Method and medical device for monitoring cardiovascular parameters

Abstract

A method for monitoring cardiovascular parameters and a medical device. The method for monitoring cardiovascular parameters comprises: acquiring arterial pressure monitoring data of a monitored subject and personal attribute information of the monitored subject (S110); according to the arterial pressure monitoring data and the personal attribute information, a target estimation model suitable for the monitored object is selected from at least two cardiovascular parameter estimation models, and the model structure algorithm logics of the at least two cardiovascular parameter estimation models are different (S120); and estimating cardiovascular parameters of the monitored object from the arterial pressure monitoring data by applying the target estimation model (S130). According to the method and the medical equipment, the target estimation model suitable for the monitored object is selected from the at least two different cardiovascular parameter estimation models to estimate the cardiovascular parameters of the detected object according to the arterial pressure monitoring data of the monitored object and the personal attribute information of the monitored object, and the accuracy of cardiovascular parameter monitoring can be improved.

Description

Method and medical device for monitoring cardiovascular parameters

[0001] manual Technical Field

[0002] The present application relates to the field of medical devices, and more particularly to a method and medical device for monitoring cardiovascular parameters. Background Art

[0003] Monitoring of cardiovascular parameters can provide an intuitive parameter basis for the diagnosis and treatment of patients. Cardiac output (CO) refers to the amount of blood ejected per minute by the left or right ventricle, and is one of the important indicators for assessing cardiac function. The gold standard for cardiac output monitoring is the thermodilution monitoring method of right heart cannulation, but the monitoring method of right heart cannulation is very traumatic to the patient and has a high risk of complications. At the same time, it has high requirements for medical operation, which seriously limits the promotion and use of cardiac output monitoring methods. Based on the triangular relationship between cardiac output, vascular tension and arterial pressure, cardiac output can be derived from arterial pressure through mathematical modeling. This monitoring method only requires arterial puncture and catheterization, which is a minimally invasive method for monitoring cardiac output.

[0004] Currently, there are several commonly used models for estimating cardiac output based on arterial blood pressure. Although these models vary in their expression, their fundamental principle is based on simplified mathematical models. Different cardiac output estimation models utilize different features, capturing waveform characteristics related to cardiac output from different physiological perspectives. Their estimation performance also varies in different scenarios, and a single theoretical model cannot be applicable to all scenarios.

[0005] Summary of the Invention

[0006] The Summary of the Invention introduces a series of simplified concepts that will be further described in the Detailed Description of the Invention. The Summary of the Invention of this application is not intended to limit the key features and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0007] A first aspect of an embodiment of the present application provides a method for monitoring cardiovascular parameters, comprising:

[0008] Acquiring arterial pressure monitoring data of a monitored subject and personal attribute information of the monitored subject;

[0009] selecting, based on the arterial pressure monitoring data and the personal attribute information, a target estimation model suitable for the monitored subject from at least two cardiovascular parameter estimation models, wherein the at least two cardiovascular parameter estimation models have different model structure algorithm logics;

[0010] The target estimation model is applied to estimate cardiovascular parameters of the monitored subject based on the arterial pressure monitoring data.

[0011] A second aspect of an embodiment of the present application provides a medical device, which includes a memory and a processor, wherein the memory stores a computer program run by the processor, and when the computer program is run by the processor, it executes the steps of the method for monitoring cardiovascular parameters as described above.

[0012] According to the method and medical device for monitoring cardiovascular parameters of the embodiments of the present application, based on the arterial pressure monitoring data and personal attribute information of the monitored object, a target estimation model suitable for the monitored object is selected from at least two different cardiovascular parameter estimation models to estimate the cardiovascular parameters of the monitored object, thereby improving the accuracy of cardiovascular parameter monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0014] In the attached figure:

[0015] FIG1 is a schematic flowchart of a method for monitoring cardiovascular parameters according to an embodiment of the present application;

[0016] FIG2A shows an equivalent circuit model of a cardiovascular system according to another embodiment of the present application;

[0017] FIG2B shows an equivalent circuit model of a cardiovascular system according to another embodiment of the present application;

[0018] FIG2C shows an equivalent circuit model of a cardiovascular system according to another embodiment of the present application;

[0019] FIG3 shows a schematic block diagram of a medical device according to an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the present application more apparent, the following is a detailed description of example embodiments of the present application with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application described in this application, all other embodiments obtained by those skilled in the art without creative work should fall within the scope of protection of this application.

