Method and device for detecting blood pressure

Abstract

The present application discloses a method and apparatus for blood pressure detection, applicable to electronic devices. The method may include: acquiring a user's first photoplethysmography (PPG) signal, and determining the user's first electrocardiogram (ECG) signal based on the first PPG signal; performing peak detection on the first PPG signal and the first ECG signal to determine the user's first pulse transit time; performing a first quality test on the first ECG signal and the first pulse transit time, and if the result of the first quality test is determined to be normal, determining the user's blood pressure value based on the first pulse transit time. The present application can improve the accuracy of blood pressure detection results and reduce the cost of blood pressure detection.

Description

Technical Field

[0001] The present application relates to the field of smart terminal technology, and in particular to a method and device for blood pressure detection. Background Art

[0002] Hypertension is a common chronic disease characterized by persistently elevated blood pressure. It can easily lead to cardiovascular and cerebrovascular diseases, seriously impacting human health. Currently, over 245 million people in China suffer from hypertension, a significant number. Regular blood pressure monitoring is crucial for patients with hypertension. This helps them better understand their condition, adjust treatment plans promptly, and avoid cardiovascular and cerebrovascular diseases.

[0003] Traditional blood pressure monitoring not only produces poor signal quality but also requires specialized medical personnel to operate and interpret the test, making it inconvenient and costly. Therefore, improving the accuracy of blood pressure testing results and reducing its cost are pressing technical challenges. Summary of the Invention

[0004] The technical problem to be solved by the embodiments of the present application is to provide a method and device for blood pressure detection, which can improve the accuracy of blood pressure detection results and reduce the cost of blood pressure detection.

[0005] In a first aspect, an embodiment of the present application provides a method for blood pressure detection, which is applied to an electronic device; the method may include: obtaining a first photoplethysmography (PPG) signal of a user, and determining a first electrocardiogram (ECG) signal of the user based on the first PPG signal; performing peak detection on the first PPG signal and the first ECG to determine a first pulse transit time of the user; performing a first quality detection on the first ECG signal and the first pulse transit time, and if the result of the first quality detection is determined to be normal, determining the blood pressure value of the user based on the first pulse transit time.

[0006] Most wearable devices currently on the market need to simultaneously record the subject's ECG signal and photoplethysmography (PPG) signal when performing blood pressure testing in order to obtain more accurate blood pressure test results. However, there are relatively few devices that support the acquisition of both ECG and PPG signals, and the resulting price costs are generally high. To address this technical issue, the embodiments of the present application can determine the user's blood pressure value based on the PPG signal without collecting the user's ECG signal, and perform quality testing on various types of user data during the blood pressure testing process, thereby reducing the cost of blood pressure testing while ensuring the accuracy of the test results. Specifically, in an embodiment of the present application, the user's PPG signal is first obtained (for example, data collection can be based on a wearable device such as a smart watch or smart bracelet), and the user's ECG signal is determined based on the PPG signal (for example, it can be calculated based on a pre-trained machine algorithm model); further, peak detection can be performed on the above-mentioned ECG signal and PPG signal to determine the user's pulse conduction time (for example, the R wave peak of the ECG signal and the pulse wave peak of the PPG signal can be determined based on peak detection, and the time difference between the peaks corresponding to the two signals can be calculated); finally, the above-mentioned ECG signal and pulse conduction time can be quality-checked (for example, the difference in the number and time difference between the R wave peak of the ECG and the pulse wave peak of the PPG signal can be compared with preset thresholds to determine the quality detection result of the ECG signal and pulse conduction time). When the quality detection result shows a normal condition, the user's blood pressure value is calculated based on the pulse conduction time, thereby ensuring the accuracy of the user's blood pressure detection result. In summary, in the embodiments of the present application, the user's blood pressure value can be determined based on the PPG signal, thereby reducing the cost of blood pressure detection (i.e., the PPG signal can be converted into an ECG signal, and there is no need to collect the ECG signal, which reduces the rigid requirements for the equipment). By ensuring that the blood pressure value is calculated when the quality detection indicators are normal, the accuracy of the blood pressure detection is ensured, which can greatly reduce the detection quality problems caused by equipment failure, incorrect usage or external interference.

[0007] In one possible implementation, determining the user's blood pressure value based on the first pulse conduction time includes: inputting the first pulse conduction time into a calibrated first blood pressure regression model, and outputting the user's blood pressure value. In an embodiment of the present application, the user's blood pressure value can be output by inputting the user's first pulse conduction time into a calibrated first blood pressure regression model. Specifically, the user's pulse conduction time can be used as an input parameter and input into a calibrated blood pressure regression model. For example, the pulse conduction velocity can be calculated based on the pulse conduction time and the distance between different blood vessels, and the blood pressure value can be further calculated based on the data. In summary, the embodiment of the present application can calculate the blood pressure value by inputting the user's data into the model, which not only improves the accuracy of blood pressure detection, but also provides the user with a fast and convenient blood pressure detection method.

[0008] In one possible implementation, the method further includes: if the calibration result of the user is detected, obtaining the first blood pressure regression model based on the calibration result. In an embodiment of the present application, the accuracy of blood pressure measurement can be optimized by detecting whether the user has performed a calibration operation and then obtaining the first blood pressure regression model based on the calibration result. Specifically, after the user completes the calibration, the system obtains the corresponding calibration data and obtains the blood pressure regression model corresponding to the user based on the calibration data. The method of obtaining the corresponding blood pressure regression model based on individual calibration data in the embodiment of the present application can more accurately reflect the specific blood pressure condition of each user, improve the reliability and effectiveness of blood pressure monitoring, and thus help to better guide the user's health management and disease prevention.

[0009] In one possible implementation, the method further includes: if the calibration result of the user is not detected, obtaining the calibration data of the user, wherein the calibration data includes the second PPG signal and the gold standard blood pressure value of the user; determining the second ECG signal of the user based on the second PPG signal, and determining the second pulse transit time of the user based on the second PPG signal and the second ECG signal; performing a second quality test on the second ECG signal and the second pulse transit time; if the result of the second quality test is determined to be normal, calibrating the second blood pressure regression model based on the second pulse transit time and the gold standard blood pressure value to determine the first blood pressure regression model. In an embodiment of the present application, after the calibration data of the user is not detected, the calibration data of the user can be collected, and further processing can be performed based on the calibration data to ultimately achieve calibration of the blood pressure regression model corresponding to the user. Specifically, when the user turns on the calibration function, or when the calibration result of the current user is not detected, the user's PPG signal (e.g., collected by a wearable device such as a smart watch or smart bracelet) and the gold standard blood pressure value (e.g., collected based on a cuff) can be collected, and the user's pulse transit time can be further determined based on the PPG signal (e.g., the PPG signal can be converted into an ECG signal, and the pulse transit time can be determined based on the PPG signal and the ECG signal), and the ECG signal and pulse transit time can be quality tested to ensure the accuracy of the calibration data; further, the pulse transit time and the blood pressure gold standard value can be input into the blood pressure regression model to calibrate the blood pressure regression model. In summary, in the embodiment of the present application, data of an uncalibrated user can be collected, and the user's pulse transit time can be calculated based on the data. Finally, the corresponding blood pressure regression model can be calibrated based on the user's pulse transit time and the blood pressure gold standard value. Different blood pressure regression models are calibrated for different users' data. When blood pressure is subsequently tested, it can more accurately reflect individual blood pressure changes, improve the accuracy of the measurement results, and ensure that the user's blood pressure value is closer to the true value.

[0010] In one possible implementation, the first PPG signal includes M first R peaks, and the first ECG signal includes N second R peaks, where M and R are both integers greater than 0; performing peak detection on the first PPG signal and the first ECG to determine the user's first pulse transit time includes determining the user's first pulse transit time based on the time difference between the M first R peaks and the N second R peaks. In this embodiment of the present application, the user's pulse transit time can be accurately determined by performing peak detection on the first PPG signal and the first ECG signal. Specifically, in this embodiment of the present application, peak detection can be performed on the PPG signal and the ECG signal to determine multiple R peaks (i.e., peak values) of the two signals, and the time difference can be calculated based on the M first R peaks and the N second R peaks (for example, PPG signals and ECG signals that are adjacent in time sequence can be determined as a group, and the time difference can be calculated for each group of signals), and finally the user's pulse transit time can be determined. In summary, in the embodiment of the present application, the time difference can be calculated based on the peak values ​​of the ECG signal and the PPG signal, so that a more accurate pulse conduction time can be calculated, and more accurate data can be used for blood pressure detection in the subsequent blood pressure detection process, thereby ensuring the accuracy of blood pressure detection.

[0011] In one possible implementation, the first R peak is the R wave peak of the ECG signal; the second R peak is the pulse wave peak of the PPG signal. In an embodiment of the present application, by accurately identifying and distinguishing the R wave peak in the ECG signal and the pulse wave peak in the PPG signal, the system can more accurately calculate the pulse conduction time and obtain more accurate blood pressure detection results. Specifically, the accurate identification of the first R peak (R wave peak of the ECG signal) and the second R peak (pulse wave peak of the PPG signal) can enable the system to process and analyze these two different types of physiological signals separately, and more accurately calculate the time difference between the peaks based on the two signals, thereby more accurately determining the pulse conduction time to increase the accuracy of subsequent blood pressure detection.

[0012] In one possible implementation, the determining of the user's first pulse conduction time based on the time difference between the M first R peaks and the N second R peaks includes: determining the temporally adjacent R wave peaks and pulse wave peaks among the M R wave peaks and the N pulse wave peaks as a group; and determining the first pulse conduction time based on the time difference between each group of R wave peaks and pulse wave peaks. In an embodiment of the present application, a more accurate and reliable blood pressure detection method is provided by determining the user's first pulse conduction time based on the time difference between the M R wave peaks and the N pulse wave peaks. Specifically, in an embodiment of the present application, by effectively identifying temporally adjacent R wave peaks and pulse wave peaks, and determining every two temporally adjacent ECG signals and PPG signals as a group, the time difference between each group of R wave peaks and pulse wave peaks is accurately calculated, and finally the pulse conduction time is determined based on the time difference, thereby improving the accuracy of blood pressure detection. Furthermore, compared to traditional methods that may rely on a single signal source (for example, measuring blood pressure based only on PPG signals) or a simpler data processing method, the embodiments of the present application are based on comprehensive signal analysis (i.e., determining PPG signals and ECG signals that are adjacent in time sequence as a group) and calculating the time difference, which can provide patients with more accurate test results.

[0013] In one possible implementation, performing a first quality check on the first ECG signal and the first pulse transit time includes determining that the result of the first quality check is normal if the difference between M and N is greater than a preset first threshold, and the time difference between the M R wave peaks and the N pulse wave peaks is greater than a preset second threshold. In this embodiment, by performing quality checks on the first ECG signal and the first pulse transit time, the normality of the physiological signal can be more accurately determined. Specifically, by setting a preset first threshold and a preset second threshold, when the difference between M and N exceeds the first threshold (for example, determining whether the number of ECG signals converted from the PPG signal is consistent with the PPG signal), and the time difference between the M R wave peaks and the N pulse wave peaks exceeds the second threshold (for example, determining whether the pulse transit time determined based on the ECG signal and the PPG signal exceeds the normal value, such as being abnormal), if both test results are "yes," the system can determine that the result of this quality check is normal, allowing subsequent blood pressure testing. In summary, in the embodiment of the present application, quality detection can be performed on two situations at the same time (for example, quality detection of ECG signals and PPG signals can be performed to determine whether the difference in the number and time difference of peak values ​​exceeds the corresponding threshold value) to ensure the accuracy of blood pressure detection parameters and improve the accuracy of subsequent blood pressure detection.

