Systems, devices, and methods for non-invasive cardiac monitoring

Abstract

The devices, systems, and methods herein relate to non-invasive cardiac monitoring of cardiac parameters such as blood pressure. These systems and methods can receive and process user and cohort cardiac data to produce mechanical cardiac parameter values for estimating blood pressure. In some variations, methods can include a step of receiving mechanical cardiac data of a user measured using an accelerometer. Mechanical cardiac parameter values for a first time period and a second time period can be produced from the mechanical cardiac data. The blood pressure of the user can be estimated based on a change in the mechanical cardiac parameter values between the first and second time periods.

Description

[0001] Cross-reference to related applications

[0002] This application claims priority to U.S. Provisional Application No. 62 / 827,726, filed April 1, 2019, the contents of which are hereby incorporated herein by reference in their entirety. Technical Field

[0003] The devices, systems, and methods described in this article relate to noninvasive cardiac monitoring for estimating the physiological characteristics of patients. Background Technology

[0004] For example, vital signs such as blood pressure and heart rate are commonly used to indicate a subject's status and health condition. Blood pressure measurements are typically performed in a clinical setting using a blood pressure cuff and sphygmomanometer, which can be cumbersome and impractical for continuous and / or ambulatory blood pressure monitoring. Other blood pressure measurement techniques involve calculations based on pulse conduction time (PTT). PTT uses electrocardiogram (ECG) and vascular volumetric (PPG) measurements to measure the time it takes for a pulse wave to travel between two arterial sites. However, the accuracy of blood pressure estimation using PTT can be adversely affected by factors such as the subject's age, sex, body composition, strength level, weight, and anatomical variations. Furthermore, blood pressure estimation relying on the time point indicated by the ECG R wave may be inaccurate due to the inclusion of the pre-ejection phase (PEP), during which the left ventricle of the heart undergoes isovolumetric contraction and blood has not yet left the chamber through the aortic valve. A more accurate estimate of the blood pulse wave traveling from the heart to distal locations can rely on mechanical indicators of valve opening. Therefore, additional devices, systems, and methods may be required for blood pressure estimation. Summary of the Invention

[0005] This document describes cardiac monitoring devices, systems, and methods for providing real-time, non-invasive monitoring of one or more cardiac parameters, which can be used to estimate a patient's physiological characteristics, such as blood pressure. These systems and methods may, for example, receive a user's cardiac data (e.g., reference blood pressure, ECG data, scintigraphy (SCG) data), process the data to produce a value for at least one mechanical cardiac parameter, and estimate the physiological characteristic of interest, such as blood pressure, based on the received and processed data. This can, for example, allow for insights into the user's vital signs on a continuous or semi-continuous real-time basis. The devices described herein for estimating physiological characteristics can be compact and portable, allowing for continuous or semi-continuous real-time monitoring without limiting the user's daily activities.

[0006] In some variations, the method for estimating blood pressure may include: receiving mechanical heart data of a user measured using an accelerometer; generating mechanical heart parameter values ​​for a first time period and a second time period from the mechanical heart data; and estimating the user's blood pressure based on changes in the mechanical heart parameter values ​​between the first and second time periods. In some variations, the method may further include receiving the user's reference blood pressure and cohort heart data associated with the user. In some of these variations, the estimated blood pressure may also be based on the reference blood pressure and the cohort heart data.

[0007] In some variations, generating mechanical cardiac parameter values ​​may include generating mechanical cardiac parameter values ​​for multiple mechanical cardiac parameters. In these variations, the estimated blood pressure may be based on the sum of the changes in the mechanical cardiac parameter values ​​for multiple mechanical cardiac parameters between first and second time periods.

[0008] In some of these variations, at least one of the multiple mechanical heart parameters is selected from the group consisting of: SCG wave amplitude, maximum L2 distance, area under the power spectral density curve, sample entropy, and R-wave wavelength. In some variations, the mechanical heart data may include an SCG wave. In some of these variations, the SCG wave may include both SCG1 and SCG2 waves.

[0009] In some variations, the mechanocardiogram (MCG) data may include multiple SCG waves for each of the first and second time periods. Generating mechanocardiogram parameter values ​​may include generating average SCG waves for the first and second time periods. In some of these variations, the mechanocardiogram parameter values ​​may be derived from the average SCG waves. In some of these variations, the mechanocardiogram parameters may include SCG wave amplitude, maximum L2 norm or distance, area under the power spectral density curve, sample entropy, or R-wave wavelength. In some variations, the accelerometer may be an electrocardiogram (SCG) sensor.

[0010] In some variations, the method may further include receiving electrical cardiac data measured using electrodes. In some of these variations, the electrodes may be electrocardiogram (ECG) electrodes and the accelerometer may be an electrocardiogram (SCG) sensor. In some of these variations, the method may further include measuring electrical cardiac data as an ECG signal and mechanical cardiac data as an SCG signal.

[0011] In some of these variations, the method may further include generating multiple electrical cardiac parameter values ​​from electrical cardiac data. Electrical cardiac parameters may include one or more of heart rate, R-wave timing points, and T-wave timing points. In some of these variations, generating multiple electrical cardiac parameter values ​​may include generating R-wave timing points on the ECG waveform using sliding window integration. In some of these variations, generating multiple electrical cardiac parameter values ​​may include generating T-wave timing points on the ECG waveform using the R-wave timing points and derivatives of the electrical cardiac data. In some of these variations, the method may further include generating SCG wave timing points from mechanical cardiac data using the R-wave and T-wave timing points.

[0012] In some variations, the mechanical heart data may include first, second, and third seismogram waveforms measured along corresponding axes. The method may further include generating a fourth seismogram waveform that includes the first, second, and third seismogram waveforms.

[0013] In some variations, blood pressure is estimated using the following formula: Where i is the number of mechanical heart parameters, BP est It is the user's estimated blood pressure, BP. ref It is the user's reference blood pressure, β i It is the mechanical heart parameter value of the i-th queue, x 1,i It is the first value of the i-th mechanical heart parameter, and x 2,i It is the second value of the i-th mechanical heart parameter.

[0014] In some variants, the accelerometer can be releasably attached to the user's skin via the left thoracic cavity. In other variants, the accelerometer can be attached to the user perpendicular to the interclavicular line and near the intersection of the fifth intercostal space and the interclavicular line.

[0015] In some variations, cohort cardiac data may be grouped by one or more of age, sex, race, and body mass index. In some variations, the estimated blood pressure includes one or more of systolic and diastolic pressure. In some variations, the first time period may be a reference time period. Mechanical cardiac data may be initially measured using an accelerometer during the reference time period.

[0016] In some variations, the method for estimating blood pressure may include the following steps: receiving a user's reference blood pressure, cohort cardiac data associated with the user, and cardiac data of the user during first and second time periods. The method may further include measuring the cardiac data using electrodes and an accelerometer. The cardiac data may be processed to generate first and second values ​​of mechanical cardiac parameters corresponding to the respective first and second time periods. The method may further include estimating the user's blood pressure based on changes between the reference blood pressure, cohort cardiac data, and the first and second values ​​of the mechanical cardiac parameters.

[0017] In some variations, the method for estimating blood pressure may include the steps of receiving a user's reference blood pressure and the user's cardiac data during first and second time periods. The method may further include measuring cardiac data using an electrocardiogram (ECG) sensor and an electrocardiogram (SCG) sensor, each sensor attached to the skin of the user's left chest. The method may further include processing the cardiac data to generate electrical cardiac parameter values ​​corresponding to R-wave and T-wave time points, and generating first and second values ​​of mechanical cardiac parameters corresponding to the respective first and second time periods, at least in part, based on the R-wave and T-wave time points. The method may further include receiving cohort cardiac data of the mechanical cardiac parameters associated with the user. The method may further include estimating the user's blood pressure based on the variation between the reference blood pressure, the cohort cardiac data, and the first and second values ​​of the mechanical cardiac parameters.

[0018] The system is also described herein. In some variations, the cardiac monitoring system may include a cardiac monitor comprising a cardiac sensor including an accelerometer. The cardiac sensor may be configured to releasably attach to the skin of a user's left chest and measure cardiac data during first and second time periods. The cardiac monitor may further include communication means configured to establish a communication channel. A non-transitory processor-readable storage medium may be configured to be executed by a processor and include instructions for receiving cardiac data using the communication channel. The instructions may further include generating mechanical cardiac parameter values ​​from cardiac data during the first and second time periods. The instructions may further include estimating the user's blood pressure based on changes in the mechanical cardiac parameter values ​​between the first and second time periods.

[0019] In some variants, the non-transitory processor-readable storage medium further includes instructions for retrieving a user's reference blood pressure and queued cardiac data associated with the user. In some variants, the estimated blood pressure may also be based on the reference blood pressure and the queued cardiac data.

[0020] In some variations, the instructions for generating mechanical cardiac parameter values ​​may include instructions for generating mechanical cardiac parameter values ​​for multiple mechanical cardiac parameters. The estimated blood pressure may be based on the sum of the changes in the mechanical cardiac parameter values ​​of multiple mechanical cardiac parameters between first and second time periods.

[0021] In some variations, mechanical cardiac parameters may include SCG wave amplitude, maximum L2 norm, area under the power spectral density curve, sample entropy, and R-wave wavelength. In some variations, the cardiac monitor may further include electrodes configured to measure cardiac data associated with the heart's electrical activity. In some of these variations, the electrodes may be electrocardiogram (ECG) electrodes and the accelerometer may be an electrocardiogram (SCG) sensor.

[0022] The apparatus and system described herein can perform one or more steps of the methods described in more detail herein. In some variations, a non-transitory processor-readable storage medium can be configured to be executed by a processor and can include instructions for receiving cardiac data of a user during a first time period and a second time period. The instructions may further include retrieving the user's reference blood pressure and queued cardiac data associated with the user. The instructions may further include processing the cardiac data to generate first and second values ​​of mechanical cardiac parameters corresponding to the respective first and second time periods. The instructions may further include estimating the user's blood pressure based on the variation between the reference blood pressure, the queued cardiac data, and the first and second values ​​of the mechanical cardiac parameters.

[0023] Apparatus is also described herein. In some variations, the apparatus for estimating blood pressure may include a transceiver configured to receive a user's reference blood pressure, queued cardiac data associated with the user, cardiac data at first and second time periods, and first and second values ​​of mechanical cardiac parameters corresponding to the respective first and second time periods. A processor may be configured to estimate the user's blood pressure based on variations between the reference blood pressure, the queued cardiac data, and the first and second values ​​of the mechanical cardiac parameters.

[0024] In some variations, a cardiac monitor may be provided, and the cardiac monitor may include a cardiac sensor comprising electrodes and an accelerometer. The cardiac sensor may be configured to releasably attach to the skin on the user's left chest and measure cardiac data during a first and second time period. A memory may be configured to store the measured cardiac data, the user's reference blood pressure, and queued cardiac data associated with the user. A processor may be configured to process the cardiac data to generate first and second values ​​of mechanical cardiac parameters corresponding to the respective first and second time periods. The processor may be further configured to estimate the user's blood pressure based on changes between the reference blood pressure, the queued cardiac data, and the first and second values ​​of the mechanical cardiac parameters. Attached Figure Description

[0025] Figure 1A and 1B This is a top and bottom perspective view of an illustrative variant of a heart monitor that can be used to estimate blood pressure. Figure 1C yes Figure 1A The image shows a side view of a heart monitor. Figure 1D yes Figure 1A The image shows a cross-sectional side view of the heart monitor.

[0026] Figure 2 This is an exploded perspective view of an illustrative variant of a heart monitor.

[0027] Figure 3A This is a plan view illustrating a variant of the adhesive portion used in a heart monitor. Figure 3B yes Figure 3A An exploded perspective view of the adhesive portion shown.

[0028] Figure 4 This is an illustrative diagram of a variant attached to a user's heart monitor.

[0029] Figure 5 This is a schematic block diagram illustrating a variant of a cardiac monitoring system.

[0030] Figure 6A It is a perspective view of an illustrative variation of the mating parts. Figure 6B yes Figure 6A The cross-sectional side view of the mating parts shown.

[0031] Figure 7 This is a flowchart illustrating a variation of a method for estimating blood pressure.

[0032] Figure 8A and 8B It is an illustrative variation of the electrocardiogram (ECG) waveform of electrical heart activity and the electrocardiogram (SCG) waveform of mechanical heart activity.