[0021] In the following description, a large number of specific details are provided to provide a more thorough understanding of the present application. However, it will be apparent to those skilled in the art that the present application can be implemented without one or more of these details. In other examples, some technical features well known in the art are not described in order to avoid confusion with the present application.

[0022] It should be understood that the present application can be implemented in different forms and should not be interpreted as being limited to the embodiments set forth herein. On the contrary, providing these embodiments will make the disclosure thorough and complete and will fully convey the scope of the present application to those skilled in the art.

[0023] The purpose of the terms used herein is only to describe specific embodiments and is not intended to limit the present application. When used herein, the singular forms "a", "an" and "said / the" are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "comprising" and / or "comprising", when used in this specification, determine the presence of the features, integers, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or groups. When used herein, the term "and / or" includes any and all combinations of the relevant listed items. In addition, the present application can be implemented in a variety of different forms and is not limited to the embodiments described in this embodiment. The purpose of providing the following specific embodiments is to facilitate a clearer and more thorough understanding of the disclosure of the present application, wherein words indicating directions such as up, down, left, right, etc. are only with respect to the position of the structure shown in the corresponding drawings.

[0024] In order to fully understand the present application, a detailed structure will be provided in the following description to illustrate the technical solution proposed by the present application. The optional embodiments of the present application are described in detail below. However, in addition to these detailed descriptions, the present application may also have other implementation methods.

[0025] Below, a method for monitoring cardiovascular parameters according to an embodiment of the present application will be described with reference to the accompanying drawings. First, reference is made to FIG1 , which is a schematic flow chart of a method 100 for monitoring cardiovascular parameters according to an embodiment of the present application. As shown in FIG1 , the method 100 for monitoring cardiovascular parameters according to an embodiment of the present application includes the following steps:

[0026] In step S110, arterial pressure monitoring data of a monitored subject and personal attribute information of the monitored subject are obtained;

[0027] In step S120, based on the arterial pressure monitoring data and the personal attribute information, a target estimation model suitable for the monitored subject is selected from at least two cardiovascular parameter estimation models, wherein the at least two cardiovascular parameter estimation models have different model structure algorithm logics;

[0028] In step S130, the target estimation model is applied to estimate cardiovascular parameters of the monitored subject based on the arterial pressure monitoring data.

[0029] According to the method 100 for monitoring cardiovascular parameters in an embodiment of the present application, based on the arterial pressure monitoring data and personal attribute information of the monitored object, a target estimation model suitable for the monitored object is selected from at least two different cardiovascular parameter estimation models to estimate the cardiovascular parameters of the monitored object, thereby improving the accuracy of cardiovascular parameter monitoring.

[0030] For example, the method 100 for monitoring cardiovascular parameters according to an embodiment of the present application can be implemented in a monitoring device. The cardiovascular parameters obtained based on the method 100 for monitoring cardiovascular parameters can be output by the monitoring device, for example, displayed on a monitoring interface of the monitoring device. The monitoring device can also monitor whether the cardiovascular parameters are within a preset threshold range and generate an alarm message when the cardiovascular parameters exceed the preset threshold range. In addition to monitoring devices, the offline assessment method 100 according to an embodiment of the present application can also be implemented in a central station, a cloud service system, or other medical device.

[0031] In step S110, arterial pressure monitoring data and personal attribute information of the monitored subject are obtained. Exemplarily, arterial pressure monitoring data can be obtained through arterial puncture and catheterization. Specifically, through arterial puncture, a catheter is placed within an artery at the subject's location, with the outer end of the catheter connected to a transducer. Catheterization sites include the radial artery, ulnar artery, and brachial artery. Because fluids transmit pressure, the pressure within the artery is transmitted to the external transducer via the fluid within the catheter. The transducer can then be connected to a monitoring device, allowing the monitoring device to obtain a dynamic waveform of real-time changes in arterial pressure within the artery. Compared to non-invasive blood pressure measurement methods, invasive blood pressure measurement based on arterial catheterization can more promptly, accurately, and dynamically reflect changes in arterial pressure. Arterial pressure monitoring data can also be obtained through non-invasive continuous blood pressure monitoring methods to minimize trauma to the subject. Exemplary non-invasive continuous blood pressure monitoring methods include volume compensation, arterial tonometry, pulse wave velocity measurement, and pulse wave morphology.

