CN113171069A

CN113171069A - Embedded graphic blood pressure measuring system

Info

Publication number: CN113171069A
Application number: CN202110247130.5A
Authority: CN
Inventors: 李龙
Original assignee: Shanghai Lihetai Medical Technology Co ltd
Current assignee: Shanghai Lihetai Medical Technology Co ltd
Priority date: 2021-03-05
Filing date: 2021-03-05
Publication date: 2021-07-27
Anticipated expiration: 2041-03-05
Also published as: CN113171069B

Abstract

The invention provides an embedded graphic blood pressure measuring system, and belongs to the field of continuous non-invasive blood pressure measurement and calculation. According to the invention, based on the littlevGL and the freeRTOS system, the freeRTOS system provides a kernel operating system framework of a bottom layer, the littlevGL provides littlevGL middleware required by embedded graphic display, functions of a memory management mechanism, a hardware equipment management mechanism, a thread scheduling mechanism, a file system management mechanism, interrupt management and the like of the freeRTOS system and embedded graphic display bottom layer drive of the littlevGL are utilized, the measurement accuracy is improved by an optimized continuous noninvasive blood pressure measurement method, and the blank of blood pressure measurement functions in the fields of the freeRTOS and the littlevGL system can be made up by building each functional module.

Description

Embedded graphic blood pressure measuring system

Technical Field

The invention relates to the technical field of continuous non-invasive blood pressure measurement, in particular to an embedded graphic blood pressure measurement system.

Background

Blood pressure is one of the important parameters reflecting the functional status of the cardiovascular system; the blood pressure monitoring is not a stable numerical value of the blood pressure of a human body, which has important significance in the monitoring of the physiological state of clinical and military operation personnel, and the blood pressure monitoring can change along with the change of the physiological state, and important parameters reflecting the psychophysiological state can be extracted through the change of the blood pressure. Beat-to-beat measurement of arterial blood pressure is therefore particularly important. However, the conventional auscultation measurement method generally used at present can only provide a single blood pressure value, and cannot detect beat-to-beat blood pressure. The change in blood pressure in a short time cannot be observed. The artery intubation method can realize beat-by-beat blood pressure measurement, and the result is the most accurate, but the application range is limited because the artery intubation method is invasive measurement. Research shows that the noninvasive beat-to-beat arterial blood pressure can be accurately measured by adopting an arterial tension method, but the method is not easy to operate and is sensitive to the position and the action of a human body: not conducive to long time measurements). In recent years, noninvasive blood pressure measurement devices finapres and portapres based on the volume clamp technique have been widely used in laboratory studies. The method measures the pulse of the finger (namely the blood pressure at the finger tip), but not the brachial artery blood pressure in the common meaning of the method, and is easily influenced by factors such as vasoconstriction, microcirculation disturbance and the like. The measurement precision is influenced due to venous congestion during long-time measurement; this method is less comfortable because of the need to maintain a certain pressure at the site to be measured. Compared with the method, the pulse wave transit time (PWTF) is a noninvasive blood pressure measuring method with strong practicability because the measurement is simple and easy to realize.

LittlevGL is a free open source graphics library that provides everything needed to create an embedded GUI, and LittlevGL is a complete graphics framework that does not need to consider drawing primitive shapes, and a GUI can be built from easy-to-use building blocks (e.g., buttons, diagrams, images, lists, sliders, switches, keyboards, etc.). Has easy-to-use graphic elements, beautiful visual effect and low memory occupation.

The FreeRTOS is a mini real-time operating system kernel. As a lightweight operating system, the functions include: task management, time management, semaphores, message queues, memory management, logging functions, software timers, threads, processes, etc., can basically meet the needs of smaller systems.

At present, an embedded pattern noninvasive blood pressure measuring system based on a LittlevGL and a freeRTOS system is not available temporarily, and in order to make up for the deficiency, the invention provides the embedded pattern noninvasive blood pressure measuring system which is based on the LittlevGL and the freeRTOS system and realizes the embedded pattern noninvasive blood pressure measuring system.

The prior art has at least the following disadvantages:

1. in the prior art, a linux system framework is generally adopted, and the operating system has a huge and graceful structure and is not simple and flexible enough;

2. in the prior art, TouchGFX and Embededwizard are adopted as embedded UIs, but the systems are commercial systems and pay high fees;

3. some continuous non-invasive blood pressure measurement methods in the prior art are not supported by an embedded operating system or an embedded UI system, and meanwhile, a freeRTOS system and littleevGL middleware are not used in the field to realize a non-invasive blood pressure measurement product, so that the technology is a filling of the blank product field.

Disclosure of Invention

In order to solve the technical problems in the prior art, the invention provides an embedded graphic blood pressure measuring system. The invention provides an embedded graphic blood pressure measuring system based on littlevGL and freeRTOS, which is used for a blood pressure measuring device and comprises: the system comprises a UI module, an application layer, a resource layer, a middleware layer and an inner core layer, wherein a freeRTOS system provides a core operating system framework of a bottom layer, littlevGL provides littlevGL middleware required by embedded graphic display, functions such as a memory management mechanism, a hardware equipment management mechanism, a thread scheduling mechanism, a file system management mechanism, interrupt management and the like in an embedded operating system framework of the freeRTOS system are utilized, the embedded graphic display bottom layer drive of the littlevGL is utilized, and the blank of blood pressure measurement functions in the fields of the freeRTOS and the littlevGL system can be made up by building each functional module; meanwhile, the measurement accuracy is improved by an optimized continuous non-invasive blood pressure measurement method.