[0014] In one possible implementation, the first quality check of the first ECG signal and the first pulse transit time includes: if the difference between M and N is less than the first threshold, determining that the result of the first quality check is abnormal and prompting the user to retest; or if the time difference between the M R wave peaks and the N pulse wave peaks is less than the second threshold, determining that the result of the first quality check is abnormal and prompting the user to retest. In this embodiment of the present application, when an abnormality is found in the quality check of the first ECG signal and the first pulse transit time, the user is prompted to retest. Specifically, in the embodiment of the present application, by setting two thresholds - a first threshold and a second threshold - the system can determine whether there are abnormalities in the ECG and pulse conduction time. If the difference between M and N is less than the first threshold (that is, it is determined that the number of ECG signals converted from the PPG signal exceeds the preset first threshold), or the time difference between the M R wave peaks and the N pulse wave peaks is less than the second threshold (that is, it is determined that the pulse conduction time has an abnormality that exceeds the preset second threshold), when one of the two situations occurs, the system will determine these results as abnormal conditions and prompt the user to retest, avoiding interference with the accuracy of the final blood pressure detection results due to partial data errors, improving the overall efficiency and accuracy of the test, and thus enhancing the reliability of the blood pressure detection solution.

[0015] In one possible implementation, determining the first electrocardiogram (ECG) signal based on the first PPG signal includes: inputting the first PPG signal into an electrocardiogram (ECG) signal conversion model, and outputting the first ECG signal. In an embodiment of the present application, the user's PPG signal can be input into a signal quality detection model to obtain an ECG signal converted from the PPG signal, which can significantly improve processing efficiency, especially when processing large-scale data. Furthermore, the ECG signal conversion model can be a pre-trained machine learning algorithm model, and the ECG signal is determined based on the ECG signal conversion model, thereby improving the speed of subsequent blood pressure detection, increasing the accuracy of blood pressure detection, and reducing the cost of blood pressure detection (that is, the blood pressure detection device can be a wearable device that supports the acquisition of PPG signals, without the need to simultaneously integrate PPG signals and ECG signals).

[0016] In one possible implementation, the ECG signal conversion model is obtained by inputting Q third PPG signals as sample data in the server and training with a preset third ECG signal as a label, or by training in the server based on historical sample documents. In an embodiment of the present application, the ECG signal conversion model can be trained in the server by inputting a pre-prepared PPG signal as sample data and using a preset ECG signal as a label, or the ECG signal conversion model can be trained based on historical sample documents, which may include PPG signals of which ECG signals have been determined. By analyzing these historical data, the ECG signal conversion model can learn and train from past events to more accurately convert PPG signals into ECG signals. In summary, the embodiment of the present application can train the ECG signal conversion model by inputting PPG signals as sample data or analyzing historical events, so that the model can effectively identify and distinguish high-quality and low-quality conversion results, thereby enhancing the adaptability and accuracy of the ECG signal conversion model when processing actual data, so as to obtain higher-quality ECG signals, and further improve the accuracy of blood pressure detection based on the ECG signals.

[0017] In a second aspect, an embodiment of the present application provides a blood pressure detection device, which may include:

[0018] a first ECG signal determining unit, configured to obtain a first photoplethysmography (PPG) signal of a user, and determine a first electrocardiogram (ECG) signal of the user based on the first PPG signal;

[0019] a first pulse transit time determining unit, configured to perform peak detection on the first PPG signal and the first ECG signal to determine a first pulse transit time of the user;

[0020] The blood pressure value determination unit is configured to perform a first quality detection on the first ECG signal and the first pulse transit time, and if the result of the first quality detection is determined to be normal, determine the blood pressure value of the user based on the first pulse transit time.

[0021] Most wearable devices currently on the market need to simultaneously record the subject's ECG signal and photoplethysmography (PPG) signal when performing blood pressure testing in order to obtain more accurate blood pressure test results. However, there are relatively few devices that support the acquisition of both ECG and PPG signals, and the resulting price costs are generally high. To address this technical issue, the embodiments of the present application can determine the user's blood pressure value based on the PPG signal without collecting the user's ECG signal, and perform quality testing on various types of user data during the blood pressure testing process, thereby reducing the cost of blood pressure testing while ensuring the accuracy of the test results. Specifically, in an embodiment of the present application, the user's PPG signal is first obtained (for example, data collection can be based on a wearable device such as a smart watch or smart bracelet), and the user's ECG signal is determined based on the PPG signal (for example, it can be calculated based on a pre-trained machine algorithm model); further, peak detection can be performed on the above-mentioned ECG signal and PPG signal to determine the user's pulse conduction time (for example, the R wave peak of the ECG signal and the pulse wave peak of the PPG signal can be determined based on peak detection, and the time difference between the peaks corresponding to the two signals can be calculated); finally, the above-mentioned ECG signal and pulse conduction time can be quality-checked (for example, the difference in the number and time difference between the R wave peak of the ECG and the pulse wave peak of the PPG signal can be compared with preset thresholds to determine the quality detection result of the ECG signal and pulse conduction time). When the quality detection result shows a normal condition, the user's blood pressure value is calculated based on the pulse conduction time, thereby ensuring the accuracy of the user's blood pressure detection result. In summary, in the embodiments of the present application, the user's blood pressure value can be determined based on the PPG signal, thereby reducing the cost of blood pressure detection (i.e., the PPG signal can be converted into an ECG signal, and there is no need to collect the ECG signal, which reduces the rigid requirements for the equipment). By ensuring that the blood pressure value is calculated when the quality detection indicators are normal, the accuracy of the blood pressure detection is ensured, which can greatly reduce the detection quality problems caused by equipment failure, incorrect usage or external interference.

[0022] In a possible implementation, the blood pressure value determining unit is specifically configured to:

[0023] The first pulse transit time is input into a calibrated first blood pressure regression model, and the blood pressure value of the user is output.

[0024] In a possible implementation, the apparatus further includes:

[0025] The first blood pressure regression model acquisition unit acquires the first blood pressure regression model based on the calibration result if the calibration result of the user is detected.

[0026] In a possible implementation, the apparatus further includes:

[0027] a calibration data acquisition unit, which acquires calibration data of the user if no calibration result of the user is detected, wherein the calibration data includes a second PPG signal and a gold standard blood pressure value of the user;

[0028] a second ECG signal determining unit, configured to determine a second ECG signal of the user based on the second PPG signal, and determine a second pulse transit time of the user based on the second PPG signal and the second ECG signal;

[0029] a second pulse transit time determining unit, configured to perform a second quality detection on the second ECG signal and the second pulse transit time;

[0030] The calibration unit calibrates the second blood pressure regression model based on the second pulse transit time and the gold standard blood pressure value to determine the first blood pressure regression model if the result of the second quality detection is determined to be normal.

[0031] In one possible implementation, the first PPG signal includes M first R peaks, the first ECG signal includes N second R peaks, and M and R are both integers greater than 0; and the first pulse transit time determining unit is specifically configured to:

[0032] A first pulse transit time of the user is determined based on a time difference between the M first R peaks and the N second R peaks.

[0033] In a possible implementation, the first R peak is the R wave peak of the ECG signal; the second R peak is the pulse wave peak of the PPG signal.

[0034] In a possible implementation, the first pulse transit time determining unit is specifically configured to:

[0035] Among the M R wave peaks and the N pulse wave peaks, determining temporally adjacent R wave peaks and pulse wave peaks as a group;

[0036] The first pulse transit time is determined based on the time difference between the peak value of each group of R waves and the peak value of the pulse wave.

[0037] In a possible implementation, the blood pressure value determining unit is specifically configured to:

[0038] If the difference between M and N is greater than a preset first threshold, and the time difference between the M R wave peaks and the N pulse wave peaks is greater than a preset second threshold, the result of the first quality detection is determined to be normal.

[0039] In a possible implementation, the blood pressure value determining unit is specifically configured to:

[0040] If the difference between M and N is less than the first threshold, the result of the first quality test is determined to be abnormal, and the user is prompted to retest; or,

[0041] If the time difference between the M R wave peaks and the N pulse wave peaks is less than the second threshold, the result of the first quality detection is determined to be abnormal, and the user is prompted to retest.

[0042] In a possible implementation, the first ECG signal determining unit is specifically configured to:

[0043] The first PPG signal is input into an electrocardiogram signal conversion model, and the first ECG signal is output.

[0044] In one possible implementation, the electrocardiogram signal conversion model is obtained by inputting Q third PPG signals as sample data in the server and training the preset third ECG signal as a label, or is obtained by training based on historical sample documents in the server.

[0045] In a third aspect, an embodiment of the present application provides a computer storage medium for storing computer software instructions used in a blood pressure detection device provided in the second aspect above, which includes a program designed for executing the above aspect.

[0046] In a fourth aspect, an embodiment of the present application provides a computer program, which includes instructions. When the computer program is executed by a computer, the computer can execute the process executed in the blood pressure detection device in the second aspect above. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background technology, the drawings required for use in the embodiments of the present application or the background technology will be described below.

[0048] Figure 1A This is a schematic diagram of a system architecture provided in an embodiment of the present application.

[0049] Figure 1B This is a schematic diagram of a medical detection device provided in an embodiment of the present application.

[0050] Figure 1C This is another system architecture diagram provided in an embodiment of the present application.

[0051] Figure 2 This is a schematic diagram of a blood pressure detection module provided in an embodiment of the present application.

[0052] Figure 3A This is a flow chart of a blood pressure detection process provided in an embodiment of the present application.

[0053] Figure 3B This is a schematic diagram of blood pressure detection provided in an embodiment of the present application.

[0054] Figure 3C This is a schematic diagram of a PPG signal provided in an embodiment of the present application.

[0055] Figure 3D This is another schematic diagram of blood pressure detection provided in an embodiment of the present application.

[0056] Figure 3E This is a schematic diagram of an ECG signal conversion model module provided in an embodiment of the present application.

[0057] Figure 3F This is a schematic diagram of an ECG signal provided in an embodiment of the present application.

[0058] Figure 3G This is a schematic diagram of ECG signal comparison provided in an embodiment of the present application.

[0059] Figure 3H This is an example diagram of a PTT calculation module provided in an embodiment of the present application.

[0060] Figure 3I This is a schematic diagram of calculating pulse transmission time provided in an embodiment of the present application.

[0061] Figure 3J This is another schematic diagram of calculating pulse transmission time provided in an embodiment of the present application.

[0062] Figure 3K This is an example diagram of abnormal blood pressure detection provided in an embodiment of the present application.

[0063] Figure 3L This is a schematic diagram of age-based blood pressure value comparison provided in an embodiment of the present application.

[0064] Figure 3M This is a schematic diagram of the detection results provided in the examples of this application.

[0065] Figure 4A A schematic diagram of a blood pressure calibration process provided in an embodiment of the present application.

[0066] Figure 4B This is a schematic diagram of blood pressure before calibration provided in an embodiment of the present application.

[0067] Figure 4C This is a blood pressure calibration diagram provided in an embodiment of the present application.

[0068] Figure 5 It is a structural schematic diagram of a blood pressure detection device provided in an embodiment of the present application.

[0069] Figure 6A This is a hardware structure block diagram of the electronic device provided in the embodiment of the present application.

[0070] Figure 6B This is a software structure block diagram of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0071] The embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0072] The terms "first," "second," "third," and "fourth," etc., in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, rather than to describe a specific order. In addition, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements, but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0073] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0074] As used in this specification, the terms "component," "module," "system," and the like are used to refer to computer-related entities, hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or a computer. By way of illustration, both an application running on a computing device and a computing device can be a component. One or more components can reside in a process and / or an execution thread, and a component can be located on a computer and / or distributed between two or more computers. In addition, these components can be executed from various computer-readable media having various data structures stored thereon. For example, a component can communicate via a local and / or remote process based on a signal having one or more data packets (e.g., data from two components interacting with another component on a local system, a distributed system, and / or a network, such as the Internet interacting with other systems via signals).

[0075] First, some terms in this application are explained to facilitate understanding by those skilled in the art.

[0076] (1) Electrocardiography (ECG) refers to the process of recording the heart's electrical activity through an electrocardiogram (ECG) device. It is an important component of cardiology and medical diagnosis, and is used in heart health monitoring, disease diagnosis, and treatment effect evaluation. The ECG recording process typically includes steps such as electrode placement, signal acquisition, waveform analysis, and diagnostic interpretation. It aims to accurately and comprehensively reflect the electrophysiological state of the heart, helping users diagnose heart disease and monitor its progression.

