A method, system, and related device for pneumatic pressurization control of an electronic blood pressure monitor.

Abstract

This application discloses a method, system, and related device for pneumatic pressurization control of an electronic blood pressure monitor, which improves the comfort and safety of the test subject and enhances the overall measurement efficiency of the blood pressure measurement process. The method includes: component separation of the photoplethysmography (PPG) signal to obtain a pulsating component reflecting the periodic changes in arterial blood flow, and constructing an amplitude characteristic sequence updated with the pressurization process; calculating the decay rate of the amplitude characteristic sequence relative to a reference sequence within the current pressure range; constructing a nonlinear response trend model of the pulsating amplitude to the pressurization process; determining whether the distal arterial blood flow has entered a critical occlusion state based on a preset background noise floor; if so, controlling the cuff to stop further pressurization, and determining an adaptive pressurization upper limit parameter for this measurement based on the cuff pressure at the corresponding moment; and controlling the blood pressure monitor to switch from the pressurization control process to the deflation measurement process based on the adaptive pressurization upper limit parameter.

Description

Technical Field

[0001] This application relates to the field of medical device technology, and in particular to a method, system and related device for pneumatic pressure control of an electronic blood pressure monitor. Background Technology

[0002] Electronic blood pressure monitors, as commonly used physiological parameter detection devices, are widely used in home health monitoring, primary healthcare, and clinical auxiliary diagnosis. Electronic blood pressure monitors typically measure blood pressure parameters such as systolic and diastolic pressure by applying cuff pressure to the limb being measured, causing detectable pressure changes or pulsation signals in the arterial blood flow during the inflation and deflation process.

[0003] In existing technologies, electronic blood pressure monitors often use preset pressure upper limits or empirical pressure values ​​as the termination condition for inflation control. For example, a fixed inflation target pressure is set, or the upper limit is simply adjusted based on the user's historical blood pressure information before measurement. Some existing technologies also indirectly determine whether the required measurement pressure range has been reached by analyzing cuff pressure signals or changes in oscillation amplitude.

[0004] However, due to significant differences in blood pressure levels, vascular compliance, and physiological conditions among individuals, using a fixed upper limit for inflation can easily lead to discrepancies between the inflation process and the actual blood flow occlusion state. For individuals with low blood pressure or sensitive blood vessels, an excessively high upper limit for inflation may cause unnecessary limb discomfort; while for individuals with high blood pressure, insufficient inflation may require secondary inflation, affecting measurement efficiency and user experience. Summary of the Invention

[0005] This application provides a method, system, and related device for pneumatic pressurization control of an electronic blood pressure monitor, which improves the comfort of the subject and the safety of measurement, thereby enhancing the overall efficiency of the blood pressure measurement process.

[0006] The first aspect of this application provides a method for pneumatic pressurization control of an electronic blood pressure monitor, comprising:

[0007] According to the measurement start command of the blood pressure monitor, while applying cuff pressure to the limb being measured, photoplethysmography (PPG) signals from at least one sampling position of the limb being measured are simultaneously acquired.

[0008] The photoplethysmography (PPG) signal is subjected to component separation to obtain the pulsatile component reflecting the periodic changes in arterial blood flow. An amplitude feature sequence updated with the pressurization process is constructed based on the pulsatile component, with the cardiac cycle as the unit.

[0009] The decay rate of the amplitude characteristic sequence relative to the reference sequence within the current pressure range is calculated based on the stepwise change relationship of the cuff pressure during the pressurization process. The reference sequence is the amplitude characteristic sequence corresponding to the initial pressure range.

[0010] Based on the comparison between the decay rate and the corresponding cuff pressure increment, a nonlinear response trend model of the pulsation amplitude to the pressurization process is constructed.

[0011] When the rate of change of the nonlinear response trend model is detected to meet the preset convergence condition, the distal arterial blood flow is judged to have entered the critical state of occlusion in combination with the preset background noise base.

[0012] If so, the cuff is controlled to stop applying pressure, and the adaptive pressure upper limit parameter for this measurement is determined based on the cuff pressure at the corresponding moment.

[0013] Based on the adaptive pressurization upper limit parameter, the blood pressure monitor is controlled to switch from the pressurization control process to the deflation measurement process.

[0014] Optionally, the step of performing component separation on the photoplethysmography signal to obtain the pulsatile component reflecting the periodic changes in arterial blood flow includes:

[0015] The air pump drive control signal in the blood pressure monitor is obtained, and the fundamental frequency and harmonic frequency of the air pump drive control signal are analyzed to construct a mechanical vibration noise reference model.

[0016] The photoplethysmography pulse wave signal is used as the main channel observation input, and the mechanical vibration noise reference model is used as the reference channel input. The adaptive notch filter algorithm is used to perform co-frequency interference cancellation processing, and the denoised intermediate state signal is output.

[0017] The intermediate-state signal is subjected to bandpass filtering processing covering the normal human heart rate range, the AC component is separated as the pulsation component, and the DC component in the intermediate-state signal is extracted simultaneously. The mean drift of the DC component within a preset time window is calculated, and the pulsation component is zero-point corrected based on the mean drift.

[0018] Optionally, the step of constructing a nonlinear response trend model of the pulsation amplitude to the pressurization process based on the comparison between the decay rate and the corresponding cuff pressure increment includes:

[0019] Based on the sampling timing during the pressurization process, the amplitude data in the amplitude feature sequence are time-domain aligned with the corresponding real-time cuff pressure values ​​to construct a pressure-amplitude dataset.

[0020] Calculate the relative attenuation of the amplitude feature sequence within the current detection window relative to the initial reference amplitude, and compare the relative attenuation with the corresponding cumulative cuff pressure increment within the window to calculate the response sensitivity under unit pressure change;

[0021] Using the response sensitivity as a weighting factor, a nonlinear regression analysis is performed on the pressure-amplitude dataset to fit and generate a dynamic envelope evolution curve that conforms to the characteristics of arterial unloading.

[0022] The first derivative of the dynamic envelope evolution curve at the current cuff pressure point is calculated in real time as the instantaneous decay rate, and the second derivative is calculated as the decay acceleration factor. The combined characteristics of the instantaneous decay rate and the decay acceleration factor are determined as a nonlinear response trend model.

[0023] Optionally, after determining whether distal arterial blood flow has entered a critical occlusion state based on a preset background noise baseline, and before stopping the cuff from continuing to apply pressure and determining the adaptive pressure upper limit parameter for this measurement based on the cuff pressure at the corresponding moment, the method further includes:

[0024] If the critical state of blockage is reached, the peak dispersion and cycle drift of the pulsatile component within at least three consecutive cardiac cycles are statistically analyzed to calculate the confidence level of blood flow blockage.

[0025] The control cuff stops applying pressure and determines the adaptive pressure upper limit parameter for this measurement based on the cuff pressure at the corresponding moment, including:

[0026] When the confidence level of blood flow occlusion reaches a preset threshold, the control cuff stops applying pressure, and the adaptive pressure upper limit parameter for this measurement is determined based on the cuff pressure at the corresponding moment.

[0027] Optionally, the step of statistically analyzing the peak dispersion and cycle drift of the pulsatile components over at least three consecutive cardiac cycles to calculate the confidence level of blood flow occlusion includes:

[0028] Extract the peak time points and peak amplitudes of the pulsation components within the at least three consecutive cardiac cycles to construct a set of feature points, calculate the time difference sequence of adjacent peak time points in the set of feature points, and calculate the periodic drift amount reflecting the stability of the heart rate rhythm based on the time difference sequence.

[0029] Calculate the average and standard deviation of all peak amplitudes in the feature point set, and calculate the peak dispersion to reflect the consistency of pulse intensity based on the ratio of the standard deviation to the average.

[0030] The period drift and the peak dispersion are normalized respectively, and the weighted sum of the period drift and the peak dispersion is calculated using a preset weighted evaluation function to obtain the blood flow occlusion confidence. The lower the period drift and the peak dispersion, the higher the blood flow occlusion confidence.

