CN117390513A - AI-based blood pressure calculation model construction method and device and electronic equipment - Google Patents
AI-based blood pressure calculation model construction method and device and electronic equipment Download PDFInfo
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Abstract
The invention provides a method and a device for constructing an AI-based blood pressure calculation model and electronic equipment, relating to the technical field of medical algorithms, comprising the following steps: receiving blood pressure sample data sent by a sphygmomanometer, wherein the blood pressure sample data comprises: an electrocardiosignal and a pulse wave signal of a sample user in the blood pressure measurement process are obtained; performing data intelligent analysis processing on the electrocardiosignal and the pulse wave signal to determine a target parameter set; and performing deep learning processing on the target parameter set through a preset oscillography blood pressure calculation model to determine a target blood pressure calculation model. According to the method, the target parameter set is extracted through the cloud server, and the blood pressure calculation model is constructed by utilizing the target parameter set, so that the measurement accuracy of the blood pressure calculation model can be remarkably improved.
Description
Technical Field
The invention relates to the technical field of medical algorithms, in particular to a method and a device for constructing a blood pressure calculation model based on AI and electronic equipment.
Background
The sphygmomanometer generally includes an electronic sphygmomanometer using an oscillometric method and a mercury sphygmomanometer using an auscultation method, and the electronic sphygmomanometer using the oscillometric method is widely used in public households because the mercury sphygmomanometer is difficult to use. At present, related technology proposes that the blood pressure of a user to be measured can be estimated through a sphygmomanometer chip based on an envelope amplitude coefficient method, but the measurement accuracy of the sphygmomanometer in the scheme is lower due to the fact that the cost of the sphygmomanometer chip is high and the computing capacity of the chip is limited.
Disclosure of Invention
Accordingly, the present invention aims to provide a method, an apparatus and an electronic device for constructing a blood pressure calculation model based on AI, wherein a cloud server extracts a target parameter set, and constructs the blood pressure calculation model by using the target parameter set, so that the measurement accuracy of the blood pressure calculation model can be significantly improved.
In a first aspect, an embodiment of the present invention provides a method for constructing an AI-based blood pressure calculation model, where the method is applied to a cloud server, and the method includes: receiving blood pressure sample data sent by a sphygmomanometer, wherein the blood pressure sample data comprises: an electrocardiosignal and a pulse wave signal of a sample user in the blood pressure measurement process are obtained; performing data intelligent analysis processing on the electrocardiosignal and the pulse wave signal to determine a target parameter set; and performing deep learning processing on the target parameter set through a preset oscillography blood pressure calculation model to determine a target blood pressure calculation model.
In one embodiment, the set of target parameters includes: the method comprises the steps of carrying out intelligent analysis processing on data of electrocardiosignals and pulse wave signals to determine a target parameter set, wherein the steps comprise: determining a respiration signal, a pulse wave envelope map and pulse wave filtering according to signal characteristics of the electrocardiosignal and the pulse wave signal; comparing the electrocardiosignal with the pulse wave signal, and determining the time difference between the peaks of the electrocardiosignal and the pulse wave signal; and determining the pulse wave propagation speed based on the time difference by a preset blood flow speed calculation model.
In one embodiment, the steps of determining the respiration signal, the pulse wave envelope map and the pulse wave filtering based on the signal characteristics of the electrocardiographic signal and the pulse wave signal comprise: determining a respiration signal according to the periodicity change rule of the waveform of the electrocardiosignal; and (3) performing curve characteristic analysis processing on the pulse wave signals through a preset self-adaptive spectral line enhancer, and determining pulse wave filtering and pulse wave envelope diagrams corresponding to the pulse wave signals.
In one embodiment, the blood pressure sample data further comprises: the target parameter set further comprises the following components of the air pressure signal of the air bag of the sphygmomanometer: the blocking time point information carries out data intelligent analysis processing on electrocardiosignals and pulse wave signals, and the step of determining a target parameter set further comprises the following steps: comparing the air pressure signal with the pulse wave signal; when the air pressure signal is in an ascending stage and the pulse wave signal is reduced to a preset signal threshold value, determining that the pulse wave disappears, and recording blocking time point information of the disappearance of the pulse wave.