[0077] (2) PPG signal (Photoplethysmogram Signal) is a physiological signal that measures changes in blood volume by optical methods. It mainly detects small changes in blood volume in blood vessels by placing sensors on the surface of the body and using the principle of light absorption or reflection. PPG signals have important applications in medical health monitoring, cardiovascular disease diagnosis, and physiological status assessment. Its signal waveform reflects the heartbeat cycle and can be used to analyze heart rate, blood oxygen saturation, and other circulatory system parameters. Accurate measurement of PPG signals is particularly critical for non-invasive cardiovascular monitoring, telemedicine services, and personal health management.

[0078] (3) R-wave Peak refers to the highest point reached by the R wave in the ECG waveform, representing the characteristic of rapid ventricular depolarization. It is one of the core elements of ECG analysis and cardiac electrophysiology, and is crucial in the fields of arrhythmia diagnosis, ECG signal processing, and cardiac function assessment. The R-wave peak identification process usually includes steps such as waveform signal capture, peak detection, waveform analysis, and clinical relevance assessment, aiming to accurately identify and interpret the ECG waveform to monitor heart health and guide clinical decision-making.

[0079] (4) PTT Pulse Transit Time (PTT) is a physiological parameter used to measure the time required for the pulse wave generated by the heart pumping blood to propagate along the artery to a specific part of the body. As an indirect indicator of cardiovascular function, PTT is widely used in the monitoring and evaluation of cardiovascular diseases. The basic principle of PTT measurement is to determine the time difference between the pulse wave propagation between two anatomical points. Usually, these points include the heart and distal parts of the body, such as fingers or ankles. The measurement of PTT requires the synchronous recording of electrocardiographic signals (such as ECG) and distal pulse wave signals (such as PPG) to determine the starting point of the heart beat and the moment when the pulse wave reaches the distal part.

[0080] (5) Hypertension is a common cardiovascular disease that refers to persistently high blood pressure. Blood pressure is the pressure exerted on the arterial walls by blood as it flows through the arteries. Hypertension means this pressure is abnormally high, placing an additional burden on the heart and arteries. Long-term high blood pressure increases the risk of a variety of health problems, including heart disease, stroke, and kidney disease. The diagnostic criteria for hypertension are defined based on blood pressure values.

[0081] (6) Pulse waves are pressure waves generated by blood flowing through blood vessels as the heart beats. These fluctuations reflect the rhythm of the heart's pumping and the dynamics of blood flow through the vascular system. Pulse waves not only provide information about heart rate (heartbeat rate), but also reflect the health of blood vessels and the hemodynamic characteristics of blood flow.

[0082] (7) The gold standard for blood pressure measurement refers to the most accurate and reliable method for measuring blood pressure. In clinical practice, this usually refers to the best practices for invasive arterial blood pressure measurement and non-invasive blood pressure measurement.

[0083] (8) Pulse Wave Velocity (PWV) is an important indicator used to assess the degree of arteriosclerosis and vascular health. The principle of PWV measurement is based on the relationship between the propagation speed of the pulse wave in the blood vessels and blood pressure. The lower the blood pressure, the slower the pulse wave propagates in the blood vessels; conversely, the higher the blood pressure, the faster the pulse wave propagates. Therefore, by measuring PWV, blood pressure levels can be indirectly inferred.

[0084] First, the technical problems that this application aims to solve are analyzed and proposed. In the prior art, the technologies for blood pressure detection include the following solutions:

[0085] Option 1: Professional medical staff help users measure blood pressure using professional equipment such as cuffs and stethoscopes.

[0086] The above solution is currently mainly applicable to professional medical scenarios, but it also has the following shortcomings:

[0087] Disadvantage 1: High operational complexity. Traditional blood pressure measurement methods typically require the use of specialized medical equipment such as a cuff and stethoscope, which can be complex for the average user. For example, correctly securing the cuff and adjusting it to the appropriate tightness requires skill and experience. Furthermore, accurately listening to and identifying the beating heartbeat using a stethoscope is particularly challenging for non-professionals. These operational complexities not only increase the risk of measurement error but also make it difficult for the average user to independently measure blood pressure.

[0088] Disadvantage 2: Inability to Provide Continuous Monitoring. Traditional blood pressure measurement methods typically only provide one-time blood pressure readings and cannot provide long-term continuous monitoring. This is a significant shortcoming for patients who need to closely monitor blood pressure fluctuations. For example, for patients with hypertension or cardiovascular disease, understanding their blood pressure fluctuations throughout the day is crucial for disease management and treatment. However, traditional methods cannot provide this continuous data record, limiting their application in disease management.

[0089] Option 2: Blood pressure detection based on wearable devices that can support both ECG and PPG signals.

[0090] The above solution is currently only suitable for a small number of users who can accept high costs, but it also has the following disadvantages:

[0091] Disadvantage 1: Device limitations and cost. Currently, relatively few wearable devices on the market can simultaneously record ECG (electrocardiogram) and PPG (photoplethysmography) signals, limiting consumer choice. Furthermore, wearable devices with dual ECG and PPG monitoring capabilities are often expensive. This can pose a significant financial burden for ordinary consumers, especially those with hypertension who require long-term blood pressure monitoring. This high cost can be an even more insurmountable obstacle, especially in low-income areas or environments with limited funding for medical equipment.

[0092] In order to solve the problem that the current blood pressure detection solution does not meet actual business needs and achieve the goal of meeting the needs of users and actual business, taking into account the shortcomings of the existing technology, the technical problems actually to be solved by this application include one or more of the following three aspects:

[0093] 1. Simplify the operation process and improve user-friendliness (for disadvantage 1 of solution 1): In view of the high operational complexity of traditional blood pressure measurement methods, it is necessary to develop more simplified and user-friendly blood pressure measurement technologies. This may include designing more intuitive and easy-to-use equipment so that ordinary users can easily measure blood pressure without professional training. For example, you can consider developing a system that automatically adjusts the tightness of the cuff, or using simpler sound or visual instructions to guide users to perform the correct operation. At the same time, provide a more intuitive feedback mechanism, such as displaying measurement results and guidance through applications or embedded screens to reduce dependence on professional medical knowledge.

[0094] 2. Achieve continuous and dynamic blood pressure monitoring (addressing disadvantage 2 of solution 1): To overcome the limitation of traditional methods that cannot provide continuous monitoring, it is necessary to develop technologies that can perform long-term, continuous blood pressure monitoring. This may involve the development of new portable or wearable devices that can continuously record blood pressure data during daily life, rather than being limited to one-time readings. Such devices should be able to adapt to the user's daily activities while providing accurate and reliable data to enable patients to better manage and monitor blood pressure changes.

[0095] 3. Reduce costs and increase device penetration (addressing disadvantage 1 of option 2): Given the high cost of current high-performance blood pressure measurement devices, especially in resource-limited areas, there is a need to develop more affordable blood pressure measurement solutions. This may include simplifying device design or adopting a modular design to reduce production and maintenance costs. In addition, it is also possible to consider improving the performance of existing devices through software optimization and algorithm improvements, rather than relying entirely on expensive hardware upgrades. By reducing costs, these devices can be made more popular, especially among groups with limited economic conditions, thereby improving overall public health.

[0096] In summary, the existing blood pressure detection solutions cannot meet the actual business needs of signal quality detection. Therefore, the blood pressure detection method provided in this application is used to solve the above technical problems.

[0097] Based on the technical issues raised above and to facilitate understanding of the embodiments of the present application, the following first describes one of the system architectures on which the embodiments of the present application are based. Figure 1A , Figure 1A This is an example diagram of a system architecture provided in an embodiment of the present application. The structure in the embodiment of the present application may include Figure 1A The smart wearable device 01 in the embodiment of the present application may include a system architecture based on which the smart wearable device 01 can independently complete the blood pressure detection function; optionally, see Figure 1B , Figure 1B This is a schematic diagram of a medical testing device provided in an embodiment of the present application. Figure 1B The medical monitoring device 02 can be used to assist the smart wearable device 01 in performing blood pressure calibration.

[0098] Among them, the smart wearable device 01 refers to an electronic technology product that can be worn on the body or in contact with the body. In the embodiment of the present application, the smart wearable device 01 can be a smart watch (Smartwatches), health and fitness monitors (Fitness Trackers), smart bracelets (Smart Bands), portable vital signs monitoring devices (Biometric Monitors), and other devices that are used to contact the body and collect data information from the body. The smart wearable device 01 mainly collects personal health data, such as PPG signals, through physical contact. For example, see Figure 1A , Figure 1A This is an example diagram of a system architecture provided in an embodiment of the present application. Figure 1A It may include smart wearable device 01, Figure 1A Take the smart bracelet as an example for smart wearable device 01. Figure 1A The function selection interface 01A may include a blood pressure detection 11 and a blood pressure calibration 12. Different functions can be selected based on the usage of different users. Taking the system architecture based on the smart wearable device 01 being able to independently complete the blood pressure detection function as an example, the user can independently complete the blood pressure detection function based on the smart wearable device 01 and display the blood pressure detection result on the interface of the smart wearable device 01. Furthermore, if the user has not calibrated, the user can click on the blood pressure calibration 12 in the smart wearable device 01 and then complete the calibration function in collaboration with the medical monitoring device 02, and then perform the blood pressure detection 11 to improve the accuracy of the blood pressure detection. In addition, in the system architecture described in the embodiment of the present application, the smart wearable device 01 collects PPG signals and the medical monitoring device 02 collects blood pressure gold standard values. The collected blood pressure gold standard values ​​can be input into the smart wearable device 01. The smart wearable device 01 can input the above-mentioned blood pressure gold standard values ​​and PPG signals into a trained machine algorithm model to determine the corresponding PTT signal, and the blood pressure detection results obtained based on the PTT signal calculation are displayed to the user on the smart wearable device 01.

[0099] The medical monitoring device 02, such as a cuff, is used to calibrate blood pressure. The medical monitoring device 02 may also include a pressure sensor, an electric inflation pump and an exhaust valve, etc., which can measure blood pressure around the arm and collect blood pressure gold standard values. The medical monitoring device 02 may also include other health monitoring functions, such as heart rate monitoring, activity monitoring, etc. For example, see Figure 1B , Figure 1BThis is a schematic diagram of a medical monitoring device provided in an embodiment of the present application. Figure 1B The medical monitoring device 02 may be included in Figure 1B Taking the medical monitoring device 02 as an example, which is a cuff, the user can fit the cuff tightly against the body and apply pressure to correctly measure the blood pressure, ensure that the blood pressure measurement reaches the blood pressure gold standard value, and input the blood pressure gold standard value into the smart wearable device 01 to assist in completing the blood pressure calibration function.

[0100] For different business scenarios, the system architecture used for ECG signal detection is also different. The following describes another system architecture based on the embodiment of this application. Figure 1C , Figure 1C This is another system architecture diagram provided by the embodiment of this application. The structure in this application may include Figure 1C Terminal device 03 in Figure 1C The business scenario is a schematic diagram of a system architecture in which the terminal device 03 and the smart wearable device 01 jointly complete the blood pressure detection function. For example, the system architecture in the embodiment of the present application can be a system architecture in which the smart wearable device 01 is responsible for collecting user data and displaying the blood pressure detection results in the terminal device 03. Figure 1B , Figure 1B This is a schematic diagram of a medical testing device provided in an embodiment of the present application. Figure 1B The medical monitoring device 02 can be used to assist the terminal device 03 and the smart wearable device 01 in performing blood pressure calibration.

