Intelligent blood pressure monitoring device and method for patient with atrial fibrillation, and processor

Abstract

An intelligent blood pressure monitoring device and method for a patient with atrial fibrillation, and a processor. The device comprises a blood pressure measurement module, an electrocardiogram measurement module, and a processing module. The blood pressure measurement module is used for measuring arterial blood pressure data of a target subject. The electrocardiogram measurement module is used for acquiring electrocardiogram data of the target subject and generating electrocardiographic data. The processing module is used for performing time alignment of the electrocardiographic data and the arterial blood pressure data, determining R-R interval stability in the electrocardiogram data, and selecting arterial blood pressure data or calculating an average value of the arterial blood pressure data on the basis of the R-R interval stability, P wave variability, and / or atrial fibrillation status in the electrocardiogram data. The device and method can recognize a period of stable heart rhythm, ensuring the reliability and practicability of a measurement result. By dynamically adjusting the measurement frequency and duration, and selecting data that is less affected by atrial fibrillation, the number of unnecessary measurements is effectively reduced, and meanwhile, the accuracy of blood pressure data is improved.

Description

A smart blood pressure monitoring device, method, and processor for patients with atrial fibrillation Technical Field

[0001] This invention relates to the field of blood pressure monitoring technology, and more particularly to an intelligent blood pressure monitoring device, method, and processor for patients with atrial fibrillation. Background Technology

[0002] Atrial fibrillation (AF) is a common cardiac arrhythmia, with a particularly high incidence in the elderly. The irregular heartbeat in AF patients leads to unstable cardiac output, resulting in significant fluctuations in blood pressure. This irregularity not only increases the risk of cardiovascular events but also makes blood pressure measurement more complex and challenging. Traditional blood pressure measurement methods, such as cuff blood pressure monitors, rely on a regular pulse wave for accurate measurement. However, in AF patients, the irregular heartbeat can introduce errors into these traditional methods, leading to false low blood pressure readings. False low blood pressure refers to a measurement lower than the actual blood pressure value, which can mislead clinical decisions, leading to unnecessary treatment or delays in necessary interventions.

[0003] False hypotension is associated with multiple factors. First, the irregular heartbeat in patients with atrial fibrillation leads to unstable cardiac output and large fluctuations in blood pressure, making it difficult for traditional blood pressure measurement methods to accurately capture true blood pressure values. Second, traditional blood pressure monitors typically rely on regular pulse waves to measure blood pressure, but in patients with atrial fibrillation, the irregular pulse waves can lead to inaccurate measurements. Furthermore, elderly patients with atrial fibrillation often have other cardiovascular diseases, such as hypertension, which further increases the complexity of blood pressure measurement and the risk of false hypotension. Adverse consequences of false hypotension include misdiagnosis and mistreatment, increased cardiovascular risk, and impaired treatment effectiveness. For example, false hypotension may cause doctors to misjudge a patient's blood pressure status, leading to unnecessary antihypertensive treatment or delaying necessary vasopressor treatment, thereby increasing the risk of cardiovascular events and reducing treatment efficacy.

[0004] The main reasons why current conventional blood pressure measuring devices cannot identify and avoid false hypotension are technological limitations, insufficient data processing, lack of intelligent analysis functions, and inadequate calibration and verification. Conventional blood pressure monitors typically use the oscillation method or auscultation method, which rely on regular pulse waves to measure blood pressure. However, in patients with atrial fibrillation, these methods are difficult to use accurately due to irregular pulse waves. Furthermore, conventional blood pressure monitors lack advanced data processing algorithms and cannot effectively identify and correct measurement errors caused by irregular heartbeats. Even averaging multiple measurements may not completely eliminate these errors. Simultaneously, conventional blood pressure monitors lack intelligent analysis functions and cannot adjust measurement strategies based on the patient's specific situation (such as irregular heart rate), leading to inaccurate results. Finally, the characteristics of atrial fibrillation patients may not have been fully considered during the calibration and verification process of conventional blood pressure monitors, resulting in their inability to accurately identify and avoid false hypotension in practical applications.

[0005] For example, CN117752313A discloses a blood pressure measuring device and its method for determining the presence of atrial fibrillation, as well as a readable storage medium. The method includes: pulse waveform detection and judgment; acquiring a sufficient number of pulse interval sequences during a single decompression process; determining the presence of atrial fibrillation using difference sequences and small sequences; determining whether the change in the pulse sequence exceeds a pulse change threshold using the pulse interval difference sequence; evenly distributing the pulse interval difference sequence into multiple small sequences; and determining the presence of atrial fibrillation when the sum of the sequence results exceeds a set result value. This technical solution can acquire sufficient pulse interval sequence data during the decompression process of a single blood pressure measurement for determining the presence of atrial fibrillation using difference sequences and small sequences, eliminating the need for multiple measurements. It also ensures the accuracy and reference value of the acquired multiple pulse interval sequence data, providing data assurance for the atrial fibrillation judgment and avoiding misjudgments. However, this technical solution, used for identifying atrial fibrillation and blood pressure in the presence of atrial fibrillation, cannot avoid readings of false low blood pressure.

[0006] To address the aforementioned deficiencies, this invention provides an intelligent blood pressure monitoring device and method for patients with atrial fibrillation, in order to avoid obtaining erroneous blood pressure data caused by false hypotension. Summary of the Invention

[0007] Due to significant blood pressure fluctuations, patients with atrial fibrillation require more frequent blood pressure monitoring, typically achieved through continuous monitoring using smart blood pressure monitoring devices. To obtain more accurate blood pressure data, especially in cases of arrhythmia, multiple measurements and averages are necessary. The accuracy and reliability of smart blood pressure monitoring devices may be affected by irregular heartbeats. Because of the irregular heart rate in patients with atrial fibrillation, the measurement error of traditional blood pressure monitors may increase. Therefore, even averaging multiple measurements may not completely eliminate measurement errors caused by irregular heartbeats. This necessitates the use of more advanced data processing algorithms and analysis tools to accurately interpret measurement results and prevent misinterpretation and misdiagnosis.

[0008] To address the shortcomings of existing technologies, this invention provides, from a first aspect, an intelligent blood pressure monitoring device for patients with atrial fibrillation, comprising a blood pressure measurement module, an electrocardiogram (ECG) measurement module, and a processing module. The blood pressure measurement module measures the arterial blood pressure data of the target individual. Preferably, considering the potential blood pressure fluctuations in patients with atrial fibrillation, the blood pressure measurement module should have real-time monitoring capabilities, be able to record arterial blood pressure data promptly, and provide clear interface prompts to help patients understand their blood pressure changes, ensuring that patients can perform measurements in a comfortable state to obtain more accurate blood pressure data.

[0009] The electrocardiogram (ECG) measurement module is used to collect ECG data from the target subject and generate ECG data. Preferably, the ECG measurement module should employ a high-sensitivity ECG sensor capable of accurately acquiring ECG data from patients with atrial fibrillation. To improve monitoring accuracy, the ECG measurement module should have ECG signal filtering capabilities to remove interference signals and reduce artifacts. Furthermore, considering the potential mobility limitations of elderly patients, the ECG measurement module should be designed to be lightweight and portable, allowing for convenient installation and use in a home environment.

[0010] The processing module is used to time-align electrocardiogram (ECG) data with arterial blood pressure data, and dynamically adjusts the measurement frequency and duration of the blood pressure measurement module based on changes in the ECG data, selecting arterial blood pressure data that meets the time interval requirements and is less affected by atrial fibrillation as the measurement data. Preferably, the processing module should use advanced algorithms to dynamically adjust the frequency and duration of blood pressure measurements, especially when the atrial fibrillation patient's condition is unstable, the measurement frequency should be increased to capture more arterial blood pressure data less affected by atrial fibrillation in a short time. In this case, the processing module should not only analyze the data in real time, but also have an alarm function, promptly sending alarm information to the patient and their family once abnormal ECG changes or blood pressure fluctuations are detected.

[0011] This device utilizes changes in the RR interval in the ECG signal to identify suitable measurement times, avoiding measurements during periods of significant heart rhythm fluctuations and ensuring more stable and accurate results with each measurement. Furthermore, it automatically selects heartbeat cycles with relatively regular pulse waveforms as the measurement basis, further improving data quality. This intelligent selection mechanism helps reduce measurement errors caused by arrhythmias, resulting in final blood pressure values ​​that more closely reflect the patient's actual condition.

[0012] According to a preferred embodiment, the processing module can intelligently analyze the stability of the RR interval in electrocardiogram (ECG) data to determine the optimal timing for blood pressure measurement. Specifically, when a relatively stable RR interval is detected, the processing module immediately initiates the arterial blood pressure measurement process. This real-time measurement method ensures that the acquired blood pressure data reflects the true working state of the heart under a relatively regular heart rhythm, thereby improving the accuracy and reliability of the measurement results.

[0013] Furthermore, the processing module can determine arterial blood pressure values ​​not only when the RR interval is stable in a single measurement, but also over a series of consecutive, stable RR intervals. In this way, the module can collect multiple arterial blood pressure values ​​and calculate the average of these measurements. This method is particularly suitable for patients with irregular heart rhythms, such as those with atrial fibrillation (AF), because in their cases, the variation in heart rate intervals is significant, and a single measurement may not fully reflect their true blood pressure situation. By taking multiple measurements and averaging them, errors caused by heart rhythm fluctuations can be effectively reduced, providing a more stable and reliable blood pressure assessment.

[0014] Furthermore, the processing module possesses dynamic adjustment capabilities, flexibly selecting the most suitable measurement time based on real-time monitored electrocardiogram data. For example, after detecting a long and stable RR interval, the module will choose this moment to measure blood pressure because the heart's pumping efficiency is high and the pressure within the arteries is relatively stable at this time, which helps to obtain more accurate systolic and diastolic blood pressure readings. Conversely, if a short or unstable RR interval is detected, the measurement will be postponed until the next more suitable time is found. This intelligent selection mechanism not only reduces unnecessary measurements but also maximizes the quality of each measurement, providing clinicians with more reliable blood pressure data.

[0015] According to a preferred embodiment, the processing module pays particular attention to the clarity and continuity of the P wave when analyzing electrocardiogram (ECG) data. When a clear and continuous P wave is detected, the processing module selects to measure arterial blood pressure during this period. The P wave represents the atrial depolarization process, i.e., the initiation signal of atrial contraction. A clear and continuous P wave indicates normal atrial electrical activity and the absence of abnormalities such as atrial fibrillation or atrial flutter, which provides a reliable basis for subsequent ventricular contraction.

