System for dynamic monitoring of lung compliance based on decoupling of respiratory loop pressure waveform

Abstract

The present application belongs to the technical field of medical diagnostic measurement, and relates to a lung compliance dynamic monitoring system based on respiratory loop pressure waveform decoupling, comprising: a sensing driving and registration module for acquiring output filtered real-time pressure and instantaneous flow sequence; a time sequence mask stream generation module for generating a Boolean time sequence mask in combination with a flow gradient and a threshold value; a resistance factor extraction module for updating and locking a real-time airway resistance factor according to a mask logic; a multi-stage pressure decoupling module for extracting temporary elastic pressure by deducting resistance pressure drop and reference pressure from the aforementioned sequence; an abnormal data elimination module for eliminating abnormal data and outputting an effective elastic pressure signal according to a threshold value; and a solving and feedback module for determining a compliance sequence by dividing the flow integral volume by the pressure, and extracting a trend feedback parameter adjustment. The present application solves the problem of how to decouple and continuously monitor the dynamic changes of lung compliance in real time from the airway pressure waveform in the airflow environment containing non-autonomous interference and mechanical noise.

Description

Technical Field

[0001] This invention belongs to the technical field of medical diagnostic measurement and relates to a dynamic monitoring system for lung compliance based on decoupling of respiratory loop pressure waveform. Background Technology

[0002] In mechanical ventilation and respiratory function measurement, obtaining lung compliance parameters is of clinical value for assessing the pathological state of the patient's lungs and guiding adjustments to ventilation strategies. Lung compliance reflects the ease with which lung tissue expands under external forces, and its dynamic changes can help indicate the evolution of related diseases such as pulmonary edema or pulmonary fibrosis. However, the raw airway pressure and flow rate signals acquired from the ventilator circuit are often complex waveforms resulting from the coupling and superposition of different physical effects, making it challenging to directly extract the independent pressure component driving the elastic deformation of lung tissue.

[0003] Currently, most commonly used methods for calculating lung compliance are based on respiratory mechanics equations, estimating parameters at quasi-static or specific time points. For example, static compliance is estimated by measuring airway pressure plateaus and positive end-expiratory pressure through end-inspiratory and end-expiratory occlusion. Meanwhile, some technical solutions employ overall fitting algorithms such as least squares to dynamically fit pressure, flow, and volume data of the respiratory cycle to estimate dynamic compliance, or by adding in-line flow monitoring points to compensate for environmental leakage and calculating compliance-related indicators based on pressure regulation control logic.

[0004] The aforementioned existing technologies still have certain limitations in practical applications. While static methods can provide relatively stable compliance reference points, they typically rely on specific ventilation modes and struggle to represent the continuous dynamic changes in lung tissue tension throughout the respiratory cycle. On the other hand, dynamic methods based on global fitting are highly sensitive to signal noise during calculation. When interference from patient coughing or swallowing causes nonlinear waveform distortion, the acquired data segments are prone to deviating from the true state, leading to errors in the estimated values. Furthermore, many conventional methods, when dealing with broadband noise introduced by pipeline water accumulation or mechanical vibration, lack sophisticated signal layering, decoupling, and purification mechanisms. This results in the extracted signals easily containing components such as hysteresis and dissipation, thus expanding the fluctuation range of compliance monitoring results. Summary of the Invention

[0005] To address the aforementioned problems, this invention provides a dynamic monitoring system for lung compliance based on respiratory loop pressure waveform decoupling.

[0006] A dynamic lung compliance monitoring system based on respiratory loop pressure waveform decoupling includes:

[0007] The sensor drive and registration module is used to receive the mixed raw airway pressure electrical signal input and the gas mass flow sensing value signal, generate a low-frequency pressure signal after preset filtering and bind it, and output the real-time pressure sequence and instantaneous flow sequence.

[0008] The time-series mask stream generation module is used to extract the flow change gradient from the instantaneous flow sequence, combine it with a dynamic adaptive threshold to determine the airflow stability, and generate a Boolean time-series mask sequence.

[0009] The drag factor extraction module calculates a wideband airway drag sequence based on real-time pressure and instantaneous flow sequences, and locks and saves historical values ​​or updates the output real-time airway drag factor according to the logical value of the Boolean time mask sequence.

[0010] The multi-level pressure decoupling module generates an airway resistance pressure drop sequence based on the instantaneous flow sequence and the real-time airway resistance factor, extracts the end-expiratory reference pressure corresponding to the pause node, and synthesizes a temporary elastic pressure signal by removing the airway resistance pressure drop sequence and the end-expiratory reference pressure from the real-time pressure sequence.