[0033] Figure 9 This is a flowchart illustrating a variation of a method for determining electrical cardiac parameter values ​​that include R-wave and T-wave time points.

[0034] Figure 10 This is a flowchart illustrating a variation of a method for determining the parameter values ​​of a mechanical heart.

[0035] Figure 11 This is a flowchart illustrating a variation of a method for determining the parameter values ​​of a mechanical heart.

[0036] Figure 12 This is a flowchart illustrating a variation of a method for estimating blood pressure.

[0037] Figure 13 This is a graph of measured and estimated blood pressure data for men over 70 years of age.

[0038] Figure 14 This is a graph of measured and estimated blood pressure data for women aged 20 to 29.

[0039] Figures 15A to 15D It is a histogram of demographic information of a set of test subjects. Figure 15A This is an age distribution chart. Figure 15B This is a distribution chart of body mass index (BMI). Figure 15C It is a resting systolic pressure chart. Figure 15D It is a resting diastolic pressure chart.

[0040] Figure 16A It is a graph showing the error in systolic blood pressure. Figure 16B This is a graph showing the error in diastolic blood pressure. Detailed Implementation

[0041] Systems, devices, and methods for noninvasively monitoring a patient's physiological characteristics, such as blood pressure, are described herein. These systems, devices, and methods can receive and process user cardiac data to generate mechanical cardiac parameter values ​​that can be used to estimate blood pressure. The systems, devices, and methods can further receive reference blood pressure and cohort data, which can be used in conjunction with the mechanical cardiac parameters to estimate blood pressure. In some variations, user cardiac data can be noninvasively and continuously measured using a detachably attached cardiac monitor. As used herein, cardiac data can refer to electrical and / or mechanical cardiac data measured over a predetermined time period.

[0042] Generally, the system described herein may include a cardiac monitor and one or more of a computing device, a docking device, a network, a server, and a database. The cardiac monitor can measure a user's cardiac data, and in some variations, cardiac data can be transmitted to a computing device, a docking device, a remote server, and / or a database for processing and analysis. In other variations, cardiac data can be processed and analyzed on the cardiac monitor itself. As mentioned above, cardiac data may include electrical cardiac data and / or mechanical cardiac data. Electrical cardiac data can be measured using multiple electrodes, and mechanical cardiac data can be measured using an accelerometer. Cardiac data can be processed and analyzed to generate cardiac parameters, such as mechanical cardiac parameters and electrical cardiac parameters. In some variations, electrical cardiac parameter values, such as those at ECG R-wave and T-wave time points, can be used to determine mechanical cardiac parameter values, such as those at SCG wave time points. The user's physiological characteristics, such as blood pressure (e.g., systolic and diastolic blood pressure), can then be estimated using only the values ​​of one or more mechanical cardiac parameters. For example, physiological characteristics can be estimated based solely on mechanical cardiac parameter values ​​generated from mechanical cardiac data. In other words, physiological characteristics can be estimated without directly using values ​​from electrical cardiac data.

[0043] Cardiac data measurements and estimations of user physiological characteristics can be performed at predetermined intervals or continuously. The estimation results can be output to one or more of a cardiac monitor, computing device, interface, network, server, database, or a combination thereof. Additionally (e.g., simultaneously) or alternatively, the resulting blood pressure estimate can be output to one or more of a healthcare professional and a designated user (e.g., partner, family member, support group).

[0044] I. System

[0045] A cardiac monitoring system may include one or more of the components required to measure and / or generate cardiac parameter values ​​as described herein. Figure 5 This is a block diagram of variations of a cardiac monitoring system (500). As shown, the system (500) may include a cardiac monitor (510), a docking device (512), computing devices (520, 522), a network (530), a database (540), and a server (550). The system (500) may include a cardiac monitor (510) configured to be detachably attached to a user (502) and to measure the user's cardiac data. As described in more detail herein, the cardiac monitor (510) may include a cardiac sensor comprising multiple electrodes (e.g., a pair of electrodes) and an accelerometer. In some variations, the cardiac monitor (510) may be configured to be releasably attached to the skin of the user's left chest (e.g., above the user's chest cavity or near and below the user's collarbone).

[0046] The heart monitor (510) may further include communication means configured to establish a communication channel. The heart monitor (510) may be coupled to a computing device (520) via one or more wired or wireless communication channels. The computing device (520) may be operatively coupled to one or more networks (530), databases (540), and / or servers (550). The network (530) may include one or more databases (540) and servers (550). In some variations, another user (not shown), such as a healthcare professional, may be allowed to access the heart monitor (510) via a corresponding computing device (522). In some variations, the heart monitor (510) may be directly connected to any of the network (530), database (540), and server (550). In some variations, the heart monitor (510) may be coupled to a docking device (512) for storing, recharging, transmitting data, combinations thereof, etc.

[0047] In some variations, the measured cardiac data can be processed on any of the devices in the system (500), while in other variations, processing can be distributed across multiple devices. In some variations, cardiac data processing may include filtering the data (e.g., reducing noise, averaging waveforms), identifying key events in the cardiac waveform (e.g., R and T waves in the ECG waveform, SCG1 and SCG2 waves in the SCG waveform), and generating mechanical cardiac parameter values ​​for estimating physiological characteristics. In some variations, cardiac data and user identification information may be encrypted and stored in accordance with HIPAA regulations.

[0048] Heart monitor

[0049] Generally, the heart monitor described herein can be configured to measure a user's cardiac data over multiple time periods. In some variations, the measured cardiac data can be transmitted to a computing device for data processing and blood pressure estimation as described herein. The heart monitor can be controlled from one or more computing devices. In some variations, the heart monitor described herein can be configured to perform only a subset of the measurement, processing, and estimation steps described herein.

[0050] Figure 1A and 1B This is a top and bottom perspective view of the changes in the heart monitor (100). Figure 1C It is a side view, and Figure 1D yes Figure 1AThe image shows a cross-sectional side view of the heart monitor (100). As depicted herein, the heart monitor (100) may include a substrate (110), a mechanical heart activity sensor (120), an electrical heart activity sensor (130), and a controller (140). In some variations, the substrate (110) may include a flexible, biocompatible material. For example, the substrate (110) may include silicone or any suitable plastic, such as low-hardness urethane, nylon, and polyethylene.

[0051] A substrate (110) may define a first housing (112), a second housing (114), and a third housing (116). The housings (112, 114, 116) may be configured to house the electronic components of the heart monitor. For example, the housings (112, 114, 116) may be configured to house one or more components including a mechanical cardiac activity sensor (120) (e.g., an accelerometer), an electrical cardiac activity sensor (130, 132) (e.g., electrodes), a controller (140) (including, for example, a processor and memory), a communication device (e.g., a transceiver), and a power source (e.g., a battery). Figure 1D As depicted, in some variations, the mechanical heart activity sensor (120) and controller (140) may be housed in a first housing (112), the first electrical heart activity sensor (130) may be housed in a second housing (114), and the second electrical heart activity sensor (132) may be housed in a third housing (116).

[0052] The substrate (110) may include: a first side (e.g., a flat side) configured to contact or otherwise face the user's skin when the heart monitor (100) is in use; and a second side opposite the first side, configured to face away from the user in use. Each of the heart activity sensors (120, 130, 132) may be disposed on the first side of the substrate (110), which may be configured to contact the user's skin via a hydrogel of an adhesive portion (not shown in FIG. 1). This configuration allows the sensors (120, 130, 132) to generate cardiac data noninvasively and continuously without impairing user mobility and function. The second side of the substrate (110) may be configured to protect the heart activity sensors (120, 130, 132) from damage. The second housing (114) may be located on the first portion (e.g., the left side) of the heart monitor (110), the third housing (116) may be located on the second portion (e.g., the right side) of the heart monitor (110), and the first housing (112) may be located between the second and third housings (114, 116). Thus, the first and second electrical cardiac activity sensors (130, 132) may be spaced apart from each other. In some variations, the sensors (120, 130, 132) may be positioned along the longitudinal axis of the substrate (110). Furthermore, while the first housing (112) is depicted as having a square or rectangular shape, and the second and third (114, 116) housings are depicted as having a circular shape, this is not necessarily the case. In other variations, the housings may have different shapes, including, for example, geometric shapes (e.g., spheres, polygons), symbols (e.g., letters, numbers, signs), combinations thereof, etc.

[0053] As mentioned above, the heart monitor (110) may include: a mechanical heart activity sensor (120) configured to measure and / or generate mechanical heart data; and an electrical heart activity sensor (130, 132) configured to measure and / or generate electrical heart data. In some variations, the electrical heart data may include time-correlated electrocardiogram waveforms representing the electrical activity of the heart, and the mechanical heart data may include three time-correlated accelerometer waveforms along orthogonal axes corresponding to the mechanodynamics of the heart. In some variations, the mechanical heart activity sensor (120) may be an accelerometer, and the electrical heart activity sensor (130, 132) may be electrodes. In some variations, the mechanical heart activity sensor (120) may be an electrocardiogram (SCG) sensor, and the electrical heart activity sensor (130, 132) may be an electrocardiogram (ECG) electrode. In some variations, the electrical heart activity sensor (130, 132) may have a measurement range between approximately -5 mV and approximately 5 mV, with a resolution of approximately 0.000265 mV. In some variations, the mechanical heart activity sensor (120) may include a microelectromechanical system (MEMS) device. Alternatively, the mechanical heart activity sensor (120) may be configured to measure acceleration between approximately -4g and +4g, acceleration between approximately -2g and +2g, acceleration between approximately -8g and +8g, and acceleration between approximately -16g and +16g, including all values ​​and sub-ranges therebetween, with a resolution of approximately 0.488mg. For example, the mechanical heart activity sensor (120) may be a 14-bit sensor.

[0054] In some variations, the heart monitor may include one or more visual indicators (e.g., light-emitting diodes) configured to transmit the operating status of the heart monitor (e.g., on, recharging, measuring heart data, error, low power).

[0055] Figure 2 This is an exploded perspective view of an illustrative variant of a heart monitor (200). As depicted herein, the heart monitor (200) may include a substrate (210), a battery (220), a controller (230), an adhesive portion (240), a sensor layer (250), electrical heart activity sensors (252, 256), a mechanical heart activity sensor (254), a connector (258), a first adhesive layer (260), a hydrogel (270), and a second adhesive layer (280). The heart monitor (200) may include a substrate (210), a battery (220), and a controller (230). The battery (220) and the controller (230) may be housed within a housing of the substrate (210). The controller (230) may include, for example, a processor, memory, and analog-to-digital converter (ADC) circuitry. The heart monitor may further include a communication device ( Figure 2 (Not shown in the image), the communication device includes an antenna. In some variations, the controller (230) may be configured to perform one or more of the following functions: control logic, signal and data processing, analysis, estimation, communication, calibration, diagnostics, storage, encryption, authentication, etc.

[0056] The substrate (210) may house the electronic components of the heart monitor (200) and may form a durable component of the heart monitor (200). In some variations, the substrate (210) may be releasably coupled to an adhesive portion (240), which may be configured to be attached to a user's skin for a predetermined period of time (e.g., up to about 24 hours, up to about 48 hours, up to about 72 hours, up to about 96 hours, encompassing all values ​​and sub-ranges therebetween). The substrate (210) may be a reusable portion of the heart monitor (200), and the adhesive portion (240) may be disposable and replaceable as needed. In some variations, the substrate (210) may be provided separately from the adhesive portion (240).

[0057] As previously mentioned, the heart monitor (200) may include a sensor layer (250) that may include one or more sensors and may be coupled to or integrally formed with a substrate (210). The sensor layer (250) may be flexible and may be made of a thermoplastic polymer, such as polyethylene terephthalate (PET), nylon, polyurethane, or polyethylene (PE). In some variations, the sensor layer (250) may be adhered to the substrate (210) to form a waterproof seal. A set of electrodes (252, 256) and an accelerometer (254) may be spaced apart in the sensor layer (250). The electrodes (252, 256) may be made of conductive pads configured to contact the user's skin via an adhesive portion (240). For example, in some variations, the electrodes (252, 256) may include copper pads with a thickness between about 0.5 mm and about 3 mm. Sensors (252, 254, 256) can be coupled to a controller (230) via electrical leads (not shown) in the sensor layer (250). In some variations, the sensor layer (250) may include a connector (258), for example, a set of electrical pins (e.g., charging pins) configured to connect to a power source (e.g., a mating part). For example, the connector (258) may be configured to be electrically coupled to a battery (220) for recharging and electrically coupled to the controller (230) for communication (e.g., data transfer).

