Flexible arterial blood pressure signal measurement device

Abstract

This application provides a flexible arterial blood pressure measurement device, relating to the field of blood pressure measurement technology. The device includes a sensor module and a physiological data processing module. The sensor module includes one or more sensors for detecting physiological signals, at least one of which is a thin-film sensor made of a flexible conductive material. The physiological data processing module obtains the multimodal physiological signals collected by the sensor module and uses at least one of a data-driven deep learning model, a deep learning model combining partial differential equations, or a blood pressure signal model based on physiological mechanisms to obtain the arterial blood pressure signal. This application achieves close contact with the skin for convenient wear while minimizing user discomfort, thus meeting the requirements for long-term monitoring. It has a wide range of applications and improves the accuracy of the arterial blood pressure signal.

Description

Technical Field

[0001] This application relates to the field of blood pressure measurement technology, and more specifically, to a flexible arterial blood pressure signal measurement device. Background Technology

[0002] As people pay increasing attention to their health, wearable devices are gaining popularity. These devices allow users to monitor and analyze various physiological parameters in real time, providing a clear understanding of their physical condition and enabling timely intervention. However, current wearable devices often lack a tight seal with the skin, leading to inconvenience and discomfort. Summary of the Invention

[0003] This application provides a flexible arterial blood pressure signal measurement device, which can solve the problem that current wearable devices are inconvenient and uncomfortable to wear due to their loose contact with the skin. The technical solution is as follows:

[0004] According to one aspect of the embodiments of this application, a flexible arterial blood pressure measurement device is provided, the flexible arterial blood pressure measurement device comprising:

[0005] A sensor module, including one or more sensors for detecting physiological signals, wherein at least one sensor is a thin-film sensor based on a flexible conductive material;

[0006] The physiological data processing module is used to obtain multimodal physiological signals collected by the sensor module, and to obtain arterial blood pressure signal by employing at least one of the following: a data-driven deep learning model, a deep learning model combining partial differential equations, or a blood pressure signal model based on physiological mechanisms.

[0007] As an optional embodiment, the sensor module includes a mechanical sensor, an electronic sensor, and an optical sensor. The mechanical sensor is used to obtain mechanical signals from the human body, the electronic sensor is used to obtain electrocardiogram signals and impedance signals from the human skin, and the optical sensor is used to obtain pulse wave signals from the human skin.

[0008] Both the mechanical sensor and the electronic sensor are thin-film sensors.

[0009] As an optional embodiment, the physiological data processing module includes:

[0010] A preprocessing unit is used to perform noise reduction processing on the mechanical signal, the electrocardiogram signal, the impedance signal and the pulse wave signal, and to extract the pressure signal and heart and lung sound signals from the mechanical signal;

[0011] The model processing unit is used to obtain the arterial blood pressure signal based on the pressure signal, the heart and lung sound signals, and the noise-reduced electrocardiogram signal, impedance signal, and pulse wave signal, using at least one of the data-driven deep learning model, the deep learning model combining partial differential equations, or the blood pressure signal model based on physiological mechanisms.

[0012] As an optional embodiment, the electronic sensor is divided into a first electronic sensor and a plurality of second electronic sensors. The first electronic sensor is circular, the second electronic sensors are annular, and the plurality of second electronic sensors are arranged at intervals with the center of the first electronic sensor as the center. The mechanical sensor is disposed between two second electronic sensors, and the optical sensor is disposed on the first electronic sensor.

[0013] As an optional embodiment, multiple electronic sensors are provided, and the multiple electronic sensors are arranged in a strip, with the mechanical sensor and the optical sensor located on the same side of the electronic sensors.

[0014] As an optional embodiment, the sensor module further includes an ultrasonic sensor and a temperature sensor, wherein the ultrasonic sensor is used to obtain ultrasonic signals from human skin, and the temperature sensor is used to obtain temperature signals from human skin.

[0015] As an optional embodiment, the physiological data processing module is specifically used for:

[0016] The multimodal physiological signals are input into the pre-trained data-driven deep learning model to obtain the initial arterial blood pressure map signal output by the data-driven deep learning model. The data-driven deep learning model is trained using multimodal physiological signal samples as samples and arterial blood pressure map signal samples as labels.