[0016] According to a preferred embodiment, the processing module first preprocesses the input ECG data to remove potential noise and artifacts, ensuring signal quality. Then, using advanced algorithms and techniques, such as deep learning models or adaptive filters, it automatically detects and marks the occurrence of atrial fibrillation (AF). For example, when the processing module detects the disappearance of normal P waves, replaced by f waves of varying sizes and shapes, it confirms the onset of AF. Simultaneously, the processing module continuously monitors changes in the RR interval, identifying significantly irregular heartbeat intervals, a key indicator of AF. Once AF is confirmed, the processing module does not immediately attempt to measure arterial blood pressure. Instead, it waits until the AF cycle ends—that is, when the heart rhythm gradually returns to normal and the RR interval becomes relatively stable—before initiating blood pressure measurement. This is because during AF, the heart's pumping efficiency decreases, and arterial pressure fluctuates significantly; blood pressure measurements taken during this time may not accurately reflect the patient's overall cardiovascular status. Conversely, during the stable period after the AF cycle ends, the heart regains a more regular rhythm, and arterial pressure stabilizes, providing favorable conditions for obtaining accurate blood pressure readings.

[0017] To further improve measurement accuracy, the processing module may select multiple consecutive, stable heartbeat cycles for multiple measurements and calculate the average of these values. This method effectively reduces random errors caused by a single measurement, providing a more reliable and stable blood pressure assessment. Furthermore, the processing module can incorporate other physiological parameters, such as heart rate variability (HRV), respiratory rate, and changes in patient position, to eliminate factors that may affect the blood pressure measurement results, ensuring that the final blood pressure data accurately reflects the patient's cardiovascular health.

[0018] According to a preferred embodiment, the processing module uses heart rate thresholds and heart rate variability (HRV) thresholds as screening criteria to ensure the accuracy of the real-time determined arterial blood pressure data. Specifically, the processing module first sets reasonable heart rate and HRV ranges as thresholds. When the detected heart rate exceeds the preset normal range, such as being too high or too low, or when the HRV shows abnormal fluctuations, the system automatically marks the arterial blood pressure data within these time periods as potentially inaccurate data.

[0019] This screening mechanism is based on the fact that in cases of abnormal heart rate or large fluctuations in HRV, the heart's pumping function may be affected, causing the measured arterial blood pressure value to not accurately reflect the patient's actual blood pressure status. For example, after strenuous exercise or during emotional excitement, the heart rate may increase significantly, while HRV may exhibit greater volatility. In such cases, even if the measured blood pressure value appears normal, it may be unreliable due to changes in cardiac load.

[0020] To further improve the accuracy of screening, the processing module also incorporates other physiological parameters, such as respiratory rate and postural changes, for a comprehensive evaluation. If these parameters also show abnormalities, the corresponding arterial blood pressure data will be subject to more rigorous review. In this way, the processing module can effectively identify and eliminate blood pressure readings affected by non-physiological factors, ensuring that the final retained data is obtained when the heart is in a relatively stable state, thereby improving the overall reliability of blood pressure monitoring.

[0021] According to a preferred embodiment, when the rate of change of the RR interval in the electrocardiogram (ECG) data increases significantly, the processing module automatically adjusts the operating frequency of the blood pressure measurement module, increasing its measurement frequency to capture arterial blood pressure changes more frequently. The rate of change of the RR interval is one of the key indicators for measuring cardiac rhythm stability; its increase often suggests irregular cardiac rhythm or potential cardiovascular events. In this case, increasing the frequency of blood pressure measurements ensures that more detailed blood pressure fluctuations are captured, providing richer data support for assessing cardiovascular health.

[0022] Specifically, the processing module monitors the RR interval in the ECG signal in real time. Once it detects a change rate exceeding a preset threshold, it triggers the blood pressure measurement module to enter a high-frequency operating mode. This dynamic adjustment not only helps to promptly detect blood pressure abnormalities caused by arrhythmias, but also provides doctors with continuous and intensive blood pressure records, facilitating in-depth analysis and diagnosis.

[0023] According to a preferred embodiment, when significant fluctuations in the P-wave amplitude in electrocardiogram (ECG) data occur, the processing module automatically extends the measurement duration of the blood pressure measurement module to ensure more stable and accurate arterial blood pressure data. The P-wave represents the atrial depolarization process, and its amplitude changes can reflect the intensity and stability of atrial activity. Significant fluctuations in P-wave amplitude may indicate atrial dysfunction or potential arrhythmias, which can affect blood pressure.

[0024] In this context, extending the blood pressure measurement time window reduces the impact of transient factors on the measurement results, providing a smoother and more reliable blood pressure trend. By increasing the measurement duration, the processing module can average blood pressure readings over a longer period, thereby filtering out short-term fluctuations and capturing a more accurate blood pressure level. This method is particularly suitable for patients with large blood pressure fluctuations due to atrial instability, such as those with atrial fibrillation.

[0025] According to a preferred embodiment, the processing module can dynamically adjust the operating parameters of the blood pressure measurement module based on heart rate variability (HRV) in electrocardiogram (ECG) data. When a decreasing trend in HRV is detected, it indicates that the balance of the cardiac autonomic nervous system may be biased towards increased sympathetic activity or decreased parasympathetic activity, which is often associated with stress, fatigue, or other health problems. In this case, the processing module will correspondingly reduce the frequency of blood pressure measurements while extending the duration of each measurement.

[0026] Reducing the frequency of measurements avoids discomfort caused by frequent checks, especially for individuals sensitive to the measurement process. Extending the duration of each measurement helps obtain more stable and accurate blood pressure readings because longer sampling times better average transient fluctuations, providing more reliable blood pressure trend analysis. This approach not only improves user comfort but also ensures higher quality and representativeness of the collected blood pressure data even with reduced HRV.

[0027] This invention provides, from a second aspect, a method for intelligent blood pressure monitoring in patients with atrial fibrillation. The method first involves periodically or continuously measuring the target subject's arterial blood pressure data using a professional blood pressure measurement device, such as a traditional cuff blood pressure monitor or a more advanced ambulatory blood pressure monitor. Subsequently, the device simultaneously acquires the target subject's electrocardiogram (ECG) data and generates a detailed ECG record. The ECG signal captures the heart's electrical activity patterns. To ensure consistency and comparability between the two different types of data, the next crucial step is aligning the timestamps of the ECG and arterial blood pressure data. This step ensures that even during atrial fibrillation, the acquired blood pressure readings accurately reflect the heart's actual working state. The time alignment process typically involves synchronizing the two data streams onto the same timeline, ensuring that the blood pressure value corresponding to each heartbeat cycle is accurately marked.

[0028] Based on changes in electrocardiogram (ECG) data, this invention dynamically adjusts the operating parameters of the blood pressure measurement module, including measurement frequency and duration of each measurement. For example, when atrial fibrillation or significant changes in heart rate variability (HRV) are detected, the invention may increase the measurement frequency to capture more information about blood pressure fluctuations, or extend the measurement duration to obtain a more stable average blood pressure value. This flexibility allows the system to self-optimize according to the individual's cardiac activity characteristics, thereby improving the reliability of the measurement results.

[0029] Finally, this invention selects arterial blood pressure data that meet specific time interval requirements and are less affected by atrial fibrillation as the final measurement data. This means that only when the electrocardiogram shows a relatively stable heart rhythm will the corresponding blood pressure reading be selected for further analysis or reported to the physician. This method not only reduces measurement errors caused by arrhythmias but also provides more reliable support for clinical decision-making.

[0030] According to a preferred embodiment, this method further optimizes the selection and processing of arterial blood pressure data. Specifically, the present invention determines how to select or calculate the average value of arterial blood pressure data based on characteristics such as RR interval stability, P wave changes, and the presence of atrial fibrillation (AF) in electrocardiogram (ECG) data. The RR interval is the time interval between two consecutive normal heartbeats, and its stability directly reflects the regularity of the heart rhythm; large fluctuations in the RR interval indicate possible arrhythmia. The P wave represents the process of atrial depolarization, and changes in its morphology and amplitude can indicate the state of atrial function. For patients with atrial fibrillation, due to irregular ventricular rates, arterial pressure fluctuates significantly; therefore, special attention needs to be paid to selecting blood pressure data points that are less affected by AF.

[0031] In practical applications, when a relatively stable RR interval and normal P wave morphology are detected, this invention selects the corresponding arterial blood pressure data as a reliable measurement result. Conversely, if an unstable RR interval or abnormal P wave is found, or if arrhythmia is confirmed, this invention automatically filters out the less affected data segments and calculates the average of these data segments to obtain a more accurate blood pressure assessment. This method not only improves measurement accuracy but also effectively reduces errors caused by arrhythmias, providing more reliable support for clinical diagnosis.

[0032] The present invention provides, from a third aspect, a processor for an intelligent blood pressure monitoring device for patients with atrial fibrillation, the processor being configured to: time-align electrocardiogram (ECG) data with arterial blood pressure data; determine the stability of the RR interval in the ECG data; and select arterial blood pressure data or calculate the average value of the arterial blood pressure data based on the stability of the RR interval, P wave changes, and / or atrial fibrillation in the ECG data.

[0033] According to a preferred embodiment, the processor includes a reading module and a regulating module, which together ensure the accuracy and reliability of arterial blood pressure data. The reading module is used to acquire real-time electrocardiogram (ECG) data from an electrocardiogram (ECG) device, which includes information on the electrical activity of the heart with each beat, such as the RR interval and P wave morphology. Furthermore, the reading module must also identify the presence of atrial fibrillation (AF), a common arrhythmia that causes irregular ventricular rates and thus affects the stability of arterial pressure.

[0034] The adjustment module analyzes the read electrocardiogram (ECG) data in real time and determines the optimal time to select arterial blood pressure data. It dynamically generates control commands for the blood pressure measurement module based on factors such as RR interval stability, P wave changes, and the presence of atrial fibrillation (AF). For example, when a stable RR interval and normal P wave are detected, the adjustment module selects arterial blood pressure data within this timeframe as a reliable measurement result. If RR interval instability or P wave abnormalities are detected, or AF is confirmed, the adjustment module filters out less affected data segments and calculates their average value to obtain a more accurate blood pressure assessment.