The FreeRTOS is a mini embedded real-time operating system kernel. As a lightweight operating system, the functions include: task management, time management, semaphores, message queues, memory management, logging functions, software timers, threads, processes, etc., can basically meet the needs of smaller systems.

The invention provides an embedded graphic blood pressure measuring system based on freeRTOS and LittlevGL, and makes up the defect that the embedded graphic blood pressure measuring system based on freeRTOS and LittlevGL is not available at present.

When the system is actually applied, for example, when the system is used for a blood pressure measurement watch, an arm type sphygmomanometer is configured as calibration equipment, when a user is replaced, the calibration equipment needs to be connected for calibration, multiple groups of data of the same user are collected in the calibration process, the calibration equipment is used for calibration, then the data are processed by the data processing module, the blood pressure calculation module calculates regression coefficients and regression constants in a regression equation used for blood pressure measurement of the user, and the regression equation is determined. The method is used for calculating the blood pressure of the user in real time in the subsequent actual dynamic monitoring. Meanwhile, relevant measuring equipment such as a blood pressure sensor, a heart rate sensor and the like can be configured.

The invention provides an embedded graphic blood pressure measuring system, which is based on littlevGL and freeRTOS system, is used for a blood pressure measuring device, is matched with a calibrating device and a measuring device for use, utilizes a memory management mechanism, a hardware device management mechanism, a thread scheduling mechanism, a file system management mechanism and an interrupt management mechanism of the freeRTOS system to schedule and an embedded graphic display bottom layer drive of the littlevGL to carry out calibrating device connection, measuring device connection, system calibration, blood pressure measurement and heart rate measurement, and adopts a regression equation considering heart rate factors and PWV factors to carry out continuous non-invasive blood pressure measurement of blood pressure calculation.

Preferably, the embedded graphic blood pressure measurement system comprises: the device comprises a UI module, an application layer, a resource layer, a middleware layer and a kernel layer, wherein the application layer comprises a blood pressure measuring module, a heart rate measuring module, an equipment connecting module and a calibrating module; the resource layer comprises picture library resources and font library resources; the middleware layer comprises littlevGL middleware; the kernel layer comprises a hardware abstract interface module and a Bluetooth library module;

the UI module performs operations including: interacting with the littlevGL middleware, receiving an instruction generated by UI interface operation, transmitting the instruction to the littlevGL middleware, or receiving information of the littlevGL middleware and displaying the information on the UI interface;

the resource layer is called by the UI module through the littlevGL middleware and displayed on the UI interface;

the application layer receives an instruction generated by UI interface operation through the littlevGL middleware and returns a result obtained according to the operation instruction to the UI module through the littlevGL middleware;

the blood pressure measurement module performs operations including: receiving a blood pressure measurement instruction sent by littlevGL middleware, sending a blood pressure measurement message to measurement equipment through a hardware equipment management mechanism in a freeRTOS system, receiving PWTT and heart rate PULSE measurement data returned by the measurement equipment, calculating the systolic pressure SBP and the diastolic pressure DBP in real time by using a regression equation considering heart rate factors and PWV factors in combination with calibration data, and displaying the calculation result on a UI interface through the littlevGL middleware;

the heart rate measurement module performs operations including: receiving a heart rate measurement instruction sent by littlevGL middleware, sending a heart rate measurement message to measurement equipment through a hardware equipment management mechanism in a freeRTOS system, receiving heart rate PULSE measurement data returned by the measurement equipment, and displaying the data on a UI interface through the littlevGL middleware;

the equipment connecting module comprises a calibration equipment connecting module and a measuring equipment connecting module;

the calibration device connection module is used for connecting the calibration device and the blood pressure measuring device, and the executed operations comprise: receiving a calibration equipment connection instruction sent by a littlevGL middleware, opening Bluetooth of a blood pressure measuring device through a hardware management mechanism in a freeRTOS system, connecting calibration equipment, and displaying the connection result on a UI interface through the littlevGL middleware according to the connection result;

the measuring equipment connecting module is used for connecting the measuring equipment and the blood pressure measuring device, and the executed operation comprises the following steps: receiving a measuring equipment connection instruction sent by a littlevGL middleware, opening Bluetooth of a blood pressure measuring device through a hardware management mechanism in a freeRTOS system, connecting measuring equipment, and displaying on a UI interface through the littlevGL middleware according to a connection result;

the calibration module performs operations including: receiving a calibration instruction sent by a littlevGL middleware, opening Bluetooth of a blood pressure measuring device through a hardware management mechanism in a freeRTOS system, sending a calibration equipment connection instruction to a calibration equipment connection module, performing calibration equipment connection, sending a measuring equipment connection instruction to a measuring equipment connection module, connecting measuring equipment, sending a calibration instruction to the calibration equipment through a thread scheduling mechanism in the freeRTOS system, sending a measuring instruction to the measuring equipment, receiving calibration equipment data, receiving measuring equipment data, and displaying the calibration result on a UI interface through the littlevGL middleware;

littlevtgl middleware performs the following operations: and receiving an instruction generated by UI interface operation, forwarding the instruction to a corresponding application layer, receiving data of the application layer, and displaying the data on the UI interface through a UI module.

