation, that can be quantified, for example, and not limited to, as the coefficient of variance (100* Standard deviation/ Aver age) of the multiple reading within a certain time frame, that is, for example, below 10%, below 3% or below 1% this is an indication the device and the body are not moving.
  • the time frame referred above is in the order of seconds.
  • Typical blood pressure drop values in the first rest minute after exercise ranges between 5-10% 1 . Furthermore, the amount of blood pressure drop is an indicator to a proper heart operation and each user can be characterized by such parameter. This parameter can be used to estimate the exercise BP values from the BP measurements taken several seconds after it was ended.
  • the Cancellation Method relies on taking BP measures, either continuous or intermittent, during movement; Artifact Correlated Data during the same time interval is acquired.
  • An accuracy scoring is attributed to the adjusted BP waveform on the basis of remaining noise correlated components; and, a reading of systolic and diastolic BP values are produced if critical threshold of minimum accuracy is attributed to the particular adjusted BP waveform.
  • the device includes a control unit, a memory device, an electric power source, a clock and a communication module that is used to transmit the processed data to a smart phone or computer, one or more single or multi-axis inertial sensors, at least one pressure sensor and an optional, additional Artifact Correlated Data source.
  • the device can have a display unit to display the blood pressure measures.
  • the device includes as well an activity identification component that identifies the type and intensity done by the user.
  • This feature allows discriminating between motion and rest time slots (also during intense activities) and along with a user predefined scheme - triggering accurate blood pressure measurements. For example, user may prefer timing the majority of his blood pressure measurements during exercise sessions rather than his daily, low intensity, activities.
  • Such activity tracker will identify when such a session is started and enable the rest detection module to trigger the blood pressure measurements.
  • Figure 1 is an illustration of the system that implements the methods for triggering blood pressure measurement in the proper time periods.
  • Figure 2 is an example graph illustrating the ability to identify rest time periods by applying a threshold on the variance index level of acceleration magnitude.
  • Figure 3 is a schematic illustration of the components included in the device for measurements blood pressure during rest periods.
  • Figure 4 is a schematic illustration of the components included in the device for measurements blood pressure via skin.
  • Figure 5 is a schematic illustration of the components included in the pressure sensor for blood pressure measurements.
  • Figure 6 is the flow chart that illustrates the data sources used in the process of identification of time slots suitable for blood pressure measurements without movement artifacts.
  • Figure 7 is the flow chart that illustrates the algorithm of identification of time slots suitable for blood pressure measurements without movement artifacts.
  • the device includes a control unit, a memory device, an electric power source, a clock and a communication module that is used to transmit the processed data to a smart phone or computer, one or more single or multi-axis inertial sensors, at least one pressure sensor and an optional, additional Artifact Correlated Data source.
  • the device can have a display unit to display the blood pressure measures.
  • Blood pressure can be sensed in a variety of methods.
  • the best mode of implementation is obtained by sensitive pressure sensor or sensors located close to the radial artery, and optionally, to the ulnar artery.
  • the sensor translates the pressure sensed on the skin to electrical signal as described above.
  • the signal is then processed to generate a value of adjusted pressure level of the radial artery.
  • Other methods such as Pulse Transit Time (“PTT”) or Pulse Wave Velocity (PWV) may also be employed to determine BP values.
  • PTT Pulse Transit Time
  • PWV Pulse Wave Velocity
  • the adjustment of the pressure level at rest provides a calibration reference that is important for the best mode of implementation.
  • the calibration of the pressure sensors is initialized upon wearing the device and while the arm is at complete rest.
  • a number of methods for calibrating the pressure sensors for best mode of implementations on the radial and, optionally, the ulnar arteries are described.
  • the user's arm wearing the wristband is required to be at complete rest during the calibration, and a real-time feedback may be displayed or otherwise communicated to the user to confirm that the wristband is indeed at rest. Rest is determined by lack of movement of the multi-axis inertial sensors.
  • the wristband contains markers that facilitate the calibration. When the user wears the wristband, feedback, such as in the form of changing colors, is given by the wristband or a companion device on the signal quality level that is sensed by the sensors. The user adjusts the wristband when the maximum signal quality is extracted.