[0031] Optionally, the method further includes:

[0032] During the construction of the amplitude feature sequence, the baseline drift velocity of the DC component in the photoplethysmography signal is monitored in real time.

[0033] If the baseline drift velocity exceeds a preset motion interference threshold, the construction of the amplitude feature sequence is paused until the baseline drift velocity returns to the normal range.

[0034] Optionally, determining the adaptive pressure upper limit parameter for this measurement based on the cuff pressure at the corresponding moment includes:

[0035] The cuff pressure at the corresponding moment is marked as the blocking reference pressure point. The convergence gradient feature of the nonlinear response trend model at the blocking reference pressure point is extracted, and the pressure overshoot compensation value for establishing the measurement buffer is determined based on the convergence gradient feature.

[0036] The target pressurization peak value is calculated by linearly superimposing the blocking reference pressure point and the pressure overshoot compensation value, and the adaptive pressurization upper limit parameter for this measurement is determined based on the target pressurization peak value.

[0037] Optionally, determining the adaptive pressure upper limit parameter for this measurement based on the target pressure peak includes:

[0038] Perform a safety boundary check on the target pressurization peak value to determine whether the target pressurization peak value is within the preset physiological safety pressure range;

[0039] If so, the target pressurization peak value is determined as the adaptive pressurization upper limit parameter for this measurement;

[0040] If not, the upper limit of the physiological safety pressure range will be determined as the adaptive pressurization upper limit parameter for this measurement.

[0041] A second aspect of this application provides a system for pneumatic pressurization control of an electronic blood pressure monitor, comprising:

[0042] The acquisition unit is used to simultaneously acquire photoplethysmography (PPG) signals from at least one sampling location of the limb being measured during the process of applying cuff pressure to the limb being measured, according to the measurement start command of the blood pressure monitor.

[0043] The separation unit is used to perform component separation on the photoplethysmography pulse wave signal to obtain the pulsating component that reflects the periodic changes in arterial blood flow, and to construct an amplitude feature sequence updated with the pressurization process based on the pulsating component in units of cardiac cycle.

[0044] The calculation unit is used to calculate the decay rate of the amplitude characteristic sequence relative to the reference sequence in the current pressure range based on the stepwise change relationship of the cuff pressure during the pressurization process. The reference sequence is the amplitude characteristic sequence corresponding to the initial pressure range.

[0045] A construction unit is used to construct a nonlinear response trend model of the pulsation amplitude to the pressurization process based on the comparison between the decay rate and the corresponding cuff pressure increment.

[0046] The judgment unit is used to determine whether the distal arterial blood flow has entered the critical state of occlusion when the rate of change of the nonlinear response trend model is detected to meet the preset convergence condition, in combination with the preset background noise base.

[0047] The stop unit is used to control the cuff to stop applying pressure when the judgment result of the judgment unit is yes, and to determine the adaptive pressure upper limit parameter for this measurement based on the cuff pressure at the corresponding moment.

[0048] The control unit is used to control the blood pressure monitor to switch from the pressurization control process to the deflation measurement process based on the adaptive pressurization upper limit parameter.

[0049] A third aspect of this application provides a device for pneumatic pressurization control of an electronic blood pressure monitor, the device comprising:

[0050] Processor, memory, input / output units, and bus;

[0051] The processor is connected to the memory, the input / output unit, and the bus;

[0052] The memory stores a program that the processor invokes to execute the first aspect and any optional method of the first aspect for pneumatic pressurization control of an electronic blood pressure monitor.

[0053] The fourth aspect of this application provides a computer-readable storage medium storing a program that, when executed on a computer, performs the method of pneumatic pressurization control of an electronic blood pressure monitor, as described in the first aspect and any optional method of the first aspect.

[0054] As can be seen from the above technical solutions, this application has the following advantages:

[0055] By simultaneously acquiring photoplethysmography (PPG) signals of the limb being measured during the blood pressure monitor's inflation control process, the pulsating component in the signal is separated, and a dynamically updated amplitude feature sequence is constructed using cardiac cycles as units. This allows the inflation process to not only depend on the cuff pressure change itself but also reflect the periodic changes in arterial blood flow in real time. Subsequently, the attenuation behavior of the amplitude feature sequence relative to the initial pressure range in different pressure ranges is correlated with the stepwise increments of cuff pressure to construct a nonlinear response trend model reflecting the change in pulsation amplitude during inflation. By determining whether the rate of change of the nonlinear response trend model meets a preset convergence condition and combining this with a preset background noise floor, stable identification of distal arterial blood flow entering a critical state of occlusion is achieved. This avoids the influence of single signal amplitude fluctuations, instantaneous noise interference, or individual differences on the inflation termination judgment. Based on this, the upper limit parameter of inflation required for this measurement is adaptively determined to control the timely switch of the blood pressure monitor from the inflation control process to the deflation measurement process. Therefore, this application can reduce unnecessary excessive pressure while ensuring the blood flow occlusion conditions required for measurement, thereby reducing the discomfort and potential risks to the test subject, and shortening the ineffective pressure phase, thus improving the overall measurement efficiency of the blood pressure measurement process. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 A schematic flowchart of an embodiment of the pneumatic pressurization control method for the electronic blood pressure monitor provided in this application;

[0058] Figure 2 A schematic flowchart of an embodiment of the method for pneumatic pressurization control of an electronic blood pressure monitor provided in this application, which involves component separation of the photoplethysmography pulse wave signal to obtain a pulsating component reflecting the periodic changes in arterial blood flow;

[0059] Figure 3 A schematic flowchart of an embodiment of the method for pneumatic pressurization control of an electronic blood pressure monitor provided in this application, which constructs a nonlinear response trend model of the pulsation amplitude to the pressurization process;

[0060] Figure 4 A schematic flowchart of another embodiment of the pneumatic pressurization control method for the electronic blood pressure monitor provided in this application;

[0061] Figure 5A schematic flowchart of an embodiment of the method for calculating the confidence level of blood flow occlusion in the pneumatic pressurization control of the electronic blood pressure monitor provided in this application;

[0062] Figure 6 A schematic flowchart of an embodiment of the method for pneumatic pressurization control of an electronic blood pressure monitor provided in this application, which determines the adaptive pressurization upper limit parameter for the current measurement based on the cuff pressure at the corresponding moment;

[0063] Figure 7 A schematic diagram of an embodiment of the pneumatic pressurization control system for the electronic blood pressure monitor provided in this application;

[0064] Figure 8 A schematic diagram of an embodiment of the pneumatic pressurization control device for an electronic blood pressure monitor provided in this application. Detailed Implementation

[0065] This application provides a method, system, and related device for pneumatic pressurization control of an electronic blood pressure monitor, which improves the comfort of the subject and the safety of measurement, thereby enhancing the overall efficiency of the blood pressure measurement process.

[0066] It should be noted that the pneumatic pressurization control method for an electronic blood pressure monitor provided in this application is not limited to a specific hardware entity and can be applied to any electronic device or system with data acquisition, signal processing, and electromechanical control capabilities. The executing entity can be an embedded microcontroller unit integrated within the electronic blood pressure monitor, or a digital signal processor, field-programmable gate array, or application-specific integrated circuit independent of the pneumatic structure; it can also be a smart terminal (such as a smartphone or tablet computer) communicating with the cuff and air pump assembly. For ease of description, the following embodiments will primarily use the control system built into the electronic blood pressure monitor as the executing entity, but this should not be construed as limiting the scope of protection of this application.

[0067] Please see Figure 1 , Figure 1 An embodiment of the pneumatic pressurization control method for an electronic blood pressure monitor provided in this application includes:

[0068] 101. According to the measurement start command of the blood pressure monitor, while applying cuff pressure to the limb being measured, photoplethysmography (PPG) signals from at least one sampling position of the limb being measured are simultaneously collected.

