Blood pressure monitoring method and related apparatus

Abstract

The embodiment of the application discloses a blood pressure monitoring method and related device, which are applied to a smart wearable device; the method comprises the following steps: counting physiological signals of a user in a sampling period; intercepting a motion signal in a first time window as a target motion signal; determining a motion state of the user according to the target motion signal; determining at least one second time window in a plurality of first time windows, taking a PPG signal in the second time window as a target PPG signal, and the motion state of the user corresponding to the second time window satisfying a preset state; and determining a blood pressure rhythm pattern of the user according to the motion state of the user and the target PPG signal. In this way, the physiological signals of the user are continuously monitored, the motion state, the sleep state and the blood pressure rhythm pattern of the user are analyzed, more comprehensive and accurate blood pressure monitoring is provided, the blood pressure rhythm pattern can be more accurately determined, and the risk of a cardiovascular event is reduced.

Description

Technical Field

[0001] This application relates to the field of medical device technology, and in particular to a blood pressure monitoring method and related device. Background Technology

[0002] In today's society, with the accelerated pace of life and increasing work pressure, the incidence of cardiovascular diseases is rising year by year, and hypertension has become a significant public health issue globally. Hypertension is one of the main risk factors for heart disease, stroke, and other cardiovascular diseases. Therefore, monitoring and managing blood pressure is particularly important.

[0003] Traditional blood pressure monitoring methods mainly rely on blood pressure monitors in medical institutions or home blood pressure monitoring devices. However, these devices usually only provide static data from a single measurement and cannot capture the dynamic changes in blood pressure throughout the day, nor can they provide continuous monitoring under different lifestyle conditions. Ambulatory blood pressure monitors, which can measure blood pressure at night, also have the following drawbacks: the definition of nighttime for ambulatory blood pressure monitors is relatively fixed, while the actual sleep time of different people varies greatly, leading to insufficient accuracy in blood pressure interpretation. Summary of the Invention

[0004] In view of this, embodiments of this application provide a blood pressure monitoring method and related device, which monitors the user's physiological signals for 24 consecutive hours and analyzes the user's exercise state, sleep state and blood pressure rhythm pattern through intelligent algorithms, providing more comprehensive and accurate blood pressure monitoring, and can more accurately determine the blood pressure rhythm pattern, thereby reducing the risk of cardiovascular events.

[0005] In a first aspect, embodiments of this application provide a blood pressure monitoring method applied to a smart wearable device; the method includes:

[0006] The physiological signals of users during the statistical sampling period include PPG signals and motion signals.

[0007] The motion signal within the first time window is captured as the target motion signal, and the sampling period includes multiple first time windows;

[0008] Determine the user's motion state based on the target motion signal;

[0009] At least one second time window is determined from multiple first time windows, and the PPG signal in the second time window is used as the target PPG signal. The motion state of the user corresponding to the second time window satisfies the preset state.

[0010] The user's blood pressure rhythm pattern is determined based on the user's exercise status and target PPG signal.

[0011] Secondly, embodiments of this application provide a blood pressure monitoring device applied to a smart wearable device; the blood pressure monitoring device includes: a statistical unit, an interception unit, a first determination unit, a second determination unit, and a third determination unit; wherein, the statistical unit is used to statistically analyze the user's physiological signals during the sampling period, the physiological signals including PPG signals and motion signals; the interception unit is used to intercept the motion signals within a first time window as the target motion signal, the sampling period including multiple first time windows; the first determination unit is used to determine the user's motion state based on the target motion signal; the second determination unit is used to determine at least one second time window among the multiple first time windows, using the PPG signal within the second time window as the target PPG signal, the user's motion state corresponding to the second time window satisfying a preset state; the third determination unit is used to determine the user's blood pressure rhythm pattern based on the user's motion state and the target PPG signal.

[0012] Thirdly, embodiments of this application provide an electronic device, including a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for performing steps in any method of the first aspect of this application.

[0013] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in any method of the first aspect of this application.

[0014] Fifthly, embodiments of this application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in any method of the first aspect of this application. The computer program product may be a software installation package.

[0015] As can be seen, the blood pressure monitoring method and related devices described above first statistically analyze the user's physiological signals during the sampling period, then extract the motion signal within a first time window as the target motion signal, next determine the user's motion state based on the target motion signal, then determine at least one second time window from among the multiple first time windows, and use the PPG signal within the second time window as the target PPG signal, finally determine the user's blood pressure rhythm pattern based on the user's motion state and the target PPG signal. This provides more comprehensive and accurate blood pressure monitoring, enabling more precise determination of blood pressure rhythm patterns, thereby reducing the user's risk of cardiovascular events. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a functional unit block diagram of a smart wearable device provided in an embodiment of this application;

[0018] Figure 2 This is a schematic flowchart of a blood pressure monitoring method provided in an embodiment of this application;

[0019] Figure 3 This is a schematic flowchart of another blood pressure monitoring method provided in the embodiments of this application;

[0020] Figure 4 This is a schematic diagram of a PPG signal curve provided in an embodiment of this application;

[0021] Figure 5 This is a data analysis graph of blood pressure measurement provided in an embodiment of this application;

[0022] Figure 6 This is a functional unit block diagram of a blood pressure monitoring device provided in an embodiment of this application;

[0023] Figure 7 This is a structural block diagram of an electronic device provided in this application. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0025] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0026] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article indicates that the preceding and following related objects have an "or" relationship.