[0032] The personal attribute information of the monitored subject includes the age, gender, height, weight, etc. of the monitored subject, which can affect the mapping relationship between arterial pressure monitoring data and cardiovascular parameters. The personal attribute information can be input by the user or automatically obtained by the monitoring device. For example, the information of the monitored subject can be obtained, keywords in the information can be extracted, and the personal attribute information of the monitored subject can be obtained based on the extracted keywords. The information of the monitored subject includes one or more of the patient information database, electronic medical records, examination application forms, etc. of the monitored subject. Alternatively, the personal attribute information of the monitored subject can also be obtained by any other feasible means.

[0033] Optionally, in step S110, other relevant information of the monitored object can also be obtained, such as the monitored object's diagnosis results, medical information such as past medical history, and data on other physiological parameters of the monitored object, such as blood oxygen data, electrocardiogram data and other vital sign monitoring data collected by monitoring equipment, which are used together to estimate the cardiovascular parameters of the monitored object.

[0034] After obtaining the subject's arterial pressure monitoring data and personal attribute information, a target estimation model suitable for the subject is selected from at least two cardiovascular parameter estimation models based on the subject's arterial pressure monitoring data and personal attribute information. The at least two cardiovascular parameter estimation models are constructed based on different algorithmic logic and are pre-configured in the monitoring device.

[0035] For example, a cardiovascular parameter estimation model is based on hemodynamics, combining the physiological structure of the cardiovascular system and the analogical relationship between hemodynamic parameters and electrical network parameters. Fluid mechanics shows that in the cardiovascular system, the heart and blood vessels are elastic, and blood exhibits viscosity, inertia, and compliance. However, due to the complexity of the systemic vascular system, finite element mechanical modeling of the entire vascular system is difficult to implement. Therefore, the cardiovascular system is modeled by analogy with an electrical system. Voltage represents blood pressure; current represents blood flow; resistance represents blood viscosity; inductance represents blood flow inertia; and capacitance represents vascular compliance. Based on this theoretical foundation, a digital simulation model of an analog circuit composed of circuit elements can be established, using circuit analysis methods to simulate the flow state of blood in the blood vessels. Given vascular and blood parameters, changes in blood flow when blood pressure changes can be calculated.

[0036] There are multiple algorithmic theories for current cardiovascular parameter estimation models, but when monitoring cardiovascular parameters clinically, only a cardiovascular parameter estimation model based on one algorithmic theory is usually used. Even if different application scenarios may affect the accuracy of the cardiovascular parameter estimation model, at most the model parameters of the cardiovascular parameter estimation model are adjusted according to the application scenario, without changing the algorithmic theory of the cardiovascular parameter estimation model, and the improvement in the accuracy of cardiovascular parameter estimation is limited.

[0037] To further improve the accuracy of cardiovascular parameter estimation, the at least two pre-configured cardiovascular parameter estimation models in the embodiments of the present application can correspond to at least two cardiovascular parameter algorithm theories. Exemplarily, the at least two cardiovascular parameter algorithm theories include any two of a lumped parameter circuit model algorithm, a lumped parameter instantaneous flow model algorithm, and a transmission line model algorithm. The lumped parameter circuit model algorithm treats the heart as a current source, using resistance and capacitance to simulate the vascular resistance and compliance of the circulatory system, respectively. See FIG2A , which illustrates a circuit model based on the lumped parameter circuit model algorithm. The lumped parameter instantaneous flow model algorithm adds an arterial impedance unit to the lumped parameter circuit model algorithm, simulating the heart as a voltage source with nonlinear arterial compliance. See FIG2B , which illustrates a circuit model based on the lumped parameter instantaneous flow model algorithm. The distributed transmission line circuit model algorithm utilizes a transmission line model to capture distributed characteristics such as propagation impedance and waveform reflections, thereby more accurately simulating the arterial tree. See FIG2C , which illustrates a circuit model based on the distributed transmission model algorithm. As can be seen from FIG. 2A to FIG. 2C , different cardiovascular parameter algorithm theories simulate the cardiovascular system as completely different circuits.