[0101] The terminal device 03 may be a smart phone, tablet, personal computer (PC), laptop, network terminal, or other smart device that supports the function of connecting with the smart wearable device 01 and the medical monitoring device 02. For example, the system architecture in the embodiment of the present application may be a system architecture based on the smart wearable device 01 and the terminal device 03 jointly completing the blood pressure test function. Figure 1C For example, the terminal device 03 is a smart phone. Figure 1CThe interface may include an application selection interface 03A and a smart bracelet application interface 03B; wherein the terminal device 03 can be connected to one or more of the smart wearable device 01 and the medical monitoring device 02 (for example, via Bluetooth, NFC, WiFi, wired connection, etc.). In the application selection interface 03A of the terminal device 03, click on the smart bracelet 31 to enter the smart bracelet application interface 03B. The interface displays the connected smart wearable device 01 in the RY bracelet 32, or displays the connected medical monitoring device 02 in the RY cuff 33. The user can complete the blood pressure detection function in collaboration with the smart wearable device 01 by clicking on the blood pressure detection 34, or complete the blood pressure calibration function in collaboration with the smart wearable device 01 and the medical monitoring device 02 by clicking on the blood pressure calibration 35. In addition, in the system architecture described in the embodiment of the present application, the smart wearable device 01 collects PPG signals, and the medical monitoring device 02 collects blood pressure gold standard values. The two devices can input the collected blood pressure gold standard values ​​and PPG signals into the terminal device 03. The terminal device 03 can calculate the PTT signal based on the above-mentioned blood pressure gold standard values ​​and PPG signals, and obtain the blood pressure value based on the PTT signal calculation and display it to the user in the terminal device 03, thereby providing more accurate blood pressure monitoring results.

[0102] It is understandable that Figure 1A-Figure 1C The system architecture in the embodiment is only one or more exemplary implementations in the embodiments of the present application. The structure in the embodiments of the present application includes but is not limited to the above system architecture.

[0103] Based on the system architecture in which the smart wearable device 01 can independently complete the blood pressure detection function, the smart wearable device 01 can include a blood pressure detection module. Figure 2 , Figure 2 This is a schematic diagram of a blood pressure detection module provided in an embodiment of the present application. Figure 2 The blood pressure detection module 10 may include a blood pressure detection module 10, and the blood pressure detection module 10 may include a calibration module 102 and a detection module 103.

[0104] in:

[0105] The calibration module 102 can be used to calibrate the user data collected by the smart wearable device 01 and the medical monitoring device 02 to ensure the accuracy and reliability of blood pressure measurement. For example, the calibration module 102 may include a user calibration prompt unit 1021, a calibration PPG data collection unit 1022, a blood pressure gold standard collection unit 1023, a first PTT calculation module unit 1024, a first signal quality detection unit 1025, and a calibration blood pressure regression formula unit 1026; specifically, when the user uses the device for blood pressure detection for the first time, the smart wearable device 01 can remind the user to calibrate through the user calibration prompt unit 1021, and collect the user's PPG signal through the calibration PPG data collection unit 1022, and obtain the user's gold standard blood pressure value (i.e., a blood pressure reference value that meets high standards) collected by the medical monitoring device 02 through the blood pressure gold standard collection unit 1023, so as to obtain the user data required for blood pressure calibration. User data is obtained and the user data is input into the smart wearable device 01; further, the first PTT calculation module unit 1024 in the smart wearable device 01 can calculate the PTT signal based on the above-mentioned PPG signal (for example, the ECG signal is restored based on the PPG signal, and the PTT signal is calculated based on the peak matching of the PPG signal and the ECG signal), and the quantity of the restored ECG signal and the quality of the PTT signal are detected based on the first signal quality detection unit 1025. Finally, the above-mentioned blood pressure gold standard value and the PTT signal are input into the calibration blood pressure regression formula unit 1026 for calibrating the blood pressure regression formula. In the detection module 103, the smart wearable device 01 can calculate the blood pressure value based on the calibrated blood pressure regression formula.

[0106] In the detection module 103, the smart wearable device 01 can collect the user's PPG signal in real time during the blood pressure detection, and input the pulse transmission time into the calibrated blood pressure regression formula in the calibration module 102 to calculate the user's blood pressure value. For example, the detection module 103 may include a PPG data collection unit 1031, a second PTT calculation module unit 1032, a second signal quality detection unit 1033, a blood pressure regression formula input unit 1034 and a predicted blood pressure acquisition unit 1035; specifically, for each different user, if the user is using it for the first time, they need to calibrate through the calibration module 102 first. After the calibration module 102 completes the calibration work, blood pressure detection can be performed through the detection module 103. The smart wearable device 01 can collect the user's PPG signal in real time through the PPG data collection unit 1031, and obtain the predicted blood pressure through the second PTT calculation module 1032. During the blood pressure detection process, the second PTT calculation module unit 1032 calculates the PTT signal in real time (for example, by restoring the ECG signal based on the PPG signal, and then calculating the PTT signal based on peak matching between the PPG signal and the ECG signal). The second signal quality detection unit 1033 detects the quantity of the restored ECG signal and the quality of the PTT signal. Finally, the user's blood pressure value is determined in the predicted blood pressure obtaining unit 1035 by inputting the above-mentioned PTT signal into the above-mentioned blood pressure regression formula (i.e., the blood pressure regression formula established by the calibration blood pressure regression formula unit 1026 in the calibration module 102). Furthermore, in the calibration module 102 and the detection module 103, the second PTT calculation module unit 1032 and the first PTT calculation module unit 1024 can be the same module or different modules, and the first signal quality detection unit 1025 and the second signal quality detection unit 1033 can be the same module or different modules, which are not specifically limited in the embodiments of the present application.

[0107] based on Figure 1A-1C The provided system architecture and the blood pressure detection module in the smart wearable device 01, combined with the blood pressure detection method provided in the embodiment of this application, specifically analyze and solve the technical problems raised in this application.

[0108] See also Figure 3A , Figure 3A This is a flow chart of a blood pressure detection process provided by an embodiment of the present application. Taking the system architecture of the smart wearable device 01 that can independently complete the blood pressure detection function as an example, the system architecture can be used to support and execute Figure 3A The method flow shown in FIG. 1 includes steps S300 to S302. The method may include the following steps S300 to S302.

[0109] Step S300: Acquire a first photoplethysmography (PPG) signal of a user, and determine a first electrocardiogram (ECG) signal of the user based on the first PPG signal.

[0110] Specifically, in the blood pressure detection process of the embodiment of the present application, the user's PPG signal can be recorded and collected based on the smart wearable device 01 (for example, a smart watch, a smart bracelet, etc.) to measure blood flow changes, and the user's PPG signal can be converted into an ECG signal to further perform blood pressure detection. For example, taking the system architecture in which the smart wearable device 01 can independently complete the blood pressure detection function as an example, please refer to Figure 3B , Figure 3B This is a schematic diagram of a blood pressure test provided in an embodiment of the present application. Figure 3B The smart wearable device 01 may include a wristband blood pressure detection front interface 01B and a wristband blood pressure detection middle interface 01C; as shown in the wristband blood pressure detection front interface 01B, the user can perform blood pressure detection by clicking the start detection button 18 and enter the wristband blood pressure detection middle interface 01C. As shown in the wristband blood pressure detection middle interface 01C, when performing blood pressure detection, the smart wearable device 01 can collect PPG signals by detecting the photoelectric capacitance change of the blood vessels under the user's arm. The collection process usually involves the LED in the smart wearable device 01 emitting light and the photosensor receiving reflected light. The smart wearable device 01 converts the collected data into a visual waveform, which can be seen in FIG. Figure 3C , Figure 3C A schematic diagram of a PPG signal provided in an embodiment of the present application is shown in FIG. Figure 3C The PPG signal shown in can show the dynamic changes of blood passing through blood vessels, and subsequent blood pressure detection can be performed based on the PPG signal; further, the smart wearable device 01 can analyze the specific characteristics of the above-mentioned PPG signal, such as peak value, waveform duration, etc., to determine the corresponding ECG signal (for example, based on the PPG2ECG autoencoder to convert the PPG signal into an ECG signal). In addition, in a possible implementation method, the system architecture in the embodiment of the present application can also be an example of a system architecture that uses the smart wearable device 01 and the terminal device 03 to jointly complete the blood pressure detection function, which can be seen in Figure 3D , Figure 3D This is another blood pressure detection schematic diagram provided in an embodiment of the present application. Figure 3D The terminal device 03 may include a function selection interface 03B, a test preparation interface 03C, and a terminal device test interface 03D; as shown in the function selection interface 03B, the function selection interface 03B may include a wristband device 32, a cuff device 33, start blood pressure detection 34, and start blood pressure calibration 35. The user can check the connection status of the personal device by viewing the wristband device 32 and the cuff device 33, and then click on the start blood pressure detection 34 to enter the function selection interface. Figure 3DThe preparation detection interface 03C may include the user's basic personal information and start detection 36. The user can perform blood pressure detection by clicking start detection 36 to start the collection of PPG signals and determine the corresponding ECG signal based on the PPG signal. The detection interface can refer to the terminal device detection interface 03D.

[0111] In one possible implementation, determining the first electrocardiogram (ECG) signal based on the first PPG signal includes: inputting the first PPG signal into an electrocardiogram (ECG) signal conversion model, and outputting the first ECG signal. In an embodiment of the present application, the user's PPG signal can be input into a signal quality detection model to obtain an ECG signal converted from the PPG signal, which can significantly improve processing efficiency, especially when processing large-scale data. Furthermore, the ECG signal conversion model can be a pre-trained machine learning algorithm model, and the ECG signal is determined based on the ECG signal conversion model, thereby improving the speed of subsequent blood pressure detection, increasing the accuracy of blood pressure detection, and reducing the cost of blood pressure detection (that is, the blood pressure detection device can be a wearable device that supports the acquisition of PPG signals, without the need to simultaneously integrate PPG signals and ECG signals).

[0112] In one possible implementation, the ECG signal conversion model in the embodiment of the present application can be a PPG2ECG autoencoder, that is, a machine learning model that can be trained in a server to implement the function of determining the ECG signal from the PPG signal. For example, see Figure 3E , Figure 3E This is a schematic diagram of an ECG signal conversion model module provided in an embodiment of the present application. Taking the training model in the server as an example, the server may include a PPG2ECG model module 105. The PPG2ECG model module 105 may include a PPG+ECG data acquisition unit 1051, a data set production unit 1052, a training PPG2ECG autoencoder unit 1053 and an acquisition PPG2ECG model unit 1054. The server may first obtain a PPG signal and an ECG signal through the PPG+ECG data acquisition unit 1051 (these two signals may be prepared in advance or directly collected), and determine the corresponding PPG data set and ECG data set through the data set production unit 1052, and input the PPG data set as sample data into the training PPG2ECG autoencoder unit 1053, and train with the ECG data set as a label, and finally obtain a trained PPG2ECG model unit 1054; further, see Figure 3F , Figure 3F This is a schematic diagram of an ECG signal provided in an embodiment of the present application. In the blood pressure detection process, it is assumed that Figure 3Cis the currently collected PPG signal, which can be obtained by Figure 3C The PPG signal in is input into the above-mentioned PPG2ECG autoencoder. After training, the encoder can parse and convert the PPG signal and finally output the following Figure 3F In addition, for the same target user, the amplitude comparison of the ECG signal restored based on the PPG2ECG autoencoder and the ECG signal acquired based on the device supporting ECG signal acquisition can be seen in Figure 3G , Figure 3G This is a schematic diagram of ECG signal comparison provided in an embodiment of the present application, such as Figure 3G As shown, the high peak is the amplitude of the original ECG signal, and the peak at the middle black mark is the amplitude of the restored ECG signal. Except for the difference in signal amplitude, the phases of the two ECG signals are relatively close. Further, the data comparison in actual application can be seen in Table 1. Table 1 is a comparison diagram of the algorithm effects of different ECG signals provided in the embodiment of the present application. As shown in Table 1, Table 1 shows a data table of blood pressure measurement error, which includes statistical error indicators of systolic blood pressure (SBP) and diastolic blood pressure (DBP) under two different ECG signal states (resting state and exercise state), where the indicators may include mean error (ME), mean absolute error (MEA), and mean absolute error (DBP). Error, MAE), standard deviation (Standard Deviation, STD), for SBP and DBP under the original scheme (i.e., ECG signal collected by the device) and this scheme (i.e., ECG signal restored by PPG signal through PPG2ECG autoencoder), the table shows the values ​​of the above three indicators respectively; for example, taking the systolic SBP data comparison of the two schemes as an example, the average error of systolic SBP under the original scheme is 0.17, the average absolute error is 17.50, and the standard deviation is 12.46, while the average error of systolic SBP in this scheme is -0.25, the average absolute error is 17.62, and the standard deviation is 12.56. After data comparison, it can be found that the results obtained by this scheme are basically consistent with the results obtained using the original ECG signal. This scheme can be used in subsequent blood pressure detection to reduce costs while ensuring the accuracy of blood pressure detection (i.e., blood pressure detection no longer needs to be based on equipment that needs to collect PPG signals and ECG signals at the same time. The blood pressure detection function can be completed by equipment that supports collecting PPG signals, avoiding high costs).