[0027] In this application's embodiments, "multiple" refers to two or more. In this application's embodiments, "connection" refers to various connection methods, such as direct or indirect connections, to achieve communication between devices; this application's embodiments do not impose any limitations on this.

[0028] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0029] The following describes the relevant content, concepts, meanings, technical issues, technical solutions, and beneficial effects involved in the embodiments of this application.

[0030] Photoplethysmograph (PPG) is a non-invasive detection method that uses photoelectric means to detect changes in blood volume in living tissue. The waveform of the PPG signal is similar to that of arterial blood pressure, and the periodicity of the PPG signal corresponds to the heart rhythm.

[0031] In today's society, with the accelerated pace of life and increased work pressure, the incidence of cardiovascular diseases is rising year by year, and hypertension has become a significant public health issue globally. Hypertension is one of the main risk factors for heart disease, stroke, and other cardiovascular diseases. Therefore, blood pressure monitoring and management are particularly important. Traditional blood pressure monitoring methods mainly rely on blood pressure monitors in medical institutions or home blood pressure monitoring devices. However, these devices typically only provide static data from a single measurement and cannot capture the dynamic changes in blood pressure throughout the day, nor can they provide continuous monitoring under different lifestyle conditions. Ambulatory blood pressure monitors, which can measure blood pressure at night, also have the following drawbacks: the definition of nighttime for ambulatory blood pressure monitors is relatively fixed, while the actual sleep time varies significantly among different populations, leading to insufficient accuracy in blood pressure interpretation.

[0032] To address the aforementioned issues, this application provides a blood pressure monitoring method and related device. By monitoring a user's physiological signals for 24 consecutive hours and analyzing the user's exercise state, sleep state, and blood pressure rhythm pattern through intelligent algorithms, it provides more comprehensive and accurate blood pressure monitoring, enabling more accurate determination of blood pressure rhythm pattern and thereby reducing the risk of cardiovascular events.

[0033] First, the method in this application embodiment is applied to smart wearable devices, including but not limited to: augmented reality (AR) / virtual reality (VR) / mixed reality (MR) head-mounted all-in-one devices, smart audio glasses, Bluetooth headsets, wearable speakers, smartwatches / smart bracelets / smart rings, smart clothing / smart sneakers, etc. Combined with... Figure 1 A blood pressure monitoring method according to an embodiment of this application will be described. Figure 1 This is a functional unit block diagram of a smart wearable device provided in an embodiment of this application. The smart wearable device 100 includes a microcontroller 110, an optical sensor 120, and an accelerometer 130. The microcontroller 110 first collects and analyzes the user's physiological signals during a sampling period, including PPG signals and motion signals. Next, the microcontroller 110 extracts the motion signal within a first time window as the target motion signal. The sampling period includes multiple first time windows. Then, the microcontroller 110 determines the user's motion state based on the target motion signal determined by the accelerometer 130. Next, the microcontroller 110 determines at least one second time window within the multiple first time windows, and uses the PPG signal acquired by the optical sensor 120 within the second time window as the target PPG signal. The user's motion state corresponding to the second time window satisfies a preset state. Finally, the microcontroller 110 determines the user's blood pressure rhythm pattern based on the user's motion state and the target PPG signal.

[0034] Specifically, the optical sensor 120 can calculate heart rate and harmonic characteristics within a specific time window, and the accelerometer 130 can analyze the user's motion state to ensure that blood pressure data is collected in a static state, thereby improving the accuracy of blood pressure measurement.

[0035] The following is combined Figure 2 This application describes a blood pressure monitoring method according to an embodiment. Figure 2 This is a flowchart illustrating a blood pressure monitoring method provided in an embodiment of this application, which specifically includes the following steps:

[0036] Step S210: Statistically analyze the user's physiological signals during the sampling period.

[0037] Physiological signals include PPG signals and motion signals; the sampling period refers to a sampling period of a whole day, which can be 24 hours in one day or 48 hours in two days. In this embodiment, a sampling period of 24 hours and a sampling frequency of 100Hz are used as an example.

[0038] Specifically, in one possible example, the user's physiological signals during the sampling period include: acquiring the user's PPG signal during the sampling period using an optical sensor; and acquiring the user's motion signal during the sampling period using an accelerometer, the motion signal being used to characterize the three-axis motion data signal in space, the accelerometer being a three-axis motion sensor.

[0039] Specifically, smart wearable devices are preferably worn on the user's wrist, through methods such as... Figure 1 The optical sensor 120 shown acquires PPG signals, and through, as shown in the figure, the optical sensor 120 ... Figure 1 The accelerometer 130 shown collects motion signals along the X, Y, and Z axes in space.

[0040] As can be seen, in this example, by collecting the user's PPG signal and triaxial motion data in space, more comprehensive and complete user health data can be obtained, reducing data interference caused by motion and improving the accuracy of subsequent blood pressure rhythm pattern determination.

[0041] Step S220: Extract the motion signal within the first time window as the target motion signal.

[0042] The sampling period includes multiple first time windows; specifically, in the embodiments of this application and subsequent examples, the first time window is 15 seconds, and the target motion signal is the motion signal collected by the motion sensor in any 15 seconds within the sampling period.

[0043] Step S230: Determine the user's motion state based on the target motion signal.