[0058] The adhesive portion (240) may comprise multiple adhesive layers. For example, in some variations, the adhesive portion (240) may include a first adhesive layer (260) and a second adhesive layer (280). The first adhesive layer (260) may be configured to releasably attach to the sensor layer (254), and the second adhesive layer (280) may be configured to releasably attach to the user's skin. One or more hydrogels (270) may be coupled between the first adhesive layer (260) and the second adhesive layer (280), and the hydrogels may be configured to act as a signal interface between the sensor layer (254) and the skin. Each of the first and second adhesive layers (260, 280) may define an opening aligned with a sensor disposed in the sensor layer (250). The hydrogel (270) may be configured to contact and align with the sensor in the sensor layer (250) and fit within the openings of the first and second adhesive layers (260, 280). When the heart monitor (200) is attached to the user's skin, the sensors (252, 254, 256) can contact the skin via the hydrogel (270). As relative to... Figure 3A and 3B In more detail, the adhesive portion may include one or more release liner pads, which can facilitate storage and transportation of the adhesive portion by covering it with adhesive before use. For example, one or more release liner pads may be removed before application to a heart monitor and the user's skin.

[0059] Turning Figure 3A and 3BThe figures show plan views of variations of the adhesive portion (300) and exploded perspective views of the adhesive portion (300). The adhesive portion (300) may include a first release layer (310) (e.g., a release liner), a first adhesive layer (320), a second adhesive layer (330), a third adhesive layer (340), a set of hydrogels (350), and a second release layer (360). In some variations, the first adhesive layer (320) may be configured to releasably attach to a substrate (not shown) of a heart monitor, and the third adhesive layer (340) may be configured to releasably attach to a user's skin (not shown). Each of the first, second, and third adhesive layers (320, 330, 340) may define an opening that can be aligned with a sensor of the corresponding heart monitor. The hydrogel (350) may be configured to align with the opening in the adhesive layers (320, 330, 340). The hydrogel (350) can be configured to releasably attach to a user's skin, and the heart monitor's sensors can facilitate signal measurement between the sensor layer and the user's skin. For example, when the heart monitor is attached to the user's skin, the sensors in the sensor layer can receive signals from the skin via the hydrogel (350) disposed between the sensor and the skin. The adhesive layers (320, 330, 340) can comprise any biocompatible adhesive. The adhesive layers can comprise a thermoplastic film carrier, such as polyester (PET) with silicone or acrylic adhesives.

[0060] The user can apply the adhesive portion (300) to the heart monitor and skin before measuring cardiac data. For example, to releasably attach the heart monitor to the skin, a first release layer (310) can be peeled off from a first adhesive layer (320) using a first tab (312), and the first adhesive layer (320) can be applied over the sensor layer of the heart monitor. A second release layer (360) can then be peeled off from a third adhesive layer (340) using a second tab (362), and the third adhesive layer (340) can be applied over the user's skin. However, the release layers (310, 320) and the adhesive layers (320, 340) can be peeled off and applied in any order.

[0061] Figure 4 This is a schematic depiction of a heart monitor (450) attached to the user (400). For example... Figure 4As shown, a heart monitor (450) can be releasably attached to the skin of a user (400) located above the left chest, directly below the pectoral muscles. The position and orientation of the heart monitor (450) attached to the user (400) can determine the intensity and / or quality of the signal measured by the heart monitor (450). For example, the heart monitor (450) can be placed flat on the lower left portion of the chest, such as at the apex of the heart's beat, approximately at the location where the interclavicular line (420) of the clavicle bone (410) intersects the fifth intercostal space of the thoracic cavity (not shown) (e.g., at the point of maximal impulse (PMI)). The heart monitor (450) can be oriented perpendicular to the interclavicular line (420). This arrangement allows a mechanical cardiac activity sensor to measure high signal-to-noise ratio electrocardiogram waveforms of vibration as the beating heart strikes the chest wall. Furthermore, positioning the heart monitor (450) horizontally relative to the apex of the heart allows an electrical cardiac activity sensor to measure high signal-to-noise ratio electrocardiogram waveforms.

[0062] In other variations, the heart monitor (450) can be releasably coupled to different parts of the user's chest. For example, in some cases, the heart monitor (450) can be coupled to the user's skin near the sternum or clavicle (e.g., below the clavicle, to the left of the sternum) to measure mechanical vibrations, and in some variations, it is tilted. For example, the heart monitor (450) can be located on the upper chest to align with the propagation of electrical signals from the heart. The heart monitor (450) can be tilted relative to the sternal axis between approximately ±30 degrees, ±45 degrees, and ±60 degrees, encompassing all values ​​and subranges within this range. The heart monitor (450) can be coupled to the user's skin at any location where appropriate electrical cardiac data (e.g., ECG waveforms) can be measured.

[0063] A heart monitor (450) attached to a user (400) can synchronously, semi-continuously, or continuously measure electrocardiogram (ECG) and seismogram waveforms. In some variations, a mechanical cardiac activity sensor (e.g., an accelerometer) can be used to measure the seismogram waveform. The mechanical cardiac activity sensor can be configured to measure the seismogram waveform in three dimensions. For example, the heart monitor (450) can generate seismogram waveforms corresponding to each of the X-axis (460), Y-axis (470), and Z-axis (480). The X-axis (460) corresponds to the horizontal motion of the heart, the Y-axis (470) corresponds to the vertical motion of the heart, and the Z-axis (480) corresponds to motion along a vector perpendicular to the surface of the thoracic cavity.

[0064] Computing device

[0065] Generally, the computing device described herein may include a controller, which includes a processor (e.g., a CPU) and a memory (which may contain one or more non-transitory computer-readable storage media). The processor may incorporate data received from the memory via a communication channel to control one or more components of the system (e.g., a heart monitor (510)). The memory may further store instructions to cause the processor to perform modules, processes, and / or functions associated with the methods described herein. As used herein, a computing device may refer to, for example, Figure 5 Any of the computing device (520, 522), database (540), and server (550) depicted herein. In some variations, the memory and processor may be implemented on a single chip. In other variations, they may be implemented on separate chips.

[0066] The controller can be configured to receive and process cardiac data from a cardiac monitor, as well as other data (e.g., queue data, reference blood pressure) from other sources (e.g., computing devices (520, 522), databases (540), user input). The computing device can be configured to receive, process, compile, store, and access data. In some variations, the computing device can be configured to access and / or receive data from different sources. The computing device can be configured to receive data directly from patient input and / or measurements. Alternatively, the computing device can be configured to receive data from individual devices (e.g., smartphones, tablets, computers) and / or from storage media (e.g., flash drives, memory cards). The computing device can receive data via a network connection (as discussed in more detail herein) or via a physical connection to a device or storage medium (e.g., via a Universal Serial Bus (USB) or any other type of port). Computing devices can include any of a variety of devices, such as cellular phones (e.g., smartphones), tablet computers, laptop computers, desktop computers, portable media players, wearable digital devices (e.g., digital glasses, wristbands, watches, brooches, armbands, virtual reality / augmented reality headsets), televisions, set-top boxes (e.g., cable TV boxes, video players, video streaming devices), gaming systems, etc.

[0067] The computing device can be configured to receive various types of data. For example, the computing device can be configured to receive a patient's personal data (e.g., gender, weight, date of birth, age, height, date of diagnosis, anniversary of device use, etc.), the patient's cardiac data (e.g., blood pressure data, heart rate data), other similar general health information of the patient (e.g., cohort cardiac data), or any other relevant information. In some variations, the computing device can be configured to create, receive, and / or store patient profiles. A patient profile can contain any of the patient-specific information described above. While the aforementioned information can be received by the computing device, in some variations, the computing device can be configured to process any of the aforementioned data from the information it has received using software stored on or outside the device.

[0068] A processor can be any suitable processing device configured to run and / or execute a set of instructions or code, and may include one or more data processors, image processors, graphics processing units, physical processing units, digital signal processors, and / or central processing units. A processor can be, for example, a general-purpose processor, a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), etc. A processor can be configured to run and / or execute application processes and / or other modules, processes, and / or functions associated with the system and / or its associated network. Various component types of underlying device technologies can be provided, such as metal-oxide-semiconductor field-effect transistor (MOSFET) technology such as complementary metal-oxide-semiconductor (CMOS), bipolar technology such as emitter-coupled logic (ECL), polymer technologies (e.g., silicon conjugated polymers and metal conjugated polymer-metal structures), hybrid analog and digital, etc.

[0069] In some variations, the memory may contain a database (not shown) and may be, for example, random access memory (RAM), a memory buffer, a hard disk drive, erasable programmable read-only memory (EPROM), electrically erasable read-only memory (EEPROM), read-only memory (ROM), flash memory, etc. The memory may store instructions to cause a processor to execute modules, processes, and / or functions associated with a communication device, such as cardiac data processing, cardiac parameter estimation, cardiac monitor control, and / or communication. Some variations described herein relate to computer storage products having a non-transitory computer-readable medium (also referred to as a non-transitory processor-readable medium) having instructions or computer code thereon for performing various computer-implemented operations. A computer-readable medium (or processor-readable medium) is non-transitory in the sense that it does not itself contain transient propagation signals (e.g., propagating electromagnetic waves carrying information on a transmission medium such as space or cable). The medium and computer code (also referred to as code or algorithm) may be those designed and constructed for a particular purpose.

[0070] Examples of non-transitory computer-readable media include, but are not limited to, magnetic storage media such as hard disks, floppy disks, and magnetic tapes; optical storage media such as optical discs / digital video discs (CDs / DVDs); optical disc read-only memories (CD-ROMs) and holographic devices; magneto-optical storage media such as optical discs; solid-state storage devices such as solid-state drives (SSDs) and hybrid solid-state drives (SSHDs); carrier signal processing modules; and hardware devices specifically configured for storing and executing program code, such as application-specific integrated circuits (ASICs), programmable logic devices (PLDs), read-only memories (ROMs), and random access memories (RAMs). Other variations described herein relate to computer program products that may contain instructions and / or computer code, such as those disclosed herein.

[0071] The systems, apparatus, and / or methods described herein can be implemented by software (executing on hardware), hardware, or a combination thereof. Hardware modules can include, for example, general-purpose processors (or microprocessors or microcontrollers), field-programmable gate arrays (FPGAs), and / or application-specific integrated circuits (ASICs). Software modules (executing on hardware) can be expressed in various software languages ​​(e.g., computer code), including C, C++, etc. Python, Ruby, Visual And / or other object-oriented, procedural, or other programming languages ​​and development tools. Instances of computer code include, but are not limited to, microcode or microinstructions, machine instructions (e.g., generated by a compiler), code for generating network services, and files containing high-level instructions that are executed by a computer using an interpreter. Additional instances of computer code include, but are not limited to, control signals, encryption code, and compression code.

[0072] In some variations, the computing devices (520, 522) may further include communication devices configured to allow users and / or healthcare professionals to control one or more of the devices in the system. The communication devices may include a network interface configured to connect the computing devices to another system (e.g., the Internet, a remote server, a database) via a wired or wireless connection. In some variations, the computing devices (520, 522) may communicate with other devices via one or more wired and / or wireless networks. In some variations, the network interface may include a radio frequency receiver, a transmitter, and / or an optical (e.g., infrared) receiver and transmitter configured to communicate with one or more devices and / or a network. The network interface may communicate via wires and / or wirelessly with one or more of the cardiac monitor (510), the network (530), the database (540), and the server (550).

[0073] The network interface may include RF circuitry configured to receive and transmit RF signals. The RF circuitry can convert electrical signals into electromagnetic signals / convert electromagnetic signals into electrical signals and communicate with communication networks and other communication devices via electromagnetic signals. The RF circuitry may include known circuitry for performing these functions, including, but not limited to, antenna systems, RF transceivers, one or more amplifiers, tuners, one or more oscillators, digital signal processors, codec chipsets, subscriber identity module (SIM) cards, memory, etc.