[0038] In some embodiments, among the pre-configured multiple cardiovascular parameter estimation models, some cardiovascular parameter estimation models may correspond to the same algorithm theory. For example, at least two cardiovascular parameter estimation models based on the lumped parameter circuit model algorithm may include the mean arterial pressure model, the Windkessel model (elastic cavity model), the Windkessel RC model, the Herd model, the Liljestrand model, etc.; at least two cardiovascular parameter estimation models based on the distributed transmission model may respectively calculate cardiac output based on systolic pressure, corrected systolic pressure, vascular area and impedance, square root of pressure, etc.; the cardiovascular parameter estimation model of the lumped parameter instantaneous flow model includes the Godje model, the Wesseling Modelflow model, etc. In different cardiovascular parameter estimation models corresponding to the same algorithm theory, different mathematical models are used to estimate cardiovascular parameters. For example, in the mean arterial pressure model, CO=k*P m , where P m is the mean arterial pressure; in the Windkessel RC model, CO = k*P m / T·ln(P s / (P d )), where P s is systolic blood pressure, P d For diastolic blood pressure.

[0039] For example, a theoretical selection model can be pre-trained for use in selecting a target estimation model. The theoretical selection model can be pre-configured in a medical device for implementing method 100 for monitoring cardiovascular parameters. Arterial pressure monitoring data and the subject's personal attribute information are input into the pre-trained theoretical selection model to obtain an output from the theoretical selection model. The target estimation model is then selected based on the output from the theoretical selection model.

[0040] Among them, the pre-training process of the theoretical selection model includes: obtaining the subject's arterial pressure monitoring data, personal attribute information and cardiovascular parameter reference values; applying at least two cardiovascular parameter estimation models respectively, and obtaining the subject's cardiovascular parameter estimation values ​​based on the subject's arterial pressure monitoring data and personal attribute information; determining the cardiovascular parameter estimation model suitable for the subject based on the deviation between the cardiovascular parameter estimation values ​​and the cardiovascular parameter reference values; and training the theoretical selection model based on the subject's arterial pressure monitoring data, personal attribute information and the labels corresponding to the cardiovascular parameter estimation model suitable for the subject.

[0041] Taking two cardiovascular parameter estimation models as an example, it is assumed that the theoretical selection model is used to select a target estimation model suitable for the subjects in the first cardiovascular parameter estimation model and the second cardiovascular parameter estimation model. For multiple subjects, their personal attribute information and arterial pressure monitoring data are obtained respectively, and their cardiovascular parameter reference values ​​are obtained. Among them, the cardiovascular parameter reference values ​​can be accurate results measured by the thermodilution method or the Doppler flow method. With the personal attribute information and arterial pressure monitoring data of each subject as input, the cardiovascular parameter estimation values ​​are obtained respectively by the first cardiovascular parameter estimation model and the second cardiovascular parameter estimation model, and compared with the cardiovascular parameter reference values, it can be determined that the cardiovascular parameter reference values ​​of the subjects in group A are closer to the cardiovascular parameter estimation values ​​obtained by the first cardiovascular parameter estimation model, that is, the cardiovascular parameter estimation model applicable to the subjects in group A is the first cardiovascular parameter estimation model; the cardiovascular parameter reference values ​​of the subjects in group B are closer to the cardiovascular parameter estimation values ​​obtained by the second cardiovascular parameter estimation model, that is, the cardiovascular parameter estimation model applicable to the subjects in group B is the second cardiovascular parameter estimation model. Thus, a theoretical selection model can be trained based on the subject's arterial pressure monitoring data, personal attribute information, and labels corresponding to the cardiovascular parameter estimation model applicable to the subject, so that after the subject's arterial pressure monitoring data and personal attribute information are input into the theoretical selection model, the theoretical selection model can accurately output a cardiovascular parameter estimation model applicable to the subject; the trained theoretical selection model can be used to select an appropriate cardiovascular parameter estimation model for the monitored subject in clinical practice. For example, the theoretical selection model can be generated through artificial intelligence technologies such as decision trees, regression analysis of previous data, and random forests.