In one embodiment, the blood pressure sample data further comprises: the step of determining a target parameter set by performing intelligent data analysis processing on accelerometer signals and gyroscope signals of the sphygmomanometer gesture sensing module and aiming at electrocardiosignal and pulse wave signals, and further comprises the following steps: determining posture information of the sphygmomanometer based on the accelerometer signal and the gyroscope signal through a preset posture analysis model, wherein the posture information comprises: jitter range and tilt angle; if the shaking range of the sphygmomanometer is larger than the preset range threshold value and the preset angle threshold value or the inclination angle is larger than the preset angle threshold value, marking blood pressure sample data of the sample user as invalid data, and deleting the invalid data.
In a second aspect, an embodiment of the present invention provides an AI-based blood pressure calculation method, where the method is applied to a sphygmomanometer, and the method includes: acquiring blood pressure measurement data of a user to be measured; the method comprises the steps of preprocessing blood pressure measurement data, sending the preprocessed blood pressure measurement data to a cloud server, enabling a target blood pressure calculation model pre-built in the cloud server to conduct data analysis processing on the blood pressure measurement data after the data preprocessing, and determining a blood pressure measurement result; the target blood pressure calculation model is constructed based on the construction method of the AI-based blood pressure calculation model provided in any one of the first aspects.
In a third aspect, an embodiment of the present invention further provides a device for constructing an AI-based blood pressure calculation model, where the device is applied to a cloud server, and the device includes: the blood pressure data acquisition module is used for receiving blood pressure sample data sent by the sphygmomanometer, wherein the blood pressure sample data comprises: an electrocardiosignal and a pulse wave signal of a sample user in the blood pressure measurement process are obtained; the data processing module is used for performing data intelligent analysis processing on the electrocardiosignal and the pulse wave signal and determining a target parameter set; the model training module is used for carrying out deep learning processing on the target parameter set through a preset oscillography blood pressure calculation model to determine the target blood pressure calculation model.
In a fourth aspect, an embodiment of the present invention further provides an AI-based blood pressure calculating device, where the device is applied to a sphygmomanometer, and the device includes: the user data acquisition module acquires blood pressure measurement data of a user to be measured; the blood pressure calculation module is used for preprocessing the blood pressure measurement data and sending the data to the cloud server so that a target blood pressure calculation model pre-built in the cloud server can be used for carrying out data analysis processing on the blood pressure measurement data after the data preprocessing and determining a blood pressure measurement result; the target blood pressure calculation model is constructed based on the construction method of the AI-based blood pressure calculation model provided in any one of the first aspects.
In a fifth aspect, embodiments of the present invention also provide an electronic device comprising a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the method of any one of the first aspects.
In a sixth aspect, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions that, when invoked and executed by a processor, cause the processor to implement the method of any one of the first aspects.
The embodiment of the invention has the following beneficial effects:
according to the method, the device and the electronic equipment for constructing the blood pressure calculation model based on the AI, when the blood pressure calculation model is constructed, blood pressure sample data sent by a blood pressure meter are received, then data intelligent analysis processing is carried out on the blood pressure sample data, a target parameter set is determined, deep learning processing is carried out on the target parameter set through a preset oscillometric blood pressure calculation model, and the target blood pressure calculation model is determined.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for constructing an AI-based blood pressure calculation model according to an embodiment of the invention;
fig. 2 is a schematic flow chart of an AI-based blood pressure calculation method according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a device for constructing an AI-based blood pressure calculation model according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an AI-based blood pressure calculating device according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described in conjunction with the embodiments, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
At present, the sphygmomanometer generally comprises an electronic sphygmomanometer adopting an oscillometric method and a mercury sphygmomanometer adopting an auscultation method, and because the use difficulty of the mercury sphygmomanometer is high, the electronic sphygmomanometer adopting the oscillometric method is widely applied to public families, and the electronic sphygmomanometer mainly estimates the blood pressure of a user to be measured through a sphygmomanometer chip based on an envelope amplitude coefficient method, but because the cost of the sphygmomanometer chip is high and the computing capacity of the chip is limited, the measuring precision of the sphygmomanometer in the scheme is low.