[0074] Wireless communication via either the computing device or the measuring device may use any of a variety of communication standards, protocols, and technologies, including but not limited to: Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), High-Speed ​​Downlink Packet Access (HSDPA), High-Speed ​​Uplink Packet Access (HSUPA), Evolved Data Only (EV-DO), HSPA, HSPA+, Dual-Cell HSPA (DC-HSPDA), Long Term Evolution (LTE), Near Field Communication (NFC), Wideband Code Division Multiple Access (W-CDMA), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Bluetooth, and Wi-Fi (e.g., IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, IEEE...). The devices described herein may use protocols such as 802.11n, Voice over Internet Protocol (VoIP), Wi-MAX, email protocols (e.g., Internet Messaging Access Protocol (IMAP) and / or Post Office Protocol (POP)), instant messaging (e.g., Extensible Messaging and Presence Protocol (XMPP), Session Initiation Protocol Extended for Instant Messaging and Field Support (SIMPLE), Instant Messaging and Online Status Service (IMPS)) and / or Short Message Service (SMS) or any other suitable communication protocol. In some variations, the devices described herein may communicate directly with each other without transmitting data over a network (e.g., via NFC, Bluetooth, WiFi, RFID, etc.).

[0075] The communication device may further include a user interface configured to allow a user (e.g., a subject, a pre-selected contact such as a partner, family member, healthcare professional, etc.) to control the computing device. The communication device may allow the user to interact with and / or control the computing device directly and / or remotely. For example, the user interface of the computing device may include input devices for the user to input commands and output devices for the user to receive output (e.g., blood pressure readings on a display device).

[0076] The output device of the user interface can output blood pressure estimates and may include one or more of a display device and an audio device. Data analysis generated by the server (550) can be displayed by the output device (e.g., a display) of the computing device (520, 522). Data used for blood pressure estimation, such as the user's reference blood pressure, cohort cardiac data, and cardiac data from the cardiac monitor (510), can be received via a network interface and output visually and / or audibly via one or more output devices of the computing device (520). In some variations, the output device may include a display device comprising at least one of a light-emitting diode (LED), a liquid crystal display (LCD), an electroluminescent display (ELD), a plasma display panel (PDP), a thin-film transistor (TFT), an organic light-emitting diode (OLED), an electronic paper / electronic ink display, a laser display, and / or a holographic display.

[0077] The audio device can audibly output cardiac data, cardiac parameter data, system data, alarms, and / or notifications. For example, the audio device can output an audible alarm when the estimated blood pressure falls outside a predetermined range or when a malfunction is detected in the cardiac monitor (510). In some variations, the audio device may include at least one of a speaker, a piezoelectric audio device, a magnetostrictive speaker, and / or a digital speaker. In some variations, a user can use the audio device and a communication channel to communicate with other users. For example, a user can establish an audio communication channel (e.g., VoIP call) with a remote healthcare professional.

[0078] In some variations, the user interface may include an input device (e.g., a touchscreen) and an output device (e.g., a display device), and is configured to receive input data from one or more of a heart monitor (510), a network (530), a database (540), and a server (550). For example, user control of the input device (e.g., a keyboard, buttons, a touchscreen) may be received by the user interface and then processed by a processor and memory for the user interface to output control signals to the heart monitor (510). Some variations of the input device may include at least one switch configured to generate control signals. For example, the input device may include a touch surface for a user to provide input corresponding to the control signals (e.g., finger contact with the touch surface). The input device including the touch surface may be configured to detect contact and movement on the touch surface using any of a variety of touch-sensitive technologies, including capacitive, resistive, infrared, optical imaging, dispersive signals, acoustic pulse identification, and surface acoustic wave technology. In variations of the input device that include at least one switch, the switch may include at least one of, for example, a button (e.g., a hard key, a soft key), a touch surface, a keyboard, an analog stick (e.g., a joystick), a direction pad, a mouse, a trackball, a dial, a step switch, a rocker switch, a pointer device (e.g., a stylus), a motion sensor, an image sensor, and a microphone. The motion sensor may receive user movement data from optical sensors and classify user gestures as control signals. The microphone may receive audio data and recognize user voice as control signals.

[0079] Haptic devices can be incorporated into one or more input and output devices to provide additional sensory output (force feedback) to the user. For example, a haptic device can generate a haptic response (e.g., vibration) to acknowledge user input to an input device (e.g., a touch surface). As another example, haptic feedback can notify the user that the input is overridden by a computing device.

[0080] network

[0081] In some variations, the systems and methods described herein can communicate with other computing devices via, for example, one or more networks, each of which can be any type of network (e.g., wired network, wireless network). Communication may or may not be encrypted. A wireless network can refer to any type of digital network not connected by any type of cable. Examples of wireless communication in a wireless network include, but are not limited to, cellular, radio, satellite, and microwave communication. However, a wireless network can connect to a wired network to connect to the Internet, other carrier voice and data networks, business networks, and personal networks. Wired networks are typically carried over copper twisted-pair, coaxial, and / or fiber optic cables. Many different types of wired networks exist, including wide area networks (WANs), metropolitan area networks (MANs), local area networks (LANs), the Internet (IANs), campus networks (CANs), global area networks (GANs) (such as the Internet), and virtual private networks (VPNs). In the following text, a network refers to any combination of wireless, wired, public, and private data networks typically interconnected via the Internet to provide a unified networking and information access system.

[0082] Cellular communications can include technologies such as GSM, PCS, CDMA or GPRS, W-CDMA, EDGE or CDMA2000, LTE, WiMAX, and 5G network standards. Some wireless network deployments combine networks from multiple cellular networks or use a hybrid of cellular, Wi-Fi, and satellite communications.

[0083] When receiving or recording cardiac data, communication between the processor and the cardiac monitor may or may not be performed in real time. The processor may be located in the same housing as the cardiac monitor, or in a separate housing within the same room or building as the cardiac monitor. The processor may also be located in a location far from the cardiac monitor (e.g., in a different building, city, or country).

[0084] docking parts

[0085] In some variations, the cardiac monitoring system (500) may include a docking device (512) that can be configured to hold and secure the cardiac monitor (510) when not in use for charging, storage, transport, and handling of one or more devices. Figure 6A and Figure 6BThese are corresponding perspective and cross-sectional side views of variations of the docking member (600). The docking member (600) may include a housing (610) including an internal recess shaped to receive a heart monitor (650). The housing (610) may include a first portion (610) and a second portion (620) rotatably connected by a hinge (616). The docking member (600) may be configured to hold, for example, a reusable portion of the heart monitor (650). For example, the first portion (610) may include a plurality of recesses, each corresponding to the size and shape of the housing in the substrate of the heart monitor (650). For example, the first portion (610) may include a first circular recess, a second circular recess, and a square recess between the first and second circular recesses. The second portion (620) may include a recess corresponding to the size and shape of a first side (e.g., facing the user) of the heart monitor (650). For example, the second portion (620) may include an elliptical recess. In some variations, the docking member (600) may include a second housing (not shown) configured to retain one or more adhesive portions separate from the heart monitor (650). For example, the second housing may define an opening or compartment in a first portion (612) or a second portion (620) of the docking member (600).

[0086] In some variations, the docking device (600) may include a power and / or data connector (620) configured to establish a power and / or data connection with the heart monitor (650). The docking device (600) may be configured to recharge the internal power supply of the heart monitor (600) by electrically coupling the connector (620) of the docking device (600) to a corresponding connector of the heart monitor (650). For example, the power connector (620) may include one or more of a set of spring pins, a USB connector, or any suitable connector. The docking device (600) may include an internal power supply (e.g., a lithium-ion battery) and / or one or more connectors configured to couple to a power supply. In some variations, the docking device (600) may include one or more visual indicators (e.g., light-emitting diodes) configured to convey the operating status of the docking device (600) (e.g., ON, recharging the heart monitor, error, low power). For example, the LED of the docking device (600) can be configured to emit red light when the heart monitor (650) placed in the docking device (600) is charging, and to emit green light when the charging process is complete.

[0087] In some variations, the docking device (600) may include a communication device configured to form a communication channel with one or more of the heart monitor (650) and the computing device. For example, the docking device (600) may be configured to form a wired or wireless communication channel to receive cardiac data from the heart monitor (650). The docking device (600) may be configured to use the communication channel to transmit and / or receive data through the heart monitor (650), including, but not limited to, user data, cardiac data, configuration data, device data, firmware updates, etc.

[0088] II. Methods

[0089] Methods for noninvasively monitoring a patient's physiological characteristics using the systems and devices described herein are also described herein. Specifically, the systems, devices, and methods described herein can be used to accurately estimate and track values ​​of physiological characteristics such as blood pressure. Conventional methods for determining blood pressure typically require tools such as blood pressure cuffs and stethoscopes, which can be cumbersome, difficult for non-medical professionals to use, and difficult to use outside of clinical settings. These conventional methods also fail to provide continuous or semi-continuous monitoring, require interruption of normal daily activities, and lack privacy. The methods described herein are advantageous over conventional methods in several respects. For example, using the cardiac monitors described herein provides non-medical professionals with the ability to accurately, continuously or semi-continuously, noninvasively, and discretely determine and track blood pressure without interrupting their daily activities. The devices, systems, and methods are easy for non-medical professionals to use and require minimal training, allow for private blood pressure determination in public settings, and provide a comfortable and portable way to determine and track important health indicators. Furthermore, because heart monitors can be flexible and can include or otherwise be used with biocompatible adhesives, they can remain comfortable and continuously wearable for days to weeks without interfering with the user's activities and require minimal maintenance.

[0090] Furthermore, the method described herein advantageously provides an accurate determination of a user's blood pressure regardless of their age, sex, or current activity level. As will be described in more detail herein, the method described herein can estimate a user's blood pressure using variations in one or more mechanical cardiac parameters, and unlike conventional techniques, it does not rely on the calculation of time differences between cardiac events obtained from multiple signals or indicative signals (e.g., methods utilizing pulse conduction time). Therefore, the method described herein can accurately, and in some cases more accurately, estimate the blood pressure of users of different sexes (male or female) and ages (under 20, 20–29, 30–39, 40–49, 50–59, 60–69, 70 and above), and when the user engages in different activities (e.g., during rest, during light exercise, during vigorous exercise, during mentally stressful tasks). Moreover, the described method differs from methods utilizing a combination of proximal and distal pulse measurements using a pulse transcranial Doppler (PTT), where the position of the distal pulse measurement relative to the level of the heart may lead to poorer blood pressure estimation.

[0091] The identified physiological characteristics, such as blood pressure, can be used in various ways. For example, as described herein, physiological characteristics can be displayed to the user on a computing device and / or stored on the computing device or a server for later viewing on the computing device. In some cases, the described physiological characteristics can be used in conjunction with data from other devices or mobile applications (e.g., activity or fitness trackers, sleep trackers, blood glucose meters, internet-enabled scales and / or body composition devices, meditation trackers, etc.) to provide the user or healthcare professional with a more comprehensive view of the user's health status. In some variations, the identified physiological characteristics, cardiac data, mechanical cardiac parameters, and / or electrical cardiac parameters can be exported to or used by a mobile application or device, which can analyze them in conjunction with other health-related data (e.g., activity data, fitness data, sleep data, weight, body fat percentage, temperature, blood glucose, etc.). In some variations, the values ​​of the identified physiological characteristics and / or trends derived from said values ​​can be used to predict future health (e.g., cardiac) events. Alternatively or concurrently, in some variations, physiological characteristics can be used to remotely monitor and / or manage users. For example, mobile applications can be used to more aggressively and comprehensively monitor users with known risk factors for cardiovascular disease or other heart conditions. Healthcare providers, such as primary care physicians and / or cardiac care teams, can use this information to adjust medication regimens, conduct clinical assessments, and / or inform treatment decisions. In another instance, users with risk factors for hypertension or comorbidities can be monitored to inform clinicians about treatment effectiveness and / or prevent further disease progression.

[0092] Estimate blood pressure

[0093] When the physiological characteristic is blood pressure, methods for estimating a user's blood pressure typically include receiving mechanical heart data of the user measured using an accelerometer, and using the mechanical heart data to generate mechanical heart parameter values ​​for a first time period and a second time period. The user's blood pressure can be estimated based on the changes in the mechanical heart parameter values ​​between the first and second time periods. In some variations, blood pressure estimation may be further based on a regression model that receives the user's reference blood pressure and cohort cardiac data associated with the user as input. It should be understood that any of the systems and apparatuses described herein can be used in the methods described herein.