  • the user can touch with her or his index a predefined contact pad on the wristband to close a loop with the other hand, and therefore enabling acquisition of ECG signal which is considered as the gold standard source for heart rate measurements. That accurate heart rate values and beat to beat periods can be used to synchronize the analysis algorithm while processing the blood pressure waveform.
  • a blood pressure patch or armband (“Brachial Patch”) that contains a pressure sensor, a control unit and communications, is placed on the brachial artery at the level of the heart.
  • the measurement that is extracted from that Brachial Patch is used to calibrate coefficients of the measurements that are applied to the wristband sensors that are on the radial and, optionally, the ulnar arteries.
  • a certain level of pressure sensed by the pressure sensors at the wrist is optimally leveled to reconstruct an accurate blood pressure waveform, and the systolic and diastolic values.
  • the calibration can be repeated from time to time.
  • the device includes a control unit, e.g., a microprocessor, which is responsible for real-time quantitative analysis of dynamics index variance.
  • a control unit e.g., a microprocessor
  • that index can be proportional to the moving coefficient of variation of the 3 dimensional acceleration magnitude.
  • the acceleration magnitude is the momentary root mean square of the 3 dimensional acceleration vector. That magnitude waveform is divided into an overlapping block of 5 seconds (or other predefined intervals), and for each block the coefficient of variation is calculated as the ratio between the standard deviation and the sample mean.
  • the popular CPU architecture ARM Cortex M3 with the appropriate peripherals such as analog to digital converters and/or serial communication interfaces can be programmed to acquire and perform such processing methods.
  • the control unit samples the accelerometer at a frequency that is high enough to be able to reconstruct the noise artifact waveform, for example 400Hz, and so as to calculate and extract statistical index of the variation of the acceleration magnitude. If the index exceeds predefined threshold level then the user is considered in motion.
  • the device also includes multi- axial inertial sensor that continuously or intermittently measures the acceleration and/or rotation levels and transmit it to the control unit.
  • multi-axial inertial sensor is a three-dimensional accelerometer.
  • that device can be utilized to operate according to the Cancellation Method.
  • the inertial dynamic measurements from the inertial sensors are treated as a Noise correlated Data, i.e has similar statistical characteristics compared to the noise.
  • That data is combined with the raw blood pressure waveform into an adaptive filter that estimates the noisy component of the raw blood pressure.
  • Adaptive filtering methods are extensively used for such tasks but one can use alternative filtering schemes to extract the noisy component from the raw blood pressure waveform.
  • the device also includes at least one pressure sensor on the radial artery and an optical heart beat monitor (as an additional Artifact Correlated Data source). Additional pressure sensors can be placed on the ulnar artery.
  • the systolic and diastolic values can be extracted from the pressure waveform by several methods.
  • Prior arts dealing with the task of extracting blood pressure readings from non-invasive pressure sensors are based on the measurement of Pulse Transition Time (PTT) and Pulse Wave Velocity (PWV).
  • PTT Pulse Transition Time
  • PWV Pulse Wave Velocity
  • the blood pressure values are estimated by applying machine learning techniques on a vector of features extracted from the blood pressure waveform and other several user characteristics. That technique requires a preliminary process of training the algorithm to accurately estimate the blood pressure values, while minimizing the error with respect to a gold standard blood pressure monitor.
  • the device also includes a battery and voltage regulator and may include or may not include peripheral components such as the following:
  • Display unit e.g. LCD is used to display the blood pressure measures.
  • Communication module e.g., Bluetooth or wired interface
  • Communication module is able to transmit blood pressure measures, alerts and statuses to an external display module, smartphone or a PC. Also it able to receive relevant data needed to calibrate the analysis process in the control unit.
  • the device includes an inertial sensor to identify the time period where the user does not moves his hand. If the dynamic measurement values very low variation, that can be quantified for example as the coefficient of variance (100* Standard deviation/ Average) of the multiple reading within a certain time frame, that is below 10%, below 3% or below 1% this is an indication the device and the body are not moving.
  • the coefficient of variance 100* Standard deviation/ Average
  • the device includes a control and processing unit and a pressure sensor located on the skin just above a significantly pulsating artery such as the radial artery on the wrist.
  • a significantly pulsating artery such as the radial artery on the wrist.
  • low noise MEMS capacitive pressure sensors are suitable for such application.