[0017] The multimodal physiological signals are input into a pre-trained deep learning model combining partial differential equations to obtain an initial arterial blood pressure map signal output by the deep learning model combining partial differential equations. The deep learning model combining partial differential equations is trained using multimodal physiological signals as samples and arterial blood pressure map signals as labels.

[0018] The multimodal physiological signals are input into the blood pressure signal model based on physiological mechanisms to obtain the initial arterial blood pressure map signal output by the blood pressure signal model based on physiological mechanisms.

[0019] The arterial blood pressure signal is obtained by fusing features of multiple initial arterial blood pressure signal signals through a fusion network.

[0020] As an optional embodiment, the flexible arterial blood pressure measurement device further includes a wireless transmission module.

[0021] When the physiological data processing module is mounted on the sensor module, the wireless transmission module is used to send the multimodal physiological signals to the physiological data processing module, and the wireless transmission module is used to output the arterial blood pressure graph signal obtained by the physiological data processing module.

[0022] When the physiological data processing module and the sensor module are set up separately, the wireless transmission module is used to send the multimodal physiological signals to the physiological data processing module.

[0023] As an optional embodiment, the flexible arterial blood pressure signal measurement device further includes:

[0024] The sensor module is mounted on the substrate, which can be attached to human skin, and the substrate is made of a flexible material;

[0025] The sensor module is located between the substrate and the protective part.

[0026] As an optional embodiment, the physiological data processing module is also used to obtain heart sounds, lung sounds, electrocardiogram, systolic blood pressure, diastolic blood pressure, blood pressure change rate, heart rate, heart rate variability, pulse wave conduction time, pulse wave conduction velocity, body temperature, and bioimpedance. The heart sounds include a first heart sound, a second heart sound, a third heart sound, and a fourth heart sound.

[0027] The beneficial effects of the technical solution provided in this application are as follows: This application provides a flexible arterial blood pressure measurement device. Compared with the prior art which can only be worn on the wrist, since at least one sensor in the sensor module is a thin-film sensor made of flexible conductive material, it can make close contact with the skin, making it convenient for users to wear while minimizing user discomfort, thus meeting the requirements for long-term monitoring and having a wide range of applications. In addition, the arterial blood pressure signal of this application adopts at least one of the following: a data-driven deep learning model, a deep learning model combined with partial differential equations, or a blood pressure signal model based on physiological mechanisms, which improves the accuracy of the arterial blood pressure signal. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below.

[0029] Figure 1 This is a schematic diagram of the structure of a flexible arterial blood pressure measurement device provided in an embodiment of this application;

[0030] Figure 2 This is a schematic diagram of the structure of a flexible arterial blood pressure measurement device provided in an embodiment of this application;

[0031] Figure 3 An input / output diagram of a physiological data processing module provided in an embodiment of this application;

[0032] Figure 4 This is a schematic diagram of the structure of a sensor module provided in an embodiment of this application;

[0033] Figure 5 This is a schematic diagram illustrating the use of a flexible arterial blood pressure measurement device according to an embodiment of this application;

[0034] Figure 6 This is a schematic diagram of the structure of a sensor module provided in an embodiment of this application;

[0035] Figure 7 This is a schematic diagram illustrating the use of a flexible arterial blood pressure measurement device provided in an embodiment of this application. Detailed Implementation

[0036] The embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the embodiments described below with reference to the accompanying drawings are exemplary descriptions for explaining the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions of the embodiments of this application.

[0037] Those skilled in the art will understand that, unless otherwise stated, the singular forms “a,” “an,” and “the” used herein may also include the plural forms. It should be further understood that the terms “comprising” and “including” as used in embodiments of this application mean that the corresponding feature can be implemented as the presented feature, information, data, step, operation, element, and / or component, but do not exclude implementation as other features, information, data, step, operation, element, component, and / or combinations thereof supported by the art. It should be understood that when we say that an element is “connected” or “coupled” to another element, the one element can be directly connected or coupled to the other element, or it can mean that the one element and the other element establish a connection relationship through an intermediate element. Furthermore, “connected” or “coupled” as used herein can include wireless connection or wireless coupling. The term “and / or” as used herein indicates at least one of the items defined by the term; for example, “A and / or B” can be implemented as “A,” or as “B,” or as “A and B.”