[0011] The abnormal data removal module is used to determine the differential change rate of the temporary elastic pressure signal based on the preset physiological tolerance jump threshold, and remove abnormal values ​​that exceed the limit to output the elastic pressure signal.

[0012] The calculation and feedback module is used to integrate the instantaneous flow sequence to generate a dynamic volume signal, divide the dynamic volume signal by the elastic pressure signal to generate a lung compliance sequence, and extract the long-term trend baseline signal as a feedback parameter to modify the configuration of the dynamic adaptive threshold.

[0013] In a further embodiment of the present invention, the sensing drive and registration module is specifically used for:

[0014] Establish a hardware clock reference mapped from the preset motherboard crystal oscillator;

[0015] A hardware clock reference is used to trigger a dual-channel analog-to-digital converter to synchronously quantize the airway pressure electrical signal input stream and the flow sensor value signal.

[0016] The preset fourth-order Butterworth low-pass filter topology is used as the low-pass filter unit to calculate the weighted sum of the current sampled value, historical sampled value, and historical output value of the airway pressure electrical signal input stream. This attenuates broadband mechanical noise that is higher than the physiological respiratory frequency range and outputs a low-frequency pressure signal.

[0017] In a further embodiment of the present invention, the timing mask stream generation module is specifically used for:

[0018] Perform a second-order difference transform operation on the instantaneous flow sequence to generate a sequence composed of the gradient of flow change;

[0019] Two exponentially weighted moving average filters with different time constants are run in parallel.

[0020] The long-term baseline mean of the gradient sequence of flow change is calculated using an exponentially weighted moving average filter with a long time constant.

[0021] The short-term standard deviation of the gradient sequence of flow change is calculated using an exponentially weighted moving average filter with a short time constant.

[0022] The absolute value of the long-term baseline mean is added to the short-term standard deviation multiplied by a preset sensitivity coefficient to generate a dynamic adaptive threshold.

[0023] When the absolute value of the traffic change gradient sequence is less than or equal to the dynamic adaptive threshold, a valid feature label is assigned; when the absolute value is greater than the dynamic adaptive threshold, an abnormal interception label is assigned, and a Boolean time-series mask sequence is generated.

[0024] In a further embodiment of the present invention, the resistance factor extraction module is specifically used for:

[0025] Connect the Boolean timing mask sequence to the enable control terminal of the preset controllable data register;

[0026] When the enable control terminal receives a valid feature flag indicating stable airflow, it opens the input port of the controllable data register, writes the calculated wideband airway resistance sequence into and updates it to the real-time airway resistance factor.

[0027] When the enable control terminal receives an abnormal interception flag indicating abnormal airflow, it closes the input port of the controllable data register, locks the last valid historical value written into the controllable data register, and maintains the historical value as the current real-time airway resistance factor.

[0028] In a further embodiment of the present invention, the multi-stage pressure decoupling module is specifically used for:

[0029] Continuously scan the instantaneous flow sequence along the time axis;

[0030] Detect the zero-crossing transition point where the instantaneous flow sequence changes from negative to positive;

[0031] The zero-crossing transition point is defined as the resting point representing the stillness of airflow from the end of expiration to the beginning of inspiration;

[0032] At the resting point, read the current value of the real-time pressure sequence and save it as the end-tidal baseline pressure.

[0033] A further aspect of the present invention includes an abnormal data removal module, specifically used for:

[0034] At each sampling moment, the difference between the current sampled value of the temporary elastic pressure signal and the sampled value at the previous sampling moment is calculated, and divided by the sampling time interval to generate the first-order differential rate of change.

[0035] The upper limit of the pressure change rate, which characterizes the upper limit of the physical deformation rate of biological tissues, is extracted as the physiological tolerance jump threshold.

[0036] Compare the absolute value of the first-order differential rate of change with the physiological tolerance jump threshold.

[0037] In a further embodiment of the present invention, when the absolute value of the first-order differential rate of change does not exceed the physiological tolerance threshold, the value that has not exceeded is retained, and the current value of the temporary elastic pressure signal is written into a preset output buffer to generate an elastic pressure signal.

[0038] A further aspect of this invention is that when the absolute value of the first-order differential rate of change exceeds the physiological tolerance threshold, the current temporary elastic pressure signal value is discarded, and the valid value stored in the output buffer from the previous moment is read to replace the abnormal value that exceeds the threshold as the current output, so as to generate an elastic pressure signal.