Preferably, the Bluetooth of the blood pressure measuring device is turned on by Bluetooth middleware through a thread scheduling mechanism in a freeRTOS system;

the Bluetooth middleware is positioned at a middleware layer and executes the following operations: the method comprises the steps of turning on or turning off the Bluetooth of the blood pressure measuring device, receiving a message of connecting or disconnecting the UI interface of the blood pressure measuring device to or from the calibration equipment, receiving a message of connecting or disconnecting the UI interface of the blood pressure measuring device to or from the blood pressure measuring device, and returning the current Bluetooth connection state to the UI interface of the blood pressure measuring device.

Preferably, the middleware layer further comprises battery management middleware, sensor middleware and control command middleware;

the Bluetooth library module executes the following operations: initializing a Bluetooth module in the blood pressure measuring device, opening/closing a bottom layer interface package by Bluetooth, connecting/disconnecting the bottom layer interface package by Bluetooth and transmitting a bottom layer interface package by Bluetooth;

the battery management middleware performs the following operations: detecting the plugging state of a power supply of the blood pressure measuring device, detecting the current electric quantity information of the blood pressure measuring device, and monitoring low electric quantity alarm and battery charging state;

the sensor middleware performs the following operations: turning on and off a gyroscope/triaxial acceleration/photoelectric sensor in the blood pressure measuring device, and monitoring data of the gyroscope/triaxial acceleration/photoelectric sensor;

the control command middleware performs the following operations: receiving application layer information/command/data, encapsulating the information into a control command, sending the control command to the sensor through the serial port, receiving sensor return information, and returning control command execution result information to the middleware layer.

Preferably, the kernel layer comprises a hardware abstraction interface module, a storage module, a bluetooth library module and a debugging module;

the hardware abstraction interface module performs the following operations: the method comprises the following steps of operating system part interface packaging, power management operation bottom layer interface packaging, RAM operation bottom layer interface packaging, Flash operation bottom layer interface packaging, GPIO operation bottom layer interface packaging, USB operation bottom layer interface packaging, LCD operation bottom layer interface packaging, serial port operation bottom layer interface packaging, I2C operation bottom layer interface packaging and sensor operation bottom layer interface packaging;

the storage module performs the following operations: initializing a flash, and dividing the flash into a user information area and a measurement data storage area for management;

the debugging module performs the following operations: and packaging each interface of the debugging system, such as a system information viewing interface, a sensor state information viewing interface and each functional module state information viewing interface, and switching on and off the debugging module through the debugging switch macro.

Preferably, the real-time calculation of the systolic blood pressure SBP and the diastolic blood pressure DBP using a regression equation taking into account heart rate factors and PWV factors specifically comprises the steps of:

screening out a qualified PWTT value by adopting a PWTT screening algorithm according to the calibration data and the acquired PWTT value;

calculating a real-time PWV value according to the optimized PWV algorithm;

using the following regression equation, systolic pressure SBP and diastolic pressure DBP;

SBP＝M1+P1*PWV+Q1*PULSE；

DBP＝M2+P2*PWV+Q2*PULSE；

wherein,

m1: regression constants of the systolic pressure, obtained from the calibration data;

m2: regression constants of diastolic pressure, obtained from calibration data;

p1, Q1, P2 and Q2 are regression coefficients between SBP and PWV, SBP and PULSE, PP and PWV, PP and PULSE, respectively, obtained from calibration data;

PWV is a calculated real-time PWV value;

PULSE is a heart rate value acquired in real time.

Preferably, the regression constant M1 for systolic pressure and the regression constant M2 for diastolic pressure are calculated by:

M1＝B-P1*A-Q1*U；

M2＝C-P2*A-Q2*U；

wherein,

a is the mean of the PWV of the calibration data;

b is the mean systolic pressure of the calibration data;

c is the mean value of the pulse pressure values of the calibration data;

u is the mean value of the heart rate PULSE of the calibration data;

p1, Q1, P2 and Q2 are regression coefficients between SBP and PWV, SBP and PULSE, PP and PWV, PP and PULSE, respectively, obtained from calibration data.

Preferably, the regression coefficients P1, Q1, P2 and Q2 between SBP and PWV, SBP and PULSE, PP and PWV, PP and PULSE are calculated by the following method:

P1＝(SYS_Ray-SYS_Rby*SYS_Rab)/(1-SYS_Rab*SYS_Rab)*(E/D)；

Q1＝(SYS_Rby-SYS_Ray*SYS_Rab)/(1-SYS_Rab*SYS_Rab)*(E/J)；

P2＝(DIS_Ray-DIS_Rby*DIS_Rab)/(1-DIS_Rab*DIS_Rab)*(F/D)；

Q2＝(DIS_Rby-DIS_Ray*DIS_Rab)/(1-DIS_Rab*DIS_Rab)*(F/J)；

wherein: d is the PWV standard deviation of the calibration data; e is the SBP standard deviation of the calibration data, and F is the PP standard deviation of the calibration data; j is heart rate PULSE standard deviation of calibration data; SYS _ Ray is an autocorrelation coefficient between the SBP and the PWV of the calibration data; SYS _ Rby is an autocorrelation coefficient between the SBP and PULSE of the calibration data; SYS _ Rab is an autocorrelation coefficient between PWV and PULSE of the calibration data; DIS _ Ray is the autocorrelation coefficient between PP and PWV of the calibration data; DIS _ Rby is the autocorrelation coefficient between PP and PULSE of the calibration data; DIS _ Rab is an autocorrelation coefficient between PWV and PULSE of the calibration data.