Referring to fig. 1, a flow chart of a method for constructing an AI-based blood pressure calculation model is shown, and the method mainly includes the following steps S102 to S106:
step S102, receiving blood pressure sample data sent by a sphygmomanometer, wherein the blood pressure sample data comprises: the electrocardiograph signal, the pulse wave signal, the air pressure signal of the air bag of the sphygmomanometer and the accelerometer signal and the gyroscope signal of the gesture sensing module of the sphygmomanometer are obtained by a sample user in the blood pressure measurement process, and in one implementation mode, the electrocardiograph signal and the pulse wave signal of the wrist part of the sample user can be collected by a photoelectric sensor in the sphygmomanometer.
Step S104, performing data intelligent analysis processing on the electrocardiosignal and the pulse wave signal, and determining a target parameter set, wherein the target parameter set comprises: in one embodiment, the blood pressure sample data sent by the sphygmomanometer is compressed data, the data is required to be decompressed and then subjected to data filtering processing, and then the filtered data is subjected to intelligent analysis processing to determine a target parameter set, wherein the target parameter set is a blood pressure measurement parameter corresponding to a sample user in the blood pressure measurement process.
Step S106, performing deep learning processing on the target parameter set through a preset oscillometric blood pressure calculation model to determine the target blood pressure calculation model, wherein in one embodiment, more than 1000 groups of sample users are required to perform deep learning processing on the target parameter set and the calibration data set respectively corresponding to the sample users to determine the target blood pressure calculation model, wherein the calibration data set is a mercury blood pressure count value measured by two persons.
According to the method for constructing the AI-based blood pressure calculation model, provided by the embodiment of the invention, the cloud server is used for extracting the target parameter set, and the blood pressure calculation model is constructed by utilizing the target parameter set, so that the measurement accuracy of the blood pressure calculation model can be remarkably improved.
The embodiment of the invention also provides an implementation mode of the cloud server for carrying out data intelligent analysis processing on blood pressure sample data and constructing a blood pressure calculation model, and the implementation mode is specifically described in the following (1) to (3):
(1) And comparing the electrocardiosignal with the pulse wave signal, determining the time difference between the electrocardiosignal and the wave crest of the pulse wave signal, and determining the pulse wave propagation speed based on the time difference through a preset blood flow speed calculation model, wherein the time difference ptt between the electrocardiosignal and the wave crest of the pulse wave signal represents the time difference from the heart beat to the test place (such as a wrist), and in one embodiment, the propagation time is negligible to be 0 as the electrocardiosignal propagation acquisition principle, and a certain time is required for blood to flow to the wrist, so ptt is mainly influenced by the blood flow speed, and the blood flow speed is mainly influenced by the blood pressure, so the distance from the heart to the wrist is divided by the time, and the pulse wave propagation speed PWV is determined.