[0042] For example, during cardiovascular parameter monitoring, the target estimation model applicable to the monitored subject can be selected in real time. Specifically, arterial pressure monitoring data and the subject's personal attribute information are input into a theoretical selection model in real time. If the cardiovascular parameter estimation model selection result output by the theoretical selection model is inconsistent with the currently applied cardiovascular parameter estimation model, the cardiovascular parameter estimation model output by the theoretical selection model can be used as the target estimation model applicable to the monitored subject to estimate the subject's cardiovascular parameters. This allows for timely adjustments to be made when the cardiovascular parameter estimation model applicable to the monitored subject changes, ensuring the accuracy of cardiovascular parameter monitoring.

[0043] Alternatively, the target estimation model applicable to the monitored object can be selected according to a set frequency. For example, the theoretical selection model can be applied at preset intervals to determine the target estimation model currently applicable to the monitored object, thereby reducing the amount of computation of the theoretical selection model. In some embodiments, the set frequency can also be adjusted according to the fluctuation amplitude of the arterial pressure monitoring data. Exemplarily, the set frequency is proportional to the fluctuation amplitude of the arterial pressure monitoring data. If the fluctuation amplitude of the arterial pressure monitoring data is large, the set frequency is high; if the fluctuation amplitude of the arterial pressure monitoring data is large, the set frequency is low. When the arterial pressure monitoring data fluctuates violently, the target estimation model applicable to the monitored object is more likely to change, so a higher set frequency can be applied to ensure timely adjustment of the target estimation model; when the arterial pressure monitoring data is relatively stable, the target estimation model applicable to the monitored object is less likely to change, so a lower set frequency can be applied to reduce the amount of computation.

[0044] In some embodiments, in order to avoid errors due to factors such as noise, when it is determined at least twice that the monitored object is applicable to the same estimation model, it can be determined as the target estimation model. If it is determined only once that the monitored object is applicable to another cardiovascular parameter estimation model that is different from the currently applied cardiovascular parameter estimation model, the cardiovascular parameter estimation model may not be adjusted temporarily. For example, if it is determined multiple times within a preset time that the monitored object is applicable to the same estimation model among at least two cardiovascular estimation models, the same estimation model is determined as the target estimation model. Alternatively, if it is determined N times in a row that the monitored object is applicable to the same estimation model among at least two cardiovascular parameter estimation models, the same estimation model is determined as the target estimation model, where N is an integer not less than 2.

[0045] In step S130, the target estimation model is applied to estimate cardiovascular parameters of the monitored subject based on the arterial pressure monitoring data. Cardiovascular parameters include at least cardiac output (CO); optionally, cardiovascular parameters may also include stroke volume (SV), stroke volume variation (SVV), vascular resistance (SVR), and the like.

[0046] Exemplarily, a target estimation model is applied to estimate the cardiovascular parameters of the monitored subject based on the arterial pressure monitoring data, specifically comprising: extracting waveform features from the arterial pressure monitoring data, and inputting the waveform features and personal attribute information into the target estimation model to obtain the cardiovascular parameters of the monitored subject. The waveform features extracted from the arterial pressure monitoring data include at least one of the following: pulse timing features, pulse timing higher-order derivatives, spectral features, energy features, and entropy features. Pulse timing features include amplitude, interval, area under the curve, statistical moments, and the like. Exemplarily, the arterial pressure monitoring data can be subjected to data processing such as amplification, filtering, and analog-to-digital conversion to obtain the above-mentioned waveform features. Different cardiovascular parameter estimation models can correspond to different waveform features.

[0047] In some embodiments, the pulse wave transit time (PWTT) of the monitored subject can also be obtained, and the cardiovascular parameters obtained in step S130 can be corrected according to the pulse wave transit time. The pulse wave is formed by the interaction of the intermittent fluctuations of the heart and the various resistances encountered by the blood flowing in the blood vessels. It contains rich physiological and case information of the cardiovascular system and is closely related to the changes in cardiovascular parameters. With the increase of blood pressure, the increase of arterial dilation pressure and the decrease of arterial compliance, the PWTT will shorten. Therefore, the PWTT can be involved in the estimation of cardiovascular parameters. Correcting cardiovascular parameters according to the pulse wave transit time can further improve the accuracy of cardiovascular parameter estimation.