[0113] Table 1

[0114]

[0115] In one possible implementation, the PPG2ECG model is obtained by inputting Q second PPG signals as sample data and training with a preset second ECG signal as a label, or is obtained by training based on historical sample documents. In an embodiment of the present application, the ECG signal conversion model can be trained in the server by inputting a pre-prepared PPG signal as sample data and using a preset ECG signal as a label, or the ECG signal conversion model can be trained based on historical sample documents, which may include a PPG signal of an already determined ECG signal. By analyzing these historical data, the ECG signal conversion model can learn and train from past events to more accurately convert the PPG signal into an ECG signal. In summary, the embodiment of the present application can train the ECG signal conversion model by inputting a PPG signal as sample data or analyzing historical events, so that the model can effectively identify and distinguish high-quality and low-quality conversion results, thereby enhancing the adaptability and accuracy of the ECG signal conversion model when processing actual data, so as to obtain a higher quality ECG signal, and further improve the accuracy of blood pressure detection based on the ECG signal.

[0116] Step S301: Perform peak detection on the first PPG signal and the first ECG signal to determine a first pulse transit time of the user.

[0117] Specifically, in the process of blood pressure detection, first based on Figure 3A-3G After obtaining the ECG signal according to the embodiment steps described in the embodiment, the pulse transit time can be determined based on the PPG signal and the ECG signal in the embodiment of the present application. For example, see Figure 3H , Figure 3HThis is an example diagram of a PTT calculation module provided in an embodiment of the present application. Taking the operation of the PTT calculation module in the smart wearable device 01 as an example, the smart wearable device 01 may include a PTT calculation module 104, wherein the PTT calculation module 104 may include a PPG signal acquisition unit 1041, an input PPG2ECG model unit 1042, a first peak detection unit 1043, a predicted ECG signal acquisition unit 1044, a second peak detection unit 1045, a peak matching unit 1046 and a PTT calculation unit 1047; first, the smart wearable device 01 obtains the user's PPG signal through the PPG signal acquisition unit 1041, and inputs the PPG signal into the PPG2ECG model unit 1042 (the The PPG2ECG model unit 1042 may be a trained machine algorithm model), and the ECG signal is determined based on the predicted ECG signal unit 1044. Please refer to step S300 in the above embodiment, which will not be described in detail in the embodiment of the present application. Furthermore, the smart wearable device 01 may also obtain the peak value corresponding to the PPG signal based on the first peak detection unit 1043, and obtain the peak value of group A corresponding to the ECG signal based on the second peak detection unit 1045, and perform peak matching on the PPG signal and the ECG signal based on the peak matching unit 1046. Finally, based on the calculation of PTT unit 1047, the time difference of the peak of each group of corresponding PPG signals and ECG signals is calculated to obtain the pulse transit time PTT. The pulse transit time PTT can be used for subsequent blood pressure detection for further analysis to obtain more accurate blood pressure detection results.

[0118] In one possible implementation, the first PPG signal includes M first R peaks, and the first ECG signal includes N second R peaks, where M and R are both integers greater than 0; performing peak detection on the first PPG signal and the first ECG to determine the first pulse transit time of the user includes: determining the first pulse transit time of the user based on the time difference between the M first R peaks and the N second R peaks. In an embodiment of the present application, the pulse transit time of the user can be accurately determined by performing peak detection on the first PPG signal and the first ECG signal. Specifically, in an embodiment of the present application, peak detection can be performed on the PPG signal and the ECG signal to determine multiple R peaks (i.e., high peaks) of the above two signals, and the time difference can be calculated based on the M first R peaks and the N second R peaks (for example, the PPG signal and ECG signal that are adjacent in time sequence can be determined as a group, and the time difference can be calculated for each group of signals), and finally the pulse transit time of the user is determined. In summary, in the embodiment of the present application, the time difference can be calculated based on the peak values ​​of the ECG signal and the PPG signal, so that a more accurate pulse conduction time can be calculated, and more accurate data can be used for blood pressure detection in the subsequent blood pressure detection process, thereby ensuring the accuracy of blood pressure detection.

[0119] In one possible implementation, the first R peak is the R wave peak of the ECG signal; the second R peak is the pulse wave peak of the PPG signal. In an embodiment of the present application, by accurately identifying and distinguishing the R wave peak in the ECG signal and the pulse wave peak in the PPG signal, the system can more accurately calculate the pulse conduction time and obtain more accurate blood pressure detection results. Specifically, the accurate identification of the first R peak (R wave peak of the ECG signal) and the second R peak (pulse wave peak of the PPG signal) can enable the system to process and analyze the above two different types of physiological signals separately, and more accurately calculate the time difference between the peaks based on the two signals, thereby more accurately determining the pulse conduction time to increase the accuracy of subsequent blood pressure detection.

[0120] In one possible implementation, the determining of the user's first pulse conduction time based on the time difference between the M first R peaks and the N second R peaks includes: determining the temporally adjacent R wave peaks and pulse wave peaks among the M R wave peaks and the N pulse wave peaks as a group; and determining the first pulse conduction time based on the time difference between each group of R wave peaks and pulse wave peaks. In an embodiment of the present application, a more accurate and reliable blood pressure detection method is provided by determining the user's first pulse conduction time based on the time difference between the M R wave peaks and the N pulse wave peaks. Specifically, in an embodiment of the present application, by identifying temporally adjacent R wave peaks and pulse wave peaks, and determining every two temporally adjacent ECG signals and PPG signals as a group, the time difference between each group of R wave peaks and pulse wave peaks is accurately calculated, and finally the pulse conduction time is determined based on the time difference, thereby improving the accuracy of blood pressure detection. Furthermore, after obtaining the ECG signal and the PPG signal, the system (such as the smart wearable device 01) can perform peak detection to determine each R-wave peak in the ECG signal. These R waves represent the rapid depolarization of the ventricles and are the most significant peaks in each heart cycle. At the same time, the system will also detect the pulse wave peaks in the PPG signal. These peaks correspond to the changes in the optical signal caused by the blood pulsating in the blood vessels. On this basis, the system pairs each R-wave peak with the corresponding pulse wave peak, calculates the time difference between each pair of peaks, that is, the pulse conduction time, and performs subsequent blood pressure detection based on the pulse conduction time.

[0121] For example, see Figure 3I , Figure 3I This is a schematic diagram of calculating pulse transit time provided in an embodiment of the present application. Figure 3I The corresponding ECG signal waveform and PPG signal waveform may be included (for example, the ECG signal and PPG signal adjacent in time sequence may be determined as a group), Figure 3IThe upper part shows the waveform of the ECG signal, wherein the "R-Wave Peak" is highlighted, indicating the highest point (i.e., R-wave peak) in the ECG waveform during ventricular contraction; Figure 3I The lower half of the diagram shows the waveform of the PPG signal, which can be obtained by measuring changes in blood flow using an optical sensor (such as the smart wearable device 01) on the surface of the arm, wherein "Pulse Peak" is marked, i.e., the pulse wave peak value; after determining the R wave peak value of the ECG signal and the pulse wave peak value of the PPG signal, the smart wearable device 01 can match the temporally adjacent R wave peak values ​​and the pulse wave peak values ​​into a group, for example, the first R wave peak value will be paired with the first pulse wave peak value detected, the second R wave peak value will be paired with the subsequently detected pulse wave peak value, and so on; further, the pulse transit time PTT can be the time difference between the R wave peak value of the ECG signal and the pulse wave peak value of the PPG signal, Figure 3I In the figure, the correspondence between the two peaks can be seen through the vertical dotted line. The horizontal arrow from the R wave peak to the pulse wave peak represents the time delay between these two points. This time is PTT. In addition, the embodiment of the present application only provides one possible implementation method. The specific time unit and peak matching correspondence are not specifically limited in the embodiment of the present application. For example, the calculation of PTT based on PPG signal and ECG signal can be referred to. Figure 3J , Figure 3J This is another schematic diagram of calculating pulse transit time provided in an embodiment of the present application. Figure 3J The diagram may include continuous ECG signal waveforms and PPG signal waveforms, and each two adjacent dotted lines include a set of corresponding PPG signals and ECG signals. PTT can be calculated based on the peak matching results of the PPG signal and the ECG signal. Time marks can be included at the top of each set of adjacent dotted lines, and these marks can be PTT measurement results; for example, "0.128" displayed at the top of the third pair of adjacent dotted lines may indicate that the actual measured value of PTT is 128 milliseconds. Through continuous waveform detection, blood pressure can be continuously detected to obtain more accurate monitoring results.

[0122] Step S302: performing a first quality check on the first ECG signal and the first pulse transit time. If the result of the first quality check is determined to be normal, determining the blood pressure value of the user based on the first pulse transit time.

[0123] Specifically, the system (e.g., smart wearable device 01) first performs a quality test on the collected ECG signal. If the result of the first quality test is confirmed to be normal, that is, the ECG signal and pulse transit time both meet the quality standards, the system will proceed to the next step; at this stage, the system can calculate the user's blood pressure value based on the pulse transit time. For example, the system may use an algorithm to model the relationship between pulse transit time and known blood pressure values, so as to accurately estimate the current blood pressure value. In this way, the system can not only provide instant blood pressure detection, but also issue early warnings for potential blood pressure problems, providing users and medical professionals with important health information.

[0124] In one possible implementation, performing a first quality check on the first ECG signal and the first pulse transit time includes determining that the result of the first quality check is normal if the difference between M and N is greater than a preset first threshold, and the time difference between the M R wave peaks and the N pulse wave peaks is greater than a preset second threshold. In this embodiment, by performing quality checks on the first ECG signal and the first pulse transit time, the normality of the physiological signal can be more accurately determined. Specifically, by setting preset first and second thresholds, when the smart wearable device 01 detects that the difference between M and N exceeds the first threshold (for example, determining whether the number of ECG signals converted from the PPG signal is consistent with the PPG signal) and detects that the time difference between the M R wave peaks and the N pulse wave peaks exceeds the second threshold (for example, determining whether the pulse transit time or Pearson correlation coefficient determined based on the ECG signal and the PPG signal exceeds a normal value, such as an abnormality), the smart wearable device 01 can determine that the result of this quality check is normal, and proceed with subsequent blood pressure testing. In summary, in the embodiment of the present application, quality detection can be performed on two situations at the same time (for example, quality detection of ECG signals and PPG signals can be performed to determine whether the difference in the number and time difference of peak values ​​exceeds the corresponding threshold value) to ensure the accuracy of blood pressure detection parameters and improve the accuracy of subsequent blood pressure detection.

[0125] In one possible implementation, the first quality check of the first ECG signal and the first pulse transit time includes: if the difference between M and N is less than the first threshold, determining that the result of the first quality check is abnormal and prompting the user to retest; or if the time difference between the M R wave peaks and the N pulse wave peaks is less than the second threshold, determining that the result of the first quality check is abnormal and prompting the user to retest. In this embodiment of the present application, when an abnormality is found in the quality check of the first ECG signal and the first pulse transit time, the user is prompted to retest. Specifically, in the embodiment of the present application, by setting two thresholds - a first threshold and a second threshold - the smart wearable device 01 can determine whether there are abnormalities in the ECG and pulse conduction time. If the smart wearable device 01 detects that the difference between M and N is less than the first threshold (i.e., it is determined that the number of ECG signals converted from the PPG signal exceeds the preset first threshold), or the smart wearable device 01 detects that the time difference between the M R wave peaks and the N pulse wave peaks is less than the second threshold (i.e., it is determined that the pulse conduction time has an abnormality that exceeds the preset second threshold), when one of the two situations occurs, the smart wearable device 01 will determine that these results are abnormal and prompt the user to retest, thereby avoiding interference with the accuracy of the final blood pressure detection result due to partial data errors, improving the overall efficiency and accuracy of the test, and thus enhancing the reliability of the blood pressure detection solution. For example, see Figure 3K , Figure 3K This is an example diagram of a blood pressure detection abnormality provided in an embodiment of the present application, which includes a detection abnormality interface 01D. When the system detects that the number of electrocardiogram (ECG) signals is less than a preset threshold, or when the pulse transit time (PTT) is less than a preset threshold range, the system prompts the user to retest.

