CN113171069B - Embedded graphic blood pressure measurement system - Google Patents
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- 238000009530 blood pressure measurement Methods 0.000 title claims abstract description 51
- 238000005259 measurement Methods 0.000 claims abstract description 60
- 230000007246 mechanism Effects 0.000 claims abstract description 44
- 238000004364 calculation method Methods 0.000 claims abstract description 35
- 238000000034 method Methods 0.000 claims abstract description 31
- 230000006870 function Effects 0.000 claims abstract description 12
- 230000036772 blood pressure Effects 0.000 claims description 101
- 238000007726 management method Methods 0.000 claims description 49
- 238000004806 packaging method and process Methods 0.000 claims description 42
- 230000035488 systolic blood pressure Effects 0.000 claims description 42
- 238000012216 screening Methods 0.000 claims description 25
- 230000035485 pulse pressure Effects 0.000 claims description 24
- 230000035487 diastolic blood pressure Effects 0.000 claims description 21
- 238000009532 heart rate measurement Methods 0.000 claims description 20
- 238000004422 calculation algorithm Methods 0.000 claims description 17
- 238000012544 monitoring process Methods 0.000 claims description 9
- 230000001133 acceleration Effects 0.000 claims description 6
- 230000005540 biological transmission Effects 0.000 claims description 5
- 238000013500 data storage Methods 0.000 claims description 3
- 230000008569 process Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000004872 arterial blood pressure Effects 0.000 description 3
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- 230000035790 physiological processes and functions Effects 0.000 description 3
- 230000007547 defect Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000003205 diastolic effect Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 206010005746 Blood pressure fluctuation Diseases 0.000 description 1
- 206010020565 Hyperaemia Diseases 0.000 description 1
- 206010047139 Vasoconstriction Diseases 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000002555 auscultation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 210000002302 brachial artery Anatomy 0.000 description 1
- 210000000748 cardiovascular system Anatomy 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
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- 230000004089 microcirculation Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
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- 230000025033 vasoconstriction Effects 0.000 description 1
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/021—Measuring pressure in heart or blood vessels
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/021—Measuring pressure in heart or blood vessels
- A61B5/02141—Details of apparatus construction, e.g. pump units or housings therefor, cuff pressurising systems, arrangements of fluid conduits or circuits
Abstract
The invention provides an embedded graphic blood pressure measurement system, and belongs to the field of continuous noninvasive blood pressure measurement and calculation. The invention is based on the littlevGL and the freeRTOS system, the freeRTOS system provides a kernel operating system architecture of the bottom layer, the littlevGL provides a littlevGL middleware required by embedded graphic display, and functions of a memory management mechanism, a hardware device management mechanism, a thread scheduling mechanism, a file system management mechanism, interrupt management and the like of the freeRTOS system and an embedded graphic display bottom layer driver 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 systems can be filled by building each functional module.
Description
Technical Field
The invention relates to the technical field of continuous noninvasive blood pressure measurement, in particular to an embedded graphic blood pressure measurement system.
Background
Blood pressure is one of the important parameters that reflects the functional state of the cardiovascular system; blood pressure monitoring is not a stable value of blood pressure of a human body with significance in clinical and military personnel physiological state monitoring, and can change along with the change of physiological states, and important parameters reflecting psychological and physiological states can be extracted through the change of blood pressure. Beat-to-beat measurements of arterial blood pressure are therefore particularly important. However, the traditional auscultation measurement method which is commonly 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. Arterial catheterization can realize beat-by-beat blood pressure measurement, and the result is the most accurate, but the arterial catheterization method is invasive measurement and has limited application range. Studies have shown that the arterial tension method can be used to measure noninvasive beat-to-beat arterial blood pressure more accurately, but the method is not easy to operate and is sensitive to position and human body actions: unfavorable for long-term measurement). In recent years, noninvasive blood pressure measuring devices finaps and portapres based on the volumetric clamp technology are widely used in laboratory research. The method measures finger pulse (namely blood pressure at the finger tip), but not brachial artery blood pressure in the common sense, and is easily influenced by factors such as vasoconstriction, microcirculation disturbance and the like. Venous congestion during long-time measurement affects measurement accuracy; the method is less comfortable because of the need to maintain a certain pressure at the site being tested. Compared with the method, the pulse wave transit time method (pulse wave transit time; PWTF) is a noninvasive blood pressure measuring method with high practicability because the pulse wave transit time method is simple to measure and easy to realize.
LittlevGL is a free open source graphic library, providing everything needed to create an embedded GUI, littlevGL is a complete graphic framework that can be built from easy-to-use building blocks (e.g., buttons, charts, images, lists, sliders, switches, keyboards, etc.) without regard to drawing the original shape. Has easy-to-use graphic elements, beautiful visual effect and low memory occupation.
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., may substantially meet the needs of smaller systems.
At present, no embedded graph noninvasive blood pressure measuring system based on LittlevGL and freeRTOS systems exists, and in order to make up for the defect, the invention provides an embedded graph noninvasive blood pressure measuring system which is based on LittlevGL and freeRTOS systems and achieves the embedded graph 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 framework is huge, total and not compact and flexible;
2. The prior art embedded UI adopts TouchGFX, embeddedWizard, but these systems are commercial systems and are expensive to pay;
3. some continuous noninvasive blood pressure measurement methods in the prior art have no embedded operation system support and no embedded UI system support, and meanwhile, no product for realizing noninvasive blood pressure measurement by using a freeRTOS system and a littlevGL middleware exists in the field, so that the technology is also a filling of the field of blank products.
Disclosure of Invention
The invention provides an embedded graphic blood pressure measuring system for solving the technical problems in the prior art. The invention provides an embedded graph blood pressure measuring system, which is based on littlevGL and freeRTOS systems and 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 a kernel layer, wherein a kernel operation system architecture of a bottom layer is provided by a freeRTOS system, a littlevGL provides a littlevGL middleware required by embedded graphic display, functions of a memory management mechanism, a hardware device management mechanism, a thread scheduling mechanism, a file system management mechanism, interrupt management and the like in the embedded operation system framework of the freeRTOS system are utilized, and the embedded graphic display bottom layer of the freeRTOS and the littlevGL can be used for making up the blank of blood pressure measurement functions in the fields of the freeRTOS and the littlevGL systems by constructing each functional module; meanwhile, the measurement accuracy is improved by an optimized continuous noninvasive blood pressure measurement method.