Preferably, the correlation coefficient between PWV, systolic blood pressure SBP, heart rate PULSE and PULSE pressure difference PP is calculated by:

calculating the average value of PWV, the systolic blood pressure SBP, the heart rate PULSE and the PULSE pressure difference PP;

calculating standard difference values of PWV, systolic pressure SBP, heart rate PULSE and PULSE pressure difference PP;

calculating covariance values of PWV, systolic pressure SBP, heart rate PULSE and PULSE pressure difference PP;

calculating correlation coefficients among PWV, the systolic blood pressure SBP, the heart rate PULSE and the PULSE pressure difference PP;

the correlation coefficients between the PWV, the systolic blood pressure SBP, the heart rate PULSE and the PULSE pressure difference PP are as follows:

autocorrelation coefficient between SBP and PWV: SYS _ Ray ═ G1/(D ═ E);

autocorrelation coefficient between SBP and PULSE: SYS _ Rby ═ S1/(E ═ J);

autocorrelation coefficient between PWV and PULSE: SYS _ Rab ═ N1/(D ═ J);

autocorrelation coefficient between PP and PWV: DIS _ Ray ═ G2/(D ═ F);

autocorrelation coefficient between PP and PULSE: DIS _ Rby ═ S2/(F × J);

wherein G1 is the covariance of D and E; g2 is the covariance of D and F; n1 is the covariance of D and J; s2 is the covariance of F and J.

Preferably, the method comprises the following steps of screening out a qualified PWTT value by adopting a PWTT screening algorithm according to the calibration data, and calculating a PWV value according to an optimized PWV algorithm:

calibration data were obtained by the following screening procedure:

primary acquisition and primary screening of PWTT;

s1000: collecting N PWTT values, and discarding the previous P PWTT values;

s2000: calculating the mean value of the remaining (N-P) PWTT values, and calculating a first confidence interval according to the confidence coefficient M%;

s3000: discarding PWTT values which are not in the first confidence interval in the remaining (N-P) PWTT values, determining the PWTT values as calibration data if the number of the remaining PWTT values meets the preset lowest PWTT number requirement, and executing the PWTT continuous acquisition and screening step if the number of the remaining PWTT values does not meet the preset lowest PWTT number requirement;

continuously acquiring and screening PWTT;

s4000; continuously acquiring m PWTT values to enable the total number of PWTT to reach the preset lowest PWTT number;

s5000: calculating the mean value of the PWTT values, and calculating a second confidence interval according to the confidence coefficient M%; the second confidence interval calculated at each iteration is related to the PWTT value currently participating in the calculation of the confidence interval;

s6000: discarding PWTT values whose PWTT values are not within the second confidence interval;

s7000: if the number of the residual PWTT values meets the preset lowest PWTT number requirement, determining the residual PWTT values as calibration data, otherwise, continuing to execute the step S4000 until the preset lowest PWTT number requirement is met;

repeating the steps S4000 to S6000 to screen the PWTT collected in real time until the preset requirement of the number of the lowest PWTT is met;

a PWV calculation step;

s8000: calculating the mean value S of the reserved PWTT values;

s9000: calculating a PWV value; the PWV value is calculated by adopting the following formula;

wherein,

PWV is the finally calculated pulse wave velocity;

l is the arm length of the person to be measured;

a is the average distance from the shoulders to the heart of a normal person;

s is the average value of the PWTT values which are finally reserved;

the confidence interval (a1, a2) is calculated using the following formula:

A1＝A-A*M％；

A2＝A+A*M％；

wherein,

a is the mean value of the PWTT value which is reserved currently;

m% is confidence;

a1 is the lower confidence interval limit;

a2 is the upper confidence interval limit.

Compared with the prior art, the invention has the following beneficial effects:

(1) the noninvasive blood pressure measurement is realized by using a freeRTOS system and a littlevGL middleware technology, and the system architecture fills the blank of the product field.

(2) According to the invention, the accuracy of the acquired data is directly improved by discarding the data with larger initial error.

(3) According to the invention, the confidence interval is set, the data which are not in the current overall average confidence interval are discarded, and the reliability of the acquired data is increased, so that the accuracy of the acquired data is improved.

(4) The invention adopts the confidence interval for PWTT data screening for a plurality of times, prevents the shaking of abnormal data in the data acquisition process and the occurrence of data with larger error, and screens the data which are not in the confidence interval by adopting the method of the confidence interval, so that the whole data accuracy is higher, and the PWV result of the subsequent final calculation is more accurate.

(5) According to the invention, the relationship between the systolic blood pressure SBP, the PULSE pressure PP, the heart rate PULSE and the blood pressure is considered, the autocorrelation coefficient, the regression coefficient and the regression constant among the SBP, the PWV, the PP and the PULSE in each group of data are calculated, and a regression equation comprising the regression coefficient and the regression constant of each parameter is constructed to calculate the blood pressure value, so that the result is more accurate.

Drawings

FIG. 1 is a system architecture diagram of one embodiment of the present invention;

FIG. 2 is a flow chart of blood pressure measurement according to an embodiment of the present invention;

FIG. 3 is a heart rate measurement flow diagram of one embodiment of the present invention;

FIG. 4 is a calibration device connection flow diagram of one embodiment of the present invention;

FIG. 5 is a flow diagram of a measurement device connection of one embodiment of the present invention;

FIG. 6 is a flowchart of the operation of the calibration module of one embodiment of the present invention

FIG. 7 is a flow chart of calculating the current diastolic and systolic pressures according to one embodiment of the present invention;

FIG. 8 is a diagram illustrating exemplary fluctuations in PWTT values when collecting PWTT in one embodiment of the present invention.