[0038] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0039] The technical solutions of this application and their effects are described below through several exemplary embodiments. It should be noted that the following embodiments can be referenced, borrowed from, or combined with each other. Identical terms, similar features, and similar implementation steps in different embodiments will not be repeated.

[0040] This application provides a flexible arterial blood pressure signal measurement device, such as... Figure 1 As shown, an exemplary flexible arterial blood pressure measurement device 10 is illustrated. The flexible arterial blood pressure measurement device 10 includes a sensor module 101 and a physiological data processing module 102. The sensor module 101 includes one or more sensors for detecting physiological signals, wherein at least one sensor is a thin-film sensor made of flexible conductive material. The physiological data processing module 102 is used to obtain multimodal physiological signals collected by the sensor module and to obtain arterial blood pressure signals by employing at least one of the following: a data-driven deep learning model, a deep learning model combining partial differential equations, or a blood pressure signal model based on physiological mechanisms.

[0041] In this embodiment, the sensor module includes multiple sensors for detecting different modal physiological signals. In other embodiments, the sensor module may include only one type of sensor, which is an optical sensor used to obtain pulse wave signals. This embodiment does not impose specific limitations on this.

[0042] Physiological signals are bioelectrical signals generated by the interaction of human organs, which can spontaneously reflect the true physical condition of the human body. Physiological signals include temperature signals, impedance signals, electrocardiogram signals, pulse wave signals, pressure signals, and ultrasound signals. Since these physiological signals are acquired and measured in different ways, and each physiological signal targets different human characteristics, these physiological signals belong to different modalities. Therefore, by obtaining physiological signals of different modalities, the accuracy of the arterial blood pressure graph signal output by the physiological data processing module 102 can be guaranteed.

[0043] In addition, thin-film sensors are lightweight and can fit snugly against human skin without affecting the user's normal activities, ensuring user comfort. For example, the flexible conductive material is graphene.

[0044] Understandably, compared to existing technologies that can only be worn on the wrist, the sensor module 101, with at least one sensor being a thin-film sensor made of flexible conductive material, allows for close contact with the skin, facilitating user wear while minimizing discomfort. This enables long-term monitoring and has a wide range of applications. Furthermore, the arterial blood pressure signal in this application employs at least one of the following: a data-driven deep learning model, a deep learning model combining partial differential equations, or a blood pressure signal model based on physiological mechanisms, thereby improving the accuracy of the arterial blood pressure signal.

[0045] Based on the above embodiments, as an optional embodiment, the sensor module includes a mechanical sensor, an electronic sensor, and an optical sensor. The mechanical sensor is used to obtain mechanical signals from the human body, the electronic sensor is used to obtain electrocardiogram signals and impedance signals from the human skin, and the optical sensor is used to obtain pulse wave signals from the human skin. Both the mechanical sensor and the electronic sensor are thin-film sensors.

[0046] Understandably, mechanical signals refer to signals generated by mechanical sensors when the human heart or blood vessels vibrate; electrocardiogram (ECG) signals refer to signals generated by electronic sensors when the human heart is excited by electrical activity; impedance signals refer to signals generated by electronic sensors when a certain alternating current is input into the human body to obtain changes in the body's electrical impedance; and pulse wave signals refer to signals obtained by optical sensors when light is emitted by the human body, based on the differences in the absorption and attenuation of each wavelength by blood, to obtain the change in the human blood volume over time. Since arterial blood pressure monitoring signals are related to the human heart, blood vessels, and blood, mechanical signals, ECG signals, impedance signals, and pulse wave signals are obtained to ensure the accuracy of the arterial blood pressure monitoring signal.