  • the pressure sensor provides a raw in-arterial proportional pressure waveform that is analyzed on the processing unit to provide Systolic and Diastolic blood pressure estimations.
  • the device also includes a communication module that is used to transmit the processed data to a smart phone or computer. Alternatively the device can have a display unit to display the measurement results.
  • the device is a wearable device that can be worn as a bracelet, or as a ring, or as an earring or as a necklace.
  • the blood pressure measurement is considered valid and reported to the user only if all the following conditions are fulfilled:
  • the read out from the inertial sensors indicate that the subject is not moving. This classification is based on a sequential measurement of the dynamic values of the user's hand. If the dynamic values shows very low variation, that can be quantified for example as the coefficient of variance (100* Standard deviation/ Average) of the multiple reading within a certain time frame, that is below 10%, below 3% or below 1% this is an indication the device and the body are not moving.
  • the coefficient of variance 100* Standard deviation/ Average
  • the read out of the blood pressure sensor indicate a steady measurement with low variation. This classification is based on a sequential measurement of the BP during time segment of, 10 seconds, 30 seconds, 1 minute, or 5 minutes, or 10 minutes. If the readout shows very low variation, that can be quantified for example as the coefficient of variance (100* Standard deviation/Average) of the multiple reading within a certain time frame, that is below 5% , below 1% or below 0.5% this is an indication that the blood pressure measurement is steady.
  • the coefficient of variance 100* Standard deviation/Average
  • the systolic has to be in preconfigured ranges, typically, between 90 to 240 mm Hg.
  • the diastolic is in the preconfigured ranges, typically, between 50 to 110 mm Hg.

Abstract

A method and device for determining the right time periods to trigger pressure based blood pressure measurement based on the variation of multi-dimensional dynamic values of the hand.

Description

A WEARABLE DEVICE AND METHOD FOR NON-INVASIVE MONITORING CONTINUOUS BLOOD PRESSURE AND OTHER PHYSIOLOGICAL PARAMETERS WITH REDUCED MOVEMENT ARTIFACTS
FIELD OF THE INVENTION
The present invention pertains to a device and method for non-invasive monitoring continuous blood pressure and other physiological parameters and more particularly to a wearable device and method for monitoring physiological parameters with reduced movement artifacts.
BACKGROUND
The use of wearable devices for monitoring non-invasively, body physiological parameters (e.g. blood pressure, heart rate (HR) pulse, body temperature, blood glucose level, movement patterns) continuously and/or intermittently for extended periods of time, are becoming popular as a way to monitor and improve health.
Blood pressure can be estimated by measuring the mechanical pressure applied by the blood volume fluctuations in arteries under the tissue. Sensitive pressure sensors and small signals converters attached to the skin sensing the mechanical movements of the tissue. The mechanical changes reflected from the skin are coupled with the blood density in the artery and is affected by the mechanical characteristics of it
The pressure sensor converts that pressure to electrical voltage that is proportional to it. The measured voltage waveform is characterized by a set of features that were correlated with the actual blood pressure. A micro controller extracts those features from the pressure waveform and applies a linear filter on it in order to estimate the real blood pressure values. The coefficients of the filter are pre-determined using machine learning methods.
The major problem of the pressure sensing method is its high sensitivity to physical motion. Any measurement that involves measurement of blood flow features within living bodies, particularly by a wearable device, is prone to movement artifact. The two main sources are: 1, the relative movement of the device or sensor relative to the body, and 2, changes in the tissue due to the body movement, e.g. pressure wave in the blood circulation system that affects volume and shape of blood vessels during the movement. This pressure wave is measured as undesirable artifact in the pressure signal, which is sometimes much more significant than the typical pulse fluctuations making the latter unnoticeable.
Current solutions for constant and under way blood pressure meters are based on pump and sensing which is robust against motion interference but it requires clumsy sensing methods which are not applicable for wearable devices. Such methods are quite expensive in terms of system and energy resources that may prevent its use in wearable device that have to have ultra-low energy consumption.
The following prior art patents deal with extraction of blood pressure readings by pressure sensors: US 2013/0304112 Al and WO 2014/195578 Al.
BRIEF SUMMARY OF THE INVENTION
The present invention relies on direct pressure sensing of the radial artery at the wrist. The best mode of implementation describes a calibration step that is applied when wearing the wristband.