[0069] The electronic blood pressure monitor first receives a measurement start command triggered by the user, then enters a blood pressure measurement process. This start command can originate from a button operation, touch control, or remote control signal, marking the beginning of the measurement process. Once the start command is issued, the monitor controls the air pump to inflate the cuff, gradually applying pressure to the limb being measured. During this inflation process, continuous photoelectric detection is simultaneously performed on the limb at at least one preset sampling location to acquire the corresponding photoplethysmography (PPG) signal in real time. This PPG signal is an electrical signal that optically detects the periodic changes in blood volume within biological tissue caused by heartbeats. The PPG signal indirectly reflects the state of arterial blood flow. The sampling location refers to a site located distal to the limb, suitable for optical transmission or reflection detection, such as the fingers, wrist, or earlobe. This PPG signal differs from the internal pressure sensor signal of the blood pressure monitor and is a physiological signal characterizing the actual blood flow state. For example, in a common upper arm electronic blood pressure monitor application scenario, the cuff is set on the upper arm, while the photoelectric acquisition unit is set on the fingertip on the same side. When the air pump gradually increases the pressure, the photoelectric sensor at the fingertip continuously outputs a photoplethysmography (PPG) signal that reflects the blood flow pulsation.

[0070] 102. Perform component separation on the photoplethysmography pulse wave signal to obtain the pulsating component that reflects the periodic changes in arterial blood flow, and construct an amplitude feature sequence updated with the pressurization process based on the pulsating component with the cardiac cycle as the unit.

[0071] After acquiring the synchronously acquired photoplethysmography (PPG) pulse wave signal, the system needs to further process the signal through component separation to extract effective information that truly reflects the pulsating characteristics of arterial blood flow. Since the original photoelectric signal typically contains a DC component caused by non-pulsatile tissues such as skin, bone, muscle, and venous blood, as well as an AC component caused by cardiac pumping, and baseline drift due to cuff compression often occurs during pressurization, component separation technology is needed to remove the DC baseline and background interference, extracting only the pulsating component that reflects the periodic volume changes of arterial blood flow. Based on this, the pulsating component is further characterized using the cardiac cycle as the unit of analysis. The cardiac cycle refers to the time interval between two adjacent heartbeats, which can serve as a natural time scale for dividing the pulse signal structure. In practice, the boundaries of the cardiac cycle can be determined by detecting the peaks or troughs in the pulsating component, and the corresponding pulsation amplitude is extracted within each cardiac cycle to construct an amplitude feature sequence. This amplitude feature sequence is a dynamically growing dataset that records the pulsation amplitude corresponding to each cardiac cycle during pressurization in sequence. Specifically, as the cuff pressure continues to increase, the system captures each newly generated heartbeat waveform in real time, calculates its amplitude, and adds it to the amplitude feature sequence in sequence, thereby forming a trajectory that can completely depict the evolution of blood flow pulsation intensity after the blood vessel is compressed.

[0072] 103. Calculate the decay rate of the amplitude characteristic sequence relative to the reference sequence within the current pressure range based on the stepwise change relationship of the cuff pressure during the pressurization process. The reference sequence is the amplitude characteristic sequence corresponding to the initial pressure range.

[0073] After constructing the amplitude feature sequence, the system further correlates the amplitude feature sequence with the pressure changes during cuff inflation to analyze the trend of pulsation amplitude changes with increasing pressure. The pressure interval refers to several adjacent pressure ranges divided according to a preset step or continuous increase method during cuff inflation; the baseline sequence refers to the amplitude feature sequence corresponding to the initial stage of inflation, before the cuff pressure exerts significant pressure on arterial blood flow, which characterizes the reference level of pulsation amplitude in the subject under near-natural blood flow conditions.

[0074] Specifically, after the blood pressure monitor begins inflation and enters a stable pressurization state, the control system divides the entire pressurization process into multiple continuous pressure intervals. For example, each fixed pressure increment forms a new pressure interval, or each increment of 5-10 mmHg constitutes an interval. Within each pressure interval, the system statistically updates the amplitude feature sequence obtained in step 102 based on the corresponding cardiac cycle. Simultaneously, the amplitude feature sequence obtained in the current pressure interval is compared with a reference sequence to calculate the degree of change in the current amplitude relative to the reference amplitude, thereby obtaining the decay rate reflecting the attenuation of pulse intensity. In actual measurement, this manifests as follows: when the cuff first begins inflation, arterial blood flow is essentially unrestricted, and the amplitude feature sequence obtained at this time is set as the reference sequence; as the cuff pressure gradually increases and enters subsequent pressure intervals, the external pressure forces the arterial wall to gradually collapse, causing the energy of the pulsating blood flow through the sampling location to attenuate, and the pulse amplitude within the corresponding cardiac cycle gradually decreases. By comparing with the reference sequence, the rate and amplitude of this decrease can be quantified, thus forming the decay rate. This decay rate not only reflects the extent to which the current pressure level affects blood flow, but also provides a comparable indicator of change across different individuals.

[0075] 104. Based on the comparison between the decay rate and the corresponding cuff pressure increment, a nonlinear response trend model of the pulsation amplitude to the pressurization process is constructed.

[0076] After obtaining the decay rate of the pulsating component with pressure, the system further correlates the decay rate with the cuff pressure increment that triggers the decay to construct a mathematical model that describes the deformation law of arteries under pressure. The cuff pressure increment refers to the change in cuff pressure between adjacent pressure intervals, reflecting the degree of advancement of the pressurization process. Due to the complex viscoelastic characteristics of the human arterial wall, its diameter reduction and closure process under external pressure is not a nonlinear uniform change, but rather exhibits a nonlinear characteristic of initial resistance to deformation followed by accelerated collapse. Therefore, a nonlinear response trend model needs to be constructed to fit this physical process.

[0077] In practical implementation, the control system does not simply treat the decay rate and cuff pressure increment as independent variables. Instead, it compares and analyzes the correspondence between the two over time and during the pressurization process to establish response characteristics that reflect the pulsation amplitude changes caused by unit pressure variations. In the initial stage of pressurization, although the cuff pressure increases, the pulsation amplitude decay rate is low, indicating that blood flow is still relatively unobstructed. However, as pressurization continues and the pressure gradually approaches the arterial blood flow's tolerance limit, the pulsation amplitude will show significant decay with smaller pressure increments. At this point, the decay rate's response to pressure increments will change significantly. By continuously tracking this change process, the overall trend of the pulsation amplitude response transitioning from a gradual phase to a rapidly changing phase can be identified. Based on the above comparison results, the system constructs a nonlinear response trend model of the pulsation amplitude to the pressurization process, enabling this model to comprehensively reflect the sensitivity of blood flow to external pressure at different pressurization stages. It should be noted that the nonlinear response trend model does not rely on a single pressure point or instantaneous signal, but rather characterizes the arterial blood flow state through the overall evolution of attenuation behavior within a continuous pressure range, thereby enabling it to more accurately capture the characteristic changes in blood flow before it enters a state of occlusion.

[0078] 105. When the rate of change of the nonlinear response trend model is found to meet the preset convergence condition, the distal arterial blood flow is judged to have entered the critical state of occlusion in combination with the preset background noise base.

[0079] By continuously monitoring the changes in the nonlinear response trend model during inflation, it is possible to determine whether arterial blood flow is approaching a critical state of occlusion. As the cuff pressure gradually increases, the system continuously tracks the rate of change of the nonlinear response trend model at the current pressure point. This rate of change reflects the sensitivity of the pulsation amplitude to changes in pressure. When arterial blood flow is still unobstructed or partially restricted, the rate of change typically adjusts continuously with pressure changes. However, as the cuff pressure approaches the stage where arterial blood flow is about to be occluded, the further decay of the pulsation amplitude tends to saturate, manifested as the rate of change of the response trend model gradually decreasing and stabilizing, thus satisfying the preset convergence condition. This convergence characteristic reflects the typical physiological behavior of blood flow transitioning from a compressible state to a near-complete occlusion state.