[0094] Figure 7 This is a flowchart illustrating a variant of the method for estimating blood pressure (700). Figure 7 In the variations described herein, the method may include receiving reference blood pressure (702); receiving cohort cardiac data (704); measuring cardiac data (706); determining R-wave and T-wave time points (708); determining SCG wave time points (710); determining mechanical cardiac parameter values ​​(712); and estimating the user's blood pressure (714). As used herein, blood pressure may include one or more of systolic and diastolic pressure. Furthermore, reference blood pressure refers to a blood pressure measurement taken using a conventional blood pressure monitoring device, such as the blood pressure monitor and blood pressure cuff described herein. Reference blood pressure may be measured once or at predetermined intervals (e.g., weekly, bi-weekly, monthly) using, for example, a blood pressure cuff. Reference blood pressure measurements may be stored in a computing device, such as any of the computing devices described herein. For example, the user's healthcare provider may input the reference blood pressure into the healthcare provider's computing device. Devices for estimating blood pressure (e.g., computing device, cardiac monitor) may receive reference blood pressure data from any computing device storing the user's reference blood pressure. In some variations, reference blood pressure data may be included in measurements taken under different physiological conditions (e.g., resting and non-resting conditions, such as during different levels of physical and mental activity). In some variations, a single blood pressure measurement taken at rest can be used as the reference blood pressure, while in others, multiple blood pressure measurements (e.g., two, three, four, five, six, or more) can be used to determine the reference blood pressure. In variations using multiple blood pressure measurements, all measurements may be taken under the same conditions (e.g., at rest), or under combinations of different conditions (e.g., at rest, during or after physical activity, or during or after mental activity). In variations using reusable cardiac monitors, a new blood pressure measurement may be taken each time the cardiac monitor is reapplied to the user's skin (e.g., applying new adhesive), and this new measurement may be used alone or in combination with the previous reference blood pressure measurement.

[0095] In some variations, an oscillometric blood pressure monitor containing a blood pressure cuff (e.g., a sphygmomanometer) can be used to measure a user's reference blood pressure. For example, the cuff can be attached to the user's left arm at the same height as the heart. The user can sit with their feet on the floor and their left hand palm-up on a surface such as a table. In some variations, the reference blood pressure can be measured in approximately one minute while the user is comfortably resting. In other variations, the reference blood pressure can be measured under non-resting conditions, such as during different levels of physical and mental activity. For example, blood pressure can be measured under multiple conditions, including when the user is solving arithmetic problems, exposed to low-intensity stimuli (e.g., listening to relaxing music, watching a beach scene using virtual reality headphones), exposed to high-intensity stimuli (e.g., experiencing a roller coaster ride using virtual reality headphones), etc. This allows blood pressure estimates to be calibrated to the user's activity level when a heart monitor is attached to the user.

[0096] In step 704, the device for estimating blood pressure (e.g., a computing device, a heart monitor) may receive and use or store queued cardiac data associated with the user for later use. In some variations, users may be categorized into queues (e.g., demographic groups, peer groups), which may contain a group of users grouped by one or more of age, sex, ethnicity, and body mass index. For example, a user may input demographic data containing information such as their age, sex, ethnicity, weight, height, body mass index, etc., into the computing device to determine the user's queue. In some variations, the queued cardiac data may include lookup tables (LUTs) stored in memory and / or algorithms stored in memory. The queued cardiac data may include values ​​of one or more of the electrical and mechanical cardiac parameters described herein (e.g., heart rate, blood pressure, SCG1 and SCG2 wave timings, SCG wave amplitude, etc.). In some variations, the queued cardiac data may be pre-programmed and stored in memory, or may be received (e.g., updated) via a communication channel.

[0097] In step 706, the heart monitor can measure the user's heart data. (See also: Regarding...) Figure 4 As described, the heart monitor can be releasably attached flat to the user's skin directly below and above the left thoracic cavity (e.g., at the intersection of the interclavicular line of the clavicle and the fifth intercostal space of the thoracic cavity). In some variations, attaching the heart monitor to a clean and substantially hairless area of ​​skin may be advantageous. The sensors of the heart monitor can be configured to continuously or at predetermined intervals measure electrical and mechanical cardiac data, such as ECG and SCG signal waveforms, as the user performs any of their daily activities.

[0098] As mentioned above, the measured cardiac data can include electrical and mechanical signal waveforms. Figure 8A and 8B In the corresponding first time period ( Figure 8A ) and the second time period ( Figure 8B Illustrative variations of ECG waveforms (800, 810) of electrical cardiac activity and SCG waveforms (802, 804, 806, 812, 814, 816) of mechanical cardiac activity measured within the timeframe. Measurements of cardiac activity during the first and second timeframes allow for comparative hemodynamic analysis in the blood pressure estimation process described herein. Figure 8A The ECG and SCG waveforms measured synchronously during the first time period are shown, including the ECG waveform (800), the X-axis SCG waveform (802), the Y-axis SCG waveform (804), and the Z-axis SCG waveform (806). Similarly, Figure 8B The diagram shows ECG and SCG waveforms measured synchronously during a second time period, including the ECG waveform (810), X-axis SCG waveform (812), Y-axis SCG waveform (814), and Z-axis SCG waveform (816). Measuring the SCG waveforms along the X, Y, and Z axes allows for the generation of position-invariant three-dimensional SCG waveforms. This can improve cardiac parameter estimation and / or reduce the need to place the cardiac monitor in an optical position within the chest.

[0099] ECG waveforms (800, 810) correspond to the electrical activity of the heart. For example, R waves (820, 830) correspond to ventricular depolarization and contraction of the large ventricular myocardium. T waves (822, 832) correspond to ventricular depolarization. R waves (820, 830) and T waves (822, 832) divide the ECG waveform (800, 810) into systolic and diastolic segments. The systolic and diastolic segments of SCG waveforms (802, 804, 806, 812, 814, 816) are associated with turbulent blood flow caused by the closure of heart valves. For example, SCG1 waves (824, 834) correspond to the closure of the mitral and tricuspid valves during systole. SCG2 waves (826, 836) correspond to the closure of the aortic and pulmonary valves during the first half of diastole. For users with heart failure or certain other heart conditions, a rapid rhythm of SCG3 and SCG4 (not shown) may occur during the second half of diastole.

[0100] ECG and SCG signal waveforms can be generated by a cardiac monitor in any known digital ECG and SCG format, or alternative image formats (e.g., .jpg, .gif, etc.). In some variations, the ECG signal format may include, but is not limited to, standard communication protocols such as Computer-Assisted Electrocardiography (SCP-ECG), HL7 Annotated ECG (HL7 aECG), Medical Digital Imaging and Communication (DICOM) Waveform Supplement 30, and Medical Waveform Format Encoding Rules (MFER). Any known digital ECG and SCG format can be used in conjunction with the apparatus and methods described herein. In some variations, ECG signal data can be recorded at resolutions ranging from about 8 bits to about 64 bits, about 16 bits to about 32 bits, and about 24 bits, at rates ranging from about 125 Hz to about 250 Hz, about 500 Hz to about 1 kHz, and about 1 kHz to about 16 kHz.

[0101] The ECG and SCG signal waveforms measured in step 706 can be affected by noise from one or more different sources, including physiological and non-physiological sources. Examples of physiological noise include axis shift, biphasic QRS morphology, and QRS amplitude variations. Non-physiological noise sources may include 50 / 60Hz electric field lines, electrode motion artifacts, electromyography, and baseline drift. In some variations, the measured ECG and SCG signal waveforms can be processed to reduce noise. For example, a cardiac monitor can preprocess the measured cardiac data before transmitting the data to a computing device for blood pressure estimation. In some variations, the ECG waveform can be preprocessed to filter out noise and improve the signal-to-noise ratio. For example, the ECG waveform can be processed by an infinite impulse response (IIR) bandpass filter configured to suppress frequencies other than approximately 2Hz to approximately 40Hz, thereby removing low-frequency noise (due to baseline drift and respiration) and high-frequency noise (due to motion artifacts) from the ECG waveform.

[0102] In some variations, a communication channel can be used to transmit one or more of the raw cardiac data and preprocessed cardiac data from the cardiac monitor to a computing device. A communication channel can be established between the cardiac monitor and the computing device. The communication channel can be a wired or wireless connection and can use any communication protocol, including but not limited to those described herein, such as Bluetooth and NFC. The communication channel can be established at predetermined intervals based on one or more of the following: time (e.g., hourly, daily, weekly, etc.), device usage (when cardiac data is measured, after the device is powered on, before entering sleep mode, memory usage, battery level, establishment of the communication channel, etc.), connection requests, etc.

[0103] Alternatively, the user can manually establish a communication channel at any desired time. The heart monitor can establish a communication channel directly or indirectly with one or more computing devices described herein (e.g., smartphones, docking stations, databases, remote servers, the Internet, etc.). For indirect connections, an intermediate device can establish an additional communication channel. For example, a docking station can establish a connection with a smartphone to initially transmit cardiac data. The smartphone can then transmit the cardiac data to a cloud database and / or any other computing device (e.g., a remote server). In some variations, the heart monitor can attempt to find a computing device to establish a communication channel within a predetermined time period (e.g., one minute) after measuring cardiac data. The heart monitor can preferably be connected to an identified and / or authorized computing device, such as the patient's smartphone, laptop computer, and / or desktop computer.

[0104] Before determining mechanical cardiac parameter values ​​using the measured ECG and SCG waveforms, characteristics of the SCG waveform, such as the time points of the SCG features, can be determined. In some variations, the SCG wave characteristics can be accurately determined using the R-wave and T-wave time points. In step 708, the computing device can be used to determine the R-wave and T-wave time points in the measured ECG waveform. Figure 9 and 10 This is a flowchart relating to the processing of ECG and SCG waveforms used to determine electrical and mechanical cardiac parameter values ​​for estimating physiological characteristics. In some variations, ambient noise and motion artifacts in the measured signal waveforms can be detected and removed using a cardiac monitor.

[0105] Figure 9This is a flowchart describing a variation of a method (900) for determining electrical cardiac parameter values ​​at time points including R-wave and T-wave. In the method shown here, the ECG waveform (901) can be preprocessed to filter out noise and improve the signal-to-noise ratio. The ECG waveform (e.g., a filtered ECG waveform) can then be input in parallel to the R-wave detection process (902, 904, 906, 908, 910) and the T-wave detection process (912, 914, 916, 918, 920). During the R-wave detection process, the ECG waveform can be input to a filter (902), such as a dual median filter, which removes baseline drift to suppress the non-QRS portion of the ECG waveform. For example, the dual median filter can contain window sizes of 7 and 11 samples. Continuous differences between each sample can be calculated (904). A sliding window integral (906) can be performed on the continuous differences, where the result of each iteration is the sum of values ​​within a predetermined window size. In some variations, the R-wave time point (908) can be determined when: 1) the integral value at the predetermined index is the maximum value within a predetermined window (e.g., 200 ms) centered at the predetermined index; and 2) the integral value at the predetermined index is greater than μ + σ, where μ and σ represent the mean and standard deviation of the integral value over a predetermined time period (e.g., the last three seconds), respectively. The resulting R-wave time point (910) can be output and can be used as input to a local minimum / maximum detector (916).

[0106] During T-wave detection, the coarse second derivative of the ECG waveform (901) can be calculated (e.g., where h = 7) (912). The second derivative can be calculated using the midpoint difference quotient given by equation (1):

[0107]

[0108] In equation (1), f represents the ECG waveform and f″′ represents its second derivative. An infinite impulse response (IIR) filter (914) can be applied to the output of the second derivative (912), and its output can be input to a local minimum / maximum detector (916). The local minimum / maximum detector (916) can also receive R-wave time points (910) as input from a sliding window threshold (908). The R-wave time points (910) can be used to define a search window for detecting T-waves. Within these search windows, minimum and maximum values ​​(916) can be detected. When the signs of the minimum and maximum values ​​of the second derivative f″′ are appropriately alternating (e.g., from positive to negative to positive, or from negative to positive to negative) (918), the time point with the maximum second derivative (in absolute value) can be output as the T-wave time point (920). The R-wave and T-wave time points can then be used to determine the SCG1 or SCG2 wave time points in the corresponding SCG waveform, which can be used to determine mechanical heart parameters.

[0109] Figure 10 This is a flowchart describing a variation of a method (1000) for determining mechanical cardiac parameter values. In the variation shown here, four time-related waveforms can be used to determine mechanical cardiac parameter values: an ECG waveform (1002); an X-axis SCG waveform (1004); a Y-axis SCG waveform (1006); and a Z-axis SCG waveform (1008). The ECG and SCG waveforms can be used in the method (1000) to determine mechanical cardiac parameter values ​​for each time period of interest. Each time period of the ECG and SCG waveforms can include multiple heartbeats (e.g., segments) each having an R wave, T wave, SCG wave, etc. Since the ECG and SCG waveforms are measured synchronously, the R-wave time point and T-wave time point from the ECG waveform correspond to, for example, Figure 8A and 8B The SCG1 wave time point and SCG2 wave time point are shown in the figure.