[0047] In this embodiment, the electronic sensor and the mechanical sensor can be made of graphene material. In other embodiments, the electronic sensor can also be made of indium tin oxide (ITO) or flexible fabric electrodes, and the mechanical sensor can also be made of other piezoelectric or piezoresistive materials other than graphene. This embodiment does not impose any specific limitations.

[0048] It should be explained that this flexible arterial blood pressure measurement device obtains arterial blood pressure signals by acquiring optical signals (in this embodiment, the optical signal acquired is a pulse wave signal), mechanical signals, and electrical signals (in this embodiment, the electrical signals acquired include electrocardiogram signals and impedance signals). This technique is called optical-mechano-electro-tonoarteriography (oMETAG).

[0049] Based on the above embodiments, as an optional embodiment, the physiological data processing module includes:

[0050] The preprocessing unit is used to perform noise reduction processing on mechanical signals, electrocardiogram signals, impedance signals and pulse wave signals, and to extract pressure signals and heart and lung sound signals from mechanical signals.

[0051] The model processing unit is used to obtain arterial blood pressure signal based on pressure signal, heart and lung sound signal, and noise-reduced electrocardiogram signal, impedance signal and pulse wave signal, using at least one of the following: a data-driven deep learning model, a deep learning model combining partial differential equations, or a blood pressure signal model based on physiological mechanisms.

[0052] Furthermore, in order to ensure the accuracy of the pressure signal and the heart and lung sound signals, the preprocessing unit extracts the pressure signal and the heart and lung sound signals from the noise-reduced mechanical signal.

[0053] Based on the above embodiments, as an optional embodiment, the sensor module further includes an ultrasonic sensor and a temperature sensor, wherein the ultrasonic sensor is used to obtain ultrasonic signals from human skin and the temperature sensor is used to obtain temperature signals from human skin.

[0054] Considering that arterial blood pressure signals are related to blood vessel diameter and body temperature, an ultrasound sensor is used to obtain ultrasound signals that indicate changes in the diameter of human blood vessels, and a temperature sensor is used to obtain the temperature signals of human skin, thereby improving the accuracy of arterial blood pressure signals.

[0055] Based on the above embodiments, as an optional embodiment, the physiological data processing module is also used to obtain heart sounds, lung sounds, electrocardiogram, systolic blood pressure, diastolic blood pressure, blood pressure change rate, heart rate, heart rate variability, pulse wave conduction time, pulse wave conduction velocity, body temperature and bioimpedance, and the heart sounds include the first heart sound, the second heart sound, the third heart sound and the fourth heart sound.

[0056] Understandably, because the mechanical sensors in multimodal sensors can obtain heart and lung sound signals, they can directly generate phonocardiograms and obtain heart sounds, generate lung sound charts and obtain lung sounds, and the electronic sensors can obtain electrocardiogram signals, thus directly obtaining electrocardiograms, heart rates and heart rate variability, obtain pulse wave conduction time and pulse wave conduction velocity based on pulse wave signals, and obtain systolic blood pressure, diastolic blood pressure and blood pressure change rate based on arterial blood pressure chart signals, thereby providing users with comprehensive human body parameter information to ensure the user's health.

[0057] Based on the above embodiments, as an optional embodiment, such as Figure 2 As shown, it exemplarily illustrates a structural schematic diagram of a flexible arterial blood pressure signal measurement device, such as... Figure 3The diagram illustrates the input and output of a physiological data processing module. The sensor module includes a mechanical sensor, an electronic sensor, an optical sensor, an ultrasonic sensor, and a temperature sensor. The physiological data processing module includes a preprocessing unit and a model processing unit. The preprocessing unit is used to denoise the mechanical signal, electrocardiogram signal, impedance signal, pulse wave signal, ultrasonic signal, and temperature signal, and extract the pressure signal and heart and lung sound signals from the mechanical signal. The model processing unit is used to obtain an arterial blood pressure signal based on the pressure signal, heart and lung sound signals, and the denoised electrocardiogram signal, impedance signal, pulse wave signal, ultrasonic signal, and temperature signal, using at least one of the following: a data-driven deep learning model, a deep learning model combining partial differential equations, or a blood pressure signal model based on physiological mechanisms.