[0044] In one possible example, determining the user's motion state based on the target motion signal includes:

[0045] The target motion signal is broken down into multiple sub-target motion signal segments, and the signal strength of each sub-target motion signal is calculated. Based on the signal strength of each sub-target motion signal and its average value, the standard deviation of each sub-target motion signal is determined, and this standard deviation is correlated with the intensity of change in the sub-target motion signal. The standard deviation of each sub-target motion signal is compared with preset intensity data, including a first preset intensity, a second preset intensity, and a third preset intensity, where the first preset intensity is less than the signal strength represented by the second preset intensity, and the second preset intensity is less than the signal strength represented by the third preset intensity. If the standard deviation of each sub-target motion signal is less than the first preset intensity, the user's motion state is determined to be stationary. If the standard deviation of each sub-target motion signal is less than the second preset intensity, the user's motion state is determined to be relatively stationary. If the standard deviation of each sub-target motion signal is less than the third preset intensity, the user's motion state is determined to be low-intensity motion. If the standard deviation of each sub-target motion signal is greater than or equal to the third preset intensity, the user's motion state is determined to be high-intensity motion.

[0046] Specifically, ACCx[n], ACCy[n], and ACCz[n] represent the readings of the continuous X, Y, and Z axes of the motion sensor (accelerometer signal), respectively. First, the target motion signal, i.e., the 15-second ACCx[n], ACCy[n], and ACCz[n] signals, is divided into three sub-target motion signal segments, each lasting 5 seconds. The signal strength of each sub-target motion signal is then calculated using the following formula:

[0047] ACC[n] = ACCx[n] 2 +ACCy[n] 2 +ACCz[n] 2

[0048] Where ACC[n] represents the energy of the triaxial acceleration signal, and the intensity of the energy change reflects the intensity of motion. If ACC[n] is stationary, it is 100% contributed by gravitational acceleration. Here, it is assumed that the AC energy of the first segment of the triaxial sensing signal ACC[n] (0-5 seconds), the second segment of the triaxial sensing signal ACC[n] (5.01-10 seconds), and the third segment of the triaxial sensing signal ACC[n] (10.01-15 seconds) are represented by ACCseg1 to ACCseg3 respectively, and presented through standard deviation data, as shown in the following formula:

[0049]

[0050] in, These represent the average energy of three 5-second acceleration sensing signals. ACCseg1 to ACCseg3 represent the representative energy intensity of the AC signal.

[0051] Furthermore, the standard deviation data of each sub-target motion signal is compared with the preset intensity data. For example, the first preset intensity is 1500, the second preset intensity is 3000, and the third preset intensity is 5000.

[0052] (1) When ACCseg1 < 1500, ACCseg2 < 1500, and ACCseg3 < 1500, it means that the smart wearable device did not perform any slight movements during the three 5-second intervals, and the user was in a static state for 15 seconds. (2) When ACCseg1 < 3000, ACCseg2 < 3000, and ACCseg3 < 3000, it means that the smart wearable device performed at least one slight movement during the three 5-second intervals, and the user was in a relatively static state. (3) When ACCseg1 < 5000, ACCseg2 < 5000, and ACCseg3 < 5000, it means that the smart wearable device performed at least one low-intensity movement during the three 5-second intervals, and the user was in a low-intensity movement state.

[0053] As can be seen in this example, by using smart wearable devices to collect dynamic physiological signals from the user's wrist in daily life, and by determining the user's exercise status through motion data, the effects of different life states on blood pressure can be distinguished, reducing data interference caused by exercise and improving the quality of blood pressure monitoring.

[0054] In other possible examples, the method also includes: statistically analyzing the motion state of users corresponding to multiple first time windows within a third time window, wherein the third time window includes multiple first time windows and the duration of the third time window is less than the duration of the sampling period; determining the proportion of users in a stationary state and a relatively stationary state within the third time window; if the proportion is greater than a preset ratio, determining that the user is in a resting state within the current third time window; if the user is determined to be in a resting state within multiple consecutive third time windows, determining that the user is in a sleeping state.

[0055] Specifically, in this embodiment, a 20-minute period is preferred as the third time window, but other durations are also possible in other embodiments. Each 20-minute period includes 80 first time windows. The percentage of time the user is in a stationary or relatively stationary state is determined. If this percentage exceeds a preferred preset ratio of 70%, the 20 minutes are defined as a rest period. Further, if multiple consecutive third time windows are all determined to be in a rest period, the user is determined to be in a sleep state. These multiple consecutive third time windows can be 3 or 4, and during the sampling time, the user is in a non-sleep state, excluding rest and sleep states, and is active during the day.

[0056] As can be seen in this example, sleep status is determined by the user's movement patterns and physiological signals, making nighttime blood pressure monitoring more accurate, avoiding individual differences in sleep time, and helping to improve the adaptability and personalization of nighttime blood pressure assessment.

[0057] Step S240: Determine at least one second time window from among the multiple first time windows, and use the PPG signal within the second time window as the target PPG signal.

[0058] Among them, the user's motion state corresponding to the second time window meets the preset state;

[0059] In one possible example, the time window for determining the user's motion state as stationary and the time window for determining the relative stationary state is used as a second time window.

[0060] The use of stillness and relative stillness in blood pressure measurements is crucial for ensuring accuracy and reliability. Maintaining a still state reduces errors, as movement or emotional fluctuations can cause blood pressure to rise or fall, introducing errors. A still state minimizes the impact of these external factors, leading to more accurate results. It also ensures consistency; maintaining the same conditions (sitting posture, arm position, measurement time) is essential for multiple blood pressure measurements. A still state helps ensure consistency, making comparison and analysis easier. Finally, it improves measurement efficiency; frequent movement or changes in posture during blood pressure measurement can take longer to stabilize. A still state speeds up the measurement process and increases efficiency.