[0080] In this process, it is also necessary to further analyze the current pulsation amplitude level by combining it with a preset background noise baseline. This background noise baseline refers to the level of system noise and environmental interference still present in the photoplethysmography (PPG) signal when there is no obvious blood flow pulsation or when blood flow has been significantly suppressed. It serves as a reference baseline to distinguish between real pulsation signals and noise signals. For example, in practical applications, even if the rate of change of the response trend model tends to converge, if the currently detected pulsation amplitude is still significantly higher than the background noise baseline, it indicates that the arterial blood flow has not yet entered the critical state of occlusion. Conversely, when the pulsation amplitude is close to or lower than the background noise baseline, and the rate of change of the response trend model meets the convergence condition, the system can determine that the distal arterial blood flow has entered the critical state of occlusion. By jointly judging the convergence behavior of the nonlinear response trend model with the background noise baseline, the risk of misjudgment caused by instantaneous interference, individual differences, or signal fluctuations can be reduced, thereby obtaining an occlusion point that more accurately reflects the true physiological state of blood flow.

[0081] When the system determines that the distal arterial blood flow has entered a critical state of blockage, step 106 is executed.

[0082] 106. Control the cuff to stop applying pressure and determine the adaptive pressure upper limit parameter for this measurement based on the cuff pressure at the corresponding moment;

[0083] When the system confirms that the distal arterial blood flow has entered a critical occlusion state based on the joint judgment results of the nonlinear response trend model and the background noise floor, it sends a control command to the air pump to stop the cuff from continuing to pressurize, thereby locking the current pressurization state. At this time, the system reads and records the cuff pressure value corresponding to this moment, and uses this pressure value as the key pressure point reflecting that the blood flow has just entered the critical occlusion state in this measurement. This key pressure point is directly derived from the synergistic analysis of real-time physiological signals and pressurization behavior, and can truly reflect the subject's current vascular compliance and blood flow compression characteristics. By determining the above key pressure point as the adaptive pressurization upper limit parameter for this measurement, the blood pressure monitor does not need to increase the cuff pressure to a uniform preset maximum value in subsequent measurements, but instead uses this adaptive pressurization upper limit as an upper limit constraint for control.

[0084] For subjects with low blood pressure or good vascular compliance, this adaptive upper limit parameter is often significantly lower than the traditional fixed inflation pressure, thus significantly reducing the feeling of pressure during the inflation process; while for subjects with high blood pressure, this upper limit parameter can be reasonably increased according to the actual blood flow obstruction, avoiding repeated measurements due to insufficient inflation, and improving the comfort of the subject.

[0085] 107. Based on the adaptive pressurization upper limit parameter, the blood pressure monitor is controlled to switch from the pressurization control process to the deflation measurement process.

[0086] After determining the adaptive upper limit parameter for inflation, the system uses this parameter as the termination boundary of the inflation phase. When the cuff pressure reaches and stabilizes at the pressure level corresponding to this upper limit parameter, the system determines that the inflation control process is complete and triggers a switch from the inflation control process to the deflation measurement process. During this switch, the blood pressure monitor first shuts off or reduces the inflation drive of the air pump, preventing further inflation of the cuff. Simultaneously, the deflation valve is opened in a controlled manner, gradually releasing the gas pressure within the cuff using a preset deflation strategy. The pressure change rate, sampling cycle, and signal processing method during the deflation measurement process are all configured and adjusted according to this adaptive upper limit parameter, ensuring that the initial deflation pressure is above the state of complete or near-complete blood flow occlusion. By using the adaptive upper limit parameter as the basis for process switching, the measurement process can automatically enter the optimal measurement range based on the actual physiological response of the subject, avoiding measurement errors or discomfort caused by insufficient or excessive inflation, while also improving the signal-to-noise ratio and stability of the pulsation signal during the deflation measurement phase.

[0087] In this embodiment, by simultaneously acquiring the photoplethysmography (PPG) signal of the limb being measured during the blood pressure monitor's inflation control process, the pulsation component in the signal is separated, and an amplitude feature sequence dynamically updated with the cuff inflation process is constructed using cardiac cycles as units. This ensures that the inflation process not only depends on the cuff pressure change itself but also reflects the periodic changes in arterial blood flow in real time. Subsequently, the attenuation behavior of the amplitude feature sequence relative to the initial pressure range in different pressure ranges is correlated with the stepwise increment of cuff pressure to construct a nonlinear response trend model reflecting the change in pulsation amplitude with the inflation process. By determining whether the rate of change of the nonlinear response trend model meets the preset convergence condition and combining it with the preset background noise floor, a stable identification of the distal arterial blood flow entering the critical state of blockage is achieved. This avoids the influence of single signal amplitude fluctuations, instantaneous noise interference, or individual differences on the inflation termination judgment. Based on this, the upper limit parameter of inflation required for this measurement is adaptively determined to control the blood pressure monitor to switch from the inflation control process to the deflation measurement process in a timely manner. Therefore, this application can reduce unnecessary excessive pressure while ensuring the blood flow occlusion conditions required for measurement, thereby reducing the discomfort and potential risks to the test subject, and shortening the ineffective pressure phase, thus improving the overall measurement efficiency of the blood pressure measurement process.

[0088] Please see Figure 2 According to some embodiments of the present invention, in step 102, the photoplethysmography (PPG) signal is subjected to component separation to obtain a pulsating component reflecting the periodic changes in arterial blood flow. Specifically, this may include, but is not limited to, the following:

[0089] 201. Obtain the air pump drive control signal from the sphygmomanometer, and analyze the fundamental frequency and harmonic frequency of the air pump drive control signal to construct a mechanical vibration noise reference model.

[0090] Considering that the high-speed operation of the air pump during pneumatic pressurization generates significant mechanical vibration, this vibration is physically coupled to the human body through the inflation tubing and cuff, and ultimately transmitted to the photoelectric sensor at the sampling position, forming co-frequency noise interference in the photoplethysmography (PPG) signal that is highly correlated with the air pump speed. In this embodiment, when performing component separation on the PPG signal to obtain the pulsating component reflecting the periodic changes in arterial blood flow, noise reference information related to the blood pressure monitor's pressurization process can be introduced to specifically suppress non-physiological interference in the signal, thereby improving the reliability of pulsating component extraction.

[0091] Specifically, the system first acquires the air pump drive control signal, which is typically a pulse width modulation (PWM) signal or a voltage-driven waveform. Its frequency characteristics directly determine the frequency of mechanical vibration. By performing spectral analysis on this air pump drive control signal, its fundamental frequency and corresponding harmonic frequencies are extracted, thereby constructing a noise reference model that characterizes the mechanical vibration characteristics of the air pump. This noise reference model characterizes the frequency distribution characteristics of the interference signals inevitably introduced by the air pump's operation at the current moment.

[0092] 202. The photoplethysmography pulse wave signal is used as the main channel observation input, and the mechanical vibration noise reference model is used as the reference channel input. The adaptive notch filter algorithm is used to perform co-frequency interference cancellation processing, and the denoised intermediate state signal is output.

[0093] Based on the noise reference model, the photoplethysmography (PPG) signal is used as the observation input of the main channel, and the mechanical vibration noise reference model is used as the reference channel input. An adaptive notch filter algorithm is used to cancel interference components with the same frequency characteristics in both channels. This dynamically suppresses the mechanical vibration noise that varies with the operating conditions of the air pump without relying on fixed filter parameters, outputting an intermediate state signal after removing the main same-frequency interference. It should be noted that this adaptive notch filter algorithm refers to a digital signal processing algorithm that can automatically adjust the filter parameters according to the reference signal, such as the Least Mean Square Error (LMS) algorithm or the Recursive Least Squares (RLS) algorithm; the specific algorithm type is not limited here.