[0058] Optionally, the sensor module uses time-division multiplexing and time-division multiplexing processing methods to enable the electronic sensor to simultaneously obtain electrocardiogram signals and impedance signals, thereby reducing the overall size of the flexible arterial blood pressure measurement device and lowering production costs.

[0059] Based on the above embodiments, as an optional embodiment, such as Figure 4 As shown, it exemplarily illustrates a schematic diagram of the structure of a sensor module, wherein... Figure 4 (a) and Figure 4 (b) are schematic diagrams of the sensor module from different perspectives. The electronic sensor 200 includes a first electronic sensor and multiple second electronic sensors. The first electronic sensor is circular, and the second electronic sensors are annular. The multiple second electronic sensors are arranged at intervals with the center of the first electronic sensor as the center. The mechanical sensor 201 is disposed between two second electronic sensors, and the light emitter 2022 of the optical sensor is disposed on the first electronic sensor.

[0060] Optionally, the optical sensor includes multiple photodetectors 2021 and light emitters 2022, each emitting light at a different wavelength.

[0061] Furthermore, the sensor module also includes a temperature sensor 203 and at least two ultrasonic sensors 204. The temperature sensor 203 is located between two second electronic sensors, and multiple photodetectors 2021 are arranged around the temperature sensor 203. One ultrasonic sensor 204 is located between the mechanical sensor 201 and the second electronic sensor, and the other ultrasonic sensor 204 is located between the second electronic sensor and the light emitter 2022.

[0062] Understandably, if the flexible conductive material is graphene, the electronic sensor 200 utilizes graphene's high conductivity to ensure close contact with human skin, reducing the impedance between the electronic sensor 200 and the skin, thus ensuring the acquisition of electrocardiogram signals; the temperature sensor 203 utilizes graphene's temperature-sensitive properties to ensure the acquisition of temperature signals; considering that heart and lung sound signals carry a large amount of information about the cardiovascular system, and that current methods for detecting heart and lung sound signals using radar, lasers, and accelerometers have problems with poor anti-interference capabilities and heavy weight, resulting in devices using these methods being inflexible and not wearable, the mechanical sensor in this embodiment... 201 utilizes the pressure-sensitive properties of graphene to sense weak vibrations and, through the piezoresistive effect, acquires pressure signals and heart and lung sound signals. The ultrasonic sensor 204 consists of a high-performance piezoelectric element array to acquire ultrasonic signals representing blood vessel diameter information. The light emitter 2022 employs a small patch-type multi-wavelength LED to acquire multi-wavelength pulse wave signals. This flexible arterial blood pressure measurement device can fit closely to the skin, reducing motion noise, exhibiting good deformation repeatability, a large deformation range, high sensitivity, no restrictions on wearing position, and good biocompatibility, greatly improving wearing comfort. Furthermore, the device is easy to manufacture and has low cost. Of course, in other embodiments, the arrangement and specific relative positions of the mechanical sensor, electronic sensor, temperature sensor, and ultrasonic sensor can be adjusted according to actual conditions; this embodiment does not impose specific limitations.

[0063] In this embodiment, there are three second electronic sensors. The electrocardiogram (ECG) signal is divided into Laplace's signal and lead I ECG signal, combined with... Figure 5 As shown, an exemplary schematic diagram of the use of a flexible arterial blood pressure measurement device is illustrated. The flexible arterial blood pressure measurement device can be placed on a person's chest, back, or wrist. When attached to the wrist, three Laplace electrocardiogram signals are obtained through three second electronic sensors, and a lead I electrocardiogram signal is obtained by touching the flexible arterial blood pressure measurement device with the other hand. Impedance signals are obtained by injecting high-frequency alternating current into the first electronic sensor, the outermost second electronic sensor, and the innermost second electronic sensor, respectively.

[0064] Based on the above embodiments, as an optional embodiment, such as Figure 6 As shown, it exemplarily illustrates a schematic diagram of the structure of a sensor module, wherein... Figure 6 (a) Figure 6 (b) and Figure 6 (c) are schematic diagrams of the sensor module from different perspectives. Multiple electronic sensors 300 are provided and arranged in a strip. The mechanical sensor 301 and the light emitter 3021 of the optical sensor are located on the same side of the electronic sensors.