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., may substantially meet the needs of smaller systems.
LittlevGL is a free open source graphic library, providing everything needed to create an embedded GUI, littlevGL is a complete graphic framework that can be built from easy-to-use building blocks (e.g., buttons, charts, images, lists, sliders, switches, keyboards, etc.) without regard to drawing the original shape. Has easy-to-use graphic elements, beautiful visual effect and low memory occupation.
The invention provides an embedded graph blood pressure measuring system based on a freeRTOS and LittlevGL, which overcomes the defect that no embedded graph blood pressure measuring system based on the freeRTOS and LittlevGL exists at present.
When the system is actually applied, for example, the system is used for a blood pressure measuring watch, an arm type sphygmomanometer is configured as a calibration device, when a user is replaced, the calibration device is connected for calibration, multiple groups of data of the same user are collected in the calibration process and calibrated through the calibration device, then the data processing module processes the data, the blood pressure calculating module calculates regression coefficients and regression constants in a regression equation for measuring the blood pressure of the user, and the regression equation is determined. The method is used for calculating the blood pressure of the user in real time during the follow-up actual dynamic monitoring. At the same time, relevant measuring devices such as blood pressure sensors, heart rate sensors, etc. are also provided.
The invention provides an embedded graphic blood pressure measurement system, which is based on littlevGL and a freeRTOS system, is used for a blood pressure measurement device, is matched with calibration equipment and measurement equipment, and is used for carrying out continuous noninvasive blood pressure measurement of blood pressure calculation by using a memory management mechanism, a hardware equipment management mechanism, a thread scheduling mechanism, a file system management mechanism, an interrupt management mechanism scheduling and an embedded graphic display bottom layer driver of the littlevGL of the freeRTOS system, wherein the calibration equipment connection, the measurement equipment connection, the system calibration, the blood pressure measurement and the heart rate measurement are carried out by adopting a regression equation considering heart rate factors and PWV factors.
Preferably, the embedded graphic blood pressure measurement system comprises: the system 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, a device connecting module and a calibration module; the resource layer comprises picture library resources and font library resources; the middleware layer contains littlevGL middleware; the kernel layer comprises a hardware abstraction interface module and a Bluetooth library module;
the UI module performs operations including: the method comprises the steps of interacting with a 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 a UI interface;
The resource layer is called by the UI module through a littlevGL middleware and displayed on a 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 a 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, and calculating systolic pressure SBP and 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 a calculation result on a UI interface through the littlevGL middleware;
the heart rate measurement module performs operations including: receiving heart rate measurement instructions sent by a littlevGL middleware, sending heart rate measurement information 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 device connection module comprises a calibration device connection module and a measurement device connection 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 device connection instruction sent by a littlevGL middleware, opening Bluetooth of a blood pressure measuring device through a hardware management mechanism in a freeRTOS system, performing calibration device connection, and displaying on a UI interface through the littlevGL middleware according to a connection result;
the measuring equipment connecting module is used for connecting the measuring equipment with the blood pressure measuring device, and the executed operations comprise: receiving a measuring device 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 devices, and displaying the measuring device connection instruction on a UI interface through the littlevGL middleware according to a connection result;
the operations performed by the calibration module include: receiving a calibration command 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 device connection command to a calibration device connection module, performing calibration device connection, sending a measurement device connection command to a measurement device connection module, connecting the measurement device, sending a calibration command to the calibration device through a thread scheduling mechanism in the freeRTOS system, sending measurement command to the measurement device, receiving calibration device data, receiving the measurement device data, and displaying on a UI interface through the littlevGL middleware according to a calibration result;
The littlegl middleware performs the following operations: receiving an instruction generated by UI interface operation, forwarding the instruction to a corresponding application layer, receiving application layer data, and displaying the application layer data on a UI interface through a UI module.
Preferably, the bluetooth of the blood pressure measuring device is turned on by the bluetooth middleware through a thread scheduling mechanism in the freeRTOS system;
the Bluetooth middleware is located in the middleware layer and performs the following operations: the method comprises the steps of opening or closing a Bluetooth of the blood pressure measuring device, receiving a message of connecting or disconnecting a calibration device by a UI interface of the blood pressure measuring device, receiving a message of connecting or disconnecting the blood pressure measuring device by the UI interface of the blood pressure measuring device, and returning a current Bluetooth connection state to the UI interface of the blood pressure measuring device.
Preferably, the middleware layer further comprises a battery management middleware, a sensor middleware and a control command middleware;
the bluetooth library module performs the following operations: initializing a Bluetooth module in the blood pressure measuring device, packaging a Bluetooth opening/Guan Deceng interface, and packaging a Bluetooth connection/disconnection bottom layer interface and a Bluetooth data transmission bottom layer interface;
the battery management middleware performs the following operations: detecting the power plug state 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: opening and closing 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: and receiving application layer information/command/data, packaging the information into a control command, sending the control command to a sensor through a serial port, receiving sensor return information, and returning control command execution result information to a 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: 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 memory 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 debug module performs the following operations: and packaging all interfaces of the debugging system, such as a system information checking interface, a sensor state information checking interface and all function module state information checking interfaces, and switching the debugging module through a debugging switch macro.
Preferably, calculating the systolic pressure SBP and the diastolic pressure DBP in real time using regression equations taking into account heart rate factors and PWV factors specifically comprises the steps of:
screening out qualified PWTT values by adopting a PWTT screening algorithm according to the calibration data and the acquired PWTT values;
calculating a real-time PWV value according to an optimized PWV algorithm;
the following regression equations are used, systolic SBP and diastolic DBP;
SBP＝M1+P1*PWV+Q1*PULSE；
DBP＝M2+P2*PWV+Q2*PULSE；
wherein,
m1: regression constants of systolic blood pressure, obtained from calibration data;
m2: regression constants of diastolic blood pressure, obtained from 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 the calibration data;
PWV is the calculated real-time PWV value;
PULSE is the heart rate value acquired in real time.