[0035] This method not only improves measurement accuracy but also effectively reduces errors caused by arrhythmias, providing more reliable support for clinical diagnosis. Simultaneously, the adjustment module can modify parameters of the blood pressure measurement module, such as measurement frequency and duration per measurement, to adapt to different cardiac activity patterns, thereby optimizing the entire monitoring process.

[0036] According to a preferred embodiment, the adjustment module in the processor is subdivided into three sub-modules: a time alignment module, a timing selection module, and an arterial blood pressure verification module, to ensure the accuracy and reliability of the arterial blood pressure data. The time alignment module internally incorporates a time series prediction model that processes and synchronizes the timestamps of electrocardiogram (ECG) data and arterial blood pressure data, ensuring consistency between the two over time. By precisely aligning the received ECG data with the arterial blood pressure data, the time alignment module provides a solid foundation for subsequent analysis.

[0037] The timing selection module receives ECG and arterial blood pressure data processed by the time alignment module. Based on factors such as RR interval stability, P wave changes, and the presence of atrial fibrillation (AF), it dynamically determines the optimal timing for selecting arterial blood pressure data. If irregular heart rhythms or abnormal ECG activity are detected, this module also generates control commands to adjust the parameters of the blood pressure measurement module, such as measurement frequency and single measurement duration, to adapt to the current cardiac state, thereby optimizing the measurement results. This real-time adjustment mechanism helps improve the accuracy and reliability of the measurement.

[0038] The arterial blood pressure verification module further enhances the system's accuracy. It not only retrieves electrocardiogram (ECG) and arterial blood pressure data but also performs secondary verification on the timing selection module, assessing whether the selected arterial blood pressure data truly meets the optimal conditions. By calculating the number of correct responses and / or the probability of correct responses from the timing selection module, the arterial blood pressure verification module can continuously monitor system performance and provide data support for potential improvements.

[0039] According to a preferred embodiment, the primary function of the timing determination module in the processor is to instantly determine the arterial blood pressure value when the RR interval (i.e., the time interval between two adjacent heartbeats) is detected to be stable. The stability of the RR interval is a direct indicator of the regularity of the heart rhythm; when fluctuations within this time period are small, it means the heartbeat is regular, and the arterial blood pressure value measured at this time is more reliable. Therefore, the timing determination module captures and records the arterial blood pressure value at the instant the RR interval stabilizes, providing an immediate and accurate blood pressure reading.

[0040] Furthermore, to further improve the accuracy and representativeness of measurements, the timing selection module can also identify at least one arterial blood pressure value within a stable RR interval period and calculate the average of these values. This method not only considers the instantaneous nature of a single measurement but also reduces the influence of random factors on the results through averaging, thus providing a more stable and reliable blood pressure assessment. Specifically, the timing selection module continuously monitors the RR interval; once it detects that multiple consecutive RR intervals remain stable, it selects several arterial blood pressure data points within this period and then calculates the average of these data points.

[0041] According to a preferred embodiment, the timing determination module in the processor is configured to intelligently respond to changes in electrocardiogram (ECG) data when an increase in the RR interval variability rate is detected. To more accurately capture arterial blood pressure fluctuations under such unstable conditions, the timing determination module automatically increases the measurement frequency of the blood pressure measurement module. This means that when the RR interval variability rate increases significantly, the present invention will collect arterial blood pressure data more frequently, thereby obtaining more information about dynamic changes in blood pressure.

[0042] In this way, the timing-based judgment module can not only reflect instantaneous changes in cardiac activity in a timely manner, but also provide more detailed blood pressure trend analysis. The increased measurement frequency helps identify short-term blood pressure fluctuation patterns. Furthermore, this method can help physicians better understand patients' physiological responses in specific situations, such as cardiovascular behavior during exercise, emotional fluctuations, or disease flare-ups. Ultimately, this dynamic adjustment mechanism ensures high-quality arterial blood pressure data even when cardiac rhythms are unstable, providing strong support for clinical diagnosis and treatment planning. Attached Figure Description

[0043] Figure 1 is a schematic diagram of the hardware connection relationship of the intelligent blood pressure monitoring device for elderly patients with atrial fibrillation provided by the present invention in the hospitalization state;

[0044] Figure 2 is a schematic diagram of the hardware connection relationship of the intelligent blood pressure monitoring device for elderly patients with atrial fibrillation provided by the present invention in the home setting;

[0045] Figure 3 is a schematic diagram of the display interface of the terminal of the intelligent blood pressure monitoring device for elderly patients with atrial fibrillation provided by the present invention;

[0046] Figure 4 is a schematic diagram of the composition structure of the intelligent blood pressure monitoring device for elderly patients with atrial fibrillation provided by the present invention.

[0047] Figure 5 is a logical schematic diagram of the operating principle of the intelligent blood pressure monitoring device for elderly patients with atrial fibrillation provided by the present invention;

[0048] Figure 6 shows the aortic pressure curves for patients with atrial fibrillation and patients with sinus rhythm;

[0049] Figure 7 is a schematic diagram of the module connection of the processor provided by the present invention;

[0050] Figure 8 is a logical schematic diagram of the processor provided by the present invention.

[0051] The attached diagram shows the following reference numerals: 100: Electrocardiogram (ECG) measurement module; 200: Blood pressure measurement module; 300: Processing module; 310: Reading module; 320: Adjustment module; 321: Time alignment module; 322: Timing selection judgment module; 323: Arterial blood pressure verification module; 324: Input port; 325: Output port; 326: Storage module; 400: Terminal; 410: ECG display interface; 420: Arterial blood pressure value display interface. Detailed Implementation

[0052] The following is a detailed explanation with reference to the accompanying drawings.

[0053] Atrial fibrillation (AF) is a common cardiac arrhythmia involving rapid and irregular beatings of the upper chambers of the heart (atria). In AF, the atrial firing rate abnormally increases to 350 to 600 beats per minute, causing the atria to lose their ability to contract in a coordinated manner, and the ventricular beats become irregular.

[0054] Symptoms of atrial fibrillation may include palpitations, chest discomfort, shortness of breath, fatigue, dizziness, and presyncope. Due to the rapid, irregular fibrillation of the atria, blood clots can easily form within the atria, increasing the risk of complications such as embolic stroke.

[0055] Treatment for atrial fibrillation typically involves using medication to control the ventricular rate, preventing thromboembolism, and sometimes converting the atrial fibrillation to a normal sinus rhythm through medication or cardiopulmonary bypass. Furthermore, the diagnosis of atrial fibrillation primarily relies on an electrocardiogram (ECG), whose characteristic features include the disappearance of P waves and the presence of fine, irregular f waves (fibrillation waves) between QRS complexes, with absolutely irregular RR intervals.

[0056] It is worth noting that the incidence of atrial fibrillation increases with age, reaching a high proportion in people over 80 years of age. Atrial fibrillation is classified into paroxysmal, persistent, long-term persistent, and permanent types, and different types of atrial fibrillation require different treatment and management strategies.

[0057] Persistent atrial fibrillation is an independent risk factor for orthostatic hypotension, especially in patients aged ≥60 years and those with uncontrolled hypertension. Poor blood pressure control can worsen symptoms and increase the frequency of attacks in patients with atrial fibrillation, and reduce the success rate of cardioversion. Accurate blood pressure measurement is a prerequisite for effective blood pressure management in patients with atrial fibrillation.

[0058] Hemodynamic characteristics of patients with atrial fibrillation include: irregular ventricular rate leading to significant fluctuations in intra-arterial pressure. Strokes following longer RR intervals have higher intra-arterial pressure, while strokes following shorter RR intervals have lower intra-arterial pressure, sometimes even to the point of being unmeasurable. Figure 6 shows the aortic pressure curves of patients with atrial fibrillation and those with sinus rhythm, demonstrating a significant difference in their intra-arterial pressure curves.

[0059] However, current electrocardiograms (ECGs) are only used to evaluate the accuracy of blood pressure measurement devices. For example, clinicians can evaluate the accuracy of oscillometric systolic blood pressure measurements by referring to the ventricular rate (auscultation for 30–60 seconds) and pulse rate variability across three blood pressure measurements in patients with atrial fibrillation. Oscillometric systolic blood pressure readings are highly reliable when the ventricular rate is <90 or 100 beats / min and the pulse rate variability across three measurements is <10 beats / min. However, the large size and high cost of professional ECG equipment limit their use in home settings.

[0060] For patients with atrial fibrillation, this may prevent them from obtaining electrocardiogram (ECG) data in a home environment, making it impossible to correlate blood pressure measurements with cardiac status. This can increase the error in blood pressure measurements and affect the accuracy of blood pressure monitoring. Patients with atrial fibrillation can only obtain accurate arterial blood pressure data by relying on professional medical personnel to measure their blood pressure.

[0061] While some portable electrocardiogram (ECG) measurement devices exist, patients with atrial fibrillation cannot directly link ECG data with blood pressure monitors. Furthermore, newly emerging integrated ECG and blood pressure devices simply display both ECG and arterial blood pressure values ​​on the device itself or on a mobile device (such as a smartphone). The displayed arterial blood pressure values ​​are those for general patients; they are not specially selected, and the measurement frequency and duration are not personalized. Therefore, integrated ECG and blood pressure devices measure arterial blood pressure in the same way as ordinary blood pressure monitors, and cannot provide accurate arterial blood pressure values ​​for patients with atrial fibrillation.

[0062] Therefore, the present invention aims to solve the problem of how to provide a device and system suitable for patients with atrial fibrillation that can reduce the number of measurements while obtaining accurate arterial blood pressure data.

[0063] This invention provides a portable blood pressure measuring device suitable for patients with atrial fibrillation, ideal for their home environment. This device enables patients with atrial fibrillation to achieve accurate blood pressure measurement at home, reducing reliance on professional medical personnel.

[0064] This invention also provides a blood pressure measurement system and method capable of interacting with hospital systems. When the analysis results of blood pressure measurement data or electrocardiogram data from a patient with atrial fibrillation indicate a high health risk, the system can promptly send relevant information, including warnings and medical advice, to the patient via terminal 400, helping the patient take timely measures to avoid potential dangers. This device and method not only improve the accuracy of blood pressure monitoring but also enhance the patient's self-management ability, contributing to improved quality of life for patients with atrial fibrillation.