[0048] Exemplarily, pulse wave transit time is the time it takes for a pulse wave to travel between two points on the human body. To obtain pulse wave transit time, at least two physiological signals from the subject need to be acquired. These at least two physiological signals can be at least two blood oxygenation signals, each of which is a photoelectric signal collected by a blood oxygenation sensor. Specifically, the blood oxygenation sensor radiates light of different wavelengths into the subject's tissue region and detects the light signal transmitted or reflected by the tissue region. With each heartbeat, the contraction and expansion of blood vessels affects the transmission or reflection of light. Therefore, the photoelectric signal can reflect the characteristics of blood flow, and the pulse wave signal generated by light absorption in the tissue region can be extracted from the photoelectric signal. Processing the pulse wave signal can extract the pulse wave waveform. By comparing the pulse wave waveforms obtained from the two blood oxygenation signals, the pulse wave transit time between the measurement locations of the two blood oxygenation signals can be obtained.

[0049] Alternatively, the at least two physiological signals may include at least one blood oxygen signal and at least one electrocardiogram (ECG) signal. The ECG signal may be collected by an ECG sensor of a monitoring device. The peak of the ECG signal originates from ventricular contraction, while the peak of the pulse wave signal originates from vasoconstriction. Therefore, the transmission time from the heart to the blood oxygen signal measurement location, i.e., the pulse wave transmission time, can be determined based on the ECG and pulse wave signals.

[0050] After obtaining the pulse wave transit time, the cardiovascular parameters obtained in step S130 are corrected based on the pulse wave transit time. Specifically, a correction coefficient for the cardiovascular parameters can be calculated based on the pulse wave transit time, and the cardiovascular parameters obtained in step S130 are multiplied by the correction coefficient to obtain the final calculated cardiovascular parameters.

[0051] In summary, the method 100 for monitoring cardiovascular parameters of an embodiment of the present application estimates the cardiovascular parameters of the monitored object by selecting a target estimation model suitable for the monitored object from at least two different cardiovascular parameter estimation models based on the arterial pressure monitoring data and personal attribute information of the monitored object, thereby improving the accuracy of cardiovascular parameter monitoring.

[0052] On the other hand, an embodiment of the present application provides a medical device. Referring to Figure 3, the medical device 300 includes a memory 310 and a processor 320. The memory 310 stores a computer program run by the processor 320. When the computer program is run by the processor 310, it executes the steps of the method for monitoring cardiovascular parameters as described above to obtain the cardiovascular parameters of the monitored object.

[0053] The medical device 300 of the present embodiment includes, but is not limited to, any one or a combination of a monitor, a local central station, a remote central station, a cloud service system, and a mobile terminal. The monitor is used to monitor the physiological parameters of the monitored subject in real time and may include a bedside monitor, a wearable monitor, etc. The central station is used to receive monitoring data sent by the monitor and other medical devices and centrally monitor the monitoring data.

[0054] The processor 320 of the medical device 300 can be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor 320 is the control center of the medical device 300 and connects the various components of the entire medical device 300 using various interfaces and circuits.

[0055] Medical device 300 also includes a memory 310. Memory 310 is used to store data on a ventilation subject associated with medical device 300. Memory 310 also stores program code, and processor 320 is used to call the program code in memory 310 to execute the steps of method 100 for monitoring cardiovascular parameters described above. Memory 310 may primarily include a program storage area and a data storage area, wherein the program storage area may store an operating system, application programs required for multiple functions, and the like. Furthermore, memory 310 may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart memory card, a secure digital card, a flash memory card, multiple disk storage devices, flash memory devices, or other volatile solid-state storage devices.

[0056] In some embodiments, medical device 300 further includes a display for displaying cardiovascular parameters of the monitored subject. The display is configured to provide a user with a visual display output. Specifically, the visual display interface provided by the display includes, but is not limited to, a monitoring interface, an operation interface, a parameter setting interface, an alarm interface, and the like. Exemplarily, the display can be implemented as a touch display or a display with an input panel, i.e., the display can function as an input / output device.