Preferably, the regression constant M1 for systolic and the regression constant M2 for diastolic pressures are calculated as follows:
M1＝B-P1*A-Q1*U；
M2＝C-P2*A-Q2*U；
wherein,
a is the mean value of PWV of the calibration data;
b is the mean value of the systolic pressure of the calibration data;
c is the mean value of pulse pressure values of the calibration data;
u is the average of 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 the 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, F is the PP standard deviation of the calibration data; j is the heart rate PULSE standard deviation of the calibration data; SYS_Ray is the autocorrelation coefficient between SBP and PWV of the calibration data; SYS_Rby is the autocorrelation coefficient between SBP and PULSE of the calibration data; SYS_Rab is the 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 the autocorrelation coefficient between PWV and PULSE of the calibration data.
Preferably, the correlation coefficients between PWV, systolic pressure SBP, heart rate PULSE and PULSE pressure difference PP are calculated by the following steps:
calculating the average value of PWV, systolic pressure SBP, heart rate PULSE and PULSE pressure difference PP;
calculating standard deviation values of PWV, systolic pressure SBP, heart rate PULSE and PULSE pressure difference PP;
calculating a covariance value of PWV, a systolic pressure SBP, a heart rate PULSE and a PULSE pressure difference PP;
calculating correlation coefficients among PWV, systolic pressure SBP, heart rate PULSE and PULSE pressure difference PP;
The correlation coefficients between PWV, systolic pressure SBP, heart rate PULSE and PULSE pressure difference PP are as follows:
autocorrelation coefficients between SBP and PWV: sys_ray=g1/(d×e);
autocorrelation coefficients between SBP and PULSE: sys_rby=s1/(e×j);
autocorrelation coefficient between PWV and PULSE: sys_rab=n1/(d×j);
autocorrelation coefficients between PP and PWV: dis_ray=g2/(d×f);
autocorrelation coefficients 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, a PWTT screening algorithm is adopted to screen out qualified PWTT values according to the calibration data, and the PWV values are calculated according to an optimized PWV algorithm, which specifically includes the following steps:
calibration data were obtained by the following screening steps:
preliminary collecting and preliminary screening of PWTT;
s1000: collecting N PWTT values, and discarding the previous P PWTT values;
s2000: calculating the average value of the remaining (N-P) PWTT values, and calculating a first confidence interval according to the confidence M%;
s3000: discarding the 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 PWTTs meets the preset minimum PWTT number requirement, and executing the PWTT continuous acquisition and screening steps if the number of the remaining PWTTs does not meet the preset minimum PWTT number requirement;
Continuously collecting and screening PWTT;
s4000; continuously collecting m PWTT values, so that the total number of PWTTs reaches the preset minimum number of PWTTs;
s5000: calculating the mean value of the PWTT value, and calculating a second confidence interval according to the confidence coefficient M%; the second confidence interval calculated in each iteration is related to the PWTT value calculated in the current participation confidence interval;
s6000: discarding PWTT values for which the PWTT value is not within the second confidence interval;
s7000: if the number of the remaining PWTT values meets the preset minimum PWTT number requirement, determining the number as calibration data, otherwise, continuing to execute the step S4000 until the preset minimum PWTT number requirement is met;
repeating the step S4000-the step S6000 for screening the PWTT acquired in real time until the preset minimum PWTT number requirement is met;
a PWV calculation step;
s8000: calculating the mean S of the reserved PWTT values;
s9000: calculating PWV value; wherein the PWV value is calculated using the following formula;
wherein,
PWV is the pulse wave velocity obtained by final calculation;
l is the arm length of the person to be measured;
a is the average distance from the shoulder to the heart of a normal person;
s is the average value of the PWTT values 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 average value of the PWTT value reserved currently;
m% is confidence;
a1 is the lower limit of the confidence interval;
a2 is the upper confidence interval limit.
Compared with the prior art, the invention has the following beneficial effects:
(1) The invention realizes noninvasive blood pressure measurement 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, after the data with larger initial error are discarded, the accuracy of the acquired data is directly improved.
(3) According to the invention, the confidence interval is set, and the data which is not in the confidence interval of the current overall average value is discarded, so that the reliability of the acquired data is increased, and the accuracy of the acquired data is improved.
(4) The invention screens the PWTT data for a plurality of times by adopting the confidence interval, prevents abnormal data from shaking in the process of data acquisition and data with larger errors from occurring, screens the data which are not in the confidence interval by adopting the confidence interval method, and ensures that the overall data accuracy is higher, thereby ensuring that the PWV result of the subsequent final calculation is more accurate.
(5) According to the invention, the relation between the systolic 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 SBP, PWV, PP, 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 one embodiment of the present invention;
FIG. 3 is a flow chart of heart rate measurement according to one embodiment of the invention;
FIG. 4 is a calibration device connection flow diagram of one embodiment of the present invention;
FIG. 5 is a measurement device connection flow diagram of one embodiment of the present invention;
FIG. 6 is a workflow diagram of a calibration module of one embodiment of the invention
FIG. 7 is a flow chart of calculating the current diastolic and systolic pressures in accordance with one embodiment of the present invention;
fig. 8 is a graph showing an example of fluctuation of PWTT values when the PWTT is acquired in one embodiment of the present invention.
Detailed Description
The following describes in detail the embodiments of the present invention with reference to fig. 1-8.
The invention provides an embedded graphic blood pressure measurement system, which is based on littlevGL and a freeRTOS system, is used for a blood pressure measurement device, is matched with calibration equipment and measurement equipment, and is used for carrying out continuous noninvasive blood pressure measurement of blood pressure calculation by using a memory management mechanism, a hardware equipment management mechanism, a thread scheduling mechanism, a file system management mechanism, an interrupt management mechanism scheduling and an embedded graphic display bottom layer driver of the littlevGL of the freeRTOS system, wherein the calibration equipment connection, the measurement equipment connection, the system calibration, the blood pressure measurement and the heart rate measurement are carried out by adopting a regression equation considering heart rate factors and PWV factors.