This invention provides a new method and device for pressure sensor based blood pressure monitoring while avoiding motion artifact problems. The method is based on identifying the proper time slots in between for the static BP meter to be triggered.
An object of the present invention is to provide a wearable device and method for non-invasive monitoring continuous (or intermittent) blood pressure and other physiological parameters with reduced movement artifacts. This invention provides a new method and device for pressure sensor based blood pressure monitoring while avoiding motion artifact problems. Pressure sensing is obtained by locating at least one sensitive pressure sensor upon the radial artery. The calibration of the wristband as provided in the best mode of implementation is pursued in order to provide the best signal to noise values from the pressure sensor/s. Additional sensors can be located on the ulnar artery to increase the precision that can be extracted from the pressure sensors.
The method is based on identifying the proper time slots in between for the static BP meter to be triggered ("Avoidance Method"). In addition, the method includes a real time movement cancellation on the basis of other movement-correlated data ("Artifact Correlated Data"), such as, but not limited to, single or multiple inertial sensors based movement characteristics, optical-based detection inputs, and/or frequency analysis algorithms ("Cancellation Method").
The Avoidance Method is based on identification of a time period where the user does not significantly move the body part the device is attached to, for example, the wrist. This classification is based on measurements of the dynamic motion values of the user's body part to which the device is attached. If the rotation and/or acceleration values show low variation, that can be quantified, for example, and not limited to, as the coefficient of variance (100* Standard deviation/ Aver age) of the multiple reading within a certain time frame, that is, for example, below 10%, below 3% or below 1% this is an indication the device and the body are not moving. The time frame referred above is in the order of seconds.
Typical blood pressure drop values in the first rest minute after exercise ranges between 5-10%1. Furthermore, the amount of blood pressure drop is an indicator to a proper heart operation and each user can be characterized by such parameter. This parameter can be used to estimate the exercise BP values from the BP measurements taken several seconds after it was ended.
The Cancellation Method relies on taking BP measures, either continuous or intermittent, during movement; Artifact Correlated Data during the same time interval is acquired. An http://digitalcommons.wku.edu/cgi/viewcontent.cgi?article=1169&context=ijes adaptive filtering technique is applied upon the BP readings with the Artifact Correlated Data, and an adjusted BP waveform signal is produced. An accuracy scoring is attributed to the adjusted BP waveform on the basis of remaining noise correlated components; and, a reading of systolic and diastolic BP values are produced if critical threshold of minimum accuracy is attributed to the particular adjusted BP waveform.
The device includes a control unit, a memory device, an electric power source, a clock and a communication module that is used to transmit the processed data to a smart phone or computer, one or more single or multi-axis inertial sensors, at least one pressure sensor and an optional, additional Artifact Correlated Data source. Alternatively the device can have a display unit to display the blood pressure measures.
In another embodiment of the invention the device includes as well an activity identification component that identifies the type and intensity done by the user. This feature allows discriminating between motion and rest time slots (also during intense activities) and along with a user predefined scheme - triggering accurate blood pressure measurements. For example, user may prefer timing the majority of his blood pressure measurements during exercise sessions rather than his daily, low intensity, activities. Such activity tracker will identify when such a session is started and enable the rest detection module to trigger the blood pressure measurements.
BRIEF DESCRIPTION OF THE DRAWINGS
Figure 1 is an illustration of the system that implements the methods for triggering blood pressure measurement in the proper time periods.
Figure 2 is an example graph illustrating the ability to identify rest time periods by applying a threshold on the variance index level of acceleration magnitude.
Figure 3 is a schematic illustration of the components included in the device for measurements blood pressure during rest periods.
Figure 4 is a schematic illustration of the components included in the device for measurements blood pressure via skin. Figure 5 is a schematic illustration of the components included in the pressure sensor for blood pressure measurements.
Figure 6 is the flow chart that illustrates the data sources used in the process of identification of time slots suitable for blood pressure measurements without movement artifacts.
Figure 7 is the flow chart that illustrates the algorithm of identification of time slots suitable for blood pressure measurements without movement artifacts.
DETAILED DESCRIPTION AND BEST MODE OF IMPLEMENTATION
The device includes a control unit, a memory device, an electric power source, a clock and a communication module that is used to transmit the processed data to a smart phone or computer, one or more single or multi-axis inertial sensors, at least one pressure sensor and an optional, additional Artifact Correlated Data source. Alternatively the device can have a display unit to display the blood pressure measures.