[0126] In one possible implementation, determining the user's blood pressure value based on the first pulse transit time includes: inputting the first pulse transit time into a calibrated first blood pressure regression model, and outputting the user's blood pressure value. Specifically, the smart wearable device 01 can use the user's pulse transit time as an input parameter and input it into a calibrated blood pressure regression model. The blood pressure regression model can calculate the pulse conduction velocity based on the pulse transit time and the distance between different blood vessels, and calculate the blood pressure value based on the data. Furthermore, the above-mentioned blood pressure regression model can be a blood pressure regression formula, which is obtained by optimizing statistical methods and machine learning techniques after collecting the user's blood pressure data and related physiological parameters for calibration. For example, the historical calibration data of a specific user may include the relationship between his blood pressure value and pulse transit time (PTT), and the blood pressure value can be restored based on a specific blood pressure regression formula. The blood pressure regression formula can include a linear model. When the heart contracts, blood is pumped into the aorta to form pulse waves. These pulse waves propagate outward along the arterial system and are closely related to changes in blood pressure. By measuring the pulse wave arrival time difference PTT at different vascular locations and the distance L between different vascular locations, the pulse wave conduction velocity PWV in the blood vessels can be calculated.

[0127]

[0128] The relationship between blood pressure and conduction velocity PWV can be established according to the Moens-Korteweg equation, where E0 is the elastic modulus of the blood vessel, h is the thickness of the blood vessel wall, α is a constant, ρ is the blood density, and r is the radius of the blood vessel.

[0129]

[0130] Since E0, h, α, ρ, and r in the above formula are all fixed values, in actual operation, a large number of samples are often used to perform linear fitting on blood pressure P and pulse wave arrival time difference PTT, that is, blood pressure P is linearly mapped through PTT.

[0131] P=αlnPTT+β

[0132] For example, after the smart wearable device 01 determines the pulse transit time, the data can be input into the calibrated blood pressure regression formula to determine the corresponding blood pressure value. In addition, the blood pressure regression formula may consider multiple variables, such as age, gender, weight, and historical blood pressure values, to more accurately predict the user's blood pressure value; see Figure 3L , Figure 3L This is a schematic diagram of age-based blood pressure comparison provided in the embodiments of this application. Figure 3LAs shown, the hypertension rate may be highest among those aged 35-44. Based on different age categories, other parameters can be set for different ages to jointly calculate the blood pressure value, and the corresponding blood pressure test results can be given to the user based on the blood pressure value. For example, see Figure 3M , Figure 3M This is a schematic diagram of a detection result provided in an embodiment of the present application. Figure 3M The blood pressure detection result interface 01E may be included. As shown in the blood pressure detection result interface 01E, assuming that the current user's blood pressure value is higher than the normal average value (for example, the blood pressure average value may be calculated based on different age groups), it is determined that the current user has hypertension, or based on the detection results over a period of time, it is determined whether the user's blood pressure value has become higher, so as to remind the user of his or her physical health condition.

[0133] Optionally, in the above method steps S300 to S302, the blood pressure value may be determined based on the user's PPG signal. For details on the blood pressure calibration process, please refer to Figure 4A , Figure 4A FIG. 1 is a flow chart of a blood pressure calibration process provided in an embodiment of the present application; Figure 4A As shown, the following steps S400 to S403 may also be included:

[0134] Step S400: If the calibration result of the user is not detected, the calibration data of the user is obtained.

[0135] Specifically, the calibration data includes the user's second PPG signal and the gold standard blood pressure value. In the embodiment of the present application, when the user turns on the calibration function, or when the calibration result of the current user is not detected, the user's PPG signal (for example, collected by a wearable device such as a smart watch or smart bracelet) and the gold standard blood pressure value (for example, collected based on a cuff) can be collected. For example, see Figure 4B , Figure 4B This is a schematic diagram before blood pressure calibration provided in an embodiment of the present application. Figure 4B The blood pressure calibration reminder interface 01F and the blood pressure calibration pre-interface 01G of the smart wearable device 01 may be included; when the smart wearable device 01 does not include the user's historical calibration data (for example, it may be caused by the user's first use or the time interval is too long), as shown in the blood pressure calibration reminder interface 01F, the smart wearable device 01 pops up a detection reminder, prompting the user to perform blood pressure calibration before blood pressure detection. The user can click to start blood pressure calibration 13 to enter the blood pressure calibration pre-interface 01G; as shown in the blood pressure calibration pre-interface 01G, the interface may include the connection status of a personal device such as the RY cuff, and prompt the user to wear the above two devices correctly. The user can perform the blood pressure calibration function by clicking the start calibration button 14; further, see Figure 4C , Figure 4C This is a blood pressure calibration diagram provided in an embodiment of the present application. Figure 4C It may include the smart wearable device 01 bracelet blood pressure detection interface 01H and the medical monitoring device 02; Figure 4C As shown, the smart wearable device 01 and the medical monitoring device 02 have established a communication connection (for example, it can be through Bluetooth, NFC, WiFi, wired connection, etc.). When performing blood pressure testing, the smart wearable device 01 (such as a smart bracelet) can detect the photoelectric capacitance changes of the blood vessels under the user's arm to collect PPG signals, and determine the blood pressure gold standard value based on the medical monitoring device 02. Assuming that the medical monitoring device 02 is a cuff, a cuff can be placed around the arm to determine the user's gold standard blood pressure value, and blood pressure verification can be performed based on the above two data subsequently.

[0136] In one possible implementation, if the calibration result of the user is detected, the first blood pressure regression model is obtained based on the calibration result. In an embodiment of the present application, the accuracy of blood pressure measurement can be optimized by detecting whether the user has performed a calibration operation and then obtaining the first blood pressure regression model based on the calibration result. Specifically, after the user completes the calibration, the system obtains the corresponding calibration data and obtains the blood pressure regression model corresponding to the user based on the calibration data. The method of obtaining the corresponding blood pressure regression model based on individual calibration data in the embodiment of the present application can more accurately reflect the specific blood pressure condition of each user, improve the reliability and effectiveness of blood pressure monitoring, and thus help to better guide the user's health management and disease prevention.

[0137] Step S401: Determine a second ECG signal of the user based on the second PPG signal, and determine a second pulse transit time of the user based on the second PPG signal and the second ECG signal.

[0138] Specifically, see Figure 3A The embodiment described in step S302 will not be repeated in the embodiments of this application.

[0139] Step S402: Perform a second quality detection on the second ECG signal and the second pulse transit time.

[0140] Specifically, see Figure 3A The embodiment described in step S302 will not be repeated in the embodiments of this application.

[0141] Step S403: If it is determined that the result of the second quality detection is normal, the second blood pressure regression model is calibrated based on the second pulse transit time and the gold standard blood pressure value to determine the first blood pressure regression model.

[0142] Specifically, in an embodiment of the present application, the user's pulse transit time can be determined based on the PPG signal (for example, the PPG signal can be converted into an ECG signal, and the pulse transit time can be determined based on the PPG signal and the ECG signal), and the ECG signal and pulse transit time can be quality-tested to ensure the accuracy of the calibration data; further, the pulse transit time and the gold standard blood pressure value can be input into the blood pressure regression model to calibrate the blood pressure regression model. For example, by analyzing multiple sets of pulse signals and corresponding blood pressure values ​​in the server, the parameters of the blood pressure prediction model can be learned and adjusted to improve its prediction accuracy. Assuming that the smart wearable device 01 has measured the user's PPG signal and the gold standard blood pressure value by a non-invasive method, the relationship between the pulse signal and the blood pressure reading can be identified by comparing the two sets of data, and the blood pressure regression model can be adjusted accordingly. The calibration process may involve multiple iterations, and each iteration adjusts the model based on the newly collected data until the predetermined accuracy standard is achieved. Furthermore, the above-mentioned blood pressure regression model can be a blood pressure regression formula, and the calibration step of the blood pressure regression formula can include: the smart wearable device 01 first inputs the collected PTT data and the gold standard blood pressure value into the preset blood pressure regression formula P=αln(PTT)+β, wherein α and β can be coefficients to be determined, which are obtained by statistical analysis of a series of data sets with known blood pressure and PTT values. The regression technology in the analysis process can include the least squares method to find the optimal α and β values ​​so that the formula can accurately predict the blood pressure value. Finally, in the actual blood pressure detection process, after the PPG signal is collected by the smart wearable device 01, the pulse transit time PTT determined based on the PPG signal can be input into the calibrated blood pressure regression formula to determine the user's blood pressure value and provide the user with accurate blood pressure detection results.

[0143] The above describes in detail the method of the embodiment of the present application, and the following provides the relevant device of the embodiment of the present application.

[0144] See Figure 5 , Figure 5 is a structural diagram of a blood pressure detection device provided in an embodiment of the present application. The blood pressure detection device 50 may include a first ECG signal determination unit 501, a first pulse transit time determination unit 502, a blood pressure value determination unit 503, a first blood pressure regression model acquisition unit 504, a calibration data acquisition unit 505, a second ECG signal determination unit 506, a second pulse transit time determination unit 507 and a calibration unit 508, wherein each unit is described in detail as follows.

[0145] A first ECG signal determining unit 501 is configured to obtain a first photoplethysmography (PPG) signal of a user and determine a first electrocardiogram (ECG) signal of the user based on the first PPG signal;

[0146] In a possible implementation, the first ECG signal determining unit 501 is specifically configured to:

[0147] The first PPG signal is input into an electrocardiogram signal conversion model, and the first ECG signal is output.

[0148] In one possible implementation, the electrocardiogram signal conversion model is obtained by inputting Q third PPG signals as sample data in the server and training the preset third ECG signal as a label, or is obtained by training based on historical sample documents in the server.

[0149] A first pulse transit time determining unit 502 is configured to perform peak detection on the first PPG signal and the first ECG signal to determine a first pulse transit time of the user;

[0150] In one possible implementation, the first PPG signal includes M first R peaks, and the first ECG signal includes N second R peaks, where M and R are both integers greater than 0; and the first pulse transit time determining unit 502 is specifically configured to:

[0151] A first pulse transit time of the user is determined based on a time difference between the M first R peaks and the N second R peaks.

[0152] In a possible implementation, the first pulse transit time determining unit 502 is specifically configured to:

[0153] Among the M R wave peaks and the N pulse wave peaks, determining temporally adjacent R wave peaks and pulse wave peaks as a group;

[0154] The first pulse transit time is determined based on the time difference between the peak value of each group of R waves and the peak value of the pulse wave.

[0155] In a possible implementation, the first R peak is the R wave peak of the ECG signal; the second R peak is the pulse wave peak of the PPG signal.

[0156] The blood pressure value determination unit 503 is configured to perform a first quality check on the first ECG signal and the first pulse transit time, and if the result of the first quality check is determined to be normal, determine the user's blood pressure value based on the first pulse transit time.

[0157] In a possible implementation, the blood pressure value determining unit 503 is specifically configured to:

[0158] The first pulse transit time is input into a calibrated first blood pressure regression model, and the blood pressure value of the user is output.

[0159] In a possible implementation, the blood pressure value determining unit 503 is specifically configured to:

[0160] If the difference between M and N is greater than a preset first threshold, and the time difference between the M R wave peaks and the N pulse wave peaks is greater than a preset second threshold, the result of the first quality detection is determined to be normal.

[0161] In a possible implementation, the blood pressure value determining unit 503 is specifically configured to:

[0162] If the difference between M and N is less than the first threshold, the result of the first quality test is determined to be abnormal, and the user is prompted to retest; or,

[0163] If the time difference between the M R wave peaks and the N pulse wave peaks is less than the second threshold, the result of the first quality detection is determined to be abnormal, and the user is prompted to retest.