[0061] Step S250: Determine the user's blood pressure rhythm pattern based on the user's exercise status and target PPG signal.

[0062] Among them, blood pressure rhythm pattern refers to the specific pattern of blood pressure changes throughout the day. Normal blood pressure often shows a certain rhythm of change, that is, blood pressure is low at night, rises in the morning, and forms two peaks and one trough within a day. This rhythm is usually called "dipper" blood pressure.

[0063] As can be seen, in this embodiment, the user's physiological signals are first statistically analyzed during the sampling period. Then, motion signals within a first time window are extracted as the target motion signal. Next, the user's motion state is determined based on the target motion signal. Then, at least one second time window is determined from among the multiple first time windows, and the PPG signal within the second time window is used as the target PPG signal. Finally, the user's blood pressure rhythm pattern is determined based on the user's motion state and the target PPG signal. This provides more comprehensive and accurate blood pressure monitoring, enabling more accurate determination of blood pressure rhythm patterns, thereby reducing the user's risk of cardiovascular events.

[0064] In one possible example, please refer to [link / reference]. Figure 3 , Figure 3 This is a schematic flowchart of another blood pressure monitoring method provided in an embodiment of this application, as shown below. Figure 3 As shown, the user's blood pressure rhythm pattern is determined based on the user's exercise status and target PPG signal, including the following steps:

[0065] Step S301: Determine the target heart rate data and target harmonic characteristic data based on the target PPG signal.

[0066] Specifically, in one possible example, determining target heart rate data and target harmonic feature data based on the target PPG signal includes: determining multiple heart rate troughs based on the target PPG signal, and determining the target heart rate based on the multiple heart rate troughs; determining harmonic feature data of the pulse wave between any two interval heart rate troughs among the multiple heart rate troughs, and determining the target harmonic feature data based on the harmonic feature data.

[0067] Please refer to details. Figure 4 , Figure 4 This is a schematic diagram of a PPG signal curve provided in an embodiment of this application. Preferably, the heart rate trough value under each pulse wave is found by the Pantompkings method or other sliding window detection method. The trough position (local lowest point) is: foot1-foot15. The calculation formula for the target heart rate data is as follows:

[0068]

[0069] Where footi is the i-th data point m of the heart rate detection.

[0070] Calculate the k-th harmonic feature Ck,i of the i-th pulse wave between each footi and footi+1. The formula for calculating the target harmonic feature data is as follows:

[0071]

[0072] ω=2π×HRi

[0073] Among them, C 0,i ω represents the average value of the i-th pulse wave; ω represents the base frequency converted from the period of the i-th pulse wave.

[0074] Step S302: Determine the target heart rate statistical parameters based on the target heart rate data; and determine the target harmonic characteristic statistical parameters based on the target harmonic characteristic data.

[0075] Specifically, in one possible example, based on the target heart rate data, the target heart rate statistical parameters are determined, including: determining the average heart rate over multiple second time windows; and determining the target heart rate statistical parameters over a third time window based on the average heart rate.

[0076] The formula for calculating the average heart rate (HRavg) within the second time window is as follows:

[0077]

[0078] Where N represents the number of pulse waves.

[0079] Furthermore, based on the average heart rate, the target heart rate statistical parameters within the third time window are determined. Preferably, every 20 minutes, the average heart rate HRavg,m,p within all 15-second time windows is recorded and a weighted average is taken, as shown in the following formula:

[0080]

[0081] Where HRavg,m,p represents the average heart rate calculated within the m-th 15-second time window of the p-th 20-minute period over a 24-hour period. If the individual is exercising during this time window, it is not included in the calculation. HRp,20min is the target heart rate statistical parameter for the p-th 20-minute period.

[0082] Specifically, in one possible example, the target harmonic characteristic statistical parameters are determined based on the target harmonic characteristic data, including: determining the average value of the harmonic characteristics within multiple second time windows; and determining the target harmonic characteristic statistical parameters within a third time window based on the average value of the harmonic characteristics.

[0083] Preferably, every 20 minutes, the average heart rate Ck,represent,p,m within all 15-second time windows is recorded and a weighted average is taken, as shown in the following formula:

[0084]

[0085] Where Ck,represent,p,m represents the representative value of the k-th harmonic calculated within the m-th 15-second time window of the p-th 20-minute period in 24 hours. If this time window is in motion, it is not included in the calculation. Ck,p,20min is the target harmonic characteristic statistical parameter of the p-th 20-minute period.

[0086] Step S303: Based on the user's exercise status, determine the difference between the target heart rate statistical parameters and the target harmonic characteristic data when the user is in a sleep state and not in a sleep state.

[0087] Specifically, in one possible example, determining the difference in target heart rate statistics when the user is asleep and not asleep includes:

[0088] In the third time window, determine the first target heart rate statistics parameter for the time window when the user is asleep; and...

[0089] Determine the second target heart rate statistics parameter for the time window when the user is in a non-sleep state;

[0090] Based on the first target heart rate statistical parameters, the second target heart rate statistical parameters, and the first and second parameters, the difference between the target heart rate statistical parameters is calculated and determined. The first parameter is used to characterize the number of third time windows when the user is in a sleep state, and the second parameter is used to characterize the number of third time windows when the user is not in a sleep state.