Blood pressure can be sensed in a variety of methods. The best mode of implementation is obtained by sensitive pressure sensor or sensors located close to the radial artery, and optionally, to the ulnar artery. The sensor translates the pressure sensed on the skin to electrical signal as described above. The signal is then processed to generate a value of adjusted pressure level of the radial artery. Other methods such as Pulse Transit Time ("PTT") or Pulse Wave Velocity (PWV) may also be employed to determine BP values. The adjustment of the pressure level at rest provides a calibration reference that is important for the best mode of implementation.
The calibration of the pressure sensors is initialized upon wearing the device and while the arm is at complete rest. A number of methods for calibrating the pressure sensors for best mode of implementations on the radial and, optionally, the ulnar arteries are described. The user's arm wearing the wristband is required to be at complete rest during the calibration, and a real-time feedback may be displayed or otherwise communicated to the user to confirm that the wristband is indeed at rest. Rest is determined by lack of movement of the multi-axis inertial sensors. (1) The wristband contains markers that facilitate the calibration. When the user wears the wristband, feedback, such as in the form of changing colors, is given by the wristband or a companion device on the signal quality level that is sensed by the sensors. The user adjusts the wristband when the maximum signal quality is extracted.
(2) Optionally, the user can touch with her or his index a predefined contact pad on the wristband to close a loop with the other hand, and therefore enabling acquisition of ECG signal which is considered as the gold standard source for heart rate measurements. That accurate heart rate values and beat to beat periods can be used to synchronize the analysis algorithm while processing the blood pressure waveform.
(3) In best mode of implementation for highly accurate measurements, a blood pressure patch or armband ("Brachial Patch") that contains a pressure sensor, a control unit and communications, is placed on the brachial artery at the level of the heart. The measurement that is extracted from that Brachial Patch is used to calibrate coefficients of the measurements that are applied to the wristband sensors that are on the radial and, optionally, the ulnar arteries.
Once calibrated, a certain level of pressure sensed by the pressure sensors at the wrist is optimally leveled to reconstruct an accurate blood pressure waveform, and the systolic and diastolic values. The calibration can be repeated from time to time.
The device includes a control unit, e.g., a microprocessor, which is responsible for real-time quantitative analysis of dynamics index variance. As an example, that index can be proportional to the moving coefficient of variation of the 3 dimensional acceleration magnitude. The acceleration magnitude is the momentary root mean square of the 3 dimensional acceleration vector. That magnitude waveform is divided into an overlapping block of 5 seconds (or other predefined intervals), and for each block the coefficient of variation is calculated as the ratio between the standard deviation and the sample mean. As an example, the popular CPU architecture ARM Cortex M3 with the appropriate peripherals such as analog to digital converters and/or serial communication interfaces can be programmed to acquire and perform such processing methods. The control unit samples the accelerometer at a frequency that is high enough to be able to reconstruct the noise artifact waveform, for example 400Hz, and so as to calculate and extract statistical index of the variation of the acceleration magnitude. If the index exceeds predefined threshold level then the user is considered in motion.
The device also includes multi- axial inertial sensor that continuously or intermittently measures the acceleration and/or rotation levels and transmit it to the control unit. An example of a multi-axial inertial sensor is a three-dimensional accelerometer.
In addition, that device can be utilized to operate according to the Cancellation Method. In that case, the inertial dynamic measurements from the inertial sensors are treated as a Noise correlated Data, i.e has similar statistical characteristics compared to the noise. That data is combined with the raw blood pressure waveform into an adaptive filter that estimates the noisy component of the raw blood pressure. Adaptive filtering methods are extensively used for such tasks but one can use alternative filtering schemes to extract the noisy component from the raw blood pressure waveform.
The device also includes at least one pressure sensor on the radial artery and an optical heart beat monitor (as an additional Artifact Correlated Data source). Additional pressure sensors can be placed on the ulnar artery.