As a preferred embodiment, the embedded graphic blood pressure measurement system includes: the system 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, a device connecting module and a calibration module; the resource layer comprises picture library resources and font library resources; the middleware layer contains littlevGL middleware; the kernel layer comprises a hardware abstraction interface module and a Bluetooth library module;
the UI module performs operations including: the method comprises the steps of interacting with a 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 a UI interface;
the resource layer is called by the UI module through a littlevGL middleware and displayed on a 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 a 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, and calculating systolic pressure SBP and 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 a calculation result on a UI interface through the littlevGL middleware; if the measurement is overtime, the user is informed of the measurement failure through the littlevGL middleware, otherwise, the systolic pressure SBP and the diastolic pressure DBP are calculated in real time through the littlevGL middleware and displayed on the UI interface as blood pressure measurement values.
The heart rate measurement module performs operations including: receiving heart rate measurement instructions sent by a littlevGL middleware, sending heart rate measurement information 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; if the measurement is overtime, the user is informed of the failure of measurement through the littlevGL middleware, otherwise, the heart rate measurement value is displayed on the UI interface through the littlevGL middleware.
The device connection module comprises a calibration device connection module and a measurement device connection 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 device connection instruction sent by a littlevGL middleware, opening Bluetooth of a blood pressure measuring device through a hardware management mechanism in a freeRTOS system, performing calibration device connection, and displaying on a UI interface through the littlevGL middleware according to a connection result; if the connection is successful, the connected icon is displayed on the UI interface through the littlevGL middleware, otherwise, the connection is repeated for three times, if the connection is successful, the connected icon is displayed on the UI interface through the littlevGL middleware, otherwise, the connection is failed, and the user is informed of the connection failure through the littlevGL middleware on the UI interface.
The measuring equipment connecting module is used for connecting the measuring equipment with the blood pressure measuring device, and the executed operations comprise: receiving a measuring device 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 devices, and displaying the measuring device connection instruction on a UI interface through the littlevGL middleware according to a connection result; if the connection is successful, displaying a 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 operations performed by the calibration module include: receiving a calibration command 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 device connection command to a calibration device connection module, performing calibration device connection, sending a measurement device connection command to a measurement device connection module, connecting the measurement device, sending a calibration command to the calibration device through a thread scheduling mechanism in the freeRTOS system, sending measurement command to the measurement device, receiving calibration device data, receiving the measurement device data, and displaying on a UI interface through the littlevGL middleware according to a calibration result; if the calibration is successful, the user is informed of the calibration failure in the UI interface through the littlevGL middleware, and if the calibration is failed, the user is informed of the calibration failure in the UI interface through the littlevGL middleware.
According to a specific embodiment, the application layer may further include a data transmission module, where the data transmission module performs the following operations: judging whether the data is read or written when the flow 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.
The littlegl middleware performs the following operations: receiving an instruction generated by UI interface operation, forwarding the instruction to a corresponding application layer, receiving application layer data, and displaying the application layer data on a UI interface through a UI module.
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;
the Bluetooth middleware is located in the middleware layer and performs the following operations: the method comprises the steps of opening or closing a Bluetooth of the blood pressure measuring device, receiving a message of connecting or disconnecting a calibration device by a UI interface of the blood pressure measuring device, receiving a message of connecting or disconnecting the blood pressure measuring device by the UI interface of the blood pressure measuring device, and returning a current Bluetooth connection state to the UI interface of the blood pressure measuring device.
As a preferred embodiment, the middleware layer further comprises a battery management middleware, a sensor middleware and a control command middleware;
the bluetooth library module performs the following operations: initializing a Bluetooth module in the blood pressure measuring device, packaging a Bluetooth opening/Guan Deceng interface, and packaging a Bluetooth connection/disconnection bottom layer interface and a Bluetooth data transmission bottom layer interface;
the battery management middleware performs the following operations: detecting the power plug state 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: opening and closing 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: and receiving application layer information/command/data, packaging the information into a control command, sending the control command to a sensor through a serial port, receiving sensor return information, and returning control command execution result information to a middleware layer.
As a preferred implementation, 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: 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 memory 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 debug module performs the following operations: and packaging all interfaces of the debugging system, such as a system information checking interface, a sensor state information checking interface and all function module state information checking interfaces, and switching the debugging module through a debugging switch macro.
As a preferred embodiment, calculating the systolic pressure SBP and the diastolic pressure DBP in real time using regression equations taking into account heart rate factors and PWV factors specifically comprises the steps of:
screening out qualified PWTT values by adopting a PWTT screening algorithm according to the calibration data and the acquired PWTT values;
calculating a real-time PWV value according to an optimized PWV algorithm;
the following regression equations are used, systolic SBP and diastolic DBP;
SBP＝M1+P1*PWV+Q1*PULSE；
DBP＝M2+P2*PWV+Q2*PULSE；
Wherein,
m1: regression constants of systolic blood pressure, obtained from calibration data;
m2: regression constants of diastolic blood pressure, obtained from 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 the calibration data;
PWV is the calculated real-time PWV value;
PULSE is the heart rate value acquired in real time.
As a preferred embodiment, the regression constant M1 of systolic pressure and the regression constant M2 of diastolic pressure are calculated by the following method:
M1＝B-P1*A-Q1*U；
M2＝C-P2*A-Q2*U；
wherein,
a is the mean value of PWV of the calibration data;
b is the mean value of the systolic pressure of the calibration data;
c is the mean value of pulse pressure values of the calibration data;
u is the average of 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 the calibration data.
As a preferred embodiment, 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, F is the PP standard deviation of the calibration data; j is the heart rate PULSE standard deviation of the calibration data; SYS_Ray is the autocorrelation coefficient between SBP and PWV of the calibration data; SYS_Rby is the autocorrelation coefficient between SBP and PULSE of the calibration data; SYS_Rab is the 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 the autocorrelation coefficient between PWV and PULSE of the calibration data.