[0065] It is understandable that the size of the multiple electronic sensors 300 does not need to be exactly the same; the main purpose is to acquire electrocardiogram signals and impedance signals. This embodiment does not impose specific limitations.

[0066] Optionally, the optical sensor includes multiple light emitters 3021 and photodetectors 3022, each of which emits light at a different wavelength.

[0067] Furthermore, the sensor module also includes a temperature sensor 303 and at least two ultrasonic sensors 304. The temperature sensor 303 and the force sensor 301 are spaced apart and located on the same side of the electronic sensor 300. One ultrasonic sensor 304 is located between the force sensor 301 and the temperature sensor 303, and the other ultrasonic sensor 304 is located between the two electronic sensors 300. Of course, in other embodiments, the arrangement and relative positions of the force sensor, electronic sensor, temperature sensor, and ultrasonic sensor can be adjusted according to actual conditions, and this embodiment does not impose specific limitations.

[0068] In this embodiment, four electronic sensors 300 are provided, and the electrocardiogram (ECG) signal is divided into Laplace signal and lead I ECG signal, combined with... Figure 7 As shown, an exemplary schematic diagram of the use of a flexible arterial blood pressure measurement device is illustrated. The flexible arterial blood pressure measurement device can be placed on a person's chest, back, or wrist. When attached to the chest, at least two electronic sensors 300 can be attached to the left and right chest respectively. When attached to the wrist, the electronic sensors 300 obtain a lead I electrocardiogram signal by touching the flexible arterial blood pressure measurement device with the other hand. An impedance signal is obtained by injecting high-frequency alternating current into two of the electronic sensors 300.

[0069] Based on the above embodiments, as an optional embodiment, the model processing unit of the physiological data processing module is specifically used for:

[0070] Multimodal physiological signals are input into a pre-trained data-driven deep learning model to obtain an initial arterial blood pressure map signal output by the data-driven deep learning model. The data-driven deep learning model is trained using multimodal physiological signal samples as samples and arterial blood pressure map signal samples as labels.

[0071] Multimodal physiological signals are input into a pre-trained deep learning model combining partial differential equations to obtain the initial arterial blood pressure map signal output by the deep learning model combining partial differential equations. The deep learning model combining partial differential equations is trained using multimodal physiological signals as samples and arterial blood pressure map signals as labels.

[0072] Multimodal physiological signals are input into a blood pressure signal model based on physiological mechanisms to obtain the initial arterial blood pressure map signal output by the blood pressure signal model based on physiological mechanisms.

[0073] Arterial blood pressure signals are obtained by feature fusion of multiple initial arterial blood pressure signals using a fusion network.

[0074] Specifically, data-driven deep learning models include, but are not limited to, Physical Information Neural Networks (PINN), Weak Adversarial Networks (WAN), and Deep Generative Model Networks (DGM). Deep learning models combining partial differential equations include, but are not limited to, multidimensional Navier-Stokes equations and blood flow-pulse wave velocity relationship equations. Blood pressure signal models based on physiological mechanisms include, but are not limited to, physiological models based on pulse wave velocity and physiological models based on changes in vessel diameter, such as multi-wavelength pulse wave models, models combining pulse wave propagation time and photoplethysmography (PIR), and multi-wavelength pulse wave PIR models. Fusion networks include, but are not limited to, Multiple Perceptrons (MLPs) or Soft Max functions, which fuse features from multiple initial arterial blood pressure map signals to obtain an arterial blood pressure map signal.

[0075] Understandably, data-driven deep learning models, deep learning models combining partial differential equations, and blood pressure signal models based on physiological mechanisms are three completely different methods for obtaining initial arterial blood pressure maps. Data-driven and deep learning models combining partial differential equations involve training and samples, making the initial arterial blood pressure maps they obtain more targeted. Furthermore, deep learning models combining partial differential equations are more general but less targeted than data-driven deep learning models. Blood pressure signal models based on physiological mechanisms obtain initial arterial blood pressure maps directly through mathematical formulas, making them more universal than the other two models. Combining these three methods can improve the accuracy of arterial blood pressure maps.