[0065] Example 1

[0066] This embodiment provides an intelligent blood pressure monitoring device for elderly patients with atrial fibrillation, as shown in Figures 1 to 4, including a blood pressure measurement module 200, an electrocardiogram (ECG) measurement module 100, and a processing module 300. The blood pressure measurement module 200 and the ECG measurement module 100 establish communication connections with the processing module 300 via wired or wireless means. Preferably, the wireless means include WiFi communication, Bluetooth communication, etc. Preferably, the blood pressure measurement module 200 and the ECG measurement module 100 also include an NFC communication module. When the blood pressure measurement module 200 and the ECG measurement module 100 are close to the terminal 400, and driven by the NFC communication module within the terminal 400, the blood pressure measurement module 200 and the ECG measurement module 100 respectively transmit the measured data to the terminal 400 via NFC communication.

[0067] Preferably, as shown in Figures 1 and 5, the blood pressure measurement module 200 is used to measure the arterial blood pressure data of a target object. The blood pressure measurement module 200 includes a pressure sensor, an inflation pump, a deflation valve, a cuff, a microcontroller unit (MCU), a wireless transmission module, a power supply, and a wired communication port. The inflation pump and deflation valve are respectively located at the air inlet and outlet of the cuff. The pressure sensor is connected to the MCU to send the measured arterial blood pressure data to the MCU and adjusts the measurement frequency and adjustment duration based on the control commands from the MCU. The MCU is connected to the wireless transmission module and the wired communication port to enable information interaction with the outside world. The inflation pump and deflation valve are connected to the MCU to inflate and deflate the pressure at a specified speed based on the control commands sent by the MCU. The power supply is connected to the pressure sensor, inflation pump, deflation valve, MCU, wireless transmission module, and wired communication port to provide power to each component.

[0068] A pressure sensor, typically a strain gauge or piezoelectric sensor, is used to detect and record changes in arterial blood pressure. An inflation pump and deflation valve automatically control the inflation and slow deflation of the cuff to measure arterial fluctuations at different pressures. The cuff is an inflatable band worn around the upper arm to transmit and receive pressure signals from the arteries. A microcontroller unit (MCU) receives and processes signals from the pressure sensor and controls the operation of the inflation pump and deflation valve. A wireless transmission module, such as Bluetooth or Wi-Fi, transmits the measured arterial blood pressure data to the processing module 300. Power supplies, including a battery and power management circuitry, ensure continuous power to all components within the blood pressure measurement module 200.

[0069] As shown in Figures 1 and 5, the electrocardiogram (ECG) measurement module 100 is used to collect ECG data from the target object and generate ECG data. Unlike traditional ECG measurement modules 100, as shown in Figure 2, the ECG measurement module 100 in this invention can be a portable ECG measurement module 100 for home use by patients with atrial fibrillation.

[0070] The electrocardiogram (ECG) measurement module 100 may include several electrodes, a signal amplification circuit module, and a microprocessor. Preferably, the number of electrodes is at least one. Generally, the number of electrodes is two. The electrodes can be attached to the body or worn close to the body using a wearable device. Preferably, the ECG measurement module 100 also includes a memory for storing ECG data. The signal amplification circuit module and the microprocessor are installed in a housing and are used to capture ECG signals and form ECG data in a time-related manner. The ECG measurement module 100 has a power supply to provide power to the electrodes, signal amplification circuit module, microprocessor, memory, and other devices. Preferably, the ECG measurement module 100 can also be installed in an auxiliary wearable device so that patients with atrial fibrillation can wear the ECG measurement module 100 near their heart. The ECG measurement module 100 also includes a wireless transmission module and a wired transmission port. The wireless transmission module is used to send ECG data to the processing module 300 in real time. The wired transmission port is used to send ECG data to the processing module 300 in real time via an information transmission line. Preferably, as shown in Figure 2, the processing module 300 can be a hospital server, receiving electrocardiogram (ECG) data and arterial blood pressure data via a terminal 400. The terminal 400 can be a portable device such as a computer, mobile phone, tablet, smartwatch, or smartphone, which can remotely transmit the received ECG data and arterial blood pressure data to the hospital server for arterial blood pressure data determination by running the corresponding application module.

[0071] As shown in Figures 1, 2, and 5, the processing module 300 executes the intelligent blood pressure monitoring method for elderly patients with atrial fibrillation according to the present invention. As shown in Figure 5, the processing module 300 analyzes and determines accurate arterial blood pressure data. Specifically, the processing module 300 can be a server, a dedicated integrated chip, or a processor capable of analyzing electrocardiogram (ECG) data and arterial blood pressure data. The processing module 300 may also be connected to a memory to store the arterial blood pressure data and ECG data measured each time by the atrial fibrillation patient, and may even store the atrial fibrillation cycle data and arterial pressure curve of the atrial fibrillation patient.

[0072] In this invention, as shown in Figure 5, after receiving the data, the processing module 300 aligns the electrocardiogram data with the arterial blood pressure data in time to ensure that the collected electrocardiogram changes can accurately correspond to the corresponding blood pressure measurement data.

[0073] For example, when the processing module 300 receives electrocardiogram (ECG) data and arterial blood pressure data, it checks the timestamp of each data point. By comparing the timestamps, it ensures that the ECG data and arterial blood pressure data at the same time correspond. If there is a timestamp inconsistency, the processing module 300 will perform time correction to make the timestamps of the two data points consistent.

[0074] In some cases, the sampling frequencies of electrocardiogram (ECG) data and arterial blood pressure data may differ. To achieve time alignment, the processing module 300 interpolates the data with the lower sampling frequency to give it the same time resolution as the data with the higher sampling frequency. This way, data alignment can be achieved even if there are differences in sampling intervals in the original data.

[0075] Because there is a certain delay in the physiological propagation of electrocardiogram (ECG) and arterial blood pressure signals, the processing module 300 performs delay compensation on one of the signals according to a pre-set delay parameter. This ensures the true temporal correspondence between the two signals and improves the accuracy of data analysis.

[0076] Preferably, if the electrocardiogram data and arterial blood pressure data are acquired through different devices, the processing module 300 may use a synchronization trigger signal to ensure that data acquisition from both devices begins at the same time. This synchronization trigger signal can be an external event (such as pressing a button) or an internal clock signal, used to coordinate the data acquisition process of the different devices.

[0077] After time alignment is completed, the processing module 300 may smooth the data to reduce the impact of noise and outliers. This can be achieved through filters, moving averages, or other statistical methods, thereby improving data quality and ensuring an accurate correspondence between ECG changes and blood pressure measurements.

[0078] As described above, the processing module 300 ensures the temporal consistency between electrocardiogram (ECG) data and arterial blood pressure data through methods such as timestamp alignment, interpolation alignment, delay compensation, synchronization triggering, and data smoothing, thereby enabling the collected ECG changes to accurately correspond to the corresponding blood pressure measurement data.

[0079] This alignment method ensures data consistency, making the dynamic adjustment of measurement frequency and duration more scientifically based.

[0080] The processing module 300 dynamically adjusts the measurement frequency and duration of the blood pressure measurement module 200 based on changes in electrocardiogram data, and selects arterial blood pressure data that meets the time interval requirements and is less affected by atrial fibrillation as the measurement data.

[0081] Specifically, the processing module 300 includes a reading module 310 and an adjustment module 320. The reading module 310 reads data such as RR interval, P wave, atrial fibrillation status, heart rate, and heart rate variability from the electrocardiogram (ECG) data. Preferably, the processing module 300 may further include a data preprocessing module for filtering the received ECG data to remove noise and interference. The preprocessed ECG data is then read by the reading module 310.

[0082] The RR interval is the time interval between two consecutive R waves on an electrocardiogram (ECG). Normally, the faster the heart rate, the shorter the RR interval; the slower the heart rate, the longer the RR interval.

[0083] On an electrocardiogram (ECG), the P wave represents atrial depolarization, the electrical activity of atrial contraction. A normal P wave should be a positive wave, typically upright in leads I, II, and aVF, and inverted in lead aVR. The duration of a normal P wave is usually no more than 0.12 seconds (or 120 milliseconds). In standard leads, the amplitude of the P wave is usually no more than 2.5 mm (or 0.25 mV).

[0084] When atrial fibrillation occurs, the ventricular rate (i.e., the rate at which the ventricles respond to rapid atrial excitation) is usually rapid and irregular, generally between 100 and 180 beats per minute, but can be slower or faster. P waves disappear, RR intervals are irregular, and the QRS complex may widen. The reading module 310 can read and determine the duration of atrial fibrillation based on the electrocardiogram data.

[0085] Under normal circumstances, the relationship between the RR interval and heart rate is as follows:

[0086] RR i HR indicates the time interval between two consecutive R waves. i This indicates the number of heartbeats per minute.

[0087] The duration of a normal P wave is:

[0088] t Pi The waveform P represents atrial depolarization. i The duration.

[0089] In standard leads, the P-wave amplitude P ABPi satisfy:

[0090] or

[0091] P ampi This indicates the maximum amplitude of the P wave in an electrocardiogram (ECG). ABPiThis refers to the specific measured value or parameter representing the amplitude of the P-wave.

[0092] The ventricular rate range during atrial fibrillation is: 100 ≤ HR AF ≤180bpm(4).

[0093] HR AF This indicates the ventricular rate.

[0094] The processing module 300 also includes an adjustment module 320. The adjustment module 320 and the reading module 310 can transmit data to each other. The adjustment module 320 is used to determine the timing of arterial blood pressure data selection in real time based on electrocardiogram data and to generate control commands for the blood pressure measurement module 200. When an early warning is required, the adjustment module 320 generates an early warning command, controlling the terminal 400 to perform alarm operations such as atrial fibrillation warning and danger level warning.

[0095] Specifically, the timing of arterial blood pressure data selection by the adjustment module 320 is t. ABPi The calculation formula is:

[0096] HRV i AF represents heart rate fluctuation data. dur This indicates the duration of atrial fibrillation. k1 to k4 represent the first regulation coefficient.

[0097] Adjustment module 320 calculates the measurement frequency f measure The formula is:

[0098] Adjustment module 320 calculates measurement time t measure The formula is:

[0099] t measure =β1·RR i +β2·HR i +β3·HRV i +β4·AF dur (7).

[0100] β1 to β4 represent the second adjustment coefficient. α1 to α4 represent the third adjustment coefficient. α1 to α4 are the same as β1 to β4.

[0101] For example, the present invention provides two sets of exemplary data, as shown in the table below.

[0102] Data group 1 had no atrial fibrillation, AF dur =0.