As a preferred embodiment, the correlation coefficients between PWV, systolic pressure SBP, heart rate PULSE and PULSE pressure difference PP are calculated by the following steps:
calculating the average value of PWV, systolic pressure SBP, heart rate PULSE and PULSE pressure difference PP;
calculating standard deviation values of PWV, systolic pressure SBP, heart rate PULSE and PULSE pressure difference PP;
calculating a covariance value of PWV, a systolic pressure SBP, a heart rate PULSE and a PULSE pressure difference PP;
calculating correlation coefficients among PWV, systolic pressure SBP, heart rate PULSE and PULSE pressure difference PP;
the correlation coefficients between PWV, systolic pressure SBP, heart rate PULSE and PULSE pressure difference PP are as follows:
autocorrelation coefficients between SBP and PWV: sys_ray=g1/(d×e);
autocorrelation coefficients between SBP and PULSE: sys_rby=s1/(e×j);
autocorrelation coefficient between PWV and PULSE: sys_rab=n1/(d×j);
autocorrelation coefficients between PP and PWV: dis_ray=g2/(d×f);
autocorrelation coefficients 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.
The mean, standard deviation and covariance of each parameter of each set of calibration data are defined as follows, and can be defined by adopting different letters according to actual needs.
TABLE 1 definition of mean, standard deviation, covariance symbols for parameters
The correlation coefficient values of the calibration data of each group calculated in the step are mainly used for calculating the linear correlation degree between two groups of different variables, and can be used as parameters for calculating subsequent regression coefficients, wherein the parameters can directly influence the correlation of the regression coefficients, so that the accuracy of measuring the blood pressure value is finally indirectly influenced.
As a preferred embodiment, a PWTT screening algorithm is adopted to screen out qualified PWTT values according to the calibration data, and the PWV values are calculated according to an optimized PWV algorithm, specifically comprising the following steps:
calibration data were obtained by the following screening steps:
preliminary collecting and preliminary screening of PWTT;
s1000: collecting N PWTT values, and discarding the previous P PWTT values;
s2000: calculating the average value of the remaining (N-P) PWTT values, and calculating a first confidence interval according to the confidence M%;
s3000: discarding the 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 PWTTs meets the preset minimum PWTT number requirement, and executing the PWTT continuous acquisition and screening steps if the number of the remaining PWTTs does not meet the preset minimum PWTT number requirement;
continuously collecting and screening PWTT;
S4000; continuously collecting m PWTT values, so that the total number of PWTTs reaches the preset minimum number of PWTTs;
s5000: calculating the mean value of the PWTT value, and calculating a second confidence interval according to the confidence coefficient M%; the second confidence interval calculated in each iteration is related to the PWTT value calculated in the current participation confidence interval;
s6000: discarding PWTT values for which the PWTT value is not within the second confidence interval;
s7000: if the number of the remaining PWTT values meets the preset minimum PWTT number requirement, determining the number as calibration data, otherwise, continuing to execute the step S4000 until the preset minimum PWTT number requirement is met;
repeating the step S4000-the step S6000 for screening the PWTT acquired in real time until the preset minimum PWTT number requirement is met;
a PWV calculation step;
s8000: calculating the mean S of the reserved PWTT values;
s9000: calculating PWV value; wherein the PWV value is calculated using the following formula;
wherein,
PWV is the pulse wave velocity obtained by final calculation;
l is the arm length of the person to be measured;
a is the average distance from the shoulder to the heart of a normal person;
s is the average value of the PWTT values 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 average value of the PWTT value reserved currently;
M% is confidence;
a1 is the lower limit of the confidence interval;
a2 is the upper confidence interval limit.
Example 1
The comparison of the blood pressure measured when the heart rate regression coefficient is not added to the blood pressure calculated in the conventional method with the blood pressure of the scheme of adding the heart rate regression coefficient to the continuous non-invasive blood pressure measuring system of the present invention is given below.
1. Blood pressure measured without adding heart rate regression coefficient and standard sphygmomanometer measurement value comparison
TABLE 2 different phases (calibration/measurement) and different states (stationary/moving) of the tester
Blood pressure values of systolic pressure SBP and diastolic pressure DBP measured by calibrating sphygmomanometer
Table 3 m-value and p-value in the calculation formula of the heart rate not added used in the PWTT blood pressure measurement wristwatch
| SBP | PP | |
| p | 0.590067424 | 0.233961592 |
| m | 121.4982 | 57.8830 |
The calculation formula of the blood pressure without heart rate is: blood pressure value = m+p PWV;
in the table:
p is the regression coefficient of SBP or PP and PWV;
m is the regression constant of SBP or PP, and is the average of SBP or PP-p PWV.
TABLE 4 blood pressure measurement watch by PWTT for different states (rest/exercise) of tester
Measured PWTT value, PWV value, systolic pressure SBP and diastolic pressure DBP calculated by heart rate-free blood pressure calculation algorithm
TABLE 5 testers in different states (still/moving)
Error between blood pressure value measured by calibrating blood pressure meter and blood pressure value calculated by non-added heart rate algorithm adopted by PWTT blood pressure measuring watch
The average error of SBP measured by adopting a heart rate-free blood pressure calculation method is-4.1 mmHg; SBP standard deviation was 9.5mmHg;
the error of the DBP average value measured by adopting a blood pressure calculation method without heart rate is-2.5 mmHg; the DBP standard deviation was 4.1mmHg;
the above data shows that after calibration, the coefficient calculated by the calibration data is used to calculate the systolic pressure SBP and the diastolic pressure DBP in the measurement phase by the calculation algorithm without heart rate, and the above table shows that the standard deviation error of the measurement result is 9.5, which exceeds the requirement of the international standard 8mmHg range.
2. The continuous noninvasive blood pressure measuring system of the invention is added with the comparison between the blood pressure measured by the heart rate regression coefficient and the standard blood pressure meter measured value
Standard sphygmomanometer measurement data are shown in table 2.