[0110] The raw ECG waveform (1002) measured using a heart monitor can be input into the heart monitor's ECG filter (901) to remove noise and improve the signal-to-noise ratio, as per [reference needed]. Figure 9 Detailed description. The preprocessed ECG waveform can then be input to the R-wave and T-wave detectors (900) and as per [reference needed]. Figure 9 The process described is as follows. R-wave and T-wave time points can be provided as inputs (1028), as described in more detail herein. The outputs of the R-wave and T-wave detectors (900) can also be used to calculate heart rate (HR) (1014). For example, heart rate (HR) can be calculated in heart rate per minute using equation (2):

[0111]

[0112] In equation (2), RRI is the average distance between consecutive R waves, and f s This is the sampling rate of the ECG sensor, measured in Hertz. In some variations, the mechanical heart parameters may include heart rate.

[0113] Refer again Figure 10 The SCG waveform can be processed by first generating a position-invariant three-dimensional SCG waveform. For example, three SCG waveforms (1004, 1006, 1008) can be combined into a joint SCG waveform (1020). xyz This can be achieved using equation (3):

[0114]

[0115] In some variations, linear interpolation can be used to transform the combined SCG waveform. xyzUpsampling (1022) is performed to approximately 500 Hz. Upsampling can optionally be performed to reduce the computational load, for example, on a cardiac monitor. (1024) SCG can be calculated. xyz The signal-to-noise ratio (SNR) can be improved, and the combined SCG waveform can be filtered using an IIR filter (1026). xyz In some variations, waveform SCG can be excluded from further processing. xyz The portion of the sample that has an SNR below a predetermined threshold.

[0116] Then SCG can be determined. xyz The time points of the SCG waves (SCG1 wave, SCG2 wave) of the waveform (1028). For example, since the ECG and SCG waveforms are measured synchronously, the R-wave time points and T-wave time points from the ECG waveform can be used to help detect and / or identify the time points of the SCG1 and SCG2 waves within a certain time period (1028).

[0117] In some variations, the average SCG1 and SCG2 waves (1030) can be calculated across all segments. Using the average SCG1 and SCG2 waves can help reduce variance caused by movement, speech, and respiration. Additionally, in some cases, cubic spline interpolation can be used to rescale and correct for SCG amplitude variations due to heavy breathing. The average SCG wave can improve the accuracy of mechanical cardiac parameter calculations and subsequent blood pressure estimation. The average SCG1 wave can be used when estimating systolic blood pressure, and the average SCG2 wave can be used when estimating diastolic blood pressure.

[0118] Multiple mechanical cardiac parameters can be calculated from SCG1 and SCG2 waves (e.g., average SCG1 and average SCG2 waves) (1030). For example, SCG1 and SCG2 waves can be used to calculate amplitude (1032), maximum L2 norm (1034), area under the power spectral density cross section (PS AUC) (1038), sample entropy (SampEN) (1040), etc. As used herein, amplitude is the difference between the peak and trough heights in the average SCG1 or SCG2 wave. Maximum L2 norm is the SCG... xyz The maximum distance between any pair of points on the waveform is given by equation (4):

[0119]

[0120] In equation (4), t1 and t2 can vary within the length of the average SCG1 wave or SCG2 wave. The area under the power spectral density cross section is given by equation (5):

[0121] (5) PS AUC = ∑|FFT| 2

[0122] The FFT in equation (5) corresponds to the standard fast Fourier transform (1036) of the SCG waveform, where the summation is over the tunable frequency band.

[0123] In some variations, a set of values ​​corresponding to at least one mechanical cardiac parameter can be calculated using a computing device as described herein. Blood pressure can then be estimated using at least one mechanical cardiac parameter, for example, via the computing device. For example, in some variations, blood pressure can be estimated based on amplitude. In some variations, the accuracy of blood pressure estimation can be improved by using multiple mechanical cardiac parameters. For example, in some variations, blood pressure can be estimated based on different combinations of mechanical cardiac parameters, such as amplitude and maximum L2 norm, and area under the curve, sample entropy, and heart rate. While electrical cardiac data (e.g., ECG data) can be used to identify time points of SCG1 and SCG2 waves in SCG data, blood pressure can be estimated without directly utilizing electrical cardiac data. For example, as per [reference to...] Figure 12 To describe in more detail, blood pressure estimates can be determined using a regression model based solely on mechanical heart parameter values. In other words, it is not necessary to feed electrical heart data (e.g., ECG data) into the regression model to estimate a user's blood pressure.

[0124] In some variations, blood pressure can be estimated using a combination of mechanical cardiac parameters that include statistical moments and / or exclude heart rate. Figure 11 This is a flowchart describing a variation of the method (1100) for determining mechanical heart parameter values. In the variation shown here, four time-related waveforms can be used to determine mechanical heart parameter values: an ECG waveform (1102); an X-axis SCG waveform (1104); a Y-axis SCG waveform (1106); and a Z-axis SCG waveform (1108). The ECG and SCG waveforms can be used in the method (1100) to determine mechanical heart parameter values ​​for each time period of interest. Each time period of the ECG and SCG waveforms can include multiple heartbeats (e.g., segments) each having an R wave, T wave, SCG wave, etc. Since the ECG and SCG waveforms are measured synchronously, the R-wave time point and T-wave time point from the ECG waveform correspond to, for example, Figure 8A and 8B The SCG1 wave time point and SCG2 wave time point are shown in the figure.

[0125] The raw ECG waveform (1102) measured using a heart monitor can be input into the heart monitor's ECG filter (901) to remove noise and improve the signal-to-noise ratio, as per [reference needed]. Figure 9 Detailed description. The preprocessed ECG waveform can then be input to the R-wave and T-wave detectors (900) and as per [reference needed]. Figure 9 The process described is as follows. R-wave and T-wave time points can be provided as input (1128), as described in more detail herein. See again... Figure 11 The SCG waveform can be processed by first generating a position-invariant three-dimensional SCG waveform. For example, three SCG waveforms (1104, 1106, 1108) can be combined into a joint SCG waveform (1120). xyz This can be achieved using equation (3):

[0126]

[0127] In some variations, linear interpolation can be used to transform the combined SCG waveform. xyz Upsampling (1122) is performed to approximately 500 Hz. Upsampling can optionally be performed to reduce the computational load, for example, on a cardiac monitor. SCG (1124) can be calculated. xyz The signal-to-noise ratio (SNR) can be improved, and the combined SCG waveform can be filtered using an IIR filter (1126). xyz In some variations, waveform SCG can be excluded from further processing. xyz The portion of the sample that has an SNR below a predetermined threshold.

[0128] Then SCG can be determined. xyz The time points (1128) of the SCG waves (SCG1 (S1) wave, SCG2 (S2) wave) of the waveform (e.g., S1 / S2 segmentation). For example, since the ECG and SCG waveforms are measured synchronously, the R-wave time points and T-wave time points from the ECG waveform can be used to help detect and / or identify the time points (1128) of the SCG1 and SCG2 waves within a certain time period.

[0129] In some variations, the average SCG1 and SCG2 waves (e.g., S1, S2 waves) can be calculated across all segments (1130). Using the average SCG1 and SCG2 waves can help reduce variance caused by movement, speech, and respiration. Additionally, in some cases, cubic spline interpolation can be used to rescale and correct for SCG amplitude variations caused by heavy breathing. The average SCG wave can improve the accuracy of mechanical cardiac parameter calculations and subsequent blood pressure estimation. The average SCG1 wave can be used when estimating systolic blood pressure, and the average SCG2 wave can be used when estimating diastolic blood pressure.

[0130] Multiple mechanical heart parameters can be calculated from SCG1 and SCG2 waves (e.g., average SCG1 and average SCG2 waves) (1130). For example, SCG1 and SCG2 waves can be used to calculate amplitude (1132), maximum L2 distance (1134), area under the power spectral density cross section (PS AUC) (1138), zero-crossing rate (1140), sample entropy (SampEN) (1142), statistical moments (1144, 1146, 1148, 1150), etc. Statistical moments can include, for example, mean (1144), variance (1146), skewness (1148), and kurtosis (1150).

[0131] In some variations, the zero-crossing rate f5 (1140) can be the number of times the S wave alternates between positive and negative, or vice versa. The zero-crossing rate (1140) can be calculated individually for each of the filtered SCG waveforms (1104, 1106, 1108). In some variations, it has been empirically observed that a higher zero-crossing rate may indicate lower blood pressure and may correspond to an S wave undergoing less rapid changes.

[0132] As used in this article, amplitude is the difference between the peak and trough heights in the average SCG1 or SCG2 wave. The maximum L2 distance is the SCG... xyz The maximum distance between any pair of points on the waveform is given by equation (4):

[0133]

[0134] In equation (4), t1 and t2 can vary within the length of the average SCG1 wave or SCG2 wave. The area under the power spectral density cross section is given by equation (5):

[0135] (5) PS AUC = ∑|FFT| 2

[0136] The FFT in equation (5) corresponds to the standard fast Fourier transform (1136) of the SCG waveform, where the summation is over the adjustable frequency band.

[0137] In some variations, statistical moments including one or more of the following—mean (1144), variance (1146), skewness (1148), and kurtosis (1150)—can be calculated from the S-wave. In some variations, higher-order moments q can be obtained through a factor (E((SE(S))). 2 )) q / 2 Normalization. In some variations, it has been empirically observed that the mean (1144) and variance (1146) tend to be the most informative. Generally, a higher mean and variance relative to the calibrated values ​​indicate higher hypertension, although this may vary depending on the individual.

[0138] In some variations, a set of values ​​corresponding to at least one mechanical cardiac parameter can be calculated using a computing device as described herein. Blood pressure can then be estimated using at least one mechanical cardiac parameter, for example, via the computing device. For example, in some variations, blood pressure can be estimated based on amplitude. In some variations, the accuracy of blood pressure estimation can be improved by using multiple mechanical cardiac parameters. For example, in some variations, blood pressure can be estimated based on different combinations of mechanical cardiac parameters, such as amplitude and maximum L2 norm, and area under the curve, sample entropy, and heart rate. While electrical cardiac data (e.g., ECG data) can be used to identify time points of SCG1 and SCG2 waves in SCG data, blood pressure can be estimated without directly utilizing electrical cardiac data. For example, as per [reference to...] Figure 12 To describe in more detail, blood pressure estimates can be determined using a regression model based solely on mechanical heart parameter values. In other words, it is not necessary to feed electrical heart data (e.g., ECG data) into the regression model to estimate a user's blood pressure.

[0139] Figure 12 This is a flowchart describing a variation of a method (1200) for estimating blood pressure. The method (1200) may include receiving a user's reference blood pressure (1202), queue data associated with the user (1204), and measured ECG and SCG waveforms corresponding to a first time period (1206) and a second time period (1208). The waveform for the first time period (1206) may correspond to... Figure 8A The waveform shown, and the second time period waveform (1208) can correspond to Figure 8B The waveform shown is illustrated. In some variations, the first time period waveform (1206) may correspond to a reference time period having initial measurements obtained after attaching the heart monitor to the user and in conjunction with a reference blood pressure measurement, and the second time period (1208) may be measured continuously or semi-continuously relative to the first time period. For example, cardiac data (e.g., electrical and mechanical) may be initially measured using sensors during a reference time period (e.g., a short first time period) exactly after the user initially attaches the heart monitor to the skin. In some of these variations, blood pressure may be estimated based on the difference between one or more mechanical cardiac parameter values ​​obtained during the reference time period and the second time period following the reference time period.

[0140] A subset of the cohort heart data (1204) associated with a user can be retrieved from the cohort heart data (1210). For example, demographic categories such as age, sex, ethnicity, weight, height, body mass index, etc., can be used to classify users into appropriate cohorts. A set of values ​​for at least one mechanical heart parameter can be retrieved from the cohort heart data associated with the user (1212) and can be fed into a regression model (1240), as described in more detail herein.

[0141] The regression model (1230) can also receive a set of values ​​for at least one mechanical heart parameter. Based on the corresponding... Figure 10 and 11 The mechanical heart parameter values ​​generated by the methods (1000, 1100) shown can be used to calculate the heart parameter values ​​(1220) of user β0. For example, the heart parameter values ​​of user β0 can be calculated using equations (6) (1230):

[0142]

[0143] Where y0 is the user's reference blood pressure, n is the number of extracted features, and x 0,i The mechanical heart parameter values ​​are calculated based on the waveform of the first time period, and βi is the mechanical heart parameter value of the queue.