[0076] Based on the above embodiments, as an optional embodiment, the flexible arterial blood pressure measurement device further includes a wireless transmission module.

[0077] When the physiological data processing module is set on the sensor module, the wireless transmission module is used to send multimodal physiological signals to the physiological data processing module, and the wireless transmission module is used to output the arterial blood pressure graph signal obtained by the physiological data processing module.

[0078] When the physiological data processing module and the sensor module are set up separately, the wireless transmission module is used to send multimodal physiological signals to the physiological data processing module.

[0079] Understandably, if the physiological data processing module and the sensor module are set up separately, the physiological data processing module can be integrated into the display device for easy display to the user. The display device can be a mobile phone, tablet, computer, watch, glasses, etc.

[0080] Based on the above embodiments, as an optional embodiment, the flexible arterial blood pressure signal measurement device further includes:

[0081] The sensor module is mounted on the substrate, which can be attached to human skin and is made of a flexible material.

[0082] The protection section and sensor module are located between the substrate and the protection section.

[0083] Understandably, by using a substrate made of flexible material to mount electronic sensors, the light emitter and photodetector of the optical sensor can be aligned with the through-holes of the substrate to be directly attached to the skin, thereby acquiring pulse wave signals.

[0084] Furthermore, to improve the accuracy of the arterial blood pressure signal, multiple light emitters and photodetectors are used, each emitting light with a different wavelength, enabling the optical sensor to obtain multi-wavelength pulse wave signals. Because the pulse wave propagation time is extremely short, to ensure the acquisition of multi-wavelength pulse wave signals, multiple photodetectors are arranged around the substrate. This allows for comparison and acquisition of the optimal multi-wavelength pulse wave signal, while also increasing the distance between the light emitters and photodetectors, thus increasing the light propagation path and pulse wave propagation time. This reduces the sampling frequency required to acquire the pulse wave signal, ensuring accurate pulse wave signal acquisition while saving power.

[0085] Optionally, the protective part is made of waterproof material to extend the service life of the flexible arterial blood pressure measurement device.

[0086] The terms "first," "second," "third," "fourth," "1," "2," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in a sequence other than that shown in the figures or text.

[0087] It should be understood that although arrows indicate various operation steps in the flowcharts of this application's embodiments, the order in which these steps are implemented is not limited to the order indicated by the arrows. Unless explicitly stated herein, in some implementation scenarios of this application's embodiments, the implementation steps in each flowchart can be executed in other orders as required. Furthermore, some or all steps in each flowchart, based on the actual implementation scenario, may include multiple sub-steps or multiple stages. Some or all of these sub-steps or stages can be executed at the same time, and each sub-step or stage can also be executed at different times. In scenarios where execution times differ, the execution order of these sub-steps or stages can be flexibly configured according to requirements, and this application's embodiments do not limit this.

[0088] The above description is only an optional implementation method for some implementation scenarios of this application. It should be noted that for those skilled in the art, other similar implementation methods based on the technical concept of this application without departing from the technical concept of this application also fall within the protection scope of the embodiments of this application.