TABLE 6 the invention adds the m, p and q values in the heart rate calculation formula
| SBP | PP | |
| p | -9.8588 | -11.0822 |
| q | 1.4522 | 0.9990 |
| m | 64.5125 | 36.1852 |
The blood pressure calculation formula after adding heart rate of the invention
SBP＝M1+P1*PWV_rt+Q1*PULSE_rt；
DBP＝M2+P2*PWV_rt+Q2*PULSE_rt；
The general formula written into the blood pressure calculation is as follows: blood pressure value=m+p pwv+q PULSE;
wherein,
m is a regression constant corresponding to a systolic pressure regression constant M1 and a diastolic pressure regression constant M2 obtained according to each set of calibration data, respectively;
p is a regression coefficient, and corresponds to regression coefficients P1 and P2 between SBP and PWV, PP and PWV obtained according to each set of calibration data;
Q is a regression coefficient, and corresponds to regression coefficients Q1 and Q2 between SBP and PULSE, between PP and PULSE obtained according to each set of calibration data, respectively;
TABLE 7 tester at different stages (calibration/measurement) and different states (stationary/moving)
PWTT value, PWV value, heart rate PULSE value, systolic pressure SBP, diastolic pressure DBP and PULSE pressure PP calculated by the algorithm of the invention measured by the PWTT blood pressure measuring watch adopting the method
Table 8 testers in different states (stationary/moving)
By calibrating the error between the blood pressure measured by the sphygmomanometer and the calculated blood pressure value after adding the heart rate, and the error average value of the final result and the error standard deviation of the final result
The average error of SBP measured by adopting a heart rate-free blood pressure calculation method is-1.6 mmHg; SBP standard deviation was 8mmHg;
the error of the DBP average value measured by adopting a blood pressure calculation method without heart rate is-2.2 mmHg; the DBP standard deviation was 3.4mmHg;
after the heart rate data are increased by adopting the method, the coefficient calculated by the calibration data after the calibration can be seen from the data, the systolic pressure SBP and the diastolic pressure DBP are calculated in the measuring 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 measuring result are required to be in the range of 8mmHg of the international standard.
Example 2
According to one embodiment of the invention, the following table is the same tester, fixing arm length, N, M and a parameters, respectively calculating the effect of different numbers of PWTT values before discarding on the final PWV value.
Wherein, true pwv= 3.566243, arm length l=630 mm, n=15, m=10%, a=200 mm.
TABLE 9 influence of different numbers of PWTT values on PWV when preliminary screening
The real PWV value is measured by a certain brand AECG100 ECG/PPG and PWTT multifunctional physiological signal tester, and according to table 2, PWTT fluctuation figure 8 can be obtained, and 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 that when p=5-9, i.e. when the first 5 to the first 9 PWTT values are discarded, the PWV calculation result is closer to the real value, and when no or less than 5 PWTT values are discarded, the PWV calculation result deviates more from the real value, and the smaller the discarding deviates more. After discarding 5, the PWV calculation values basically tend to stabilize, all close to the true value. Therefore, the value range of P is set to be 5-9, more preferably, P can be 5 in order to save calculation time.
Meanwhile, it can be seen that the PWTT value is basically stable after n=6, and thus, 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 following table is the same tester, fixing arm length, N, M and a parameters, varying confidence M values compared to example 2.
Wherein, true pwv= 3.566243, arm length l=630 mm, n=15, m=5%, a=200 mm.
TABLE 10 PWV calculation results at 5% confidence level
On the basis of example 2, when the confidence level M is increased to 5% by changing only the value of the confidence level M, according to the PWV calculation step of the present invention, data not in the confidence interval is discarded, and PWTT data is collected after discarding, and finally, it can be seen that when p=0, p=3, p=5, and p=7, the PWV final calculation result is closer to the true value than when the confidence level M of example 2 is 10%, and the accuracy is improved.
Because the accuracy and jitter conditions of the acquired PWTT values are different under different hardware environments and development environments, the user can set the confidence interval M value to limit the error range according to the actual hardware environments and development environments, so that the PWV value range with corresponding accuracy is acquired. When pursuing high precision, the confidence coefficient M can be properly adjusted down, so that the final PWV calculation result is closer to a true value, the precision is improved, but the disadvantage 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 to trade off the final result according to the actual requirement scenario.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.
Claims (8)
1. The 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 a calibration device and a measurement device, and the memory management mechanism, the hardware device management mechanism, the thread scheduling mechanism, the file system management mechanism, the interrupt management mechanism scheduling and the embedded graphic display bottom layer driving of the littlevGL of the freeRTOS system are utilized to perform calibration device connection, measurement device connection, system calibration, blood pressure measurement and heart rate measurement, wherein the blood pressure measurement adopts a regression equation considering heart rate factors and PWV factors to perform continuous noninvasive blood pressure measurement of blood pressure calculation;
wherein, the real-time calculation of the systolic pressure SBP and the diastolic pressure DBP by using regression equations considering heart rate factors and PWV factors specifically comprises the following steps:
screening out qualified PWTT values by adopting a PWTT screening algorithm according to the calibration data and the acquired PWTT values;
Calculating a real-time PWV value according to an optimized PWV algorithm;
using the following regression equation, based on the calculated real-time PWV value PWV Real-time computing And heart rate value PULSE acquired in real time Real-time acquisition Calculating a real-time systolic pressure SBP and a diastolic pressure DBP;
SBP real-time computing = M1 + P1*PWV Real-time computing + Q1*PULSE Real-time acquisition ；
DBP Real-time computing =M2 + P2*PWV Real-time computing + Q2*PULSE Real-time acquisition ；
Wherein,
m1: regression constants of systolic blood pressure, obtained from calibration data;
m2: regression constants of diastolic blood pressure, obtained from the calibration data;
p1, Q1, P2 and Q2 are SBP and PWV of calibration data, respectively Real-time computing SBP and PULSE of calibration data Real-time acquisition 、PP Real-time computing With PWV Real-time computing PP and PULSE Real-time acquisition Regression coefficients between;
PWV real-time computing A real-time PWV value calculated;
PULSE real-time acquisition Heart rate values acquired in real time;
PP real-time computing For the calculated real-time pulse pressure difference;
the regression constant M1 of the systolic pressure and the regression constant M2 of the diastolic pressure are calculated by the following method:
M1=B-P1*A-Q1*U；
M2=C-P2*A-Q2*U；
wherein,
a is the mean value of PWV of the calibration data;
b is the mean value of the systolic pressure of the calibration data;
c is the mean value of pulse pressure values of the calibration data;
u is the mean of the heart rate PULSE of the calibration data.