[0103] Data set 2 showed atrial fibrillation lasting 2 seconds.

[0104] Preferably, as shown in FIG3, the user interface of the terminal 400 includes an electrocardiogram (ECG) display interface 410 and an arterial blood pressure value display interface 420. The ECG display interface 410 is used to display real-time or historical ECGs. The arterial blood pressure value display interface 420 is used to display real-time or historical arterial blood pressure values.

[0105] The adjustment module 320 is trained using electrocardiogram sample data and time-aligned arterial blood pressure sample data from several atrial fibrillation patients. Preferably, the adjustment module 320 is a time series prediction model. The time series prediction model is preferably a time series model such as LSTM (Long Short-Term Memory) or GRU (Gated Recurrent Unit). The algorithm of the time series prediction model includes mean squared error (MSE) or mean absolute error (MAE) to minimize the time series prediction error.

[0106] The sample data, labeled with RR interval, P wave, atrial fibrillation events and corresponding arterial blood pressure data, is input into the adjustment module 320 to ensure the consistency of time points between the electrocardiogram data and the arterial blood pressure data. The training adjustment module 320 outputs the selection timing of the arterial blood pressure data.

[0107] According to a preferred embodiment, the adjustment module 320 in the processing module 300 determines the stability of the RR interval in the electrocardiogram data, and determines the arterial blood pressure value in real time when the RR interval is stable.

[0108] A preset time window is used, with each window containing a certain number of RR intervals. A time window refers to a period of time preceding a given moment. RR intervals are extracted from the ECG data within each time window. RR interval stability is calculated by determining the standard deviation of the RR intervals within each time window; a smaller standard deviation indicates higher stability. A standard deviation threshold is set. If the standard deviation of the RR intervals within a given time window is less than the set threshold, the RR intervals within that time window are considered stable.

[0109] As the electrocardiogram (ECG) data dynamically changes, the stability of the RR interval within the preset time window also changes dynamically. Preferably, when the RR interval is determined to be stable, the adjustment module 320 in the processing module 300 immediately determines at least one arterial blood pressure value. Preferably, multiple arterial blood pressure values ​​are dynamically acquired, and the acquisition time interval between adjacent arterial blood pressure values ​​is at least 1 minute, allowing the processing module 300 to calculate the average arterial blood pressure. It should be noted that the average arterial blood pressure calculated here is used to verify the real-time arterial blood pressure value and is not a mandatory step. In this invention, the processing module 300 acquires multiple real-time arterial blood pressure values, which also facilitates the selection of the more accurate one based on additional conditions.

[0110] For example, let's define a time window as W, where each window contains N RR intervals. A time window refers to a period of time preceding this point in time. The RR intervals (RR1, RR2, ... RR) are extracted from the ECG data within each time window. N ).

[0111] Calculate the standard deviation σ of the RR interval within each time window. RR .

[0112] The average RR interval within the time window is:

[0113] If the standard deviation σ of the RR interval within a certain time window RR If the interval is less than the set standard deviation threshold, the RR interval within that time window is considered stable.

[0114] σ th This represents the standard deviation threshold.

[0115] When the RR interval is determined to be stable, the adjustment module 320 in the processing module 300 immediately determines at least one arterial blood pressure value (BP).

[0116] When the RR interval is stable, multiple arterial blood pressure values ​​BP1, BP2, ... BP are dynamically acquired. M Set the time interval for collecting adjacent arterial blood pressure values ​​to at least 1 minute.

[0117] Calculate the average arterial blood pressure The formula is:

[0118] M represents the number of arterial blood pressure values, and j represents the index of the arterial blood pressure value. BP j This represents the j-th arterial blood pressure value.

[0119] The stability of the RR interval directly reflects the stability of the heart rate. When the heart rate is stable, blood pressure is also relatively stable, so measurements taken during this period yield more accurate data. Furthermore, calculating the average value further enhances the stability and representativeness of the data, reducing potential errors from single measurements. By performing immediate measurements when the RR interval is stable, this invention avoids the influence of arrhythmias on blood pressure data, improving the accuracy of the measurement data.

[0120] According to a preferred embodiment, the adjustment module 320 in the processing module 300 selects the arterial blood pressure value in real time when the P wave in the electrocardiogram data is clear and continuous.

[0121] The adjustment module 320 dynamically determines the clarity of the P wave based on electrocardiogram data. Specifically, the adjustment module 320 calculates the amplitude of the P wave. An amplitude threshold is set; P waves with an amplitude greater than this threshold are considered clear. The adjustment module 320 analyzes the morphological characteristics of the P wave, such as the rising slope and falling slope, to ensure that the P wave morphology conforms to normal standards.

[0122] The adjustment module 320 calculates the time interval between adjacent P waves in real time. A time interval threshold is set to ensure that the time interval between P waves is within the normal range. During real-time monitoring, if the P waves remain clear and the time interval is stable, the P waves are considered continuous.

[0123] At the point in time when the P wave is clear and continuous, the adjustment module 320 immediately generates a control command, instructing the blood pressure measurement module 200 to measure the arterial blood pressure value. The corresponding arterial blood pressure value is recorded in real time. The P wave characteristics, time points, and arterial blood pressure values ​​are stored in a correlated manner.

[0124] For example, the adjustment module 320 calculates the amplitude A of the P wave. P A th This indicates the amplitude threshold.

[0125] A P =max(P(t))-min(P(t)) (11).

[0126] A P >A th P(t) is the P-wave portion of the electrocardiogram signal at time t.

[0127] Set an amplitude threshold A th P waves with amplitudes greater than this threshold are considered clear.

[0128] Adjustment module 320 analyzes the rising slope S of the P wave. up and the descending slope S down Set a threshold S for the rising slope that meets normal standards. up-th and the descent slope threshold S down-th .

[0129] Ascent slope S up The calculation formula is:

[0130] In the rising phase of the P wave, where t1 < t2 (12).

[0131] t1 is the starting point of the upward phase, and t2 is the ending point of the upward phase. Between these two points, the amplitude of the P wave increases. The downward slope S... down The calculation formula is:

[0132] In the descending phase of the P wave, where t3 < t4 (13).

[0133] S up ≥S up-th And S down ≤S down-th .

[0134] t3 is the starting point of the downward phase, and t4 is the ending point of the downward phase. Between these two points, the amplitude of the P wave decreases.

[0135] The adjustment module 320 calculates the time interval T between adjacent P waves in real time. PP Defined as the time difference between two adjacent P-wave peaks:

[0136] T PP =t i+1 -t i (14).

[0137] T min-th ≤T PP ≤T max-th .

[0138] t i T represents the time of the peak of the i-th P-wave. min-th T represents the minimum time interval threshold. max-th This represents the maximum time interval threshold.

[0139] During real-time monitoring, if the P wave remains clear and the time interval is stable, the P wave is considered continuous: that is, (A P >A th )∧(T min-th ≤T PP ≤T max-th At this time, the P wave is continuous.

[0140] According to a preferred embodiment, the processing module 300 identifies atrial fibrillation based on electrocardiogram data. During the stable period after the atrial fibrillation cycle ends, the processing module 300 determines arterial blood pressure data.

[0141] After an atrial fibrillation cycle ends, cardiac activity tends to stabilize, and blood pressure measurements taken at this time better reflect the patient's actual blood pressure status. By identifying the atrial fibrillation cycle and selecting a stable period for measurement, a more accurate blood pressure value can be captured. Therefore, measuring blood pressure during the stable period after the atrial fibrillation cycle ends can reduce the interference of arrhythmia on blood pressure measurement, thereby improving the reliability of the measurement results.

[0142] According to a preferred embodiment, the processing module 300 filters the instantaneously determined arterial blood pressure data based on a heart rate threshold and a heart rate variability threshold, thereby deleting inaccurate arterial blood pressure data.

[0143] For example, the regulation module 320 calculates the real-time heart rate (HR). i Heart rate can be measured by measuring the time interval T between adjacent R waves (i.e., the peak of the heartbeat on an electrocardiogram). RR To calculate:

[0144] T RRi This represents the time interval (in seconds) of the i-th heartbeat.

[0145] Calculate Heart Rate Variability (HRV). HRV can be calculated using the following formula:

[0146] It is the average of all heartbeat intervals, and N is the number of heartbeats.

[0147] Set heart rate threshold HR th Heart rate variability threshold (HRV) th .

[0148] HR min-th ≤HR i ≤HR max-th HRV i ≤HRV th .

[0149] Real-time arterial blood pressure data (BP) were filtered based on the above threshold. i Arterial blood pressure data are considered accurate only when heart rate and heart rate variability are within the threshold range.

[0150] If (HR) min-th ≤HR i ≤HR max-th )∧(HRV i ≤HRV th ), then BP i It is accurate.

[0151] Otherwise, delete real-time arterial blood pressure data (BP). i .

[0152] By filtering inaccurate blood pressure data, the processing module 300 can effectively remove abnormal data, ensuring the accuracy and consistency of the final measurement results. This is because heart rate thresholds and heart rate variability thresholds help identify unreliable data caused by large heart rate fluctuations or high heart rate variability. By setting these thresholds, the processing module 300 can identify and exclude this data, thereby improving the overall data quality.

[0153] According to a preferred embodiment, when the RR interval change rate of the electrocardiogram data increases, the processing module 300 increases the measurement frequency of the blood pressure measurement module 200 to obtain more information on arterial blood pressure changes.

[0154] An increase in the RR interval variability rate usually indicates greater heart rate variability. Increasing the measurement frequency can more accurately reflect real-time changes in blood pressure. This method is particularly suitable for patients with atrial fibrillation, as their RR interval variability rate is typically high; increasing the measurement frequency can improve the accuracy of diagnosis and treatment. Therefore, increasing the measurement frequency when the RR interval variability rate increases captures more details of blood pressure changes, providing more comprehensive information for subsequent data analysis.

[0155] The processing module 300 adjusts the inflation rate of the blood pressure measurement module 200 based on the RR interval variability in the initial electrocardiogram data. When the RR interval variability is high, the inflation rate is reduced to decrease additional stress on the patient's cardiovascular system.

[0156] For example, the following R-wave peak time points (in seconds) were extracted from electrocardiogram data: {t1,t2,t3,t4,t5}={0.0,1.0,2.2,3.3,4.4}.