[0057] In some embodiments, the medical device 300 also includes a sensor. The sensor and the processor 320 can be connected via a wired communication protocol or a wireless communication protocol so that data can be exchanged between the sensor and the processor 320. Exemplarily, the sensor includes at least a transducer connected to a puncture catheter for obtaining arterial pressure monitoring data. Wireless communication technologies include, but are not limited to: various generations of mobile communication technologies (2G, 3G, 3G and 5G), wireless networks, Bluetooth, ZigBee, ultra-wideband UWB, NFC, etc. In some embodiments, the sensor can be independently arranged outside the medical device 300 and detachably connected to the medical device 300. The processor 320 is also used to perform data processing on the monitoring data signal from the sensor. In some other embodiments, the medical device 300 may also not include a sensor, and the medical device 300 can receive monitoring data collected by an external monitoring accessory through a communication module.

[0058] The medical device 300 may further include a communication module connected to the processor 320. In some embodiments, the medical device 300 may establish data communication with a third-party device through the communication module. The processor 320 also controls the communication module to obtain data from the third-party device, or to send monitoring data collected by the sensor to the third-party device. The communication module includes but is not limited to WiFI, Bluetooth, NFC, ZigBee, ultra-wideband UWB, or 2G, 3G, 3G, 5G and other mobile communication modules. In other embodiments, the medical device 300 may further establish a connection with the third-party device via a cable. The third-party device includes other medical devices. The third-party device may also be a cloud service system or a mobile terminal such as a mobile phone, tablet computer, or personal computer.

[0059] In some embodiments, the medical device 300 further includes an alarm module connected to the processor 320 for outputting an alarm prompt so that medical personnel can take appropriate first aid measures. The alarm module includes, but is not limited to, an alarm light, an alarm speaker, etc. The alarm information can be displayed on a display, a flashing alarm light can be used to alert medical personnel, or an alarm message can be played through an alarm speaker.

[0060] In order to realize user interface and data exchange, in addition to the display, the medical device 300 may also include other input / output devices connected to the processor 320, including but not limited to input devices such as a keyboard, a mouse, a touch screen, a remote control, and output devices including but not limited to a printer, a speaker, etc.

[0061] It should be understood that Figure 3 is only an example of the components included in the medical device 300 and does not constitute a limitation on the medical device 300. The medical device 300 may include more or fewer components than shown in Figure 3, or a combination of certain components, or different components. For example, the medical device 300 may also include a power module, a positioning and navigation device, a printing device, etc.

[0062] The medical device 300 of the embodiment of the present application is used to implement the above-mentioned method 100 for monitoring cardiovascular parameters, and thus also has similar advantages.

[0063] Although example embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above example embodiments are merely illustrative and are not intended to limit the scope of the present application. Various changes and modifications may be made therein by those skilled in the art without departing from the scope and spirit of the present application. All such changes and modifications are intended to be included within the scope of the present application as required by the appended claims.

[0064] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0065] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units described is merely a logical function division. In actual implementation, other division methods may be used, such as combining or integrating multiple units or components into another device, or ignoring or not performing some features.

[0066] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0067] Similarly, it should be understood that in order to streamline the present application and aid in understanding one or more of the various inventive aspects, in the description of the exemplary embodiments of the present application, the various features of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, this approach of the present application should not be interpreted as reflecting the intention that the application claimed for protection requires more features than those explicitly recited in each claim. More precisely, as reflected in the corresponding claims, the inventive point is that the corresponding technical problem can be solved with fewer features than all the features of a single disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into the detailed description, with each claim itself serving as a separate embodiment of the present application.

[0068] It will be understood by those skilled in the art that, except where mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or apparatus disclosed herein may be combined in any combination. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature providing the same, equivalent, or similar purpose.

[0069] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of this application and to form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.

[0070] The various component embodiments of the present application can be implemented in hardware, or in a software module running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all of the functions of some modules according to the embodiments of the present application. The application can also be implemented as a part or all of a device program (e.g., a computer program and a computer program product) for performing the method described herein. Such a program implementing the present application can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0071] It should be noted that the above embodiments illustrate rather than limit the present application, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbols placed between brackets should not be construed as limiting the claims. The present application may be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names.

[0072] The above description is merely a specific embodiment or illustration of a specific embodiment of the present application, and the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. The scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for monitoring cardiovascular parameters, characterized in that: include: Acquiring arterial pressure monitoring data of a monitored subject and personal attribute information of the monitored subject; selecting, based on the arterial pressure monitoring data and the personal attribute information, a target estimation model suitable for the monitored subject from at least two cardiovascular parameter estimation models, wherein the at least two cardiovascular parameter estimation models have different model structure algorithm logics; The target estimation model is applied to estimate cardiovascular parameters of the monitored subject based on the arterial pressure monitoring data.