[0164] The first blood pressure regression model acquisition unit 504 acquires the first blood pressure regression model based on the calibration result if the calibration result of the user is detected.

[0165] The calibration data acquisition unit 505 acquires the calibration data of the user if the calibration result of the user is not detected, wherein the calibration data includes the second PPG signal and the gold standard blood pressure value of the user;

[0166] a second ECG signal determining unit 506 , configured to determine a second ECG signal of the user based on the second PPG signal, and to determine a second pulse transit time of the user based on the second PPG signal and the second ECG signal;

[0167] a second pulse transit time determining unit 507, configured to perform a second quality detection on the second ECG signal and the second pulse transit time;

[0168] The calibration unit 508 calibrates the second blood pressure regression model based on the second pulse transit time and the gold standard blood pressure value to determine the first blood pressure regression model if the result of the second quality detection is determined to be normal.

[0169] The following introduces the terminal device provided in the embodiment of the present application.

[0170] Figure 6AFIG2 shows a hardware structure diagram of a terminal device 1000 provided in an embodiment of the present application. The terminal device 1000 is used to execute the blood pressure detection method provided in the above method embodiment.

[0171] Terminal device 1000 may include a processor 1001, memory 1002, a wireless communication module 1003, a mobile communication module 1004, an antenna 1003A, an antenna 1004A, a power switch 1005, a sensor module 1006, a display screen 1009, and the like. Sensor module 1006 may include a gyroscope sensor 1006A, an accelerometer 1006B, an ambient light sensor 1006C, a PPG signal sensor 1006D, a distance sensor 1006E, and the like. Wireless communication module 1003 may include a WLAN communication module, a Bluetooth communication module, and the like. These multiple components may transmit data via a bus.

[0172] The processor 1001 may include a variety of processing units to optimize the performance of the terminal device 1000 when processing blood pressure detection. For example, these processing units may include a main application processor (AP) responsible for complex data processing tasks, a graphics processing unit (GPU) to optimize the display of blood pressure detection results, and a specially designed digital signal processor (DSP) and neural network processor (NPU) to accelerate the execution of blood pressure detection. These units work together to improve the efficiency and accuracy of the blood pressure detection solution in determining ECG signals based on PPG signals, determining pulse transit time based on PPG signals and ECG signals, quality detection, and determining blood pressure values ​​based on pulse transit time. In addition, this multi-processing unit design can support the blood pressure detection solution to process multiple tasks in parallel, thereby providing fast response and accurate detection results when processing large blood pressure data sets.

[0173] The memory 1002 is used to store the operating system, application code and other data of the terminal device 1000. For example, it may include local applications (such as blood pressure value calculation applications, etc.) and program codes and data required for blood pressure detection. The processor 1001 implements device functions and blood pressure data processing tasks by executing program codes stored in the memory 1002. The program storage area of ​​the memory 1002 is used to place the operating system and application code, while the data storage area is used to save data generated when running the application. In addition to containing fast-access random access memory (RAM), the memory 1002 may also include one or more forms of non-volatile memory, such as a hard disk drive, a solid-state drive or a flash memory device, to provide a persistent storage solution.

[0174] The wireless communication function of the terminal device 1000 can be implemented through the antenna 1003A, the antenna 1004A, the mobile communication module 1004, the wireless communication module 1003, the modem processor and the baseband processor.

[0175] Antenna 1003A and antenna 1004A can be used to transmit and receive electromagnetic wave signals. Each antenna in terminal device 1000 can be used to cover a single or multiple communication frequency bands. Different antennas can also be reused to improve antenna utilization.

[0176] The mobile communication module 1004 can provide wireless communication solutions, including 2G / 3G / 4G / 5G, for the terminal device 1000. The mobile communication module 1004 can include at least one filter, a switch, a power amplifier, a low-noise amplifier (LNA), and the like. The mobile communication module 1004 can receive electromagnetic waves via antenna 1004A, filter and amplify the received electromagnetic waves, and transmit them to the modem processor for demodulation. The mobile communication module 1004 can also amplify the signals modulated by the modem processor and convert them into electromagnetic waves for radiation via antenna 1004A.

[0177] The modem processor may include a modulator and a demodulator. The modulator converts the low-frequency PPG signal to be transmitted into a medium- or high-frequency signal for easier transmission. The demodulator on the receiving end demodulates the received medium- or high-frequency signal back into a low-frequency baseband signal. These demodulated signals are then transmitted to the baseband processor for further processing, such as amplification, filtering, and digitization. The processed PPG signals are then transmitted to the application processor, which further analyzes them to detect the user's blood pressure and displays the relevant blood pressure value or other physiological information on the display screen 1009.

[0178] The wireless communication module 1003 provides a variety of wireless communication methods suitable for the terminal device 1000. This includes wireless local area network (WLAN), Bluetooth (BT), Global Navigation Satellite System (GNSS), Frequency Modulation (FM), Near Field Communication (NFC), infrared technology (IR), etc. In particular, the wireless communication module 1003 uses Bluetooth technology to connect multiple terminal devices together to form a collaborative network. In this network, each terminal device can share PPG signal data and gold standard blood pressure values ​​to achieve collaborative blood pressure detection functions. This connection method not only enhances the scope and efficiency of data collection, but also improves the accuracy and reliability of blood pressure detection. The wireless communication module 1003 receives and sends electromagnetic wave signals through the antenna 1003A to ensure the effective transmission of data.

[0179] The power switch 1005 may be used to control the supply of power to the terminal device 1000 .

[0180] The gyroscope sensor 1006A can be used to determine the motion posture of the terminal device 1000. In some embodiments, the angular velocity of the terminal device 1000 around three axes (i.e., x, y, and z axes) can be determined by the gyroscope sensor 1006A. The gyroscope sensor 1006A can be used for anti-shake shooting. For example, when the shutter is pressed, the gyroscope sensor 1006A detects the angle of the terminal device 1000 shaking, calculates the distance that the lens module needs to compensate based on the angle, and allows the lens to offset the shaking of the terminal device 1000 through reverse movement to achieve anti-shake. The gyroscope sensor 1006A can also be used for navigation and somatosensory game scenes.

[0181] Accelerometer 1006B can detect the magnitude of acceleration of terminal device 1000 in all directions (generally three axes). When terminal device 1000 is stationary, it can detect the magnitude and direction of gravity. It can also be used to identify the terminal device's posture. For example, accelerometer 1006B can be used in applications such as landscape and portrait screen switching and pedometers.

[0182] The ambient light sensor 1006C is used to sense the ambient light brightness. The terminal device 1000 can adaptively adjust the brightness of the display screen 1009 based on the sensed ambient light brightness. The ambient light sensor 1006C can also be used to automatically adjust the white balance when taking photos.

[0183] The PPG signal sensor 1006D can be used to capture PPG signals. The PPG signal sensor 1006D may include at least one light source (e.g., an LED), a photodetector, and the necessary circuitry to process the PPG signal. The LED in the PPG signal sensor 1006D emits light (typically red or infrared) onto the user's skin, and the photodetector captures the light absorbed or reflected by the blood. Small changes in blood volume (such as those caused by heartbeats) cause variations in the amount of received light. These changes are converted into electrical signals, which, after amplification and filtering by the signal processing circuitry, produce a PPG signal reflecting the characteristics of blood flow.

[0184] The distance sensor 1006E can be used to measure distance. The terminal device 1000 can measure distance using infrared or laser. In some shooting scenarios, the terminal device 1000 can use the distance sensor 1006E to measure distance to achieve fast focusing.

[0185] Video codecs are used to compress or decompress digital images. The terminal device 1000 may support one or more video codecs. In this way, the terminal device 1000 can open or save pictures or videos in multiple encoding formats.

[0186] The terminal device 1000 can implement display functions through a GPU, display screen 1009, and an application processor. The GPU is a microprocessor for image processing that connects the display screen 1009 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The processor 1001 may include one or more GPUs, which execute program instructions to generate or change display information.

[0187] The blood pressure detection module 1007 can be used to measure and monitor blood pressure. The blood pressure detection module 1007 can determine the ECG signal based on the user's PPG signal collected by the PPG signal sensor 1006D. Based on analysis of these two signals, the blood pressure detection module 1007 can accurately calculate the user's pulse transit time (PTT), thereby estimating the user's blood pressure value. Optionally, the blood pressure detection module 1007 can also be a software module (including an algorithm model) executed by the processor 1001.

[0188] Display screen 1009 is used to display images, videos, etc. Display screen 1009 includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), a MiniLED, a MicroOLED, a Micro-OLED, or a quantum dot light-emitting diode (QLED). In some embodiments, terminal device 1000 may include one or N display screens 1009, where N is a positive integer greater than 1.

[0189] It should be understood that the structures illustrated in the embodiments of the present application do not constitute a specific limitation on the terminal device 1000. In other embodiments of the present application, the terminal device 1000 may include more or fewer components than shown, or may combine or separate certain components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0190] For details on the operations performed by each component in the terminal device 1000, please refer to Figure 3A-3B The relevant description of the embodiment in the embodiment will not be expanded in detail here.

[0191] The software system of the terminal device 1000 can adopt one or more of a layered architecture, an event-driven architecture, a microkernel architecture, a microservice architecture, or a cloud architecture, and can support local blood pressure detection and analysis functions implemented based on a blood pressure detection algorithm. For example, the application framework layer in the mobile operating system provides an application programming interface (API) and a programming framework for PPG signal analysis applications. In addition, the system library can include multiple functional modules, such as a media library and a graphics processing library for processing user physiological data, which can support the analysis, display, and storage of blood pressure values. The embodiment of the present application takes a mobile operating system with a layered architecture as an example to illustrate the software structure of the terminal device 1000.

[0192] Figure 6B It is a software structure block diagram of the terminal device 1000 of an embodiment of the present application.

[0193] A layered architecture divides software into several layers, each with distinct roles and responsibilities. Layers communicate with each other via software interfaces. In some embodiments, a mobile operating system is divided into four layers: application layer, application framework layer / core services layer, system libraries and runtime layer, and kernel layer.

[0194] The application layer can include a series of application packages.

[0195] like Figure 6B As shown, the application package may include applications such as camera, gallery, calendar, call, map, navigation, WLAN, Bluetooth, music, video, short message, etc.

[0196] The application framework layer provides an application programming interface (API) and programming framework for the applications in the application layer. The application framework layer includes some predefined functions.

[0197] like Figure 6B As shown, the application framework layer may include a window manager, a content provider, a view system, a phone manager, a resource manager, a notification manager, and the like.

[0198] The window manager is used to manage window programs. The window manager can obtain the display size, determine whether there is a status bar, lock the screen, take screenshots, etc.

[0199] Content providers can be used to store and retrieve data, making it accessible to applications. These data can then be used to access and index local data such as phone books, browsing history, and bookmarks, providing users with the necessary information for blood pressure monitoring. This data can include video, images, audio, incoming and outgoing calls, browsing history and bookmarks, and phone books.

[0200] The view system includes visual controls, such as those for displaying text and images. The view system is used to build applications. A display interface can consist of one or more views. For example, a display interface containing a text notification icon might include a view for displaying text and a view for displaying images.

[0201] The phone manager is used to provide communication functions for terminal devices, such as call status management (including answering, hanging up, etc.).

[0202] The resource manager provides various resources for applications, such as localized strings, icons, images, layout files, video files, and so on.

[0203] The Notification Manager allows applications to display notifications in the status bar. These messages can be displayed briefly and then disappear automatically, without requiring user interaction. For example, the Notification Manager can be used to notify users of completed downloads and message reminders. The Notification Manager can also display notifications in the top status bar of the system as icons or scrolling text, such as notifications from background applications, or as dialog windows on the screen. Examples include displaying text messages in the status bar, emitting alert sounds, vibrating the device, or flashing indicator lights.

[0204] The runtime can refer to all code libraries, frameworks, etc. required for the program to run. For example, a Java virtual machine and core libraries are provided to provide the necessary environment for the operation of a blood pressure monitoring solution.

[0205] The system library can include multiple functional modules, such as surface manager, media library, 3D graphics processing library (such as OpenGL ES), 2D graphics engine (such as SGL), etc.