2. The embedded graphic blood pressure measurement system according to claim 1, wherein the embedded graphic blood pressure measurement system comprises: the system 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, a device connecting module and a calibration module; the resource layer comprises picture library resources and font library resources; the middleware layer contains littlevGL middleware; the kernel layer comprises a hardware abstraction interface module and a Bluetooth library module;
The UI module performs operations including: the method comprises the steps of interacting with a 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 a UI interface;
the resource layer is called by the UI module through a littlevGL middleware and displayed on a 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 a 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, and calculating systolic pressure SBP and 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 a calculation result on a UI interface through the littlevGL middleware;
the heart rate measurement module performs operations including: receiving heart rate measurement instructions sent by a littlevGL middleware, sending heart rate measurement information 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 device connection module comprises a calibration device connection module and a measurement device connection 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 device connection instruction sent by a littlevGL middleware, opening Bluetooth of a blood pressure measuring device through a hardware management mechanism in a freeRTOS system, performing calibration device connection, and displaying on a UI interface through the littlevGL middleware according to a connection result;
the measuring equipment connecting module is used for connecting the measuring equipment with the blood pressure measuring device, and the executed operations comprise: receiving a measuring device 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 devices, and displaying the measuring device connection instruction on a UI interface through the littlevGL middleware according to a connection result;
the operations performed by the calibration module include: receiving a calibration command 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 device connection command to a calibration device connection module, performing calibration device connection, sending a measurement device connection command to a measurement device connection module, connecting the measurement device, sending a calibration command to the calibration device through a thread scheduling mechanism in the freeRTOS system, sending measurement command to the measurement device, receiving calibration device data, receiving the measurement device data, and displaying on a UI interface through the littlevGL middleware according to a calibration result;
The littlegl middleware performs the following operations: receiving an instruction generated by UI interface operation, forwarding the instruction to a corresponding application layer, receiving application layer data, and displaying the application layer data on a UI interface through a UI module.
3. The embedded graphics 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 the freeRTOS system;
the Bluetooth middleware is located in the middleware layer and performs the following operations: the method comprises the steps of opening or closing a Bluetooth of the blood pressure measuring device, receiving a message of connecting or disconnecting a calibration device by a UI interface of the blood pressure measuring device, receiving a message of connecting or disconnecting the blood pressure measuring device by the UI interface of the blood pressure measuring device, and returning a current Bluetooth connection state to the UI interface of the blood pressure measuring device.
4. The embedded graphic blood pressure measurement system according to claim 3, wherein the middleware layer further comprises battery management middleware, sensor middleware, and control command middleware;
the bluetooth library module performs the following operations: initializing a Bluetooth module in the blood pressure measuring device, packaging a Bluetooth opening/Guan Deceng interface, and packaging a Bluetooth connection/disconnection bottom layer interface and a Bluetooth data transmission bottom layer interface;
The battery management middleware performs the following operations: detecting the power plug state 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: opening and closing 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: and receiving application layer information/command/data, packaging the information into a control command, sending the control command to a sensor through a serial port, receiving sensor return information, and returning control command execution result information to a middleware layer.
5. The embedded graphic blood pressure measurement system according to claim 2, wherein the kernel layer comprises a hardware abstraction interface module, a memory module, a bluetooth library module, and a debug module;
the hardware abstraction interface module performs the following operations: 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 memory 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 debug module performs the following operations: and packaging all interfaces of the debugging system, such as a system information checking interface, a sensor state information checking interface and all function module state information checking interfaces, and switching the debugging module through a debugging switch macro.
6. The embedded graphic blood pressure measurement system according to claim 1, wherein the regression coefficients P1, Q1, P2 and Q2 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)；
wherein: d is the PWV standard deviation of the calibration data; e is the SBP standard deviation of the calibration data, F is the PP standard deviation of the calibration data; j is the heart rate PULSE standard deviation of the calibration data; SYS_Ray is the autocorrelation coefficient between SBP and PWV of the calibration data; SYS_Rby is the autocorrelation coefficient between SBP and PULSE of the calibration data; SYS_Rab is the 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 the autocorrelation coefficient between PWV and PULSE of the calibration data.
7. The embedded graphic blood pressure measurement system according to claim 6, wherein the correlation coefficients between PWV, systolic pressure SBP, heart rate PULSE and PULSE pressure difference PP are calculated by:
Calculating the average value of PWV, systolic pressure SBP, heart rate PULSE and PULSE pressure difference PP;
calculating standard deviation values of PWV, systolic pressure SBP, heart rate PULSE and PULSE pressure difference PP;
calculating a covariance value of PWV, a systolic pressure SBP, a heart rate PULSE and a PULSE pressure difference PP;
calculating correlation coefficients among PWV, systolic pressure SBP, heart rate PULSE and PULSE pressure difference PP;
the correlation coefficients between PWV, systolic pressure SBP, heart rate PULSE and PULSE pressure difference PP are as follows:
autocorrelation coefficients between SBP and PWV: sys_ray=g1/(d×e);
autocorrelation coefficients between SBP and PULSE: sys_rby=s1/(e×j);
autocorrelation coefficient between PWV and PULSE: sys_rab=dis_rab=n1/(d×j);
autocorrelation coefficients between PP and PWV: dis_ray=g2/(d×f);
autocorrelation coefficients 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.