2. The method according to claim 1, characterized in that The cardiovascular parameters include cardiac output.

3. The method according to claim 1, characterized in that The at least two cardiovascular parameter estimation models correspond to at least two cardiovascular parameter algorithm theories, and the at least two cardiovascular parameter algorithm theories include any two of a lumped parameter circuit model algorithm, a lumped parameter instantaneous flow model algorithm, and a transmission line model algorithm.

4. The method according to claim 1, wherein The step of selecting a target estimation model suitable for the monitored subject from at least two cardiovascular parameter estimation models based on the arterial pressure monitoring data and the personal attribute information includes: The arterial pressure monitoring data and the personal attribute information are input into a pre-trained theoretical selection model to obtain an output result of the theoretical selection model, and the target estimation model is selected based on the output result.

5. The method according to claim 4, characterized in that The pre-training process of the theoretical selection model also includes: Obtain the subjects' arterial pressure monitoring data, personal attribute information, and cardiovascular parameter reference values; Applying the at least two cardiovascular parameter estimation models respectively to obtain cardiovascular parameter estimation values ​​of the subject according to the arterial pressure monitoring data and personal attribute information of the subject; determining a cardiovascular parameter estimation model suitable for the subject according to a deviation between the cardiovascular parameter estimation value and the cardiovascular parameter reference value; The theoretical selection model is trained according to the arterial pressure monitoring data of the subject, personal attribute information and labels corresponding to the cardiovascular parameter estimation model applicable to the subject.

6. The method according to claim 1, characterized in that The applying the target estimation model to estimate the cardiovascular parameters of the monitored subject based on the arterial pressure monitoring data includes: Waveform features are extracted from the arterial pressure monitoring data, and the waveform features and the personal attribute information are input into the target estimation model to obtain cardiovascular parameters of the monitored subject.

7. The method according to claim 6, characterized in that The waveform feature includes at least one of the following: pulse timing feature, pulse timing high-order derivative, spectrum feature, energy feature, and entropy value feature.

8. The method according to claim 1, characterized in that The step of selecting a target estimation model suitable for the monitored subject from at least two cardiovascular parameter estimation models based on the arterial pressure monitoring data and the personal attribute information includes: During the process of monitoring the cardiovascular parameters, the target estimation model applicable to the monitored object is selected and determined in real time, or the target estimation model applicable to the monitored object is selected and determined according to a set frequency.

9. The method according to claim 8, characterized in that Also includes: The set frequency is adjusted according to the fluctuation amplitude of the arterial pressure monitoring data.

10. The method according to claim 9, characterized in that The set frequency is proportional to the fluctuation amplitude of the arterial pressure monitoring data.

11. The method according to claim 1, wherein The step of selecting a target estimation model suitable for the monitored subject from at least two cardiovascular parameter estimation models based on the arterial pressure monitoring data and the personal attribute information includes: If it is determined that the monitored object is suitable for the same candidate estimation model among the at least two cardiovascular estimation models for multiple times in a preset time, the same candidate estimation model is determined as the target estimation model.

12. The method according to claim 1, characterized in that The step of selecting a target estimation model suitable for the monitored subject from at least two cardiovascular parameter estimation models based on the arterial pressure monitoring data and the personal attribute information includes: If it is determined N times in succession that the monitored object is suitable for the same candidate estimation model among the at least two cardiovascular parameter estimation models, the same candidate estimation model is determined as the target estimation model, where N is an integer not less than 2.

13. The method according to claim 1, wherein Also includes: Obtaining the pulse wave transit time of the monitored subject; The cardiovascular parameters are corrected according to the pulse wave transit time.

14. The method according to claim 1, wherein The personal attribute information of the monitored object includes at least one of the following: height, gender, weight, and age.

15. A medical device, characterized in that: The medical device comprises a memory and a processor, wherein a computer program executed by the processor is stored in the memory, and when the computer program is executed by the processor, the steps of the method for monitoring cardiovascular parameters according to any one of claims 1 to 14 are performed.

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