[0091] Specifically, the average difference percentage between day and night heart rates, Diff_HR(%), is calculated using the following formula:

[0092]

[0093] N1 and N2 represent the number of heart rate statistics parameters for 20 minutes during daytime activity and sleep, respectively. In other words, N1 is the first parameter and N2 is the second parameter.

[0094] As can be seen, in this example, by calculating the statistical parameters of heart rate at fixed intervals, time series analysis of heart rate and blood pressure data is achieved, thereby improving the accuracy and reliability of blood pressure pattern identification.

[0095] Specifically, in one possible example, determining the difference in target harmonic feature data when the user is in a sleep state and not in a sleep state includes:

[0096] Within the third time window, determine the first target harmonic characteristic data for the time window when the user is asleep; and...

[0097] Determine the second target harmonic characteristic data for the time window when the user is in a non-sleep state;

[0098] Based on the first target harmonic characteristic data, the second target harmonic characteristic data, and the third and fourth parameters, the difference in the target harmonic characteristic data is calculated. The third parameter is used to characterize the number of third time windows when the user is in a sleep state, and the fourth parameter is used to characterize the number of third time windows when the user is not in a sleep state.

[0099] Specifically, similarly as described above, the average difference percentage of the k-th harmonic characteristic between day and night, Diff_Ck(%), is calculated using the following formula:

[0100]

[0101] Among them, N3 and N4 represent the number of 20-minute harmonic statistical parameters in the daytime activity state and the sleep state, respectively. That is, N3 is the third parameter and N4 is the fourth parameter.

[0102] As can be seen, in this example, by calculating the statistical parameters of the harmonic characteristics at fixed intervals, time series analysis of heart rate and blood pressure data is achieved, thereby improving the accuracy and reliability of blood pressure pattern identification.

[0103] Step S304: Compare the differences in heart rate statistical parameters with the empirical threshold for heart rate, and the differences in target harmonic characteristic data with the empirical threshold for harmonics, to determine the user's blood pressure rhythm pattern.

[0104] In other possible examples, determining a user's blood pressure rhythm pattern includes: determining the blood pressure rhythm pattern as a dipper rhythm when the difference between the target heart rate statistical parameters is less than the heart rate empirical threshold and the difference between the target harmonic feature data is less than the harmonic empirical threshold; and determining the blood pressure rhythm pattern as a non-dipper rhythm when the difference between the target heart rate statistical parameters is greater than or equal to the heart rate empirical threshold and the difference between the target harmonic feature data is greater than or equal to the harmonic empirical threshold.

[0105] For example, the heart rate difference Diff_HR and the difference in the 4th harmonic characteristic Diff_C4 can be used as the main parameters for determining whether a heart rate pattern is dipping or non-dipping. Please refer to [link to relevant documentation]. Figure 5 , Figure 5 This is a data analysis graph of blood pressure measurement provided in an embodiment of this application, such as... Figure 5As shown, 24-hour sensor signals from a smart wearable device were collected from 8 patients. The preset heart rate threshold α1 = 6%, and the 4th harmonic threshold α2 = 33%. A dipper-shaped blood pressure rhythm was defined as Diff_HR < α1 and Diff_C4 < α2. A non-dipper-shaped blood pressure rhythm was defined as Diff_HR < α1 or Diff_C4 < α2.

[0106] As can be seen, in this embodiment, the calculated diurnal heart rate and harmonic characteristics can effectively distinguish the nocturnal blood pressure rhythm pattern; compared with the clinical gold standard ambulatory blood pressure measurement, the accuracy of the pre-blood pressure rhythm data analysis of 8 patients (dipper and non-dipper) is reliable, and the optical characteristics can effectively distinguish different blood pressure rhythm patterns (chi-square test characteristic value of 8, p value less than 0.005), which can provide richer decision support information for clinical diagnosis and treatment.

[0107] The above primarily describes the solutions of the embodiments of this application from the perspective of the method execution process. It is understood that, in order to achieve the above functions, mobile electronic devices include corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments provided herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0108] This application embodiment can divide the electronic device into functional units according to the above method example. For example, each function can be divided into a separate functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0109] and Figure 2 The implementation is consistent with the previous one; please refer to [link / reference]. Figure 6 , Figure 6This is a functional unit block diagram of a blood pressure monitoring device provided in an embodiment of this application. The blood pressure monitoring device 600 includes: a statistical unit 610, an extraction unit 620, a first determination unit 630, a second determination unit 640, and a third determination unit 650. The statistical unit 610 is used to statistically analyze the user's physiological signals during the sampling period, including PPG signals and motion signals. The extraction unit 620 is used to extract motion signals within a first time window as target motion signals, and the sampling period includes multiple first time windows. The first determination unit 630 is used to determine the user's motion state based on the target motion signal. The second determination unit 640 is used to determine at least one second time window from the multiple first time windows, using the PPG signal within the second time window as the target PPG signal, and the user's motion state corresponding to the second time window satisfies a preset state. The third determination unit 650 is used to determine the user's blood pressure rhythm pattern based on the user's motion state and the target PPG signal.

[0110] In one possible embodiment, the smart wearable device includes: an optical sensor and an accelerometer; regarding the user's physiological signals during the statistical sampling period, the statistical unit 610 is specifically used to: acquire the user's PPG signal during the sampling period via the optical sensor; and acquire the user's motion signal during the sampling period via the accelerometer, the motion signal being used to characterize three-axis motion data signals in space, the accelerometer being a three-axis motion sensor.