[0094] 203. Perform bandpass filtering on the intermediate state signal, covering the normal heart rate range of the human body, to separate the AC component as the pulsating component, and simultaneously extract the DC component in the intermediate state signal, calculate its mean drift within a preset time window, and perform zero-point correction on the pulsating component based on the mean drift.

[0095] To further separate the effective signal, the system performs bandpass filtering on the intermediate signal, covering the normal heart rate range. This effectively separates the AC component corresponding to the cardiac cycle, serving as the pulsating component reflecting the periodic changes in arterial blood flow. The bandpass range can be set to cover both normal and pathological heart rates, for example, 0.5Hz to 5Hz, corresponding to 30bpm to 300bpm. The purpose of bandpass filtering is to filter out extremely low-frequency respiratory waves below this range and power frequency interference or electromagnetic noise above it, thereby separating the AC component representing arterial pulsation.

[0096] The DC component in the intermediate-state signal mainly reflects the overall transmittance change caused by variations in venous blood filling or cuff pressure. During pressurization, increased cuff pressure leads to tissue compression, causing a significant nonlinear drift in the DC baseline, directly affecting the accuracy of pulsation component amplitude calculation. Therefore, to avoid baseline shifts caused by tissue compression, optical path changes, or sensor operating point drift affecting pulsation component amplitude analysis during pressurization, the system can simultaneously extract the DC component from the intermediate-state signal and calculate the mean drift of the DC component within a preset time window (e.g., one cardiac cycle). This drift is then subtracted from the AC component as a correction factor to achieve zero-point correction, resulting in a physiological waveform with accurate amplitude.

[0097] In this embodiment, by performing component separation processing on the photoplethysmography (PPG) pulse wave signal, co-frequency interference caused by the mechanical vibration of the air pump during pressurization can be effectively suppressed. Simultaneously, the pulsating component reflecting the periodic changes in arterial blood flow is separated, and baseline drift is corrected, thereby obtaining a physiological signal with stable amplitude and clear periodic characteristics. This processing not only improves the accuracy and reliability of pulsating component extraction but also provides a high signal-to-noise ratio input basis for constructing amplitude feature sequences, thus enhancing the overall ability of the pressurization control process to perceive the real blood flow state and improve measurement accuracy.

[0098] Please see Figure 3 According to some embodiments of the present invention, in step 104, a nonlinear response trend model of the pulsation amplitude to the pressurization process is constructed based on the comparison between the decay rate and the corresponding cuff pressure increment. This model may specifically include, but is not limited to, the following:

[0099] 301. Based on the sampling time sequence during the pressurization process, the amplitude data in the amplitude feature sequence are aligned with the corresponding real-time cuff pressure values ​​in the time domain to construct a pressure-amplitude dataset;

[0100] In the actual inflation process, the amplitude feature sequence collected as the cuff pressure changes first needs to be matched in the time domain with the corresponding real-time cuff pressure value. That is, based on the sampling time sequence of the inflation process, the amplitude feature data corresponding to each cardiac cycle is matched one-to-one with the cuff pressure value at the time of collection, thus forming a continuous pressure-amplitude dataset. For example, if the cuff pressure increases from 100 mmHg to 140 mmHg during inflation, the amplitude feature sequence corresponding to each pressure point is recorded in the dataset, synchronized with the pressure change trajectory, avoiding data mismatch caused by sampling delay or signal drift.

[0101] 302. Calculate the relative attenuation of the amplitude feature sequence within the current detection window relative to the initial reference amplitude, and compare the relative attenuation with the corresponding cumulative cuff pressure increment within the window to calculate the response sensitivity under unit pressure change;

[0102] After obtaining the pressure-amplitude dataset, the system can quantitatively describe the blood flow compression process by calculating the attenuation of the amplitude feature sequence within the current detection window relative to the initial reference amplitude. The system first calculates the relative attenuation, i.e., the percentage reduction in current pulse amplitude compared to the initial reference amplitude before pressure is applied. This eliminates calculation errors caused by variations in the baseline pulse strength among different users. The initial reference amplitude can be taken as the average amplitude at the beginning of pressure application to reflect the pulse level before significant pressure impact.

[0103] The obtained relative attenuation is compared with the cumulative cuff pressure increment within the window to calculate the response sensitivity per unit pressure change, thus obtaining the sensitivity of the pulsation amplitude to pressure changes in each pressure range. In the initial stage of inflation, before significant vascular deformation, the response sensitivity is low; however, as the pressure approaches systolic pressure, the vessel wall enters a high compliance zone, and the sensitivity increases rapidly. By calculating this response sensitivity index, the system can identify which stage of the inflation process is currently in, such as the plateau, abrupt change, or cutoff phase, thus providing different weights for subsequent model fitting.

[0104] 303. Using response sensitivity as a weighting factor, nonlinear regression analysis was performed on the pressure-amplitude dataset to fit and generate a dynamic envelope evolution curve that conforms to the characteristics of arterial unloading.

[0105] To reconstruct the continuous vascular deformation pattern from discrete sampling points, the system performs nonlinear regression analysis on the pressure-amplitude dataset. In this process, the aforementioned response sensitivity is used as a weighting factor to perform nonlinear regression analysis on the entire pressure-amplitude dataset to fit and generate a dynamic envelope evolution curve reflecting the arterial unloading characteristics. The arterial unloading characteristics refer to the nonlinear S-shaped or exponential change in the volume of the artery as the transmural pressure (the difference between the intravascular and extravascular pressure) changes. The resulting dynamic envelope curve represents the nonlinear trend of the pulsation amplitude as a function of pressure during inflation, taking into account both the early, slight pressure response and the rapid decay during the later blood flow occlusion state.

[0106] It's important to note that the fitting process for the dynamic envelope evolution curve not only considers the overall trend of amplitude variation with pressure but also incorporates response sensitivity weighting to highlight pressure regions sensitive to blood flow occlusion, making the model more closely resemble physiological reality. Specifically, when fitting the dynamic envelope curve, each data point provides not only amplitude information but also a weight value. In nonlinear regression, the weight of a data point determines its contribution to the fitted curve: the larger the weight, the more significant the impact of that point on the fitting result. Through this weighting mechanism, amplitude changes in high-sensitivity pressure regions will be more prominently reflected on the fitted curve, thus ensuring that the model more closely reflects real blood flow characteristics in key pressure regions.

[0107] 304. The first derivative of the dynamic envelope evolution curve at the current cuff pressure point is calculated in real time as the instantaneous decay rate, and the second derivative is used as the decay acceleration factor. The combined characteristics of the instantaneous decay rate and the decay acceleration factor are determined as the nonlinear response trend model.

[0108] Finally, after model generation, the first and second derivatives of the dynamic envelope evolution curve at the current cuff pressure point are calculated in real time, corresponding to the instantaneous decay rate and decay acceleration factor, respectively. The combined characteristics of these two factors are then used to define the nonlinear response trend model. The instantaneous decay rate reflects the speed at which the pulsation amplitude decreases under the current pressure change, providing an immediate indication of the blood flow compression trend; while the decay acceleration factor reveals the acceleration of the pulsation amplitude change rate, allowing for early detection of the critical inflection point of blood flow occlusion. Through the combination of first and second-order features, this nonlinear response trend model can dynamically characterize the continuous transition of arterial blood flow from a patent to an occluded state, thereby predicting future occlusion points.

[0109] In this embodiment, the nonlinear relationship between the pulsation amplitude decay behavior and cuff pressure change is coupled and analyzed. A nonlinear response trend model is constructed using response sensitivity weighting to achieve early and accurate identification of blood flow occlusion state. This provides a reliable physiological basis for personalized upper limit control of blood pressure, while improving the safety and comfort of blood pressure measurement.

[0110] The method for pneumatic pressurization control of the electronic blood pressure monitor provided in this application is described in detail below. Please refer to... Figure 4 , Figure 4 Another embodiment of the method for pneumatic pressurization control of an electronic blood pressure monitor provided in this application includes:

[0111] 401. According to the measurement start command of the blood pressure monitor, while applying cuff pressure to the limb being measured, the photoplethysmography (PPG) signal from at least one sampling position of the limb being measured is simultaneously acquired.