Detailed Description

The following detailed description of the embodiments of the present invention is provided in conjunction with the accompanying drawings of fig. 1-8.

As a preferred embodiment, the embedded graphic blood pressure measurement system comprises: the device comprises a UI module, an application layer, a resource layer, a middleware layer and a kernel layer, wherein the application layer comprises a blood pressure measuring module, a heart rate measuring module, an equipment connecting module and a calibrating module; the resource layer comprises picture library resources and font library resources; the middleware layer comprises littlevGL middleware; the kernel layer comprises a hardware abstract interface module and a Bluetooth library module;

the blood pressure measurement module performs operations including: receiving a blood pressure measurement instruction sent by littlevGL middleware, sending a blood pressure measurement message to measurement equipment through a hardware equipment management mechanism in a freeRTOS system, receiving PWTT and heart rate PULSE measurement data returned by the measurement equipment, calculating the systolic pressure SBP and the diastolic pressure DBP in real time by using a regression equation considering heart rate factors and PWV factors in combination with calibration data, and displaying the calculation result on a UI interface through the littlevGL middleware; and if the measurement is overtime, informing the user of the measurement failure through the UI interface through littlevGL middleware, and otherwise, displaying the real-time calculated systolic pressure SBP and diastolic pressure DBP as the blood pressure measurement values on the UI interface through the littlevGL middleware.

The heart rate measurement module performs operations including: receiving a heart rate measurement instruction sent by littlevGL middleware, sending a heart rate measurement message to measurement equipment through a hardware equipment management mechanism in a freeRTOS system, receiving heart rate PULSE measurement data returned by the measurement equipment, and displaying the data on a UI interface through the littlevGL middleware; and if the measurement is overtime, informing the user of the measurement failure through the litlevGL middleware through the UI interface, and otherwise, displaying the heart rate measurement value on the UI interface through the litlevGL middleware.

the calibration device connection module is used for connecting the calibration device and the blood pressure measuring device, and the executed operations comprise: receiving a calibration equipment connection instruction sent by a littlevGL middleware, opening Bluetooth of a blood pressure measuring device through a hardware management mechanism in a freeRTOS system, connecting calibration equipment, and displaying the connection result on a UI interface through the littlevGL middleware according to the connection result; if the connection is successful, displaying the connected icon on the UI interface through the littlevGL middleware, otherwise, continuously and repeatedly connecting for three times, if the connection is successful, displaying the connected icon on the UI interface through the littlevGL middleware, otherwise, failing to connect, and informing the user of the connection failure through the littlevGL middleware on the UI interface.

The measuring equipment connecting module is used for connecting the measuring equipment and the blood pressure measuring device, and the executed operation comprises the following steps: receiving a measuring equipment connection instruction sent by a littlevGL middleware, opening Bluetooth of a blood pressure measuring device through a hardware management mechanism in a freeRTOS system, connecting measuring equipment, and displaying on a UI interface through the littlevGL middleware according to a connection result; and if the connection is successful, displaying the connected icon on the UI interface through the littlevGL middleware, otherwise, notifying the user of the connection failure through the littlevGL middleware on the UI interface.

The calibration module performs operations including: receiving a calibration instruction sent by a littlevGL middleware, opening Bluetooth of a blood pressure measuring device through a hardware management mechanism in a freeRTOS system, sending a calibration equipment connection instruction to a calibration equipment connection module, performing calibration equipment connection, sending a measuring equipment connection instruction to a measuring equipment connection module, connecting measuring equipment, sending a calibration instruction to the calibration equipment through a thread scheduling mechanism in the freeRTOS system, sending a measuring instruction to the measuring equipment, receiving calibration equipment data, receiving measuring equipment data, and displaying the calibration result on a UI interface through the littlevGL middleware; if the calibration is successful, the success of the calibration is prompted on the UI interface through the littlevGL middleware, and if the calibration is failed, the failure of the calibration is informed to the user on the UI interface through the littlevGL middleware.

According to a specific embodiment, the application layer may further include a data transmission module, and the data transmission module performs the following operations: judging whether the data is read or written when the process starts, if the data is written, acquiring user input information in a UI interface setting information function, sending a data message to a littlevGL middleware, and finally sending the data to a background server for synchronization; if the data is read, the user information on the server is read, and the information is written back to the measuring equipment.

As a preferred embodiment, the Bluetooth of the blood pressure measuring device is turned on by the Bluetooth middleware through a thread scheduling mechanism in the freeRTOS system;

As a preferred embodiment, the middleware layer further comprises battery management middleware, sensor middleware and control command middleware;

As a preferred embodiment, the kernel layer comprises a hardware abstraction interface module, a storage module, a bluetooth library module and a debugging module;

As a preferred embodiment, the real-time calculation of the systolic blood pressure SBP and the diastolic blood pressure DBP using a regression equation taking into account heart rate factors and PWV factors specifically comprises the following steps:

calculating a real-time PWV value according to the optimized PWV algorithm;

SBP＝M1+P1*PWV+Q1*PULSE；

DBP＝M2+P2*PWV+Q2*PULSE；

wherein,

m2: regression constants of diastolic pressure, obtained from calibration data;

PWV is a calculated real-time PWV value;

PULSE is a heart rate value acquired in real time.