(2) Determining a respiration signal and a pulse wave envelope map according to signal characteristics of the electrocardiosignal and the pulse wave signal: according to the periodical change law of the waveform of the electrocardiosignal, a respiratory signal is determined, curve characteristic analysis processing is carried out on the pulse wave signal through a preset self-adaptive spectral line enhancer, a pulse wave envelope graph and pulse wave filtering corresponding to the pulse wave signal are determined, wherein when the pulse wave filtering moves according to a certain movement law, images of all instantaneous positions of the pulse wave filtering are reserved, a curve can be tangent to all positions of the movement curve, the curve is determined to be an envelope curve of the movement curve of the pulse wave filtering, namely, the pulse wave envelope graph, in one embodiment, characteristic points of the electrocardiosignal can be identified through an r-wave amplitude method, the trend change of the respiratory signal is judged by utilizing the periodical change of the magnitude of the r-wave amplitude, in another embodiment, the pulse wave envelope graph corresponding to the pulse wave signal can be determined through the preset self-adaptive spectral line enhancer based on a polynomial smoothing method, and the r-wave amplitude method, and the preset self-adaptive spectral line enhancer and the polynomial smoothing method are not limited. Meanwhile, it should be understood by those skilled in the art that the r-wave amplitude method, the preset adaptive spectral line enhancer and the polynomial smoothing method are not limited to the present invention, and other algorithms for determining the respiratory signal and the pulse wave envelope map are also within the scope of the present invention.
(3) Comparing the air pressure signal with the pulse wave signal: when the air pressure signal is in an ascending stage and the pulse wave signal is reduced to a preset signal threshold value, determining that the pulse wave is disappeared, and recording the information of the blocking time point of the pulse wave disappearance, in one embodiment, since the arteriole vessel of the user is pressed by the air bag during the air bag inflation process, the pulse wave signal is blocked, when the rapid decrease of the pulse wave signal is observed, the time point can be also determined as the information of the blocking time point of the pulse wave signal. In practical application, since the pose and stability during measuring blood pressure are very important, the measurement result can be directly influenced, and therefore, the pose information of the blood pressure meter can be determined based on the accelerometer signal of the accelerometer and the gyroscope signal of the gyroscope in the blood pressure meter pose sensing module of the blood pressure meter through a preset pose analysis model, if the jitter range of the blood pressure meter is greater than a preset range threshold value and/or the inclination angle is greater than a preset angle threshold value, the blood pressure sample data of the sample user are marked as invalid data, and the invalid data are deleted, wherein the pose information comprises: jitter range and tilt angle.
Referring to fig. 2, a flow chart of an AI-based blood pressure calculation method mainly includes the following steps S202 to S204:
step S202, blood pressure measurement data of a user to be measured is obtained, wherein the blood pressure measurement data comprises: an electrocardiosignal, a pulse wave signal and an air pressure signal of an air bag of the sphygmomanometer of a user to be measured in the blood pressure measurement process, and an accelerometer signal and a gyroscope signal of the sphygmomanometer of the user to be measured in the blood pressure measurement process.
Step S204, after data preprocessing, the blood pressure measurement data is sent to a cloud server, so that a target blood pressure calculation model pre-built in the cloud server performs data analysis processing on the blood pressure measurement data after data preprocessing, and a blood pressure measurement result is determined, wherein the target blood pressure calculation model is built based on the AI-based blood pressure calculation model building method in any one of the above steps, in one embodiment, information interaction is performed between a sphygmomanometer and the cloud server through a network, the sphygmomanometer needs to perform lossless compression processing on the blood pressure measurement data, and the compressed data is sent to the cloud server, so that the cloud server performs data analysis after decompression and data filtering processing on the compressed data, and the blood pressure measurement result is determined.
According to the AI-based blood pressure calculation method provided by the embodiment of the invention, the blood pressure measurement result is obtained by uploading the blood pressure measurement data of the user to be measured to the cloud server, so that the technical problem that the operation capability of a sphygmomanometer chip is limited in the prior art can be solved, the cost is reduced, and meanwhile, the blood pressure measurement precision is obviously improved.
For the method for constructing the AI-based blood pressure calculation model provided in the foregoing embodiment, the embodiment of the present invention provides a device for constructing the AI-based blood pressure calculation model, which is applied to a cloud server, and see a schematic structural diagram of the device for constructing the AI-based blood pressure calculation model shown in fig. 3, where the device includes the following parts:
the sample data obtaining module 302 receives blood pressure sample data sent by the sphygmomanometer, where the blood pressure sample data includes: an electrocardiosignal and a pulse wave signal of a sample user in the blood pressure measurement process are obtained;
the data processing module 304 performs data intelligent analysis processing on the electrocardiosignal and the pulse wave signal to determine a target parameter set;
the model training module 306 performs deep learning processing on the target parameter set through a preset oscillography blood pressure calculation model to determine a target blood pressure calculation model.