[0112] 402. Perform component separation on the photoplethysmography pulse wave signal to obtain the pulsating component that reflects the periodic changes in arterial blood flow, and construct an amplitude feature sequence updated with the pressurization process based on the pulsating component with the cardiac cycle as the unit.

[0113] In this embodiment, steps 401-402 are similar to steps 101-102 in the previous embodiment, and will not be described again here.

[0114] 403. During the construction of the amplitude characteristic sequence, the baseline drift velocity of the DC component in the photoplethysmography signal is monitored in real time.

[0115] In this embodiment, the DC component of the photoplethysmography (PPG) signal can be monitored in real time during the construction of the amplitude feature sequence. The DC component reflects the slow trend of overall blood volume change. When the user moves their arm, clenches or relaxes, non-physiological amplitude drift is introduced. This drift interferes with the accurate extraction of the pulsatile component and the construction of the amplitude feature sequence that changes with pressure. Although during normal compression, as the cuff pressure increases, venous return in the limb is obstructed, leading to changes in tissue filling and causing a slow and monotonous trend change in the DC component, this physiological change is usually low and smooth. In contrast, when the subject moves their limb, contracts their muscles, or the sensor undergoes a slight displacement, the optical coupling efficiency between the sensor and the skin contact surface fluctuates drastically, manifesting as large oscillations or rapid rises and falls in the DC component within a short period of time.

[0116] To distinguish between normal pulsations and motion interference, the system calculates baseline drift velocity to quantify signal stability, i.e., the rate of change of the DC component over continuous sampling time. By monitoring this indicator in real time, it is possible to identify whether the current signal change is caused by slow inflation or by sudden mechanical movement.

[0117] 404. If the baseline drift velocity exceeds the preset motion interference threshold, the construction of the amplitude feature sequence will be paused until the baseline drift velocity returns to the normal range.

[0118] The preset motion interference threshold is a boundary value determined based on the limits of normal physiological changes in the human body and the upper limit of baseline drift velocity caused by pneumatic pressurization. When the detected baseline drift velocity exceeds the preset motion interference threshold, it indicates that the current signal is significantly affected by motion or external interference. At this point, the extracted pulsation amplitude can no longer accurately reflect the response characteristics of arterial blood flow to cuff pressure. Therefore, it is necessary to pause the update of the amplitude feature sequence to avoid introducing interference into the amplitude sequence, which would affect the construction of the subsequent nonlinear response trend model and the determination of blood flow obstruction. During the pause, the air pump can maintain the current pressure or reduce the pressurization rate. The system continuously monitors the baseline drift velocity until it returns to the normal range, indicating that the interference has subsided, signal stability has been restored, and the amplitude feature sequence can continue to be constructed.

[0119] 405. Calculate the decay rate of the amplitude characteristic sequence relative to the reference sequence within the current pressure range based on the stepwise change relationship of the cuff pressure during the pressurization process. The reference sequence is the amplitude characteristic sequence corresponding to the initial pressure range.

[0120] 406. Based on the comparison between the decay rate and the corresponding cuff pressure increment, a nonlinear response trend model of the pulsation amplitude to the pressurization process is constructed.

[0121] 407. When the rate of change of the nonlinear response trend model is found to meet the preset convergence condition, the distal arterial blood flow is judged to have entered the critical state of occlusion in combination with the preset background noise base.

[0122] In this embodiment, steps 405-407 are similar to steps 103-105 in the previous embodiment, and will not be described again here.

[0123] It should be noted that in this embodiment, after determining that the distal arterial blood flow has entered the critical state of blockage, in order to further improve the accuracy of the pressure control, it is necessary to calculate the confidence level of blood flow blockage to ensure that the judgment is reliable, that is, to execute steps 408 and 409.

[0124] 408. Statistically analyze the peak dispersion and cycle drift of the pulsatile components over at least three consecutive cardiac cycles, and calculate the confidence level of blood flow occlusion;

[0125] In this embodiment, when the nonlinear response trend model indicates that the distal arterial blood flow is approaching the critical occlusion state, further quantification of the confidence level of blood flow occlusion is needed to ensure the reliability of cuff compression control. Specifically, the system performs a comprehensive analysis of the collected pulsation components over at least three consecutive cardiac cycles, i.e., statistically analyzing the pulse peak value and its time interval for each cardiac cycle to assess the stability of the pulse rhythm and monitor the trend of pulse amplitude changes over time to form a reliability index reflecting the blood flow occlusion state. By comprehensively judging the fluctuation range and periodic consistency of the pulse peak value, the confidence level of blood flow occlusion is obtained, which is used to quantify whether the current blood flow has truly reached the occlusion state, thereby avoiding misjudgments caused by transient fluctuations or measurement noise.

[0126] 409. When the confidence level of blood flow occlusion reaches the preset threshold, control the cuff to stop applying pressure and determine the adaptive pressure upper limit parameter for this measurement based on the cuff pressure at the corresponding time.

[0127] When the calculated confidence level of blood flow occlusion reaches a preset threshold, the system controls the cuff to stop further inflating. Simultaneously, the cuff pressure at this point is recorded and used as the adaptive upper pressure limit parameter for this blood pressure measurement. This upper pressure limit ensures sufficient pressure for accurate measurement while avoiding overinflation that could cause discomfort or potential injury to the subject. This achieves personalized and adaptive control of the blood pressure monitor's inflation process, further improving measurement safety and comfort while maintaining the efficiency and accuracy of the overall measurement procedure.

[0128] 410. Based on the adaptive pressurization upper limit parameter, the blood pressure monitor is controlled to switch from the pressurization control process to the deflation measurement process.

[0129] In this embodiment, step 410 is similar to step 107 in the previous embodiment, and will not be described again here.

[0130] Please see Figure 5 According to some embodiments of the present invention, in step 408, the peak dispersion and cycle drift of the pulsatile components within at least three consecutive cardiac cycles are statistically analyzed to calculate the confidence level of blood flow occlusion. Specifically, this may include, but is not limited to, the following:

[0131] 501. Extract the peak time points and peak amplitudes of the pulsation components within at least three consecutive cardiac cycles to construct a set of feature points, calculate the time difference sequence of adjacent peak time points in the set of feature points, and calculate the periodic drift reflecting the stability of the heart rate rhythm based on the time difference sequence.

[0132] When the system determines that distal arterial blood flow is approaching the critical state of occlusion, a quantitative analysis of the blood flow occlusion state is required to calculate the confidence level of blood flow occlusion in order to ensure the reliability of cuff compression. The system first extracts the peak time points and corresponding amplitudes of the pulsatile components within at least three consecutive cardiac cycles, constructing a feature point set. By analyzing the time interval sequence of adjacent peaks in this feature point set, a stability index of the heart rate rhythm, namely the cycle drift, can be obtained. This cycle drift reflects the amplitude of blood flow rhythm changes over time, revealing the regularity of the pulse wave within consecutive cardiac cycles and avoiding misjudgments caused by single abnormal fluctuations.

[0133] 502. Calculate the mean and standard deviation of the amplitude of all peaks in the feature point set, and calculate the peak dispersion to reflect the consistency of pulse intensity based on the ratio of the standard deviation to the mean.

[0134] Simultaneously, the system performs statistical analysis on the amplitude of all peaks in the feature point set, calculating their mean and standard deviation. The ratio of the standard deviation to the mean is used as the peak dispersion index to reflect the consistency of pulse intensity. The smaller the peak dispersion, the more stable the pulse amplitude changes within a continuous cardiac cycle, and the more reliable the blood flow occlusion state.

[0135] 503. Normalize the period drift and peak dispersion respectively, and use the preset weighted evaluation function to calculate the weighted sum of the period drift and peak dispersion to obtain the blood flow occlusion confidence. The lower the period drift and peak dispersion, the higher the blood flow occlusion confidence.