Claims

1. A flexible arterial tonometric signal measurement device, characterized by, The method comprises the following steps: A sensor module comprising one or more sensors for detecting physiological signals, wherein at least one of the electronic sensor, temperature sensor and mechanical sensor included in the sensor module is a thin film sensor made of a flexible conductive material, and the flexible conductive material is graphene; A physiological data processing module is used to obtain the multi-modal physiological signals collected by the sensor module, and a data-driven deep learning model, a deep learning model combined with a partial differential equation, and a blood pressure signal model based on physiological mechanism are used to obtain the initial arterial blood pressure signals output by each model respectively, and the initial arterial blood pressure signals are fused to obtain the arterial blood pressure signal. The physiological data processing module is specifically used for: inputting the multi-modal physiological signals into the pre-trained data-driven deep learning model to obtain the initial arterial blood pressure signal output by the data-driven deep learning model, wherein the data-driven deep learning model is trained with multi-modal physiological signal samples as samples and arterial blood pressure signal samples as labels; inputting the multi-modal physiological signals into the pre-trained deep learning model combined with a partial differential equation to obtain the initial arterial blood pressure signal output by the deep learning model combined with a partial differential equation, wherein the deep learning model combined with a partial differential equation is trained with multi-modal physiological signals as samples and arterial blood pressure signals as labels; inputting the multi-modal physiological signals into the blood pressure signal model based on physiological mechanism to obtain the initial arterial blood pressure signal output by the blood pressure signal model based on physiological mechanism according to the mathematical formula applied by the blood pressure signal model based on physiological mechanism; fusing the initial arterial blood pressure signals through a fusion network to obtain the arterial blood pressure signal.

2. The flexible arterial tonogram signal measurement device of claim 1, wherein, The sensor module comprises an optical sensor, the mechanical sensor is used to obtain the mechanical signal of the human body, the electronic sensor is used to obtain the electrocardiogram signal and impedance signal of the human skin, and the optical sensor is used to obtain the pulse wave signal of the human skin; The mechanical sensor and the electronic sensor are both thin film sensors.

3. The flexible arterial tonogram signal measurement device of claim 2, wherein, The physiological data processing module comprises: a preprocessing unit, which is used to perform noise reduction processing on the mechanical signal, electrocardiogram signal, impedance signal and pulse wave signal, and extract the pressure signal and heart sound and lung sound signal from the mechanical signal; a model processing unit, which is used to obtain the arterial blood pressure signal by using at least one of the data-driven deep learning model, deep learning model combined with a partial differential equation or blood pressure signal model based on physiological mechanism according to the pressure signal, heart sound and lung sound signal, and electrocardiogram signal, impedance signal and pulse wave signal after noise reduction processing.

4. The flexible arterial tonogram signal measurement device of claim 2, wherein, The electronic sensor is divided into a first electronic sensor and a plurality of second electronic sensors, the first electronic sensor is circular, the second electronic sensor is circular ring, a plurality of second electronic sensors are arranged at intervals with the center of the first electronic sensor as the center, the mechanical sensor is arranged between two second electronic sensors, and the optical sensor is arranged on the first electronic sensor.

5. The flexible arterial tonogram signal measurement device of claim 2, wherein, The electronic sensor is provided with a plurality of electronic sensors arranged in a strip shape, and the mechanical sensor and the optical sensor are located on the same side of the electronic sensor.

6. The flexible arterial tonogram signal measurement device of claim 2, wherein, The sensor module further comprises an ultrasonic sensor and a temperature sensor, the ultrasonic sensor is used to obtain the ultrasonic signal of the human skin, and the temperature sensor is used to obtain the temperature signal of the human skin.

7. The flexible arterial tonogram signal measurement device of claim 1, wherein, The flexible arterial blood pressure signal measurement device further comprises a wireless transmission module, When the physiological data processing module is arranged on the sensor module, the wireless transmission module is used to send the multi-modal physiological signal to the physiological data processing module, and the wireless transmission module is used to output the arterial blood pressure signal obtained by the physiological data processing module; When the physiological data processing module is arranged separately from the sensor module, the wireless transmission module is used to send the multi-modal physiological signal to the physiological data processing module.

8. The flexible arterial tonogram signal measurement device of claim 1, wherein, The arterial blood pressure signal flexible arterial blood pressure signal measurement device further comprises: a base, the sensor module is arranged on the base, the base can be attached to the human skin, and the base is made of flexible material; a protection part, the sensor module is located between the base and the protection part.

9. The flexible arterial tonogram signal measurement device of claim 1, wherein, The physiological data processing module is also used to obtain heart sound, lung sound, electrocardiogram, systolic pressure, diastolic pressure, blood pressure change rate, heart rate, heart rate variability, heart sound, pulse wave transmission time, pulse wave transmission velocity, body temperature and bioimpedance, the heart sound includes first heart sound, second heart sound, third heart sound and fourth heart sound.

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