As a preferred embodiment, the regression constant M1 for systolic pressure and the regression constant M2 for diastolic pressure are calculated by:

M1＝B-P1*A-Q1*U；

M2＝C-P2*A-Q2*U；

wherein,

a is the mean of the PWV of the calibration data;

b is the mean systolic pressure of the calibration data;

c is the mean value of the pulse pressure values of the calibration data;

u is the mean value of the heart rate PULSE of the calibration data;

As a preferred embodiment, the regression coefficients P1, Q1, P2 and Q2 between SBP and PWV, SBP and PULSE, PP and PWV, PP and PULSE are calculated by the following methods:

P1＝(SYS_Ray-SYS_Rby*SYS_Rab)/(1-SYS_Rab*SYS_Rab)*(E/D)；

Q1＝(SYS_Rby-SYS_Ray*SYS_Rab)/(1-SYS_Rab*SYS_Rab)*(E/J)；

P2＝(DIS_Ray-DIS_Rby*DIS_Rab)/(1-DIS_Rab*DIS_Rab)*(F/D)；

Q2＝(DIS_Rby-DIS_Ray*DIS_Rab)/(1-DIS_Rab*DIS_Rab)*(F/J)；

As a preferred embodiment, the correlation coefficient between PWV, systolic blood pressure SBP, heart rate PULSE and PULSE pressure difference PP is calculated by:

autocorrelation coefficient between SBP and PWV: SYS _ Ray ═ G1/(D ═ E);

autocorrelation coefficient between SBP and PULSE: SYS _ Rby ═ S1/(E ═ J);

autocorrelation coefficient between PWV and PULSE: SYS _ Rab ═ N1/(D ═ J);

autocorrelation coefficient between PP and PWV: DIS _ Ray ═ G2/(D ═ F);

autocorrelation coefficient between PP and PULSE: DIS _ Rby ═ S2/(F × J);

The mean, standard deviation and covariance of each parameter of each set of calibration data are defined as follows, or different letters can be used for definition according to actual needs.

TABLE 1 symbol definitions of mean, standard deviation, covariance of each parameter

The correlation coefficient values of the calibration data sets calculated in the step are mainly used for calculating the linear correlation degree between two different variables, and can be used as parameters for calculating subsequent regression coefficients, and the parameters can directly influence the correlation of the regression coefficients, so that the accuracy of the measured blood pressure values is finally and indirectly influenced.

As a preferred embodiment, screening out a qualified PWTT value by adopting a PWTT screening algorithm according to calibration data, and calculating a PWV value according to an optimized PWV algorithm, specifically comprising the following steps:

calibration data were obtained by the following screening procedure:

primary acquisition and primary screening of PWTT;

s1000: collecting N PWTT values, and discarding the previous P PWTT values;

continuously acquiring and screening PWTT;

a PWV calculation step;

s8000: calculating the mean value S of the reserved PWTT values;

wherein,

PWV is the finally calculated pulse wave velocity;

l is the arm length of the person to be measured;

a is the average distance from the shoulders to the heart of a normal person;

s is the average value of the PWTT values which are finally reserved;

the confidence interval (a1, a2) is calculated using the following formula:

A1＝A-A*M％；

A2＝A+A*M％；

wherein,

a is the mean value of the PWTT value which is reserved currently;

m% is confidence;

a1 is the lower confidence interval limit;

a2 is the upper confidence interval limit.

Example 1

The blood pressure measured when the heart rate regression coefficient is not added in the blood pressure calculation in the conventional method is compared with the blood pressure measured by the scheme of adding the heart rate regression coefficient in the continuous non-invasive blood pressure measurement system.

Firstly, comparing the measured blood pressure without adding heart rate regression coefficient with the measured value of a standard sphygmomanometer

TABLE 2 tester at different stages (calibration/measurement) and different states (still/motion)

Blood pressure values of the systolic pressure SBP and the diastolic pressure DBP measured by calibrating a sphygmomanometer

TABLE 3M-and P-values in the non-additive Heart Rate calculation formulas used in the PWTT blood pressure measurement watch

	SBP	PP
			p	0.590067424	0.233961592
m	121.4982	57.8830

The formula for calculating the blood pressure without adding the heart rate is as follows: blood pressure value m + p PWV;

in the table:

p is a regression coefficient of SBP or PP and PWV;

m is the regression constant of SBP or PP, and is the average value of SBP or PP-p multiplied by the average value of PWV.

TABLE 4 wrist watch for measuring blood pressure by PWTT at different states (still/sport) of the tester

The measured PWTT value, PWV value, Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) calculated by a blood pressure calculation algorithm without adding heart rate

TABLE 5 tester at different states (still/moving)

By calibrating the error between the blood pressure value of the sphygmomanometer and the blood pressure value calculated by the PWTT blood pressure measuring watch without adding the heart rate algorithm

The error of the SBP average value measured by a blood pressure calculation method without adding the heart rate is-4.1 mmHg; SBP standard deviation 9.5 mmHg;

the error of the DBP average value measured by a blood pressure calculation method without adding the heart rate is-2.5 mmHg; DBP standard deviation was 4.1 mmHg;

the above data show that after calibration, the coefficients calculated from the calibration data are used to calculate the systolic SBP and the diastolic DBP in the measurement phase by a calculation algorithm without adding heart rate, and the above table shows that the standard deviation error of the measurement results is 9.5, which exceeds the international standard 8mmHg range.