[0136] The system further normalizes the period drift and peak dispersion to eliminate individual differences and the influence of the measurement environment. Then, a weighted evaluation function is used to fuse the normalized period drift and peak dispersion to calculate the blood flow occlusion confidence score. Specifically, the weighted evaluation function dynamically assigns weights based on the sensitivity of both factors to predicting blood flow occlusion, making the confidence score more closely reflect physiological changes in blood flow. For example, a comprehensive index can be obtained by setting the weight of period drift to 0.4 and the weight of peak dispersion to 0.6 using a linear weighting method. A higher blood flow occlusion confidence score indicates a greater likelihood of distal arterial blood flow occlusion, thus providing a reliable basis for subsequent adaptive pressurization control.

[0137] In this embodiment, by extracting the peak time point and amplitude of the pulsatile component within a continuous cardiac cycle, and combining the cycle drift and peak dispersion for normalized weighted calculation, the confidence level of blood flow occlusion is obtained. This enables more reliable identification of the critical point of blood flow occlusion and avoids misjudgment caused by transient noise or individual differences.

[0138] Please see Figure 6According to some embodiments of the present invention, in step 409, the adaptive pressure upper limit parameter for this measurement is determined based on the cuff pressure at the corresponding moment, which may specifically include, but is not limited to, the following:

[0139] 601. Mark the cuff pressure at the corresponding time as the blocking reference pressure point, extract the convergence gradient characteristics of the nonlinear response trend model at the blocking reference pressure point, and determine the pressure overshoot compensation value for establishing the measurement buffer based on the convergence gradient characteristics.

[0140] In this embodiment, once the confidence level for blood flow occlusion reaches a preset threshold, the adaptive upper limit parameter for this measurement needs to be determined based on the real-time cuff pressure. First, the cuff pressure at that moment is marked as the occlusion reference pressure point, corresponding to the position where distal arterial blood flow has just entered the critical occlusion state. Then, the convergence gradient characteristics of the nonlinear response trend model at this occlusion reference pressure point are extracted. This convergence gradient reflects the local trend of pulsation amplitude changes with pressure, i.e., the rate of decrease and acceleration of pulsation amplitude change. Based on these convergence gradient characteristics, the pressure overshoot compensation value used to establish the measurement buffer is further calculated to ensure that the cuff pressure during deflation measurement does not fall below the actual required occlusion pressure due to transient response or control lag, thereby ensuring the integrity and accuracy of the measurement.

[0141] 602. Linearly superimpose the blocking reference pressure point and the pressure overshoot compensation value to calculate the target pressurization peak value, and determine the adaptive pressurization upper limit parameter for this measurement based on the target pressurization peak value.

[0142] The target inflation peak is obtained by linearly superimposing the reference pressure point and the pressure overshoot compensation value. This target inflation peak is the actual upper limit of inflation required to complete the measurement while ensuring safety. By using this adaptive upper limit parameter, the blood pressure monitor can dynamically adjust the inflation process according to individual blood flow characteristics, avoiding overinflation that may occur with traditional preset high-pressure modes, thereby improving measurement comfort and reducing the potential risk of limb injury. In practical applications, for example, for patients with hypotension, this adaptive upper limit can be significantly lower than the traditional fixed inflation value, ensuring measurement accuracy while reducing unnecessary pain; while for patients with hypertension, this upper limit can be automatically increased to ensure complete measurement.

[0143] In some specific embodiments, a safety boundary check can also be performed on the target peak pressure to ensure that the cuff pressure remains within a physiologically tolerable range during blood pressure measurement. The system specifically executes the following steps:

[0144] Perform a safety boundary check on the target pressurization peak to determine whether the target pressurization peak is within the preset physiological safety pressure range;

[0145] If so, the target pressurization peak value will be determined as the adaptive pressurization upper limit parameter for this measurement;

[0146] If not, the upper limit of the physiological safety pressure range will be determined as the adaptive pressurization upper limit parameter for this measurement.

[0147] Specifically, the system first determines whether the target pressure peak is within a preset physiological safety pressure range. This safety range can be preset based on the vascular pressure characteristics of adults and children, or adjusted based on clinical experience and individual historical measurement data. If the target pressure peak is within this safety range, it is directly used as the adaptive pressure upper limit parameter for this measurement, thereby completing the dynamic adjustment and personalized optimization of the pressure increase process. If the target pressure peak exceeds the safety range, the system uses the upper limit of the physiological safety pressure range as the adaptive pressure upper limit parameter for this measurement to prevent overinflation from causing limb discomfort or vascular damage. Through the above verification and adjustment, the blood pressure monitor can achieve safe and personalized pressure control based on dynamically sensing changes in pulse amplitude and blood flow occlusion status.

[0148] The pneumatic pressurization control system for the electronic blood pressure monitor provided in this application is described in detail below. Please refer to [link / reference]. Figure 7 , Figure 7 One embodiment of the pneumatic pressurization control system for the electronic blood pressure monitor provided in this application includes:

[0149] The acquisition unit 701 is used to simultaneously acquire photoplethysmography (PPG) signals from at least one sampling location of the limb being measured during the process of applying cuff pressure to the limb being measured, according to the measurement start command of the blood pressure monitor.

[0150] The separation unit 702 is used to perform component separation on the photoplethysmography pulse wave signal to obtain the pulsating component that reflects the periodic changes in arterial blood flow, and to construct an amplitude feature sequence updated with the pressurization process based on the pulsating component with the cardiac cycle as the unit.

[0151] The calculation unit 703 is used to calculate the decay rate of the amplitude characteristic sequence relative to the reference sequence in the current pressure range based on the stepwise change relationship of the cuff pressure during the pressurization process. The reference sequence is the amplitude characteristic sequence corresponding to the initial pressure range.

[0152] Building unit 704 is used to construct a nonlinear response trend model of the pulsation amplitude to the pressurization process based on the comparison between the decay rate and the corresponding cuff pressure increment.

[0153] The judgment unit 705 is used to determine whether the distal arterial blood flow has entered the critical state of occlusion when the rate of change of the nonlinear response trend model is found to meet the preset convergence condition, in combination with the preset background noise base.

[0154] The stop unit 706 is used to control the cuff to stop applying pressure when the judgment result of the judgment unit 705 is yes, and to determine the adaptive pressure upper limit parameter for this measurement based on the cuff pressure at the corresponding moment.

[0155] The control unit 707 is used to control the blood pressure monitor to switch from the pressurization control process to the deflation measurement process based on the adaptive pressurization upper limit parameter.

[0156] In this embodiment, the functions of each unit are the same as described above. Figures 1-6 The steps in the method embodiments shown correspond to those in the examples, and will not be repeated here.

[0157] This application also provides a device for pneumatic pressurization control of an electronic blood pressure monitor; please refer to [link to relevant documentation]. Figure 8 , Figure 8 One embodiment of the pneumatic pressurization control device for an electronic blood pressure monitor provided in this application includes:

[0158] Processor 801, memory 802, input / output unit 803, bus 804;

[0159] The processor 801 is connected to the memory 802, the input / output unit 803, and the bus 804;

[0160] The memory 802 stores a program, and the processor 801 calls the program to execute the pneumatic pressurization control method of any of the above electronic blood pressure monitors.

[0161] This application also relates to a computer-readable storage medium storing a program that, when run on a computer, causes the computer to perform the pneumatic pressurization control method of any of the above electronic blood pressure monitors.