[0157] The time interval between adjacent R-wave peaks is:

[0158] T RR1 =1.0 - 0.0 = 1.0 seconds; T RR2 =2.2 - 1.0 = 1.2 seconds; T RR3 =3.3 - 2.2 = 1.1 seconds;

[0159] T RR4 =4.4 - 3.3 = 1.1 seconds.

[0160] The mean RR interval is:

[0161] The RR interval variability is:

[0162] Set the variability threshold RRV th It takes 0.05 seconds.

[0163] Because RRV = 0.071 seconds is greater than 0.05 seconds, the inflation frequency needs to be reduced.

[0164] Set the normal inflation frequency to F normal =1.0H Z The frequency reduction was 0.2H. ZTherefore, the adjusted inflation frequency is: F = F normal -ΔF=1.0H Z -0.2H Z =0.8H Z .

[0165] Because the RR interval variability is high (exceeding the threshold), it indicates cardiac rhythm instability. To reduce additional stress on the patient's cardiovascular system, the inflation rate was reduced to 0.8 h⁻¹. Z To reduce the frequency of the measurement process.

[0166] According to a preferred embodiment, when the P-wave amplitude of the electrocardiogram data fluctuates significantly, the processing module 300 extends the measurement time of the blood pressure measurement module 200 to ensure that more stable arterial blood pressure data is obtained.

[0167] For example, the following P wave amplitude (unit: mV) was extracted from electrocardiogram data: {A P1 A P2 A P3} = {0.2, 0.35, 0.5}.

[0168] Rate of change of adjacent P-wave amplitude:

[0169]

[0170] Set amplitude fluctuation threshold So and All values ​​are greater than the amplitude fluctuation threshold, therefore the measurement time needs to be extended.

[0171] Set the normal measurement duration to T normal =10 seconds, the increased duration ΔT = 5 seconds.

[0172] Therefore, the increased measurement time is:

[0173] T measure =T normal +ΔT=10 seconds + 5 seconds=15 seconds (21).

[0174] Because the P wave amplitude fluctuates significantly (exceeding the threshold), it indicates that the electrocardiogram signal is unstable. To ensure more stable arterial blood pressure data, the measurement time is extended to 15 seconds to obtain more data samples for averaging, thereby improving the reliability of the measurement.

[0175] The fluctuation of P wave amplitude directly reflects the stability of atrial activity. When P wave amplitude fluctuates significantly, atrial activity may be unstable, and blood pressure measurements may be inaccurate. By extending the measurement time, stable blood pressure values ​​can be better captured in patients with atrial fibrillation. Therefore, by extending the measurement time when P wave amplitude fluctuations are significant, stable blood pressure data can still be obtained under conditions of large fluctuations, thus improving the reliability of the measurement.

[0176] According to a preferred embodiment, the processing module 300 dynamically adjusts the measurement frequency and duration of the blood pressure measurement module 200 based on the heart rate variability in the electrocardiogram data. When the heart rate variability shows a decreasing trend, the processing module 300 reduces the measurement frequency of the blood pressure measurement module 200 and extends the duration of a single measurement to improve the measurement comfort of the target subject.

[0177] When a decrease in heart rate variability is detected, HRV k <HRV k-1 (twenty two).

[0178] HRV k Indicates current heart rate variability; HRV k-1 This indicates the variability of heart rate over a previous period.

[0179] The formula is now adjusted to: f new =f old ·α (23). t new =t old ·β (24).

[0180] f old This indicates the current measurement frequency, measured in times per minute. new This indicates the new measurement frequency. t old This indicates the duration of a single measurement, in minutes. t new This indicates the new duration of a single measurement. α is an adjustment factor for the measurement frequency (0 < α < 1, indicating a decrease in measurement frequency). β is an adjustment factor for the duration of a single measurement (β > 1, indicating an increase in measurement duration).

[0181] Assume the current measurement frequency is f old = 1 time / minute, current single measurement duration is t old =2 minutes, with adjustment factors of α=0.5 and β=1.5, and HRV was detected. k <HRV k-1 ,but:

[0182] The new measurement frequency is: f new =f old α = 1 · 0.5 = 0.5 times / minute.

[0183] The new duration of a single measurement is: t new =t old ·β=2·1.5=3 minutes.

[0184] Heart rate variability (HRV) is an indicator of heart rate fluctuations. When HRV decreases, heart rate stabilizes, and blood pressure tends to stabilize as well. Therefore, reducing the frequency of measurements can minimize patient discomfort and increase comfort. Conversely, extending the duration of each measurement helps obtain more accurate and stable blood pressure data when the heart rate is stable. This is particularly important for patients with atrial fibrillation, as their heart rate fluctuates significantly, and traditional frequent measurements may lead to inaccurate data and discomfort. This dynamic adjustment method allows for more effective monitoring of blood pressure, improving the scientific validity of measurements and patient compliance. Therefore, when HRV decreases, heart rate tends to stabilize, and blood pressure is relatively stable. In this case, reducing the measurement frequency will not affect the accuracy of blood pressure data, while extending the duration of each measurement ensures more stable data. This adjustment not only reduces the discomfort caused by frequent measurements but also improves the reliability of blood pressure monitoring.

[0185] Preferably, the processing module 300 adjusts the deflation frequency of the blood pressure measurement module 200 according to the pulse pressure difference in the initial arterial blood pressure data. When the pulse pressure difference is large, the deflation frequency is increased to quickly adapt to rapid changes in blood pressure and improve measurement accuracy.

[0186] Pulse pressure PP is the difference between systolic blood pressure (SBP) and diastolic blood pressure (DBP), which can be expressed by the formula: PP = SBP - DBP (25).

[0187] Let the current venting frequency be D. old (Unit: mmHg / s), the new venting frequency is D. new The formula for adjusting the venting frequency based on the pulse pressure difference can be expressed as: D new =D old ·γ(26).

[0188] When the pulse pressure difference is large, the deflation frequency increases. γ > 1 indicates an increase in the deflation frequency.

[0189] Based on the initial pulse pressure difference, the processing module 300 or the adjustment module 320 within the processing module 300 will dynamically adjust the venting frequency using the following comprehensive formula:

[0190] Among them, PP threshold It is a preset pulse pressure difference threshold used to determine whether the venting frequency needs to be increased.

[0191] Assume the current venting frequency is D old =3 mmHg / s, adjustment factor γ = 1.2, preset pulse pressure threshold is PPthreshold =40 mmHg. If the initial pulse pressure difference is PP = 45 mmHg.

[0192] Since PP ≥ PP threshold The new venting frequency is: D new =D old γ = 3 * 1.2 = 3.6 mmHg / s.

[0193] Clearly, by adjusting the deflation frequency of the blood pressure measurement module 200, this invention can improve the accuracy of blood pressure measurement. Firstly, patients with atrial fibrillation have irregular heart rates and significant blood pressure fluctuations. Traditional fixed deflation frequencies are prone to large measurement errors when dealing with such irregular heartbeats, while dynamically adjusting the deflation frequency can significantly reduce these errors, improving measurement accuracy and reliability. By dynamically adjusting the deflation frequency, the blood pressure measurement module 200 can adapt to these rapid changes in a timely manner, avoiding measurement lag and thus providing more real-time and accurate measurement data.

[0194] Secondly, dynamically adjusting the deflation frequency can effectively avoid false hypotension. False hypotension is an erroneous low value caused by the measurement process failing to respond promptly to rapid changes in blood pressure. By adjusting the deflation frequency in real time, the device can more accurately capture true blood pressure fluctuations, avoid the influence of false hypotension, and thus provide more reliable blood pressure measurement results.

[0195] Furthermore, the dynamic adjustment mechanism provides a more accurate raw data foundation for advanced data processing algorithms, enabling them to better interpret measurement results and reduce misjudgments and misdiagnoses. This high-precision data foundation enhances the flexibility of the algorithm, allowing the blood pressure measurement module 200 to maintain efficient and accurate operation even in complex atrial fibrillation situations.

[0196] Furthermore, heart rate and blood pressure variations in patients with atrial fibrillation exhibit significant individual differences. The mechanism of dynamically adjusting the deflation frequency allows the blood pressure measurement module 200 to better adapt to the specific circumstances of different patients, providing more personalized and accurate measurement results. This personalized measurement not only improves measurement accuracy but also enhances the clinical applicability of the device.

[0197] Finally, through the intelligent analysis and adjustment of the processing module 300, the blood pressure measurement module 200 possesses a more intelligent response capability, enabling it to optimize the measurement process based on the patient's real-time condition. This intelligence not only provides more accurate blood pressure readings but also accumulates more valuable health data over long-term monitoring, offering important references for medical decision-making.

[0198] Therefore, the technique of dynamically adjusting the deflation frequency can significantly improve the accuracy and reliability of blood pressure measurement in patients with atrial fibrillation, avoid false hypotension, reduce measurement errors, provide personalized measurement, and enhance the intelligence and applicability of the equipment.

[0199] The processing module 300 adjusts the inflation and deflation frequency of the blood pressure measurement module 200 based on the heart rate in the initial physiological parameters. When the heart rate is fast, the inflation and deflation frequency is reduced to reduce the burden on the heart.

[0200] Specifically, record the initial heart rate (HR). initial Determine if the heart rate exceeds a preset threshold HR. threshold If HR initial >HR threshold The adjustment factor α for the measurement frequency is recalculated.

[0201] HR max This indicates the upper limit of heart rate.

[0202] For example, the HR records initial =110 bpm; HR threshold =100 bpm; HR max =150 bpm.

[0203] f new =f old 0.8.

[0204] The processing module 300 adjusts the inflation time of the blood pressure measurement module 200 based on the QT interval in the initial electrocardiogram data. When the QT interval is long, the inflation time is extended to ensure the accuracy of blood pressure measurement and avoid measurement errors caused by arrhythmia.

[0205] Recording QT intervals QT initial Determine if the QT interval exceeds a preset threshold QT interval. threshold If QT initial >QT threshold The inflation time needs to be extended, so the adjustment factor λ for the inflation time needs to be calculated. 充气new =T 充气old ·λ.

[0206] T 充气new This indicates the adjusted inflation time, in seconds (s); T 充气old This indicates the inflation time before the adjustment.

[0207] There are two formulas for calculating the adjustment factor λ.

[0208] The formula A for calculating the adjustment factor λ is:

[0209] The formula B for calculating the adjustment factor λ is:

[0210] QT max This indicates the upper limit of the QT interval.