According to the device for constructing the AI-based blood pressure calculation model, the cloud server is used for extracting the target parameter set, and the blood pressure calculation model is constructed by using the target parameter set, so that the measurement accuracy of the blood pressure calculation model can be remarkably improved.
In one embodiment, the set of target parameters includes: the data processing module 304 is further configured to, when performing the steps of performing data intelligent analysis processing on the electrocardiographic signal and the pulse wave signal and determining the target parameter set, perform pulse wave propagation speed, the respiration signal, the pulse wave envelope map and pulse wave filtering: determining a respiration signal, a pulse wave envelope map and pulse wave filtering according to signal characteristics of the electrocardiosignal and the pulse wave signal; comparing the electrocardiosignal with the pulse wave signal, and determining the time difference between the peaks of the electrocardiosignal and the pulse wave signal; and determining the pulse wave propagation speed based on the time difference by a preset blood flow speed calculation model.
In one embodiment, when performing the step of determining the respiratory signal, the pulse wave envelope map and the pulse wave filtering according to the signal characteristics of the electrocardiographic signal and the pulse wave signal, the data processing module 304 is further configured to: determining a respiration signal according to the periodicity change rule of the waveform of the electrocardiosignal; and (3) performing curve characteristic analysis processing on the pulse wave signals through a preset self-adaptive spectral line enhancer, and determining pulse wave filtering and pulse wave envelope diagrams corresponding to the pulse wave signals.
In one embodiment, the blood pressure sample data further comprises: the target parameter set further comprises the following components of the air pressure signal of the air bag of the sphygmomanometer: the data processing module 304 is further configured to, when performing the step of performing data intelligent analysis processing on the electrocardiographic signal and the pulse wave signal to determine the target parameter set, block the time point information: comparing the air pressure signal with the pulse wave signal; when the air pressure signal is in an ascending stage and the pulse wave signal is reduced to a preset signal threshold value, determining that the pulse wave disappears, and recording blocking time point information of the disappearance of the pulse wave.
In one embodiment, the blood pressure sample data further comprises: the data processing module 304 is further configured to, when performing the step of performing data intelligent analysis processing on the electrocardiographic signal and the pulse wave signal and determining the target parameter set, perform the step of: determining posture information of the sphygmomanometer based on the accelerometer signal and the gyroscope signal through a preset posture analysis model, wherein the posture information comprises: jitter range and tilt angle; if the shaking range of the sphygmomanometer is larger than the preset range threshold value and the preset angle threshold value or the inclination angle is larger than the preset angle threshold value, marking blood pressure sample data of the sample user as invalid data, and deleting the invalid data.
For the AI-based blood pressure calculating method provided in the foregoing embodiment, the embodiment of the present invention provides an AI-based blood pressure calculating device, which is applied to a sphygmomanometer, referring to a schematic structural diagram of the AI-based blood pressure calculating device shown in fig. 4, and the device includes the following parts:
the user data acquisition module 402 acquires blood pressure measurement data of a user to be measured;
the blood pressure calculation module 404 performs data preprocessing on the blood pressure measurement data and then sends the data preprocessing to the cloud server, so that a target blood pressure calculation model pre-built in the cloud server performs data analysis processing on the blood pressure measurement data after the data preprocessing, and a blood pressure measurement result is determined; the target blood pressure calculation model is constructed by the AI-based blood pressure calculation model construction method.
According to the AI-based blood pressure calculation device provided by the embodiment of the application, the blood pressure measurement data of the user to be measured is uploaded to the cloud server, so that the blood pressure measurement result is obtained, the technical problem that the operation capability of a sphygmomanometer chip is limited in the prior art can be solved, and the blood pressure measurement precision is remarkably improved while the cost is reduced.