[0206] The surface manager is used to manage the display subsystem and provide fusion of 2D and 3D layers for multiple applications.

[0207] The media library supports playback and recording of a variety of common audio and video formats, as well as static image files. The media library can support a variety of audio and video encoding formats, such as: MPEG4, H.264, MP3, AAC, AMR, JPG, PNG, etc.

[0208] The 3D graphics processing library is used to implement 3D graphics drawing, image rendering, compositing, and layer processing.

[0209] A 2D graphics engine is a drawing engine for 2D drawings.

[0210] The kernel layer is the interface between hardware and software. It includes at least display drivers, audio drivers, and sensor drivers. For example, it provides underlying hardware support for blood pressure monitoring, enabling the blood pressure monitoring and analysis system to efficiently access and process blood pressure data stored in the file system. This underlying support ensures accurate data processing and rapid analysis of blood pressure values, thereby improving overall blood pressure monitoring performance.

[0211] It should be understood that each step in the above method embodiment can be completed by hardware integrated logic circuits in a processor or by software instructions. The method steps disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware processor, or by a combination of hardware and software modules in a processor.

[0212] The present application also provides a terminal device, which may include: a memory and a processor. The memory may be used to store a computer program; the processor may be used to call the computer program in the memory to enable the terminal device to execute the method executed on the terminal device side in any of the above embodiments.

[0213] The present application also provides a terminal device, which may include: a memory and a processor. The memory may be used to store a computer program; the processor may be used to call the computer program in the memory to enable the terminal device to execute the method executed on the terminal device side in any of the above embodiments.

[0214] The present application also provides a chip system, which includes at least one processor for implementing the functions involved in the terminal device side in any of the above embodiments.

[0215] In one possible design, the chip system further includes a memory, which is used to store program instructions and data, and the memory is located inside or outside the processor.

[0216] The chip system can be composed of chips, or can include chips and other discrete devices.

[0217] Optionally, there may be one or more processors in the chip system. The processor may be implemented in hardware or software. When implemented in hardware, the processor may be a logic circuit, an integrated circuit, etc. When implemented in software, the processor may be a general-purpose processor implemented by reading software code stored in a memory.

[0218] Optionally, the memory in the chip system may be one or more. The memory may be integrated with the processor or may be provided separately from the processor, which is not limited in the embodiments of the present application. For example, the memory may be a non-transient processor, such as a read-only memory (ROM), which may be integrated with the processor on the same chip or provided on different chips. The embodiments of the present application do not specifically limit the type of memory or the configuration of the memory and the processor.

[0219] Exemplarily, the chip system can be a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on chip (SoC), a central processor unit (CPU), a network processor (NP), a digital signal processor (DSP), a microcontroller unit (MCU), a programmable logic device (PLD) or other integrated chips.

[0220] The present application also provides a computer program product, which includes: a computer program (also referred to as code, or instructions), which, when executed, enables a computer to execute the method executed on the terminal device side in any of the above embodiments.

[0221] The present application also provides a computer-readable storage medium storing a computer program (also referred to as code or instruction). When the computer program is executed, the computer executes the method executed by the terminal device side in any of the above embodiments.

[0222] The various implementation modes of this application can be combined arbitrarily to achieve different technical effects.

[0223] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described herein are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0224] Those skilled in the art will appreciate that all or part of the process steps in the above-described method embodiments can be implemented by a computer program instructing the relevant hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can include the process steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

[0225] In short, the above description is only an embodiment of the technical solution of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent replacements, improvements, etc. made based on the disclosure of this application should be included in the scope of protection of this application.

Claims

1. A method for blood pressure detection, characterized in that: Applied to electronic equipment; the method comprises: Acquire a first photoplethysmography (PPG) signal of a user, and determine a first electrocardiogram (ECG) signal of the user based on the first PPG signal; performing peak detection on the first PPG signal and the first ECG signal to determine a first pulse transit time of the user; the first pulse transit time comprises a time difference between an R wave peak of the first ECG signal and a pulse wave peak of the first PPG signal; performing a first quality test on the first ECG signal and the first pulse transit time, and if a result of the first quality test is determined to be normal, determining a blood pressure value of the user based on the first pulse transit time; The performing a first quality detection on the first ECG signal and the first pulse transit time includes: comparing the difference in number and the time difference between the R wave peak value of the first ECG signal and the pulse wave peak value of the first PPG signal with preset thresholds to determine a result of the first quality detection; Among them, when the difference between the number of R wave peaks included in the first ECG signal and the number of pulse wave peaks included in the first PPG signal is greater than a preset first threshold, and when the time difference between the R wave peak included in the first ECG signal and the pulse wave peak included in the first PPG signal is greater than a preset second threshold, it is judged that the result of the first quality detection is normal.

2. The method according to claim 1, characterized in that The determining the blood pressure value of the user based on the first pulse transit time includes: The first pulse transit time is input into a calibrated first blood pressure regression model, and the blood pressure value of the user is output.

3. The method according to claim 2, characterized in that The method further comprises: If the calibration result of the user is detected, the first blood pressure regression model is acquired based on the calibration result.

4. The method according to claim 2 or 3, characterized in that The method further comprises: If the calibration result of the user is not detected, obtaining calibration data of the user, wherein the calibration data includes a second PPG signal and a gold standard blood pressure value of the user; determining a second ECG signal of the user based on the second PPG signal, and determining a second pulse transit time of the user based on the second PPG signal and the second ECG signal; performing a second quality detection on the second ECG signal and the second pulse transit time; If the result of the second quality detection is determined to be normal, the second blood pressure regression model is calibrated based on the second pulse transit time and the gold standard blood pressure value to determine the first blood pressure regression model.

5. The method according to any one of claims 1 to 3, characterized in that The first PPG signal includes M first R peaks, and the first ECG signal includes N second R peaks, where M and R are both integers greater than 0; and performing peak detection on the first PPG signal and the first ECG signal to determine the first pulse transit time of the user includes: A first pulse transit time of the user is determined based on a time difference between the M first R peaks and the N second R peaks.

6. The method according to claim 5, characterized in that The first R peak is the R wave peak of the ECG signal; the second R peak is the pulse wave peak of the PPG signal.

7. The method according to claim 6, characterized in that The determining the first pulse transit time of the user based on the time difference between the M first R peaks and the N second R peaks includes: Among the M R wave peaks and N pulse wave peaks, the temporally adjacent R wave peaks and pulse wave peaks are determined as a group; The first pulse transit time is determined based on the time difference between the peak value of each group of R waves and the peak value of the pulse wave.

8. The method according to claim 6 or 7, characterized in that The performing a first quality detection on the first ECG signal and the first pulse transit time includes: If the difference between M and N is greater than a preset first threshold, and the time difference between the M R wave peaks and the N pulse wave peaks is greater than a preset second threshold, the result of the first quality detection is determined to be normal.

9. The method according to claim 6 or 7, characterized in that The performing a first quality detection on the first ECG signal and the first pulse transit time includes: If the difference between M and N is less than a first threshold, the result of the first quality test is determined to be abnormal, and the user is prompted to retest; or, If the time difference between the M R wave peaks and the N pulse wave peaks is less than a second threshold, the result of the first quality detection is determined to be abnormal, and the user is prompted to retest.

10. The method according to any one of claims 1 to 3, characterized in that The determining a first electrocardiogram (ECG) signal based on the first PPG signal includes: The first PPG signal is input into an electrocardiogram signal conversion model, and the first ECG signal is output.

11. The method according to claim 10, characterized in that The electrocardiographic signal conversion model is obtained by inputting Q third PPG signals as sample data in the server and training the preset third ECG signal as a label, or is obtained by training based on historical sample documents in the server.

12. A blood pressure detection device, characterized in that: The device comprises: a first ECG signal determining unit, configured to obtain a first photoplethysmography (PPG) signal of a user, and determine a first electrocardiogram (ECG) signal of the user based on the first PPG signal; a first pulse transit time determination unit, configured to perform peak detection on the first PPG signal and the first ECG signal to determine a first pulse transit time of the user; the first pulse transit time comprising a time difference between an R wave peak of the first ECG signal and a pulse wave peak of the first PPG signal; a blood pressure value determination unit, configured to perform a first quality check on the first ECG signal and the first pulse transit time, and if the result of the first quality check is determined to be normal, determine the user's blood pressure value based on the first pulse transit time; wherein the first quality check on the first ECG signal and the first pulse transit time includes comparing a difference in the number of R wave peaks of the first ECG signal and the pulse wave peak of the first PPG signal and the time difference with preset thresholds, respectively, to determine the result of the first quality check; Among them, when the difference between the number of R wave peaks included in the first ECG signal and the number of pulse wave peaks included in the first PPG signal is greater than a preset first threshold, and when the time difference between the R wave peak included in the first ECG signal and the pulse wave peak included in the first PPG signal is greater than a preset second threshold, it is judged that the result of the first quality detection is normal.

13. The device according to claim 12, characterized in that The blood pressure value determination unit is specifically configured to: The first pulse transit time is input into a calibrated first blood pressure regression model, and the blood pressure value of the user is output.

14. The device according to claim 13, characterized in that The device further comprises: The first blood pressure regression model acquisition unit acquires the first blood pressure regression model based on the calibration result if the calibration result of the user is detected.

15. The device according to claim 13 or 14, characterized in that The device further comprises: a calibration data acquisition unit, which acquires calibration data of the user if no calibration result of the user is detected, wherein the calibration data includes a second PPG signal and a gold standard blood pressure value of the user; a second ECG signal determining unit, configured to determine a second ECG signal of the user based on the second PPG signal, and determine a second pulse transit time of the user based on the second PPG signal and the second ECG signal; a second pulse transit time determining unit, configured to perform a second quality detection on the second ECG signal and the second pulse transit time; The calibration unit calibrates the second blood pressure regression model based on the second pulse transit time and the gold standard blood pressure value to determine the first blood pressure regression model if the result of the second quality detection is determined to be normal.

16. The device according to any one of claims 12 to 14, characterized in that The first PPG signal includes M first R peaks, the first ECG signal includes N second R peaks, and M and R are both integers greater than 0; the first pulse transit time determining unit is specifically configured to: A first pulse transit time of the user is determined based on a time difference between the M first R peaks and the N second R peaks.

17. The device according to claim 16, characterized in that The first R peak is the R wave peak of the ECG signal; the second R peak is the pulse wave peak of the PPG signal.

18. The device according to claim 17, characterized in that The first pulse transit time determining unit is specifically configured to: Among the M R wave peaks and N pulse wave peaks, the temporally adjacent R wave peaks and pulse wave peaks are determined as a group; The first pulse transit time is determined based on the time difference between the peak value of each group of R waves and the peak value of the pulse wave.

19. The device according to claim 17 or 18, characterized in that The blood pressure value determination unit is specifically configured to: If the difference between M and N is greater than a preset first threshold, and the time difference between the M R wave peaks and the N pulse wave peaks is greater than a preset second threshold, the result of the first quality detection is determined to be normal.

20. The device according to claim 17 or 18, characterized in that The blood pressure value determination unit is specifically configured to: If the difference between M and N is less than a first threshold, the result of the first quality test is determined to be abnormal, and the user is prompted to retest; or, If the time difference between the M R wave peaks and the N pulse wave peaks is less than a second threshold, the result of the first quality detection is determined to be abnormal, and the user is prompted to retest.

21. The device according to any one of claims 12-14 and 17-18, characterized in that The first ECG signal determining unit is specifically configured to: The first PPG signal is input into an electrocardiogram signal conversion model, and the first ECG signal is output.

22. The device according to claim 21, characterized in that The electrocardiographic signal conversion model is obtained by inputting Q third PPG signals as sample data in the server and training the preset third ECG signal as a label, or is obtained by training based on historical sample documents in the server.

23. A computer storage medium, characterized in that The computer storage medium stores a computer program, which implements the method according to any one of claims 1 to 11 when executed by a processor.

24. A computer program, characterized in that The computer program comprises instructions, which, when executed by a computer, cause the computer to perform the method according to any one of claims 1 to 11.

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