Secondly, the continuous non-invasive blood pressure measuring system of the invention is added with the measured value comparison of the blood pressure measured by the heart rate regression coefficient and the standard sphygmomanometer

The standard sphygmomanometer measurements are shown in Table 2.

Table 6 the present invention adds m, p and q values to the heart rate calculation formula

	SBP	PP
			p	-9.8588	-11.0822
q	1.4522	0.9990
			m	64.5125	36.1852

Calculating formula of the blood pressure added with the heart rate

SBP＝M1+P1*PWV_rt+Q1*PULSE_rt；

DBP＝M2+P2*PWV_rt+Q2*PULSE_rt；

The general formula written for blood pressure calculation is: blood pressure value m + p PWV + q PULSE;

wherein,

m is a regression constant, and corresponds to a systolic pressure regression constant M1 and a diastolic pressure regression constant M2 obtained according to each group of calibration data respectively;

p is a regression coefficient and respectively corresponds to regression coefficients P1 and P2 between SBP and PWV and between PP and PWV obtained according to each group of calibration data;

q is a regression coefficient, and corresponds to the regression coefficients Q1 and Q2 between SBP and PULSE and between PP and PULSE obtained according to each group of calibration data;

TABLE 7 tester at different stages (calibration/measurement) and different states (still/motion)

The PWTT value, the PWV value, the heart rate PULSE value and the Systolic Blood Pressure (SBP), the Diastolic Blood Pressure (DBP) and the PULSE Pressure (PP) calculated by the algorithm are measured by the PWTT blood pressure measuring watch adopting the method

TABLE 8 tester at different states (still/moving)

By calibrating the error between the blood pressure value of the sphygmomanometer and the blood pressure value calculated after adding the heart rate, the error average value of the final result and the error standard deviation of the final result

The error of the SBP average value measured by a blood pressure calculation method without adding the heart rate is-1.6 mmHg; SBP standard deviation 8 mmHg;

the error of the DBP average value measured by a blood pressure calculation method without adding the heart rate is-2.2 mmHg; DBP standard deviation is 3.4 mmHg;

after the heart rate data is increased by adopting the method, the coefficient calculated by the calibration data can be seen from the data after calibration, the Systolic Blood Pressure (SBP) and the Diastolic Blood Pressure (DBP) are calculated in the measurement stage by the calculation algorithm of adding the heart rate through the calibration coefficient, and the final average value and the standard deviation error of the measurement result are both in the range of international standard 8 mmHg.

Example 2

The following table shows the effect of the same tester, fixed arm length, N, M, and a parameters, respectively, on the final PWV value for different numbers of PWTT values before discarding, according to one embodiment of the present invention.

Wherein, the real PWV is 3.566243, the arm length L is 630mm, N is 15, M is 10%, and a is 200 mm.

TABLE 9 Effect of discarding different PWTT values on PWV during Primary Screen

The real PWV value is measured by using a certain brand AECG100 ECG/PPG and PWTT multifunctional physiological signal tester, and according to the above table 2, a PWTT fluctuation graph 8 can be obtained, it can be seen that the PWTT data fluctuation of the first 5 acquisition points is relatively large, and the data after the 5 acquisition points tends to be stable, so when P is 5-9, i.e. the first 5 to the first 9 PWTT values are discarded, the PWV calculation result is closer to the real value, and when the PWTT values are not discarded or less than 5 PWTT values are discarded, the PWV calculation result deviates more from the real value, and the discarded less deviation is more. After discarding 5, the PWV calculated values substantially stabilized, all close to the true values. Therefore, the value range of P is set to be 5-9, preferably 5, in order to save the calculation time.

Meanwhile, it can be seen that after N is 6, the PWTT value is basically stable, so that N is 5< N ≦ 15, and more preferably, N may be 15 to ensure that the sample size is sufficient.

Example 3

According to one embodiment of the invention, the table below shows the same tester, fixed arm length, N, M and a parameters, with the confidence M values changed compared to example 2.

Wherein, the real PWV is 3.566243, the arm length L is 630mm, N is 15, M is 5%, and a is 200 mm.

TABLE 10 PWV calculation results with 5% confidence

On the basis of embodiment 2, only the value of the confidence M is changed, and when the confidence is increased to 5%, according to the PWV calculation step of the present invention, the data that is not within the confidence interval is discarded, and at the same time, the PWTT data is additionally collected after being discarded, and finally, when P is 0, P is 3, P is 5, and P is 7, compared with the case that the confidence M is 10% in embodiment 2, the PWV final calculation result is closer to the true value, and the accuracy is improved.

Because the accuracy and the jitter condition of the acquired PWTT value are different under different hardware environments and development environments, a user can set a confidence interval M value according to the actual hardware environment and development environment to limit an error range, and thus the PWV value range with the corresponding accuracy is acquired. When high precision is pursued, the confidence coefficient M can be properly adjusted down, so that the final calculation result of the PWV is closer to a true value, the precision is improved, but the defect is that more calculation time and calculation resources are consumed; in order to increase the calculation speed of the PWV, the confidence M may be increased, but the accuracy of the PWV may be reduced, and the user may adjust the M value according to the actual demand scenario to accept or reject the final result.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.