[0144] The blood pressure estimation method (1240) can receive a reference blood pressure, a cardiac parameter value of the user β0, and a value of at least one mechanical cardiac parameter for a second time period. For example, the blood pressure estimate can be given by equation (7) and can be formed for systolic blood pressure (1242) and diastolic blood pressure (1244):

[0145]

[0146] Where y1 is the estimated blood pressure (1240) during the second time period, and x 1,i These are mechanical cardiac parameter values ​​calculated based on the waveform of the second time period. By combining equations (6) and (7), the blood pressure estimate can be given by equation (8):

[0147]

[0148] Where i is the number of mechanical heart parameters, y1 is the user's estimated blood pressure, y0 is the user's reference blood pressure, and β i It is the mechanical heart parameter value of the i-th queue, x i x is the value of the i-th mechanical heart parameter in the first time period, and x0 is the value of the i-th mechanical heart parameter in the second time period.

[0149] As can be seen from equation (8), the estimated blood pressure can be based on the sum of the changes in the values ​​of n mechanical cardiac parameters between the first and second time periods. In some variations, at least one of the mechanical cardiac parameters can be selected from a group consisting of: SCG wave amplitude, maximum L2 distance, area under the power spectral density curve, sample entropy, one or more statistical moments, and R-wave wavelength.

[0150] Blood pressure estimates can be calculated using at least one mechanical cardiac parameter. In some variations, mechanical cardiac parameter values ​​can be calculated over a predetermined time period (e.g., continuous, discontinuous) and used to estimate systolic and diastolic blood pressure. These time-related estimates can be appended to form a continuous or semi-continuous user blood pressure tracker. The estimated blood pressure can be output to the user on any accessible computing device. For example, the user can use a graphical user interface (GUI) to view their estimated blood pressure, cardiac data, electrical cardiac parameter data, and / or mechanical cardiac parameter data using a mobile application (e.g., iOS, Android), a web browser accessing a secure website, and / or one or more cloud computing solutions. Users can register an account and log in to the application to access its features. Blood pressure and cardiac data can be presented in one or more customizable formats that allow users to gain deeper insights into their cardiac health. For example, blood pressure trends can be plotted over time and can include target information, averages, and / or color coding. Cardiac data can be displayed in tabular format. Alternatively or concurrently, the estimated blood pressure and cardiac data can be output using text, email, and / or other electronic communication methods.

[0151] III. Examples

[0152] As described herein, blood pressure estimates can be derived from electrical and mechanical cardiac data. Examples 1 and 2 below illustrate a set of monitor-based measurements compared to blood pressure estimates using the devices, systems, and methods described herein. Specifically, reference blood pressure can be measured using an oscillometric blood pressure monitor that includes a blood pressure cuff (e.g., a sphygmomanometer). Resting blood pressure is measured for approximately 1 minute with the user comfortably at rest. Blood pressure is also measured under more strenuous conditions, including when the user is solving arithmetic problems, exposed to low-intensity stimuli (e.g., listening to relaxing music, watching a beach scene using virtual reality headphones), and exposed to high-intensity stimuli (e.g., experiencing a roller coaster ride using virtual reality headphones).

[0153] For Examples 1 and 2, blood pressure and usage will be referenced. Figure 10 The differences between blood pressure estimates obtained by the methods described herein were compared with the Association for the Development of Medical Devices (AAMI) standards and the British Hypertension Society (BHS) standards. The AAMI standards require an average error of less than 5 mmHg and a standard deviation error of less than 8 mmHg. The BHS rates blood pressure estimates on a letter-based scale (e.g., A, B, C, D). For activity levels below strenuous exercise, the standard deviation error of the estimated blood pressure values ​​estimated using the devices, systems, and methods described herein is between approximately 4.75 mmHg and approximately 8 mmHg.

[0154] Similarly, Example 3 below illustrates the use of the apparatus and system described herein. Figure 11The methods used to obtain blood pressure estimates, and the devices and systems that meet and exceed the requirements of ISO / ANSI / AAMI 81060, provide minimum labeling, performance, and safety requirements for the clinical validation of medical electrical devices used for the intermittent, non-invasive, and automated estimation of arterial blood pressure, and apply to all blood pressure monitors that sense or display pulse, flow, or sound to estimate, display, or record blood pressure. For example, this includes measurements of blood pressure with more than 100 test subjects under both low-intensity and high-intensity stimuli (e.g., experiencing a roller coaster ride using virtual reality headsets).

[0155] Example 1

[0156] Figure 13 This is a graph comparing measured and estimated blood pressure data for men over 70 years of age. (Example) Figure 13 As shown, for a cohort of users (AF), the estimated blood pressure (“predicted blood pressure”) exhibited a lower error relative to blood pressure measurements based on the sphygmomanometer (“actual blood pressure”). For example, the standard deviation error was approximately 6.25 mmHg for measurements taken during exercise and approximately 7.75 mmHg for measurements taken outside of exercise. The blood pressure estimates for this cohort met the AAMI criteria and achieved BHS Grade A.

[0157] Example 2

[0158] Figure 14 This is a graph comparing measured and estimated blood pressure data for women aged 20 to 29. (Example) Figure 14 As shown, for a cohort of users (GK), the estimated blood pressure (“predicted blood pressure”) exhibited a low error relative to blood pressure measurements based on the sphygmomanometer (“actual blood pressure”). For example, the standard deviation error was approximately 6.5 mmHg for measurements taken during exercise and approximately 5.5 mmHg for measurements taken outside of exercise. The blood pressure estimates for this cohort met the AAMI criteria and achieved a BHS grade B.

[0159] Example 3

[0160] Figures 15A to 15DHistograms were used to visualize demographic information of a group of test subjects, including age distribution (1500), BMI (1502), resting systolic blood pressure (1504), and resting diastolic blood pressure (1506). Measurements were performed on 104 test subjects, exceeding the minimum of 85 subjects recommended by ISO / ANSI / AAMI 81060. Each subject participated in at least four blood pressure recordings, exceeding the minimum of three recordings per subject recommended by ISO / ANSI / AAMI 81060. The obtained measurements were used to determine the accuracy of the estimated blood pressure values ​​estimated by the apparatus, system, and method described herein. Furthermore, measurements were obtained during light exercise on a stepper, arithmetic tests, virtual reality roller coasters, and other daily activities, thus providing more challenging test cases than those recommended by ISO / ANSI / AAMI 81060.

[0161] The participant population included individuals of various ages and health statuses, ranging from underweight (e.g., BMI < 18.5) to obese (e.g., BMI > 30), and from low blood pressure (e.g., systolic blood pressure < 90 mmHg or diastolic blood pressure < 60 mmHg) to stage 3 hypertension (e.g., systolic blood pressure between 130 mmHg and 139 mmHg, or diastolic blood pressure between 80 mmHg and 89 mmHg). Additionally, some of the 12 participants were taking medication to control hypertension, and their blood pressure was recorded throughout the day to track the effects of the medication on the devices, systems, and methods described herein.

[0162] Figure 16A It is a curve of systolic blood pressure error (1600), and Figure 16B The graph (1610) shows the error of diastolic blood pressure, using a Bland-Altman plot used to assess consistency between two different measurement techniques. Each graph (1600, 1610) includes the mean error (1602, 1612) and ±2 standard deviations of error (1604, 1614). The accuracy of the estimated systolic and diastolic blood pressure is assessed relative to the thresholds outlined in ISO / ANSI / AAMI 81060 with respect to the mean error (Standard 1), the standard deviation of error (Standard 2), and the standard deviation of the intra-subject mean (Standard 3). Specifically, the estimated measurements obtained by the apparatus, system, and method described herein are within the range of mean error less than ±5 mmHg, standard deviation of error less than ±8 mmHg, and standard deviation of the intra-subject mean less than ±8 mmHg, as outlined in Table 1 below.

[0163] Table 1

[0164]

[0165] The threshold for passing Standard 2 depends on the average error of Standard 1. The larger the average error, the more difficult it is to pass Standard 2, as shown in Table 2 below. In the worst case, when the average error is ±5 mmHg, the threshold for Standard 2 is 4.79 mmHg.

[0166] Table 2

[0167] Average subject data in mmHg accepted (Standard 2)

[0168]

[0169] The specific examples and descriptions herein are exemplary in nature, and variations can be developed by those skilled in the art based on the material taught herein without departing from the scope of the invention, which is limited only by the appended claims.

Claims

1. A cardiac monitoring system, comprising: A non-transitory processor-readable storage medium configured to be executed by a processor and including instructions for performing a method of estimating blood pressure, the method comprising: Receive the user's reference blood pressure; Receives mechanical heart data from users measured using an accelerometer; Generate mechanical heart parameter values ​​for each of a plurality of mechanical heart parameters from the said mechanical heart data in a first time period and a second time period; and The user's blood pressure is estimated based on the reference blood pressure, cohort cardiac data associated with the user, and the sum of the changes in the values ​​of the plurality of mechanical cardiac parameters between the first and second time periods. The estimated blood pressure is given by the following formula: ， Where i is an index of a set of n mechanical heart parameters, BP est It is the user's estimated blood pressure, BP. ref It is the user's reference blood pressure, β i It is the mechanical heart parameter value of the i-th queue, x 1,i It is the first value of the i-th mechanical heart parameter corresponding to the first time period, and x 2,i It is the second value of the i-th mechanical heart parameter corresponding to the second time period. Furthermore, the cohort cardiac data is grouped by one or more of age, sex, ethnicity, and body mass index.

2. The cardiac monitoring system according to claim 1, wherein at least one of the plurality of mechanical cardiac parameters is selected from the group consisting of: SCG wave amplitude, maximum L2 norm, area under the power spectral density curve, zero-crossing rate, sample entropy, and statistical moments.

3. The cardiac monitoring system according to claim 1, wherein the mechanical heart data includes SCG waves.

4. The cardiac monitoring system according to claim 3, wherein the SCG wave includes SCG1 wave and SCG2 wave.

5. The cardiac monitoring system of claim 3, wherein the mechanical heart data includes a plurality of SCG waves for each of the first time period and the second time period, and wherein generating the mechanical heart parameter values ​​includes generating an average SCG wave of the plurality of SCG waves in the first time period and the second time period.

6. The cardiac monitoring system of claim 5, wherein the mechanical cardiac parameter values ​​are derived from the average SCG wave.

7. The cardiac monitoring system according to claim 5, wherein the mechanical cardiac parameters include one or more of the following: SCG wave amplitude, maximum L2 norm, area under the power spectral density curve, zero-crossing rate, sample entropy, and statistical moments.

8. The cardiac monitoring system according to claim 1, wherein the accelerometer is an electrocardiogram (SCG) sensor.

9. The cardiac monitoring system according to claim 1, wherein, The method also includes receiving electrical cardiac data measured using electrodes.

10. The cardiac monitoring system of claim 9, wherein the electrode is an electrocardiogram (ECG) electrode and the accelerometer is an electrocardiogram (SCG) sensor.

11. The heart monitoring system of claim 9, wherein the electrical heart data is measured as an ECG signal, and the mechanical heart data is measured as an SCG signal.

12. The cardiac monitoring system according to any one of claims 9 to 11, wherein, The method further includes generating multiple electrical cardiac parameter values ​​from the electrical cardiac data, wherein the electrical cardiac parameters include one or more of heart rate, R-wave time points, and T-wave time points.

13. The cardiac monitoring system of claim 12, wherein generating the plurality of electrical cardiac parameter values ​​includes generating the R-wave time points on the ECG waveform using sliding window integration.

14. The cardiac monitoring system of claim 12, wherein generating the plurality of electrical cardiac parameter values ​​includes generating the T-wave time points on the ECG waveform using the R-wave time points and the derivatives of the electrical cardiac data.

15. The cardiac monitoring system according to claim 12, wherein, The method also includes generating SCG wave time points from the mechanical heart data using the R wave and T wave time points.

16. The cardiac monitoring system of claim 1, wherein the mechanical heart data includes first, second, and third seismogram waveforms measured along respective X, Y, and Z axes, and the method further includes generating a fourth seismogram waveform including the first, second, and third seismogram waveforms.

17. The cardiac monitoring system according to claim 1, wherein, The method also includes releasably coupling the accelerometer to the skin of the user's left chest above the left thoracic cavity.