The device provided by the embodiment of the present invention has the same implementation principle and technical effects as those of the foregoing method embodiment, and for the sake of brevity, reference may be made to the corresponding content in the foregoing method embodiment where the device embodiment is not mentioned.
The embodiment of the invention provides electronic equipment, which comprises a processor and a storage device; the storage means has stored thereon a computer program which, when executed by the processor, performs the method of any of the embodiments described above.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, where the electronic device 100 includes: a processor 50, a memory 51, a bus 52 and a communication interface 53, the processor 50, the communication interface 53 and the memory 51 being connected by the bus 52; the processor 50 is arranged to execute executable modules, such as computer programs, stored in the memory 51.
The memory 51 may include a high-speed random access memory (RAM, random Access Memory), and may further include a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. The communication connection between the system network element and at least one other network element is achieved via at least one communication interface 53 (which may be wired or wireless), and the internet, wide area network, local network, metropolitan area network, etc. may be used.
Bus 52 may be an ISA bus, a PCI bus, an EISA bus, or the like. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 5, but not only one bus or type of bus.
The memory 51 is configured to store a program, and the processor 50 executes the program after receiving an execution instruction, and the method executed by the apparatus for flow defining disclosed in any of the foregoing embodiments of the present invention may be applied to the processor 50 or implemented by the processor 50.
The processor 50 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuitry in hardware in the processor 50 or by instructions in the form of software. The processor 50 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but may also be a digital signal processor (Digital Signal Processing, DSP for short), application specific integrated circuit (Application Specific Integrated Circuit, ASIC for short), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA for short), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory 51 and the processor 50 reads the information in the memory 51 and in combination with its hardware performs the steps of the above method.
The computer program product of the readable storage medium provided by the embodiment of the present invention includes a computer readable storage medium storing a program code, where the program code includes instructions for executing the method described in the foregoing method embodiment, and the specific implementation may refer to the foregoing method embodiment and will not be described herein.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, an electronic device, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Finally, it should be noted that: the above examples are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, but it should be understood by those skilled in the art that the present invention is not limited thereto, and that the present invention is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. The method for constructing the AI-based blood pressure calculation model is characterized by being applied to a cloud server and comprising the following steps:
receiving blood pressure sample data sent by a sphygmomanometer, wherein the blood pressure sample data comprises: an electrocardiosignal and a pulse wave signal of a sample user in the blood pressure measurement process are obtained;
performing data intelligent analysis processing on the electrocardiosignal and the pulse wave signal to determine a target parameter set;
and performing deep learning processing on the target parameter set through a preset oscillography blood pressure calculation model to determine a target blood pressure calculation model.
2. The method for constructing an AI-based blood pressure calculation model according to claim 1, wherein the target parameter set includes: the step of performing data intelligent analysis processing on the electrocardiosignal and the pulse wave signal to determine a target parameter set comprises the following steps of:
determining the respiratory signal, the pulse wave envelope map and the pulse wave filtering according to the signal characteristics of the electrocardiosignal and the pulse wave signal;
comparing the electrocardiosignal with the pulse wave signal to determine the time difference between the peaks of the electrocardiosignal and the pulse wave signal;
and determining the pulse wave propagation speed based on the time difference through a preset blood flow speed calculation model.
3. The method for constructing an AI-based blood pressure calculation model according to claim 2, wherein the step of determining the respiratory signal, the pulse wave envelope map, and the pulse wave filter from signal characteristics of the electrocardiographic signal and the pulse wave signal includes:
determining the respiratory signal according to the periodicity change rule of the waveform of the electrocardiosignal;
and performing curve characteristic analysis processing on the pulse wave signals through a preset self-adaptive spectral line enhancer, and determining the pulse wave filtering and the pulse wave envelope map corresponding to the pulse wave signals.