Claims

1. An embedded graphic blood pressure measurement system is characterized in that a system based on littlevGL and freeRTOS is used for a blood pressure measurement device and is matched with calibration equipment and measurement equipment for use, the memory management mechanism, the hardware equipment management mechanism, the thread scheduling mechanism, the file system management mechanism and the interrupt management mechanism scheduling of the freeRTOS system and embedded graphic display bottom layer driving of the littlevGL are utilized for carrying out calibration equipment connection, measurement equipment connection, system calibration, blood pressure measurement and heart rate measurement, and the blood pressure measurement adopts a regression equation considering heart rate factors and PWV factors to carry out continuous non-invasive blood pressure measurement of blood pressure calculation.

2. The embedded graphical blood pressure measurement system of claim 1, comprising: the device comprises a UI module, an application layer, a resource layer, a middleware layer and a kernel layer, wherein the application layer comprises a blood pressure measuring module, a heart rate measuring module, an equipment connecting module and a calibrating module; the resource layer comprises picture library resources and font library resources; the middleware layer comprises littlevGL middleware; the kernel layer comprises a hardware abstract interface module and a Bluetooth library module;

3. The embedded graphic blood pressure measurement system of claim 2, wherein bluetooth of the blood pressure measurement device is turned on by bluetooth middleware through a thread scheduling mechanism in a freeRTOS system;

4. The embedded graphical blood pressure measurement system of claim 3, wherein the middleware layer further comprises battery management middleware, sensor middleware, and control command middleware;

5. The embedded graphic blood pressure measuring system of claim 2, wherein the kernel layer comprises a hardware abstraction interface module, a storage module, a bluetooth library module and a debugging module;

6. The embedded graphic blood pressure measurement system according to claim 1, wherein the real-time calculation of the systolic blood pressure SBP and the diastolic blood pressure DBP using a regression equation taking into account heart rate factors and PWV factors comprises the following steps:

calculating a real-time PWV value according to the optimized PWV algorithm;

SBP＝M1+P1*PWV+Q1*PULSE；

DBP＝M2+P2*PWV+Q2*PULSE；

wherein,

m2: regression constants of diastolic pressure, obtained from calibration data;

PWV is a calculated real-time PWV value;

PULSE is a heart rate value acquired in real time.

7. The embedded graphic blood pressure measurement system according to claim 6, wherein the regression constant M1 for systolic pressure and M2 for diastolic pressure are calculated by:

M1＝B-P1*A-Q1*U；

M2＝C-P2*A-Q2*U；

wherein,

a is the mean of the PWV of the calibration data;

b is the mean systolic pressure of the calibration data;

c is the mean value of the pulse pressure values of the calibration data;

u is the mean value of the heart rate PULSE of the calibration data;

8. The embedded graphic blood pressure measurement system according to claim 7, wherein the regression coefficients P1, Q1, P2 and Q2 between SBP and PWV, SBP and PULSE, PP and PWV, PP and PULSE are calculated by:

P1＝(SYS_Ray-SYS_Rby*SYS_Rab)/(1-SYS_Rab*SYS_Rab)*(E/D)；

Q1＝(SYS_Rby-SYS_Ray*SYS_Rab)/(1-SYS_Rab*SYS_Rab)*(E/J)；

P2＝(DIS_Ray-DIS_Rby*DIS_Rab)/(1-DIS_Rab*DIS_Rab)*(F/D)；

Q2＝(DIS_Rby-DIS_Ray*DIS_Rab)/(1-DIS_Rab*DIS_Rab)*(F/J)；

9. The embedded graphic blood pressure measurement system according to claim 8, wherein the correlation coefficient between PWV, systolic blood pressure SBP, heart rate PULSE and PULSE pressure difference PP is calculated by:

autocorrelation coefficient between SBP and PWV: SYS _ Ray ═ G1/(D ═ E);

autocorrelation coefficient between SBP and PULSE: SYS _ Rby ═ S1/(E ═ J);

autocorrelation coefficient between PWV and PULSE: SYS _ Rab ═ N1/(D ═ J);

autocorrelation coefficient between PP and PWV: DIS _ Ray ═ G2/(D ═ F);

autocorrelation coefficient between PP and PULSE: DIS _ Rby ═ S2/(F × J);

10. The embedded graphic blood pressure measurement system of claim 6, wherein the qualified PWTT value is screened out by adopting a PWTT screening algorithm according to the calibration data, and the PWV value is calculated according to the optimized PWV algorithm, specifically comprising the steps of:

calibration data were obtained by the following screening procedure:

primary acquisition and primary screening of PWTT;

s1000: collecting N PWTT values, and discarding the previous P PWTT values;

s2000: calculating the mean value of the remaining (N-P) PWTT values, and calculating a first confidence interval according to the confidence coefficient M%; s3000: discarding PWTT values which are not in the first confidence interval in the remaining (N-P) PWTT values, determining the PWTT values as calibration data if the number of the remaining PWTT values meets the preset lowest PWTT number requirement, and executing the PWTT continuous acquisition and screening step if the number of the remaining PWTT values does not meet the preset lowest PWTT number requirement;

continuously acquiring and screening PWTT;

a PWV calculation step;

s8000: calculating the mean value S of the reserved PWTT values;

wherein,

PWV is the finally calculated pulse wave velocity;

l is the arm length of the person to be measured;

a is the average distance from the shoulders to the heart of a normal person;

s is the average value of the PWTT values which are finally reserved;

the confidence interval (a1, a2) is calculated using the following formula:

A1＝A-A*M％；

A2＝A+A*M％；

wherein,

a is the mean value of the PWTT value which is reserved currently;

m% is confidence;

a1 is the lower confidence interval limit;

a2 is the upper confidence interval limit.