[0111] In one possible embodiment, the user's motion state is determined based on the target motion signal. Specifically, the first determining unit 630 is configured to: divide the target motion signal into multiple sub-target motion signal segments and calculate the signal strength of each sub-target motion signal; determine the standard deviation data of each sub-target motion signal based on the signal strength of each sub-target motion signal and the average signal strength of each sub-target motion signal, wherein the standard deviation data of the sub-target motion signal is correlated with the change intensity of the sub-target motion signal; and compare the standard deviation data of each sub-target motion signal with preset intensity data, wherein the preset intensity data includes: a first preset intensity, a second preset intensity, and a third preset intensity. The intensity is determined as follows: a first preset intensity is less than the signal intensity represented by a second preset intensity, and the second preset intensity is less than the signal intensity represented by a third preset intensity. If the standard deviation of the motion signal of each sub-target is less than the first preset intensity, the user's motion state is determined to be a stationary state. If the standard deviation of the motion signal of each sub-target is less than the second preset intensity, the user's motion state is determined to be a relatively stationary state. If the standard deviation of the motion signal of each sub-target is less than the third preset intensity, the user's motion state is determined to be a low-intensity motion state. If the standard deviation of the motion signal of each sub-target is greater than or equal to the third preset intensity, the user's motion state is determined to be a high-intensity motion state.

[0112] In one possible embodiment, at least one second time window is determined among a plurality of first time windows. The second determining unit 640 is specifically used to: determine, within the plurality of first time windows, the time windows in which the user's motion state is stationary and the time windows in which the user's motion state is relatively stationary, as the second time windows.

[0113] In one possible embodiment, the blood pressure monitoring device 600 is further configured to: statistically analyze the user's motion state corresponding to multiple first time windows within a third time window, wherein the third time window includes multiple first time windows and the duration of the third time window is less than the duration of the sampling period; determine the proportion of the user's motion state being stationary or relatively stationary within the third time window; if the proportion is greater than a preset ratio, determine that the user is in a resting state within the current third time window; if the user is determined to be in a resting state within multiple consecutive third time windows, determine that the user is in a sleeping state.

[0114] In one possible embodiment, the user's blood pressure rhythm pattern is determined based on the user's exercise state and the target PPG signal. The third determining unit 650 is specifically used for: determining target heart rate data and target harmonic characteristic data based on the target PPG signal; determining target heart rate statistical parameters based on the target heart rate data; and determining target harmonic characteristic statistical parameters based on the target harmonic characteristic data; determining the difference between the target heart rate statistical parameters and the target harmonic characteristic data when the user is in a sleep state and not in a sleep state based on the user's exercise state; and comparing the difference in heart rate statistical parameters with a heart rate empirical threshold and the difference in target harmonic characteristic data with a harmonic empirical threshold to determine the user's blood pressure rhythm pattern.

[0115] In one possible embodiment, target heart rate data and target harmonic feature data are determined based on the target PPG signal. The third determining unit 650 is specifically used to: determine multiple heart rate troughs based on the target PPG signal, and determine the target heart rate based on the multiple heart rate troughs; determine the harmonic feature data of the pulse wave between any two interval heart rate troughs among the multiple heart rate troughs, and determine the target harmonic feature data based on the harmonic feature data.

[0116] In one possible embodiment, a target heart rate statistical parameter is determined based on the target heart rate data. The third determining unit 650 is specifically used to: determine the average heart rate within a plurality of second time windows; and determine the target heart rate statistical parameter within a third time window based on the average heart rate.

[0117] In one possible embodiment, the third determining unit 650 determines the difference in target heart rate statistical parameters when the user is in a sleep state and not in a sleep state. Specifically, it is used to: determine a first target heart rate statistical parameter for a time window when the user is in a sleep state within a third time window; and determine a second target heart rate statistical parameter for a time window when the user is not in a sleep state; and calculate the difference in the target heart rate statistical parameters based on the first target heart rate statistical parameter, the second target heart rate statistical parameter, and the first and second parameters, wherein the first parameter is used to characterize the number of third time windows corresponding to the user being in a sleep state, and the second parameter is used to characterize the number of third time windows corresponding to the user being not in a sleep state.

[0118] In one possible embodiment, the target harmonic characteristic statistical parameters are determined based on the target harmonic characteristic data. The third determining unit 650 is specifically used to: determine the average value of harmonic characteristics within a plurality of second time windows; and determine the target harmonic characteristic statistical parameters within a third time window based on the average value of harmonic characteristics.

[0119] In one possible embodiment, the third determining unit 650 determines the difference between the target harmonic feature data when the user is in a sleep state and not in a sleep state. Specifically, it is used to: determine the first target harmonic feature data of the time window when the user is in a sleep state within a third time window; and determine the second target harmonic feature data of the time window when the user is not in a sleep state; and calculate the difference between the target harmonic feature data based on the first target harmonic feature data, the second target harmonic feature data, and the third and fourth parameters, wherein the third parameter is used to characterize the number of third time windows corresponding to the user being in a sleep state, and the fourth parameter is used to characterize the number of third time windows corresponding to the user being not in a sleep state.