18. The cardiac monitoring system of claim 1, wherein the estimated blood pressure includes one or more of systolic and diastolic blood pressure.

19. The cardiac monitoring system of claim 1, wherein the first time period is a reference time period, and wherein the mechanical heart data is initially measured using the accelerometer during the reference time period.

20. The cardiac monitoring system according to any one of claims 2 and 7, wherein the statistical moments include one or more of mean, variance, skewness, and kurtosis.

21. A cardiac monitoring system, comprising: A non-transitory processor-readable storage medium configured to be executed by a processor and including instructions for performing a method of estimating blood pressure, the method comprising: The user's reference blood pressure and the user's cardiac data were received during the first and second time periods, wherein the cardiac data were measured using electrodes and an accelerometer. Process the cardiac data to generate first and second values ​​for each of a plurality of mechanical cardiac parameters corresponding to the respective first and second time periods; and The user's blood pressure is estimated based on the reference blood pressure, cohort cardiac data associated with the user, and the sum of the variations between the first and second values ​​of the plurality of mechanical cardiac parameters. The estimated blood pressure is given by the following formula: ， Where i is an index of a set of n mechanical heart parameters, BP est It is the user's estimated blood pressure, BP. ref It is the user's reference blood pressure, β i It is the mechanical heart parameter value of the i-th queue, x 1,i It is the first value of the i-th mechanical heart parameter corresponding to the first time period, and x 2,i It is the second value of the i-th mechanical heart parameter corresponding to the second time period. Furthermore, the cohort cardiac data is grouped by one or more of age, sex, ethnicity, and body mass index.

22. A cardiac monitoring system, comprising: A non-transitory processor-readable storage medium configured to be executed by a processor and including instructions for performing a method of estimating blood pressure, the method comprising: The user's reference blood pressure and the user's cardiac data were received during the first and second time periods, wherein the cardiac data were measured using an electrocardiogram (ECG) sensor and an electrocardiogram (SCG) sensor, respectively attached to the skin of the user's upper left chest. The cardiac data is processed to generate electrical cardiac parameter values ​​corresponding to R-wave and T-wave time points, and first and second values ​​of mechanical cardiac parameters corresponding to the respective first and second time periods are generated, at least in part, based on the R-wave and T-wave time points; and The user's blood pressure is estimated based on the reference blood pressure, cohort cardiac data of the mechanical heart parameters associated with the user, and the variation between the first and second values ​​of the mechanical heart parameters. The estimated blood pressure is given by the following formula: ， Where i is an index of a set of n mechanical heart parameters, BP est It is the user's estimated blood pressure, BP. ref It is the user's reference blood pressure, β i It is the mechanical heart parameter value of the i-th queue, x 1,i It is the first value of the i-th mechanical heart parameter corresponding to the first time period, and x 2,i It is the second value of the i-th mechanical heart parameter corresponding to the second time period. Furthermore, the cohort cardiac data is grouped by one or more of age, sex, ethnicity, and body mass index.

23. The cardiac monitoring system of claim 22, wherein the mechanical cardiac parameters include a plurality of mechanical cardiac parameters, and wherein the estimated blood pressure is based on the sum of the changes in the values ​​of the plurality of mechanical cardiac parameters between the first and second time periods.

24. A cardiac monitoring system comprising: A heart monitor, comprising: The cardiac sensor, including an accelerometer, is configured to be releasably attached to the skin above the user's left chest cavity and to measure cardiac data in first and second time periods. A communication device configured to establish a communication channel; and A non-transitory processor-readable storage medium configured to be executed by a processor and including instructions for performing the following operations: Receive the user's reference blood pressure; The heart data is received using the communication channel; Generate mechanical heart parameter values ​​for each of a plurality of mechanical heart parameters from the said cardiac data in a first time period and a second time period; and The user's blood pressure is estimated based on the reference blood pressure, cohort cardiac data associated with the user, and the sum of the changes in the values ​​of the plurality of mechanical cardiac parameters between the first and second time periods. The estimated blood pressure is given by the following formula: ， Where i is an index of a set of n mechanical heart parameters, BP est It is the user's estimated blood pressure, BP. ref It is the user's reference blood pressure, β i It is the mechanical heart parameter value of the i-th queue, x 1,i It is the first value of the i-th mechanical heart parameter corresponding to the first time period, and x 2,i It is the second value of the i-th mechanical heart parameter corresponding to the second time period. Furthermore, the cohort cardiac data is grouped by one or more of age, sex, ethnicity, and body mass index.

25. The system of claim 24, wherein the mechanical heart parameters include one or more of the following: SCG wave amplitude, maximum L2 norm, area under the power spectral density curve, zero-crossing rate, sample entropy, and statistical moments.

26. The system of claim 25, wherein the statistical moments include one or more of the following: mean, variance, skewness, and kurtosis.

27. The system of claim 24, wherein the heart monitor further includes electrodes configured to measure cardiac data associated with the electrical activity of the heart.

28. The system of claim 27, wherein the electrode is an electrocardiogram (ECG) electrode and the accelerometer is an angiograph (SCG) sensor.

29. A cardiac monitoring system comprising: A non-transitory processor-readable storage medium configured to be executed by a processor and including instructions for performing the following operations: Receive cardiac data from users in the first and second time periods; Retrieve the user's reference blood pressure; The cardiac data is processed to generate first and second values ​​for each of a plurality of mechanical cardiac parameters, corresponding to the respective first and second time periods. as well as The user's blood pressure is estimated based on the reference blood pressure, cohort cardiac data associated with the user, and the sum of the variations between the first and second values ​​of the plurality of mechanical cardiac parameters. The estimated blood pressure is given by the following formula: ， Where i is an index of a set of n mechanical heart parameters, BP est It is the user's estimated blood pressure, BP. ref It is the user's reference blood pressure, β i It is the mechanical heart parameter value of the i-th queue, x 1,i It is the first value of the i-th mechanical heart parameter corresponding to the first time period, and x 2,i It is the second value of the i-th mechanical heart parameter corresponding to the second time period. Furthermore, the cohort cardiac data is grouped by one or more of age, sex, ethnicity, and body mass index.

30. A device for estimating blood pressure, comprising: A transceiver configured to receive a user’s reference blood pressure, queued heart data associated with the user, heart data in first and second time periods, and one or more of first and second values ​​of a plurality of mechanical heart parameters corresponding to the respective first and second time periods. as well as A processor configured to estimate the user's blood pressure based on the sum of changes between the reference blood pressure, the queued cardiac data, and the first and second values ​​of the plurality of mechanical cardiac parameters. The estimated blood pressure is given by the following formula: ， Where i is an index of a set of n mechanical heart parameters, BP est It is the user's estimated blood pressure, BP. ref It is the user's reference blood pressure, β i It is the mechanical heart parameter value of the i-th queue, x 1,i It is the first value of the i-th mechanical heart parameter corresponding to the first time period, and x 2,i It is the second value of the i-th mechanical heart parameter corresponding to the second time period. Furthermore, the cohort cardiac data is grouped by one or more of age, sex, ethnicity, and body mass index.

31. A heart monitor, comprising: A cardiac sensor, comprising electrodes and an accelerometer, is configured to releasably attach to the skin of a user's left chest and measure cardiac data in a first time period and a second time period. A memory configured to store the measured cardiac data, the user's reference blood pressure, and queue cardiac data associated with the user; as well as A processor configured to process the cardiac data to generate first and second values ​​for each of a plurality of mechanical cardiac parameters corresponding to the respective first and second time periods, and to estimate the user's blood pressure based on the reference blood pressure, the queued cardiac data, and the sum of the variations between the first and second values ​​of the plurality of mechanical cardiac parameters. The estimated blood pressure is given by the following formula: ， Where i is an index of a set of n mechanical heart parameters, BP est It is the user's estimated blood pressure, BP. ref It is the user's reference blood pressure, β i It is the mechanical heart parameter value of the i-th queue, x 1,i It is the first value of the i-th mechanical heart parameter corresponding to the first time period, and x 2,i It is the second value of the i-th mechanical heart parameter corresponding to the second time period. Furthermore, the cohort cardiac data is grouped by one or more of age, sex, ethnicity, and body mass index.

32. A cardiac monitoring system, comprising: A non-transitory processor-readable storage medium configured to be executed by a processor and including instructions for performing a method of estimating blood pressure, the method comprising: Receive the user’s mechanical heart data, which includes a first SCG wave measured along a first axis and a second SCG wave measured along a second axis different from the first axis for each of a first time period and a second time period using an accelerometer. Mechanical heart parameter values ​​are generated from the mechanical heart data, including average SCG wave values ​​of the first SCG wave measured along the first axis and the second SCG wave measured along the second axis for the first and second time periods; and The user's blood pressure is estimated based on the changes in the mechanical cardiac parameter values ​​between the first time period and the second time period.

33. The cardiac monitoring system according to claim 32, wherein, The method also includes receiving the user's reference blood pressure and cohort cardiac data associated with the user.

34. The cardiac monitoring system of claim 33, wherein the estimated blood pressure is further based on the reference blood pressure and the cohort cardiac data.

35. The cardiac monitoring system according to any one of claims 32 to 34, wherein generating the mechanical cardiac parameter values ​​comprises generating mechanical cardiac parameter values ​​of a plurality of mechanical cardiac parameters, and wherein the estimated blood pressure is based on the sum of the changes in the mechanical cardiac parameter values ​​of the plurality of mechanical cardiac parameters between the first and second time periods.

36. The cardiac monitoring system of claim 32, wherein the SCG wave comprises an SCG1 wave and an SCG2 wave.

37. The cardiac monitoring system of claim 32, wherein the mechanical cardiac parameter values ​​are derived from the average SCG wave.

38. The cardiac monitoring system of claim 32, wherein the mechanical cardiac parameters include one or more of the following: SCG wave amplitude, maximum L2 norm, area under the power spectral density curve, zero-crossing rate, sample entropy, and statistical moments.

39. The cardiac monitoring system of claim 32, wherein the accelerometer is an electrocardiogram (SCG) sensor.

40. The cardiac monitoring system according to claim 32, wherein, The method also includes receiving electrical cardiac data measured using electrodes.

41. The cardiac monitoring system of claim 40, wherein the electrode is an electrocardiogram (ECG) electrode and the accelerometer is an electrocardiogram (SCG) sensor.

42. The heart monitoring system of claim 40, wherein the electrical heart data is measured as an ECG signal, and the mechanical heart data is measured as an SCG signal.

43. The cardiac monitoring system according to any one of claims 40 to 42, wherein, The method further includes generating multiple electrical cardiac parameter values ​​from the electrical cardiac data, wherein the electrical cardiac parameters include one or more of heart rate, R-wave time points, and T-wave time points.

44. The cardiac monitoring system of claim 43, wherein generating the plurality of electrical cardiac parameter values ​​includes generating the R-wave time points on the ECG waveform using sliding window integration.

45. The cardiac monitoring system of claim 43, wherein generating the plurality of electrical cardiac parameter values ​​comprises generating the T-wave time points on the ECG waveform using the R-wave time points and the derivatives of the electrical cardiac data.

46. ​​The cardiac monitoring system according to claim 43, wherein, The method also includes generating SCG wave time points from the mechanical heart data using the R wave and T wave time points.

47. The cardiac monitoring system of claim 32, wherein the mechanical cardiac data includes a third SCG wave measured along a third axis different from the first and second axes, and the method further includes generating a fourth SCG wave including the first, second, and third SCG waves.

48. The cardiac monitoring system of claim 34, wherein the blood pressure is estimated by the following formula: ， Where i is an index of a set of n mechanical heart parameters, BP est It is the user's estimated blood pressure, BP. ref It is the user's reference blood pressure, β i It is the mechanical heart parameter value of the i-th queue, x 1,i It is the first value of the i-th mechanical heart parameter corresponding to the first time period, and x 2,i It is the second value of the i-th mechanical heart parameter corresponding to the second time period.

49. The cardiac monitoring system according to claim 32, wherein, The method also includes releasably coupling the accelerometer to the skin of the user's left chest above the left thoracic cavity.

50. The cardiac monitoring system of claim 33, wherein the cohort cardiac data is grouped by one or more of age, sex, race, and body mass index.

51. The cardiac monitoring system of claim 32, wherein the estimated blood pressure includes one or more of systolic and diastolic blood pressure.

52. The cardiac monitoring system of claim 32, wherein the first time period is a reference time period, and wherein the mechanical heart data is initially measured using the accelerometer during the reference time period.

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