Once an adequately artifact-less signal is obtained, either by Avoidance or Cancellation methods, the systolic and diastolic values can be extracted from the pressure waveform by several methods. Prior arts dealing with the task of extracting blood pressure readings from non-invasive pressure sensors are based on the measurement of Pulse Transition Time (PTT) and Pulse Wave Velocity (PWV). In that application the blood pressure values are estimated by applying machine learning techniques on a vector of features extracted from the blood pressure waveform and other several user characteristics. That technique requires a preliminary process of training the algorithm to accurately estimate the blood pressure values, while minimizing the error with respect to a gold standard blood pressure monitor.
The device also includes a battery and voltage regulator and may include or may not include peripheral components such as the following:
i) Display unit, e.g. LCD is used to display the blood pressure measures.
ii) Communication module, e.g., Bluetooth or wired interface, is able to transmit blood pressure measures, alerts and statuses to an external display module, smartphone or a PC. Also it able to receive relevant data needed to calibrate the analysis process in the control unit.
In yet another embodiment of the invention the device includes an inertial sensor to identify the time period where the user does not moves his hand. If the dynamic measurement values very low variation, that can be quantified for example as the coefficient of variance (100* Standard deviation/ Average) of the multiple reading within a certain time frame, that is below 10%, below 3% or below 1% this is an indication the device and the body are not moving.
Another embodiment of this invention is a device and method for estimation of blood pressure measurement. The device includes a control and processing unit and a pressure sensor located on the skin just above a significantly pulsating artery such as the radial artery on the wrist. In particular, but not necessarily, low noise MEMS capacitive pressure sensors are suitable for such application. The pressure sensor provides a raw in-arterial proportional pressure waveform that is analyzed on the processing unit to provide Systolic and Diastolic blood pressure estimations. The device also includes a communication module that is used to transmit the processed data to a smart phone or computer. Alternatively the device can have a display unit to display the measurement results. The device is a wearable device that can be worn as a bracelet, or as a ring, or as an earring or as a necklace.
According to the method of this invention the blood pressure measurement is considered valid and reported to the user only if all the following conditions are fulfilled:
iii) The read out from the inertial sensors indicate that the subject is not moving. This classification is based on a sequential measurement of the dynamic values of the user's hand. If the dynamic values shows very low variation, that can be quantified for example as the coefficient of variance (100* Standard deviation/ Average) of the multiple reading within a certain time frame, that is below 10%, below 3% or below 1% this is an indication the device and the body are not moving.
iv) The read out of the blood pressure sensor, indicate a steady measurement with low variation. This classification is based on a sequential measurement of the BP during time segment of, 10 seconds, 30 seconds, 1 minute, or 5 minutes, or 10 minutes. If the readout shows very low variation, that can be quantified for example as the coefficient of variance (100* Standard deviation/Average) of the multiple reading within a certain time frame, that is below 5% , below 1% or below 0.5% this is an indication that the blood pressure measurement is steady.
v) The systolic has to be in preconfigured ranges, typically, between 90 to 240 mm Hg. The diastolic is in the preconfigured ranges, typically, between 50 to 110 mm Hg.
vi) If all the above conditions are fulfilled than the blood pressure can be extrapolated from body reliably.

Claims

CLAIMS What is claimed is:
1. A method and device for measuring continuous or intermittent BP waveform non-invasively at any time.
2. A method and device for measuring continuous or intermittent BP waveform (and values) non-invasively by applying the Avoidance Method.
3. A method and device for measuring continuous or intermittent BP waveform (and values) non-invasively by applying the Cancellation Method.
4. Technique for identifying relative rest intervals on the basis of Artifact Correlated Data analysis.
5. Technique for scoring adjusted BP waveform measures while in movement.
6. A technique to measuring or extacting BP waveform by a patch that is applied on the brachial artery.
7. A technique to use the measure taken by a brachial artery path in order to calibrate a wristband device that uses BP sensor/s on the radial, and optionally, the ulnar arteries.
8. A method and device for determining the right time periods to trigger pressure based BP measurement based on Artifact Correlated Data.