[0120] In one possible embodiment, the third determining unit 650 determines the user's blood pressure rhythm pattern by: determining the blood pressure rhythm pattern as a dipper rhythm when the difference between the target heart rate statistical parameters is less than the heart rate empirical threshold and the difference between the target harmonic feature data is less than the harmonic empirical threshold; and determining the blood pressure rhythm pattern as a non-dipper rhythm when the difference between the target heart rate statistical parameters is greater than or equal to the heart rate empirical threshold and the difference between the target harmonic feature data is greater than or equal to the harmonic empirical threshold.

[0121] It is understood that since the method embodiments and the device embodiments are different presentations of the same technical concept, the content of the method embodiment section in this application should be adapted to the device embodiment section in a synchronous manner, and will not be repeated here.

[0122] Figure 7 This is a structural block diagram of an electronic device provided in this application. For example... Figure 7As shown, the electronic device 700 may include one or more components: a processor 701 and a memory 702 coupled to the processor 701, wherein the memory 702 may store one or more computer programs, which may be configured to implement the methods described in the examples above when executed by one or more processors 701. The electronic device 700 may be as described above. Figure 1 100 intelligent wearable devices.

[0123] Processor 701 may include one or more processing cores. Processor 701 connects to various parts within the electronic device 700 using various interfaces and lines, and performs various functions and processes data of the electronic device 700 by running or executing instructions, programs, code sets, or instruction sets stored in memory 702, and by calling data stored in memory 702. Optionally, processor 701 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). Processor 701 may integrate one or more of a Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. It is understood that the aforementioned modem may also not be integrated into processor 701, but may be implemented separately through a communication chip.

[0124] The memory 702 may include random access memory (RAM) or read-only memory (ROM). The memory 702 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 702 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as touch functionality, sound playback functionality, image playback functionality, etc.), and instructions for implementing the various method examples described above. The data storage area may also store data created during the use of the electronic device 700.

[0125] It is understood that the electronic device 700 may include more or fewer structural elements than those shown in the above block diagram, such as a power module, physical buttons, WiFi (Wireless Fidelity) module, speaker, Bluetooth module, sensor, etc., without limitation.

[0126] This application also provides a computer storage medium storing a computer program / instructions thereon, which, when executed by a processor, implements some or all of the steps of any of the methods described in the above method embodiments.

[0127] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments.

[0128] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0129] In the several embodiments provided in this application, it should be understood that the disclosed methods, apparatuses, and systems can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for example, the division of units is merely a logical functional division, and there may be other division methods in actual implementation; for example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0130] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0131] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can be physically comprised separately, or two or more units can be integrated into one unit. The integrated unit described above can be implemented in hardware or in the form of hardware plus software functional units.

[0132] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute partial steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes: a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, volatile memory, or non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM), etc., which are various media capable of storing program code.

[0133] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can easily conceive of variations or substitutions without departing from the spirit and scope of the present invention, and various modifications and alterations can be made, including combinations of the different functions and implementation steps described above, as well as software and hardware implementation methods, all of which are within the protection scope of the present invention.

Claims

1. A method for monitoring blood pressure, characterized in that, Applied to smart wearable devices; the method includes: The physiological signals of users during the statistical sampling period include PPG signals and motion signals. The motion signal within a first time window is extracted as the target motion signal, and the sampling period includes multiple first time windows; The user's motion state is determined based on the target motion signal; At least one second time window is determined from multiple first time windows, and the PPG signal in the second time window is taken as the target PPG signal. The motion state of the user corresponding to the second time window satisfies a preset state. The user's blood pressure rhythm pattern is determined based on the user's exercise status and the target PPG signal.

2. The method according to claim 1, characterized in that, The smart wearable device includes: an optical sensor and an accelerometer; the user's physiological signals during the statistical sampling period include: The optical sensor is used to acquire the PPG signal of the user during the sampling period; The acceleration sensor collects the user's motion signal during the sampling period. The motion signal is used to characterize the three-axis motion data signal in space. The acceleration sensor is a three-axis motion sensor.

3. The method according to claim 1 or 2, characterized in that, Determining the user's motion state based on the target motion signal includes: The target motion signal is divided into multiple sub-target motion signal segments, and the signal strength of each sub-target motion signal is calculated. Based on the signal strength of each sub-target motion signal and the average signal strength of each sub-target motion signal, the standard deviation data of each sub-target motion signal is determined, and the standard deviation data of the sub-target motion signal is correlated with the change intensity of the sub-target motion signal; The standard deviation data of the motion signal of each sub-target is compared with preset intensity data, which includes: a first preset intensity, a second preset intensity, and a third preset intensity, wherein the first preset intensity is less than the signal intensity represented by the second preset intensity, and the second preset intensity is less than the signal intensity represented by the third preset intensity. If the standard deviation of the motion signal of each sub-target is less than the first preset intensity, then the user's motion state is determined to be a stationary state. If the standard deviation of the motion signal of each sub-target is less than the second preset intensity, then the user's motion state is determined to be a relatively stationary state. If the standard deviation of the motion signal of each sub-target is less than the third preset intensity, then the user's motion state is determined to be a low-intensity motion state. If the standard deviation of the motion signal of each sub-target is greater than or equal to the third preset intensity, then the user's motion state is determined to be a high-intensity motion state.

4. The method according to claim 3, characterized in that, Determining at least one second time window within a plurality of first time windows includes: Within the plurality of first time windows, the time window in which the user's motion state is the stationary state and the relatively stationary state is determined is used as the second time window.