[0211] For example, the current inflation time is T. 充气old = 30 seconds; QT initial = 480 milliseconds; QT threshold = 450 milliseconds; QT max = 500 milliseconds.

[0212] The formula A for calculating the adjustment factor λ is: T 充气new =T 充气old ·λ=30 seconds × 1.067≈32.01 seconds.

[0213] The formula B for calculating the adjustment factor λ is: T 充气new =T 充气old ·λ=30 seconds × 1.6≈48 seconds.

[0214] The advantage of formula A for calculating the adjustment factor λ is that it provides a smoother adjustment, preventing drastic changes in inflation time, which is particularly important for stable measurements in patients with atrial fibrillation. Since patients with atrial fibrillation may have arrhythmias, this formula can avoid over-adjustment due to heart rate fluctuations. However, in cases of large fluctuations in the QT interval, the adjustment range may be insufficient and unable to quickly adapt to large changes.

[0215] The advantages of formula B for calculating the adjustment factor λ are: it is suitable for situations requiring significant adjustment, and it can quickly adjust the inflation time when the QT interval deviates significantly from the preset threshold. When the QT interval approaches its upper limit, the adjustment factor increases more significantly, making it more sensitive to changes in the QT interval. However, in patients with atrial fibrillation, rhythm fluctuations may lead to over-adjustment, affecting the stability and accuracy of the measurement.

[0216] Therefore, for blood pressure measurement in patients with atrial fibrillation, the following combined strategy can be referenced and implemented based on their specific needs and the variability of the QT interval.

[0217] If the patient's QT interval has minimal fluctuations, it is more appropriate to use formula A, which calculates the adjustment factor λ, to determine the inflation duration. This method provides a smoother and more robust adjustment, avoiding over-adjustment caused by heart rhythm fluctuations.

[0218] If a patient's QT interval fluctuates significantly, it is more appropriate to use formula B, which calculates the adjustment factor λ, to determine the inflation duration. This is especially true when the QT interval is close to its upper limit, requiring rapid and significant adjustments to accommodate the changes.

[0219] Furthermore, the inflation duration is smoothly adjusted using formula A, which calculates the adjustment factor λ, ensuring stability in most cases. When the QT interval significantly deviates from the preset threshold and approaches the upper limit, the inflation duration is adjusted using formula B, which calculates the adjustment factor λ, to quickly adapt to larger fluctuations. This combined strategy provides more accurate and stable blood pressure measurements under different conditions, ensuring that the measurement needs of patients with atrial fibrillation are met.

[0220] The processing module 300 adjusts the deflation time of the blood pressure measurement module 200 based on the skin conductance response in the initial physiological parameters. When the skin conductance response is high, the deflation time is shortened to reduce stimulation and discomfort to the patient.

[0221] Specifically, recording the skin conductance response G initial Determine whether the skin conductivity response exceeds the preset threshold G. threshold If G initial >G threshold Calculate the adjustment factor δ for the venting frequency.

[0222] The adjusted venting time is: T 放气new =T 放气old ·δ.

[0223] G max This indicates the upper limit of skin electrical conductivity response. T 放气new This indicates the adjusted venting time, in seconds (s); T 放气old This indicates the venting time before adjustment.

[0224] For example, setting the skin conductance response G initial =5ms; G threshold =3ms; G max = 6ms.

[0225] Since 5ms > 3ms, the skin conductance response is considered to be high. T 放气new =T 放气old 0.33.

[0226] In blood pressure measurement of patients with atrial fibrillation, the processing module 300 can adjust the deflation time of the blood pressure measurement module 200 according to the skin conductance response in the initial physiological parameters, which can bring significant advantages.

[0227] (1) A high skin conductivity response usually indicates that the patient is in a state of tension or anxiety. By shortening the deflation time, the pressure and discomfort on the patient during the measurement process can be reduced, thereby improving the patient's comfort and cooperation.

[0228] (2) By adjusting the deflation time in real time, the blood pressure of patients with atrial fibrillation can be measured more accurately. This reduces the impact of dynamic blood pressure changes caused by excessive deflation time on the measurement results, thereby improving the accuracy of the measurement.

[0229] (3) Dynamically adjusting the deflation time makes the blood pressure measurement process more personalized and humane, improving the overall experience of patients.

[0230] (4) Skin conductance is one of the indicators of the body's response to stress. By adjusting the deflation time based on skin conductance, the stress response of patients during blood pressure measurement can be reduced to a certain extent, thereby avoiding the rise in blood pressure caused by stress.

[0231] (5) Shortening the deflation time can speed up the overall blood pressure measurement process, reduce waiting time, and improve measurement efficiency.

[0232] Example 2

[0233] This embodiment provides a processor, namely processing module 300, for an intelligent blood pressure monitoring device for patients with atrial fibrillation.

[0234] As shown in Figures 7 and 8, the adjustment module 320 in the processor includes a time alignment module 321, a timing selection judgment module 322, and an arterial blood pressure verification module 323. The adjustment module 320 also includes an input port 324 and an output port 325.

[0235] Input port 324 is connected to reading module 310 to receive data such as RR interval, P wave, atrial fibrillation status, heart rate and heart rate variability from the electrocardiogram data read by reading module 310.

[0236] Input port 324 is connected to time alignment module 321 via a line. Time alignment module 321 and timing selection module 322 transmit data via an internal bus or dedicated data channel. Timing selection module 322 and arterial blood pressure verification module 323 transmit data via an internal bus or dedicated data channel. Arterial blood pressure verification module 323 is connected to output port 325 via a line to output arterial blood pressure data. Timing selection module 322 and output port 325 are connected via a line to output arterial blood pressure data or control commands.

[0237] Preferably, when the adjustment module 320 is an independent hardware component, the time alignment module 321, the timing judgment module 322, and the arterial blood pressure verification module 323 are its internal functional modules.

[0238] When the adjustment module 320 is composed of multiple physical hardware components, the time alignment module 321, the timing judgment module 322, and the arterial blood pressure verification module 323 can each be a dedicated integrated chip, server, or microprocessor with different data processing functions. Preferably, the time alignment module 321, the timing judgment module 322, and the arterial blood pressure verification module 323 can also each be provided with an independently connected storage module 326.

[0239] Preferably, the time alignment module 321 is used to align the electrocardiogram (ECG) data with the arterial blood pressure data in time. Preferably, the time alignment module 321 has a preset time series prediction model. The time series prediction model adjusts the time points of the received ECG data and arterial blood pressure data to be consistent, so that the subsequent timing determination module 322 can perform the next step of processing.

[0240] Preferably, the timing judgment module 322 receives time-aligned electrocardiogram data and arterial blood pressure data sent by the time alignment module 321; in particular, it receives arterial blood pressure data and time-aligned data such as RR interval, P wave, atrial fibrillation status, heart rate, and heart rate variability.

[0241] Preferably, the timing determination module 322 has a preset timing model. The timing model is a pre-written data extraction program. The timing determination module 322 determines the stability of the RR interval in the electrocardiogram data and immediately determines the arterial blood pressure value when the RR interval is stable. If the standard deviation of the RR interval within a certain time window is less than a set threshold, the timing determination module 322 determines that the RR interval within the time window is stable. At this time, the timing determination module 322 immediately determines at least one arterial blood pressure value.

[0242] Preferably, multiple arterial blood pressure values ​​are dynamically acquired, and the time interval between acquisitions of adjacent arterial blood pressure values ​​is at least 1 minute. The timing judgment module 322 can calculate the average value of the arterial blood pressure as the final arterial blood pressure value.

[0243] Preferably, the timing determination module 322 calculates the amplitude of the P-wave. Preferably, the timing determination module 322 has a set amplitude threshold. If the amplitude of the P-wave is greater than this amplitude threshold, the timing determination module 322 determines that the P-wave is clear. In this case, the timing determination module 322 calculates the time interval between adjacent P-waves in real time. During real-time monitoring, if the P-wave remains clear and the time interval is stable, the timing determination module 322 determines that the P-wave is continuous.

[0244] At a point in time where the P wave is clear and continuous, the timing determination module 322 determines this time as the timing for measurement and immediately generates a control command for blood pressure measurement. The timing determination module 322 sends the control command to the blood pressure measurement module 200 via output port 325, instructing the blood pressure measurement module 200 to measure arterial blood pressure. It also instructs the blood pressure measurement module 200 to record the corresponding arterial blood pressure value in real time. The timing determination module 322 then sends the P wave characteristics, the time point, and the arterial blood pressure value in a correlated manner to the connected storage module 326 for storage.

[0245] Preferably, the timing judgment module 322 calculates the RR interval change rate of the electrocardiogram data. When the RR interval change rate of the electrocardiogram data increases, the timing judgment module 322 increases the measurement frequency of the blood pressure measurement module 200 to obtain more information on arterial blood pressure changes.

[0246] Preferably, the timing judgment module 322 calculates the change in the P-wave amplitude of the electrocardiogram (ECG) data. When the P-wave amplitude of the ECG data shows significant change, i.e., fluctuation, the timing judgment module 322 does not send a stop measurement command to the blood pressure measurement module 200, thereby extending the measurement time of the blood pressure measurement module 200 to ensure the acquisition of more stable arterial blood pressure data.

[0247] Preferably, the timing determination module 322 calculates the heart rate variability in the electrocardiogram data. When the heart rate variability shows a decreasing trend, the timing determination module 322 determines the moment when the duration of the decreasing trend meets a time threshold as the adjustment opportunity. At this time, the timing determination module 322 sends a control command to the blood pressure measurement module 200 to reduce the measurement frequency, and sends the control command to the blood pressure measurement module 200 through the output port 325. In response to receiving the control command to reduce the measurement frequency, the blood pressure measurement module 200 reduces the measurement frequency and extends the duration of a single measurement to improve the measurement comfort of the target subject.

[0248] Preferably, the timing determination module 322 calculates the pulse pressure difference in the initial arterial blood pressure measurement. When the pulse pressure difference is greater than the pulse pressure difference threshold, the timing determination module 322 determines that this is an opportune time for adjustment. The timing determination module 322 generates a control command to increase the deflation frequency and sends the control command to the blood pressure measurement module 200 through the output port 325. Preferably, the various control commands in this invention can be sent in an encoded form corresponding to the category of the control command. In response to the receipt of the control command to increase the deflation frequency, the blood pressure measurement module 200 increases the deflation frequency to quickly adapt to rapid changes in blood pressure and improve measurement accuracy.