4. The method for constructing an AI-based blood pressure calculation model according to claim 1, wherein the blood pressure sample data further includes: the target parameter set further comprises: blocking time point information, wherein the step of performing data intelligent analysis processing on the electrocardiosignal and the pulse wave signal and determining a target parameter set further comprises the following steps:
comparing the barometric pressure signal with the pulse wave signal;
when the air pressure signal is in an ascending stage and the pulse wave signal is reduced to a preset signal threshold value, determining that the pulse wave disappears, and recording the blocking time point information of the pulse wave disappearance.
5. The method for constructing an AI-based blood pressure calculation model according to claim 1, wherein the blood pressure sample data further includes: the step of determining a target parameter set by performing data intelligent analysis processing on the electrocardiosignal and the pulse wave signal and further comprises the following steps of:
determining, by a preset posture analysis model, posture information of a sphygmomanometer based on the accelerometer signal and the gyroscope signal, wherein the posture information includes: jitter range and tilt angle;
and if the jitter range of the sphygmomanometer is larger than a preset range threshold value and the inclination angle is larger than a preset angle threshold value, marking the blood pressure sample data of the sample user as invalid data, and deleting the invalid data.
6. An AI-based blood pressure calculation method, characterized in that the method is applied to a sphygmomanometer, the method comprising:
acquiring blood pressure measurement data of a user to be measured;
the blood pressure measurement data are sent to a cloud server after data preprocessing, so that a target blood pressure calculation model pre-built in the cloud server carries out data analysis processing on the blood pressure measurement data after data preprocessing, and a blood pressure measurement result is determined;
the target blood pressure calculation model is constructed based on the AI-based blood pressure calculation model construction method of any one of claims 1-5.
7. An AI-based blood pressure calculation model construction device, wherein the device is applied to a cloud server, and the device comprises:
the blood pressure data acquisition module is used for receiving blood pressure sample data sent by the sphygmomanometer, wherein the blood pressure sample data comprises: an electrocardiosignal and a pulse wave signal of a sample user in the blood pressure measurement process are obtained;
the data processing module is used for performing data intelligent analysis processing on the electrocardiosignal and the pulse wave signal and determining a target parameter set;
and the model training module is used for carrying out deep learning processing on the target parameter set through a preset oscillography blood pressure calculation model to determine a target blood pressure calculation model.
8. An AI-based blood pressure computing device, the device being applied to a blood pressure meter, the device comprising:
the user data acquisition module acquires blood pressure measurement data of a user to be measured;
the blood pressure calculation module is used for preprocessing the blood pressure measurement data and sending the data to a cloud server so that a target blood pressure calculation model pre-built in the cloud server can be used for carrying out data analysis processing on the blood pressure measurement data after the data preprocessing and determining a blood pressure measurement result;
the target blood pressure calculation model is constructed based on the AI-based blood pressure calculation model construction method of any one of claims 1-5.
9. An electronic device comprising a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the method of any one of claims 1 to 6.
10. A computer readable storage medium storing computer executable instructions which, when invoked and executed by a processor, cause the processor to implement the method of any one of claims 1 to 6.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| KR102394615B1 (en) * | 2020-11-25 | 2022-05-09 | 광운대학교 산학협력단 | Blood pressure measuring device wereable on wrist of user |
| US20220287579A1 (en) * | 2021-03-15 | 2022-09-15 | Covidien Lp | System and method for continuous non-invasive blood pressure measurement |
| CN115399743A (en) * | 2022-09-02 | 2022-11-29 | 广东乐心医疗电子股份有限公司 | Control method and device of blood pressure measuring instrument and server |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR102394615B1 (en) * | 2020-11-25 | 2022-05-09 | 광운대학교 산학협력단 | Blood pressure measuring device wereable on wrist of user |
| US20220287579A1 (en) * | 2021-03-15 | 2022-09-15 | Covidien Lp | System and method for continuous non-invasive blood pressure measurement |
| CN115399743A (en) * | 2022-09-02 | 2022-11-29 | 广东乐心医疗电子股份有限公司 | Control method and device of blood pressure measuring instrument and server |
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