[0162] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0163] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0164] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0165] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0166] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for pneumatic pressurization control of an electronic blood pressure monitor, characterized in that, The method includes: According to the measurement start command of the blood pressure monitor, while applying cuff pressure to the limb being measured, photoplethysmography (PPG) signals from at least one sampling position of the limb being measured are simultaneously acquired. The photoplethysmography (PPG) signal is subjected to component separation to obtain the pulsatile component reflecting the periodic changes in arterial blood flow. An amplitude feature sequence updated with the pressurization process is constructed based on the pulsatile component, with the cardiac cycle as the unit. The decay rate of the amplitude characteristic sequence relative to the reference sequence within the current pressure range is calculated based on the stepwise change relationship of the cuff pressure during the pressurization process. The reference sequence is the amplitude characteristic sequence corresponding to the initial pressure range. Based on the comparison between the decay rate and the corresponding cuff pressure increment, a nonlinear response trend model of the pulsation amplitude to the pressurization process is constructed. When the rate of change of the nonlinear response trend model is detected to meet the preset convergence condition, the distal arterial blood flow is judged to have entered the critical state of occlusion in combination with the preset background noise base. If so, the cuff is controlled to stop applying pressure, and the adaptive pressure upper limit parameter for this measurement is determined based on the cuff pressure at the corresponding moment. Based on the adaptive pressurization upper limit parameter, the blood pressure monitor is controlled to switch from the pressurization control process to the deflation measurement process; The component separation of the photoplethysmography (PPG) signal to obtain the pulsatile component reflecting the periodic changes in arterial blood flow includes: The air pump drive control signal in the blood pressure monitor is obtained, and the fundamental frequency and harmonic frequency of the air pump drive control signal are analyzed to construct a mechanical vibration noise reference model. The photoplethysmography pulse wave signal is used as the main channel observation input, and the mechanical vibration noise reference model is used as the reference channel input. The adaptive notch filter algorithm is used to perform co-frequency interference cancellation processing, and the denoised intermediate state signal is output. The intermediate state signal is subjected to bandpass filtering processing with a passband range covering the normal human heart rate range to separate the AC component as the pulsation component. The DC component in the intermediate state signal is extracted simultaneously, and its mean drift within a preset time window is calculated. The pulsation component is then zero-point corrected based on the mean drift. The nonlinear response trend model of the pulsation amplitude to the pressurization process, based on the comparison between the decay rate and the corresponding cuff pressure increment, includes: Based on the sampling timing during the pressurization process, the amplitude data in the amplitude feature sequence are time-domain aligned with the corresponding real-time cuff pressure values ​​to construct a pressure-amplitude dataset. Calculate the relative attenuation of the amplitude feature sequence within the current detection window relative to the initial reference amplitude, and compare the relative attenuation with the corresponding cumulative cuff pressure increment within the window to calculate the response sensitivity under unit pressure change; Using the response sensitivity as a weighting factor, a nonlinear regression analysis is performed on the pressure-amplitude dataset to fit and generate a dynamic envelope evolution curve that conforms to the characteristics of arterial unloading. The first derivative of the dynamic envelope evolution curve at the current cuff pressure point is calculated in real time as the instantaneous decay rate, and the second derivative is calculated as the decay acceleration factor. The combined characteristics of the instantaneous decay rate and the decay acceleration factor are determined as a nonlinear response trend model. After determining whether distal arterial blood flow has entered a critical occlusion state based on a preset background noise baseline, and before stopping the cuff from applying pressure and determining the adaptive upper limit parameter for this measurement based on the cuff pressure at the corresponding moment, the method further includes: If the critical state of blockage is reached, the peak dispersion and cycle drift of the pulsatile component within at least three consecutive cardiac cycles are statistically analyzed to calculate the confidence level of blood flow blockage. The control cuff stops applying pressure and determines the adaptive pressure upper limit parameter for this measurement based on the cuff pressure at the corresponding moment, including: When the confidence level of blood flow occlusion reaches a preset threshold, the control cuff stops applying pressure, and the adaptive pressure upper limit parameter for this measurement is determined based on the cuff pressure at the corresponding moment.

2. The method according to claim 1, characterized in that, The statistical analysis of the peak dispersion and cycle drift of the pulsatile components over at least three consecutive cardiac cycles, and the calculation of the confidence level for blood flow occlusion, includes: Extract the peak time points and peak amplitudes of the pulsation components within the at least three consecutive cardiac cycles to construct a set of feature points, calculate the time difference sequence of adjacent peak time points in the set of feature points, and calculate the periodic drift amount reflecting the stability of the heart rate rhythm based on the time difference sequence. Calculate the average and standard deviation of all peak amplitudes in the feature point set, and calculate the peak dispersion to reflect the consistency of pulse intensity based on the ratio of the standard deviation to the average. The period drift and the peak dispersion are normalized respectively, and the weighted sum of the period drift and the peak dispersion is calculated using a preset weighted evaluation function to obtain the blood flow occlusion confidence. The lower the period drift and the peak dispersion, the higher the blood flow occlusion confidence.

3. The method according to claim 1, characterized in that, The method further includes: During the construction of the amplitude feature sequence, the baseline drift velocity of the DC component in the photoplethysmography signal is monitored in real time. If the baseline drift velocity exceeds a preset motion interference threshold, the construction of the amplitude feature sequence is paused until the baseline drift velocity returns to the normal range.

4. The method according to any one of claims 1 to 3, characterized in that, The process of determining the adaptive pressure upper limit parameter for this measurement based on the cuff pressure at the corresponding moment includes: The cuff pressure at the corresponding moment is marked as the blocking reference pressure point. The convergence gradient feature of the nonlinear response trend model at the blocking reference pressure point is extracted, and the pressure overshoot compensation value for establishing the measurement buffer is determined based on the convergence gradient feature. The target pressurization peak value is calculated by linearly superimposing the blocking reference pressure point and the pressure overshoot compensation value, and the adaptive pressurization upper limit parameter for this measurement is determined based on the target pressurization peak value.

5. The method according to claim 4, characterized in that, The step of determining the adaptive pressure upper limit parameter for this measurement based on the target pressure peak includes: Perform a safety boundary check on the target pressurization peak value to determine whether the target pressurization peak value is within the preset physiological safety pressure range; If so, the target pressurization peak value is determined as the adaptive pressurization upper limit parameter for this measurement; If not, the upper limit of the physiological safety pressure range will be determined as the adaptive pressurization upper limit parameter for this measurement.

6. A system for pneumatic pressurization control of an electronic blood pressure monitor, characterized in that, The system is used to perform the method as described in claim 1, the system comprising: The acquisition unit is used to simultaneously acquire photoplethysmography (PPG) signals from at least one sampling location of the limb being measured during the process of applying cuff pressure to the limb being measured, according to the measurement start command of the blood pressure monitor. The separation unit is used to perform component separation on the photoplethysmography pulse wave signal to obtain the pulsating component that reflects the periodic changes in arterial blood flow, and to construct an amplitude feature sequence updated with the pressurization process based on the pulsating component in units of cardiac cycle. The calculation unit is used to calculate the decay rate of the amplitude characteristic sequence relative to the reference sequence in the current pressure range based on the stepwise change relationship of the cuff pressure during the pressurization process. The reference sequence is the amplitude characteristic sequence corresponding to the initial pressure range. A construction unit is used to construct a nonlinear response trend model of the pulsation amplitude to the pressurization process based on the comparison between the decay rate and the corresponding cuff pressure increment. The judgment unit is used to determine whether the distal arterial blood flow has entered the critical state of occlusion when the rate of change of the nonlinear response trend model is detected to meet the preset convergence condition, in combination with the preset background noise base. The stop unit is used to control the cuff to stop applying pressure when the judgment result of the judgment unit is yes, and to determine the adaptive pressure upper limit parameter for this measurement based on the cuff pressure at the corresponding moment. The control unit is used to control the blood pressure monitor to switch from the pressurization control process to the deflation measurement process based on the adaptive pressurization upper limit parameter.

7. A device for pneumatic pressurization control of an electronic blood pressure monitor, characterized in that, The device includes: Processor, memory, input / output units, and bus; The processor is connected to the memory, the input / output unit, and the bus; The memory stores a program, which the processor invokes to perform the method as described in any one of claims 1 to 5.

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