[0249] Preferably, when the heart rate increases, the timing judgment module 322 sends a control command to the blood pressure measurement module 200 through the output port 325 to reduce the inflation and deflation frequency, so as to reduce the burden on the heart.

[0250] Preferably, the timing determination module 322 determines whether the QT interval exceeds a preset threshold QT interval. threshold When the QT interval exceeds a preset threshold QT threshold When the timing judgment module 322 sends a control command to the blood pressure measurement module 200 through the output port 325 to extend the inflation time and the specific extension time data, so that the blood pressure measurement module 200 extends the inflation time to ensure the accuracy of blood pressure measurement and avoid measurement errors caused by arrhythmia.

[0251] If the patient's QT interval fluctuations are small, it is more appropriate to use the formula A for calculating the adjustment factor λ in the timing judgment module 322 to calculate the inflation duration. This is because this method provides a smoother and more robust adjustment, avoiding over-adjustment caused by heart rhythm fluctuations.

[0252] If the patient's QT interval fluctuates significantly, it is more appropriate to use formula B, which calculates the adjustment factor λ, in the timing judgment module 322 to calculate the inflation duration. This is especially true when the QT interval is close to its upper limit, requiring rapid and significant adjustments to adapt to the changes.

[0253] Preferably, the timing judgment module 322 determines whether the skin conductance response exceeds a preset threshold G for the skin conductance response. threshold If G initial >G threshold Calculate the adjustment factor δ for the venting frequency and the venting duration.

[0254] In the blood pressure measurement system, the timing selection module 322 is not only used to determine when to start blood pressure measurement, but also undertakes the task of optimizing the measurement process.

[0255] Specifically, when the processor detects that the RR interval (i.e., the time interval between two adjacent heartbeats) in the electrocardiogram data is stable, the timing judgment module 322 sends a deflation command and corresponding deflation duration data to the blood pressure measurement module 200 through the output port 325. During this process, the blood pressure measurement module 200 dynamically adjusts its deflation duration based on the physiological parameters obtained from the initial measurement, particularly the skin conductance response. Skin conductance response is an important indicator of the level of sympathetic nervous system activity; it can reflect an individual's emotional state and stress response, thus affecting the accuracy of blood pressure measurement. Therefore, by combining skin conductance response with the adjustment of deflation duration, the accuracy and reliability of the measurement results can be effectively improved.

[0256] The arterial blood pressure verification module 323 further ensures the accuracy and consistency of the measurement results. The arterial blood pressure verification module 323 retrieves previously recorded electrocardiogram and arterial blood pressure data from the storage module 326 and performs secondary analysis on this data to confirm whether the optimal measurement timing has been selected. The arterial blood pressure verification module 323 compares the repeated timing selection with the timing selection initially determined by the timing selection module 322, calculating the number of deviations and the probability of correctness. Here, the number of deviations refers to the number of times the difference between the repeated timing selection and the original timing selection exceeds a preset deviation time threshold (e.g., 0.5 seconds or 1 second); the probability of correctness refers to the proportion of correctly selected timings out of the total number of measurements.

[0257] To better understand this process, consider a specific scenario: Within a complete measurement cycle, the timing selection module 322 determines multiple possible measurement points based on real-time electrocardiogram data and selects the optimal measurement time. Subsequently, the arterial blood pressure verification module 323 reviews all measurement data from this period and re-evaluates the rationality of each measurement point selection. If significant deviations are found in the selection of certain measurement points, exceeding a set time threshold, these deviations are counted. Simultaneously, the arterial blood pressure verification module 323 also counts the frequency with which the timing selection module 322 correctly selects the measurement time throughout the entire measurement process and calculates the probability of correct selection.

[0258] It is worth noting that the correct probability actually refers to the ratio of the number of times the deviation in timing selection is less than or equal to the deviation time threshold to the total number of times. In other words, the correct probability reflects the extent to which the timing selection judgment module 322 can accurately predict the optimal measurement timing. When the correct probability is lower than the preset correct probability threshold, it means that the performance of the timing selection judgment module 322 may not be ideal and calibration is required. At this time, the arterial blood pressure verification module 323 will send a model calibration command to the timing selection judgment module 322.

[0259] During calibration, the timing selection module 322, with the processor establishing a communication connection with the external network, accesses a large-scale database in the cloud to compare and learn from similar models. In this way, the timing selection module 322 can obtain the latest algorithm updates and technological improvements, thereby enhancing its performance and accuracy. For example, the timing selection module 322 can learn from the successful experiences of other similar systems, optimize its parameter settings, or introduce new feature variables to enhance its understanding of complex physiological signals. Furthermore, calibration helps the timing selection module 322 adapt to the characteristics of different user groups, ensuring it can provide high-quality service globally.

[0260] As described above, the collaborative operation between the timing judgment module 322 and the arterial blood pressure verification module 323 constitutes a closed-loop control system designed to continuously optimize the blood pressure measurement process, ensuring that each measurement is performed at the most appropriate time, thereby providing users with the most accurate and reliable blood pressure data. This not only improves measurement efficiency but also enhances the user experience.

Claims

1. An intelligent blood pressure monitoring device for a patient with atrial fibrillation, characterized in that, include: Electrocardiogram (ECG) measurement module (100): Collects ECG data of the target object and generates ECG data; Blood pressure measurement module (200): Measures arterial blood pressure data of the target subject; Processing module (300): Time-aligns the electrocardiogram data with the arterial blood pressure data, and determines the stability of the RR interval in the electrocardiogram data. Based on the stability of the RR interval, P wave changes and / or atrial fibrillation in the electrocardiogram data, it selects arterial blood pressure data or calculates the average value of the arterial blood pressure data.

2. The smart blood pressure monitoring device of claim 1, wherein, The processing module (300) determines the arterial blood pressure value immediately when the RR interval is stable; and / or determines at least one arterial blood pressure value and its average value during the period when the RR interval is stable.

3. The smart blood pressure monitoring device of claim 2, wherein, When the rate of change of the RR interval in the electrocardiogram data increases, the processing module (300) increases the measurement frequency of the blood pressure measurement module (200) to obtain more information on changes in arterial blood pressure.

4. The smart blood pressure monitoring device according to any one of claims 1 to 3, characterized in that, The processing module (300) selects the arterial blood pressure value when the P wave in the electrocardiogram data is clear and continuous.

5. The intelligent blood pressure monitoring device according to any one of claims 1 to 4, characterized in that, The processing module (300) filters the real-time determined arterial blood pressure data based on heart rate threshold and heart rate variability threshold, thereby deleting inaccurate arterial blood pressure data.

6. The smart blood pressure monitoring device according to any one of claims 1 to 5, characterized in that, The processing module (300) dynamically adjusts the measurement frequency and duration of the blood pressure measurement module (200) based on the changes in the electrocardiogram data, and selects arterial blood pressure data that meets the time interval requirements and is less affected by atrial fibrillation as the measurement data.

7. A method for intelligent blood pressure monitoring for atrial fibrillation patients, characterized in that, The method includes: Measure the arterial blood pressure data of the target subject; Collect electrocardiogram (ECG) data from the target subject and generate ECG data; The electrocardiogram (ECG) data and the arterial blood pressure data are time-aligned, and the stability of the RR interval in the ECG data is determined. Based on the stability of the RR interval, P wave changes, and / or atrial fibrillation in the ECG data, arterial blood pressure data is selected or the average value of the arterial blood pressure data is calculated.

8. The intelligent blood pressure monitoring method of claim 7, wherein, The method further includes: determining an arterial blood pressure value immediately when the RR interval is stable; and / or determining at least one arterial blood pressure value and its average value during a period when the RR interval is stable.

9. The intelligent blood pressure monitoring method of claim 7 or 8, wherein, The method further includes: The measurement frequency and duration of the blood pressure measurement module (200) are dynamically adjusted based on the changes in the electrocardiogram data, and arterial blood pressure data that meet the time interval requirements and are less affected by atrial fibrillation are selected as the measurement data.

10. The intelligent blood pressure monitoring method according to any one of claims 7 to 9, characterized in that, Inaccurate arterial blood pressure data is filtered out based on heart rate thresholds and heart rate variability thresholds.

11. A processor for an intelligent blood pressure monitoring device for a patient with atrial fibrillation, characterized in that, The processor (300) is configured to: Time-align the electrocardiogram data with the arterial blood pressure data; Determine the stability of the RR interval in the electrocardiogram data; Arterial blood pressure data or the average value of arterial blood pressure data can be selected based on the stability of the RR interval, changes in the P wave, and / or atrial fibrillation in the electrocardiogram data.

12. The processor of claim 11, wherein, The processor (300) includes: The reading module (310) reads electrocardiogram data; The adjustment module (320) determines the timing of arterial blood pressure data selection in real time based on electrocardiogram data and generates control instructions for the blood pressure measurement module (200). Under the condition of adjusting the measurement parameters of the blood pressure measurement module (200), it selects arterial blood pressure data or calculates the average value of arterial blood pressure data based on the stability of RR interval, P wave changes and / or atrial fibrillation in electrocardiogram data.

13. The processor of claim 11 or 12, wherein, The adjustment module (320) in the processor (300) includes: The time alignment module (321) has a preset time series prediction model; the time series prediction model aligns the received electrocardiogram data with the time data of arterial blood pressure data. The timing selection module (322) receives time-aligned electrocardiogram data and arterial blood pressure data sent by the time alignment module (321) and determines the timing of arterial blood pressure data selection; or generates control instructions for the blood pressure measurement module (200) to adjust the measurement parameters of the blood pressure measurement module (200) and determines the timing of arterial blood pressure data selection. The arterial blood pressure verification module (323) is used to retrieve electrocardiogram data and arterial blood pressure data, and to re-determine the timing of the selection of arterial blood pressure data, and to calculate the correct number of times and / or the correct probability of the timing of selection by the timing judgment module (322).

14. The processor of any of claims 11-13, wherein, The timing determination module (322) in the processor (300) is configured as follows: Arterial blood pressure values ​​are determined immediately when the RR interval is stable; and / or at least one arterial blood pressure value and its average value are determined during the period when the RR interval is stable.

15. The processor of any of claims 11-14, wherein, The timing determination module (322) in the processor (300) is configured as follows: When the rate of change of the RR interval in the electrocardiogram data increases, the processing module (300) increases the measurement frequency of the blood pressure measurement module (200) to obtain more information on changes in arterial blood pressure.

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