8. The embedded graphic blood pressure measurement system according to claim 1, wherein the qualified PWTT value is screened out by using a PWTT screening algorithm according to the calibration data, and the PWV value is calculated according to an optimized PWV algorithm, comprising the steps of:
calibration data were obtained by the following screening steps:
Preliminary collecting and preliminary screening of PWTT;
s1000: collecting N PWTT values, and discarding the previous P PWTT values;
s2000: calculating the average value of the remaining (N-P) PWTT values, and calculating a first confidence interval according to the confidence M%;
s3000: discarding the 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 PWTTs meets the preset minimum PWTT number requirement, and executing the PWTT continuous acquisition and screening steps if the number of the remaining PWTTs does not meet the preset minimum PWTT number requirement;
continuously collecting and screening PWTT;
s4000; continuously collecting m PWTT values, so that the total number of PWTTs reaches the preset minimum number of PWTTs;
s5000: calculating the mean value of the PWTT value, and calculating a second confidence interval according to the confidence coefficient M%; the second confidence interval calculated in each iteration is related to the PWTT value calculated in the current participation confidence interval;
s6000: discarding PWTT values for which the PWTT value is not within the second confidence interval;
s7000: if the number of the remaining PWTT values meets the preset minimum PWTT number requirement, determining the number as calibration data, otherwise, continuing to execute the step S4000 until the preset minimum PWTT number requirement is met;
repeating the step S4000-the step S6000 for screening the PWTT acquired in real time until the preset minimum PWTT number requirement is met;
A PWV calculation step;
s8000: calculating the mean S of the reserved PWTT values;
s9000: calculating PWV value; wherein the PWV value is calculated using the following formula;
（1）
wherein,
PWV is the pulse wave velocity obtained by final calculation;
l is the arm length of the person to be measured;
a is the average distance from the shoulder to the heart of a normal person;
s is the average value of the PWTT values 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 average value of the PWTT value reserved currently;
m% is confidence;
a1 is the lower limit of the confidence interval;
a2 is the upper confidence interval limit.
Priority Applications (1)
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1552282A (en) * | 2003-05-29 | 2004-12-08 | 香港中文大学 | Blood pressure measuring method and device based on heart sound signal |
| KR20060081166A (en) * | 2005-01-07 | 2006-07-12 | 학교법인연세대학교 | Mobile radio terminal with blood pressure monitoring system ppg signal |
| CN201993766U (en) * | 2011-03-24 | 2011-09-28 | 西安交通大学苏州研究院 | Portable remote life multi-parameter monitoring terminal and constructed remote monitoring system |
| CN105943005A (en) * | 2016-06-01 | 2016-09-21 | 合肥芯福传感器技术有限公司 | Non-invasive blood pressure detection method based on mixing of photoelectric green-light pulses and electrocardiogram |
| CN108272446A (en) * | 2018-01-30 | 2018-07-13 | 浙江大学 | Noninvasive continuous BP measurement system and its calibration method |
| CN109960589A (en) * | 2019-03-25 | 2019-07-02 | 上海富士施乐有限公司 | Implementation method, device and the readable medium of the system software layer of embedded system |
| CN110074776A (en) * | 2019-04-30 | 2019-08-02 | 广东乐之康医疗技术有限公司 | A kind of power consumption control method and system of artificial intelligence dynamic cardiopulmonary monitoring device |
| CN110292369A (en) * | 2019-07-03 | 2019-10-01 | 浙江大学 | Chest non-invasive blood pressure detection probe and its device based on pulse wave translation time |
| JP6615970B1 (en) * | 2018-09-28 | 2019-12-04 | 学校法人福岡工業大学 | Blood pressure estimation device and blood pressure estimation program |
| CN110824218A (en) * | 2019-11-18 | 2020-02-21 | 重庆邮电大学 | Digital storage oscilloscope system based on ZYNQ |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110066044A1 (en) * | 2009-09-15 | 2011-03-17 | Jim Moon | Body-worn vital sign monitor |
| JP6913928B2 (en) * | 2017-02-15 | 2021-08-04 | 国立大学法人 東京大学 | Blood pressure measuring device, blood pressure measuring method and blood pressure measuring program |
-
2021
- 2021-03-05 CN CN202110247130.5A patent/CN113171069B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1552282A (en) * | 2003-05-29 | 2004-12-08 | 香港中文大学 | Blood pressure measuring method and device based on heart sound signal |
| KR20060081166A (en) * | 2005-01-07 | 2006-07-12 | 학교법인연세대학교 | Mobile radio terminal with blood pressure monitoring system ppg signal |
| CN201993766U (en) * | 2011-03-24 | 2011-09-28 | 西安交通大学苏州研究院 | Portable remote life multi-parameter monitoring terminal and constructed remote monitoring system |
| CN105943005A (en) * | 2016-06-01 | 2016-09-21 | 合肥芯福传感器技术有限公司 | Non-invasive blood pressure detection method based on mixing of photoelectric green-light pulses and electrocardiogram |
| CN108272446A (en) * | 2018-01-30 | 2018-07-13 | 浙江大学 | Noninvasive continuous BP measurement system and its calibration method |
| JP6615970B1 (en) * | 2018-09-28 | 2019-12-04 | 学校法人福岡工業大学 | Blood pressure estimation device and blood pressure estimation program |
| CN109960589A (en) * | 2019-03-25 | 2019-07-02 | 上海富士施乐有限公司 | Implementation method, device and the readable medium of the system software layer of embedded system |
| CN110074776A (en) * | 2019-04-30 | 2019-08-02 | 广东乐之康医疗技术有限公司 | A kind of power consumption control method and system of artificial intelligence dynamic cardiopulmonary monitoring device |
| CN110292369A (en) * | 2019-07-03 | 2019-10-01 | 浙江大学 | Chest non-invasive blood pressure detection probe and its device based on pulse wave translation time |
| CN110824218A (en) * | 2019-11-18 | 2020-02-21 | 重庆邮电大学 | Digital storage oscilloscope system based on ZYNQ |
Non-Patent Citations (1)
| Title |
|---|
| 基于 ARM 的多功能健康一体机主控系统设计;刘忠禹;中国优秀硕士学位论文全文数据库信息科技辑(第2019年第1期期);1140-2239 * |
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