5. The method according to claim 3, characterized in that, The method further includes: Within a third time window, the motion state of the user corresponding to multiple first time windows is statistically analyzed. The third time window includes multiple first time windows, and the duration of the third time window is less than the duration of the sampling period. Determine the percentage of times the user's motion state is in the stationary state and the percentage of times the user is in the relatively stationary state within the third time window; If the percentage is greater than the preset percentage, it is determined that the user is in a resting state within the current third time window; If the user is determined to be in a resting state within multiple consecutive third time windows, then the user is determined to be in a sleeping state.

6. The method according to claim 5, characterized in that, The step of determining the user's blood pressure rhythm pattern based on the user's exercise state and the target PPG signal includes: Determine the target heart rate data and target harmonic characteristic data based on the target PPG signal; Based on the target heart rate data, determine the target heart rate statistical parameters; and based on the target harmonic characteristic data, determine the target harmonic characteristic statistical parameters. Based on the user's movement state, determine the difference between the target heart rate statistical parameters and the target harmonic characteristic data when the user is in the sleep state and not in the sleep state; By comparing the differences in the heart rate statistical parameters with the empirical heart rate threshold, and the differences in the target harmonic characteristic data with the empirical harmonic threshold, the blood pressure rhythm pattern of the user is determined.

7. The method according to claim 6, characterized in that, The step of determining the target heart rate data and target harmonic characteristic data based on the target PPG signal includes: Multiple heart rate trough values ​​are determined based on the target PPG signal, and the target heart rate is determined based on the multiple heart rate trough values; Determine the harmonic characteristic data of the pulse wave between any two intervals of the plurality of heart rate troughs, and determine the target harmonic characteristic data based on the harmonic characteristic data.

8. The method according to claim 7, characterized in that, The step of determining the target heart rate statistical parameters based on the target heart rate data includes: Determine the average heart rate within the plurality of second time windows; Based on the average heart rate, the target heart rate statistical parameters within the third time window are determined.

9. The method according to claim 8, characterized in that, The difference in the target heart rate statistical parameters when determining that the user is in the sleep state and not in the sleep state includes: In the third time window, a first target heart rate statistical parameter is determined for the time window when the user is in the sleep state; and, Determine the second target heart rate statistical parameter for the time window when the user is in the non-sleep state; Based on the first target heart rate statistical parameter, the second target heart rate statistical parameter, and the first parameter and the second parameter, the difference of the target heart rate statistical parameter is calculated and determined, wherein the first parameter is used to characterize the number of the third time windows corresponding to when the user is in the sleep state, and the second parameter is used to characterize the number of the third time windows corresponding to when the user is in the non-sleep state.

10. The method according to claim 7, characterized in that, The step of determining the target harmonic characteristic statistical parameters based on the target harmonic characteristic data includes: Determine the average value of the harmonic characteristics within the plurality of second time windows; Based on the average value of the harmonic characteristics, the statistical parameters of the target harmonic characteristics within the third time window are determined.

11. The method according to claim 10, characterized in that, The difference between the target harmonic feature data when determining that the user is in the sleep state and not in the sleep state includes: In the third time window, the first target harmonic characteristic data of the time window when the user is in the sleep state is determined; and, Determine the second target harmonic characteristic data for the time window when the user is in the non-sleep state; Based on the first target harmonic feature data, the second target harmonic feature data, and the third and fourth parameters, the difference in the target harmonic feature data is calculated and determined. The third parameter is used to characterize the number of third time windows corresponding to when the user is in the sleep state, and the fourth parameter is used to characterize the number of third time windows corresponding to when the user is in the non-sleep state.

12. The method according to any one of claims 1-11, characterized in that, Determining the user's blood pressure rhythm pattern includes: When the difference between the target heart rate statistical parameters is less than the heart rate empirical threshold and the difference between the target harmonic characteristic data is less than the harmonic empirical threshold, the blood pressure rhythm pattern is determined to be a dipper-shaped blood pressure rhythm. When the difference between the target heart rate statistical parameters is greater than or equal to the heart rate empirical threshold, and the difference between the target harmonic characteristic data is greater than or equal to the harmonic empirical threshold, the blood pressure rhythm pattern is determined to be a non-dipper blood pressure rhythm.

13. A blood pressure monitoring device, characterized in that, Applied to smart wearable devices; the blood pressure monitoring device includes: a statistical unit, an interception unit, a first determination unit, a second determination unit, and a third determination unit; wherein, The statistical unit is used to statistically analyze the user's physiological signals during the sampling period, including PPG signals and motion signals. The interception unit is used to intercept the motion signal within a first time window as the target motion signal, and the sampling period includes multiple first time windows; The first determining unit is used to determine the user's motion state based on the target motion signal; The second determining unit is configured to determine at least one second time window among multiple first time windows, take the PPG signal within the second time window as the target PPG signal, and the motion state of the user corresponding to the second time window satisfies a preset state. The third determining unit is used to determine the user's blood pressure rhythm pattern based on the user's exercise state and the target PPG signal.

14. An electronic device, characterized in that, It includes a processor and a memory, the memory being used to store one or more programs and configured to be executed by the processor, the programs including instructions for performing the steps of the method as described in any one of claims 1-12.

15. A computer-readable storage medium, characterized in that, A computer program for storing electronic data interchange is provided, wherein the computer program causes a computer to perform the method as described in any one of claims 1-12.

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