9. A method and a device for determining the right time periods to trigger BP measurement.
PCT/CA2015/051104 2014-10-30 2015-10-29 A wearable device and method for non-invasive monitoring continuous blood pressure and other physiological parameters with reduced movement artifacts WO2016065476A1 (en)

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Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018081208A1 (en) * 2016-10-31 2018-05-03 Livemetric (Medical) S.A. Blood pressure signal acquisition using a pressure sensor array
US10716518B2 (en) 2016-11-01 2020-07-21 Microsoft Technology Licensing, Llc Blood pressure estimation by wearable computing device
US11000193B2 (en) 2017-01-04 2021-05-11 Livemetric (Medical) S.A. Blood pressure measurement system using force resistive sensor array
EP3860446A4 (en) * 2018-11-23 2021-11-24 Samsung Electronics Co., Ltd. Electronic device for obtaining blood pressure value using pulse wave velocity algorithm and method for obtaining blood pressure value
CN113925477A (en) * 2021-07-08 2022-01-14 哈尔滨铂云医疗器械有限公司 Automatic calibration system of electronic sphygmomanometer
US11259130B2 (en) 2018-12-14 2022-02-22 Widex A/S Hearing assistive system with sensors
US11670422B2 (en) 2017-01-13 2023-06-06 Microsoft Technology Licensing, Llc Machine-learning models for predicting decompensation risk
US11832949B2 (en) 2018-12-14 2023-12-05 Widex A/S Monitoring system comprising a master device in wireless communication with at least one slave device having a sensor

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116999054A (en) * 2017-05-19 2023-11-07 北京麦迪克斯科技有限公司 Physiological information acquisition device and method based on motion state
CA3070346A1 (en) * 2017-07-17 2019-01-24 Livemetric (Medical) S.A. Method and system for ventricular assistive device adjustment using a wearable device
KR20200021711A (en) * 2018-08-21 2020-03-02 주식회사 아모텍 System for managing body temperature based on patch type thermometer and method thereof
KR102605900B1 (en) 2018-09-18 2023-11-23 삼성전자주식회사 Apparatus and method for estimating bio-information

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2008015598A2 (en) * 2006-08-02 2008-02-07 Philips Intellectual Property & Standards Gmbh Sensor for detecting the passing of a pulse wave from a subject´s arterial system
US7674231B2 (en) * 2005-08-22 2010-03-09 Massachusetts Institute Of Technology Wearable pulse wave velocity blood pressure sensor and methods of calibration thereof
US20110166461A1 (en) * 2008-08-15 2011-07-07 Suesstrunk Heinz Equipment and method for continually measuring the blood pressure for monitoring purposes
US8313439B2 (en) * 2009-03-20 2012-11-20 Massachusetts Institute Of Technology Calibration of pulse transit time measurements to arterial blood pressure using external arterial pressure applied along the pulse transit path
EP2191771B1 (en) * 2008-11-27 2013-05-22 Samsung Electronics Co., Ltd. Portable device for measuring blood pressure and method therefor
US8672854B2 (en) * 2009-05-20 2014-03-18 Sotera Wireless, Inc. System for calibrating a PTT-based blood pressure measurement using arm height
CA2894944A1 (en) * 2012-12-13 2014-06-19 Cnv Systems Ltd. System for measurement of cardiovascular health
WO2014195578A1 (en) * 2013-06-03 2014-12-11 Medieta Oy Blood pressure measurement device
US20150112606A1 (en) * 2013-10-23 2015-04-23 Quanttus, Inc. Calculating Pulse Transit Time
WO2015107269A1 (en) * 2014-01-16 2015-07-23 Medieta Oy Device and method for measuring arterial signals

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
ATE472292T1 (en) * 2002-12-10 2010-07-15 Koninkl Philips Electronics Nv WEARABLE DEVICE FOR BIOELECTRIC INTERACTION WITH MOTION ARTIFACT CORRECTING AGENTS

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7674231B2 (en) * 2005-08-22 2010-03-09 Massachusetts Institute Of Technology Wearable pulse wave velocity blood pressure sensor and methods of calibration thereof
WO2008015598A2 (en) * 2006-08-02 2008-02-07 Philips Intellectual Property & Standards Gmbh Sensor for detecting the passing of a pulse wave from a subject´s arterial system
US20110166461A1 (en) * 2008-08-15 2011-07-07 Suesstrunk Heinz Equipment and method for continually measuring the blood pressure for monitoring purposes
EP2191771B1 (en) * 2008-11-27 2013-05-22 Samsung Electronics Co., Ltd. Portable device for measuring blood pressure and method therefor
US8313439B2 (en) * 2009-03-20 2012-11-20 Massachusetts Institute Of Technology Calibration of pulse transit time measurements to arterial blood pressure using external arterial pressure applied along the pulse transit path
US8672854B2 (en) * 2009-05-20 2014-03-18 Sotera Wireless, Inc. System for calibrating a PTT-based blood pressure measurement using arm height
CA2894944A1 (en) * 2012-12-13 2014-06-19 Cnv Systems Ltd. System for measurement of cardiovascular health
WO2014195578A1 (en) * 2013-06-03 2014-12-11 Medieta Oy Blood pressure measurement device
US20150112606A1 (en) * 2013-10-23 2015-04-23 Quanttus, Inc. Calculating Pulse Transit Time
WO2015107269A1 (en) * 2014-01-16 2015-07-23 Medieta Oy Device and method for measuring arterial signals

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
HAN ET AL.: "Development of real-time motion artifact reduction algorithm for a wearable photoplethysmography", PROCEEDINGS OF THE 29TH ANNUAL INTERNATIONAL CONFERENCE OF IEEE EMBS, 2007, Lyon, France, pages 1538 - 1541, XP031336475, Retrieved from the Internet <URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4352596> [retrieved on 20160126] *
LEE ET AL.: "Development of a Wristwatch-Type PPG array Sensor Module", PROCEEDINGS OF THE IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS, 6 September 2011 (2011-09-06), Berlin, Germany, pages 168 - 171, XP031968551, Retrieved from the Internet <URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6031811> [retrieved on 20160126] *
WEI ET AL.: "A New Wristband Wearable Sensor Using Adaptive Reduction Filter to Reduce Motion Artifact'',.", PROCEEDINGS OF THE 5TH INT INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND APPLICATIONS IN BIOMEDICINE (ITAB 2008), IN CONJUNCTION WITH THE 2ND INTERNATION SYMPOSIUM * SUMMER SCHOOL ON BIOMEDICAL AND HEALTH ENGINEERING, 30 May 2008 (2008-05-30), Shenzhen, China, pages 278 - 281, XP031289428, Retrieved from the Internet <URL:https://www.researchgate.net/publication/224320913_A_new_wristband_wearable_sensor_using_adaptive_reduction_filter_to_reduce_motion_artifact> [retrieved on 20160126] *
YOUSEFI ET AL.: "Adaptive Cancellation of Motion Artifact in Wearable Biosensors", 34TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE EMBS, 28 August 2012 (2012-08-28), San Diego, California, USA, pages 2004 - 2208, XP032463331, Retrieved from the Internet <URL:http://ecs.utdallas.edu/research/researchlabs/QoLT/files/pdfs/EMBC12_Adaptive.pdf> [retrieved on 20160126] *

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018081208A1 (en) * 2016-10-31 2018-05-03 Livemetric (Medical) S.A. Blood pressure signal acquisition using a pressure sensor array
US10722125B2 (en) 2016-10-31 2020-07-28 Livemetric (Medical) S.A. Blood pressure signal acquisition using a pressure sensor array
US10716518B2 (en) 2016-11-01 2020-07-21 Microsoft Technology Licensing, Llc Blood pressure estimation by wearable computing device
US11000193B2 (en) 2017-01-04 2021-05-11 Livemetric (Medical) S.A. Blood pressure measurement system using force resistive sensor array
US11670422B2 (en) 2017-01-13 2023-06-06 Microsoft Technology Licensing, Llc Machine-learning models for predicting decompensation risk
EP3860446A4 (en) * 2018-11-23 2021-11-24 Samsung Electronics Co., Ltd. Electronic device for obtaining blood pressure value using pulse wave velocity algorithm and method for obtaining blood pressure value
US11259130B2 (en) 2018-12-14 2022-02-22 Widex A/S Hearing assistive system with sensors
US11832949B2 (en) 2018-12-14 2023-12-05 Widex A/S Monitoring system comprising a master device in wireless communication with at least one slave device having a sensor
CN113925477A (en) * 2021-07-08 2022-01-14 哈尔滨铂云医疗器械有限公司 Automatic calibration system of electronic sphygmomanometer
CN113925477B (en) * 2021-07-08 2023-10-27 哈尔滨铂云医疗器械有限公司 Automatic calibration system of electronic sphygmomanometer

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