[0039] A further aspect of the present invention, the solution and feedback module, is specifically used for:

[0040] Starting from the beginning of the respiratory cycle, the current sampled value of the instantaneous flow sequence is multiplied by the sampling time interval and accumulated with the volume value at the previous moment to generate a dynamic volume signal characterizing the changes in lung chamber volume;

[0041] Determine whether the absolute value of the elastic pressure signal is greater than the preset pressure safety desensitization threshold;

[0042] When the absolute value of the elastic pressure signal is greater than the pressure safety desensitization threshold, the dynamic volume signal is used as the numerator and the elastic pressure signal is used as the denominator to perform a division operation, generating a continuously output lung compliance sequence.

[0043] A further aspect of this invention involves the calculation and feedback module extracting the long-term trend baseline signal as a feedback parameter to modify the dynamically adaptive threshold in reverse configuration. Specifically, this is used for:

[0044] Input the lung compliance sequence into the preset low-pass filter topology;

[0045] By using low-pass filtering topology to filter out fluctuations caused by a single respiration, the long-term trend baseline signal reflecting the long-term pathological evolution process is extracted and separated.

[0046] Transform the long-term trend baseline signal into a reverse adjustment feedback parameter;

[0047] When a downward trend is detected in the long-term trend baseline signal, the reverse adjustment feedback parameter is sent to the parameter tuning interface of the timing mask stream generation module to synchronously increase the sensitivity coefficient corresponding to the generated dynamic adaptive threshold.

[0048] In summary, the present invention has the following beneficial technical effects:

[0049] 1. This invention introduces a digital filtering unit to intercept high-frequency fluctuations in the mixed raw airway pressure signal, which helps to remove broadband mechanical noise introduced by tubing fluid resonance and equipment mechanical vibration. This processing allows the output low-frequency pressure signal to better retain effective information reflecting soft tissue deformation and internal organ blockage characteristics, thus making the pressure parameters used to calculate lung compliance more consistent with the actual lung elasticity.

[0050] 2. This invention utilizes the flow change gradient extracted from the instantaneous flow sequence, combined with a threshold with an adaptive adjustment mechanism to generate a Boolean-type time-series mask sequence, thereby controlling the enable state of the data register. This hardware-level read / write mechanism allows the system to acquire and update the airway drag factor under stable airflow conditions, while maintaining historical effective values ​​under turbulent airflow conditions. This reduces the error data introduced into drag calculations by nonlinear dissipation effects such as turbulence, which is beneficial to improving the repeatability and stability of airway drag factor measurement under dynamic breathing environments.

[0051] 3. This system employs multi-level decoupled data processing logic. By sequentially subtracting the pressure drop sequence corresponding to airway frictional resistance and the end-expiratory baseline pressure, it initially extracts the elastic pressure component representing the work done by lung tissue deformation from the composite pressure waveform. Simultaneously, by combining a preset physiological tolerance threshold for jump changes, the first-order differential rate of change of the pressure waveform is further evaluated, effectively suppressing abnormal step signals caused by inelastic events such as coughing. This signal extraction and purification mechanism provides a data foundation for subsequent stable compliance calculations.

[0052] 4. This invention calculates the time integral data of instantaneous flow rate with the purified elastic pressure signal, providing a continuous output of tissue compliance change trends. Furthermore, the scheme extracts a long-term trend baseline signal from this compliance sequence and uses this signal as a closed-loop feedback parameter to inversely adjust the sensitivity parameter of the dynamic adaptive threshold. This feedback adjustment mechanism enables the system to adaptively adjust the judgment boundary for airflow abnormalities based on the overall drift in lung compliance caused by disease progression, thereby improving the monitoring system's adaptability and robustness to slow pathological changes during long-term operation. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. The drawings are used to provide a further understanding of the present invention.

[0054] Figure 1 This is a schematic diagram of the module structure of the lung compliance dynamic monitoring system based on respiratory loop pressure waveform decoupling in an embodiment of this application.

[0055] Figure 2 This is a data processing flowchart of the lung compliance dynamic monitoring system based on respiratory loop pressure waveform decoupling in the embodiments of this application.

[0056] Figure 3 This is a schematic diagram of the multi-level pressure decoupling and abnormal data removal process in the embodiments of this application.

[0057] Figure 4 This is a schematic diagram of an instantaneous flow signal analysis segment in an embodiment of this application.

[0058] Figure 5 This is a schematic diagram of the flow change gradient and dynamic adaptive boundary in an embodiment of this application.

[0059] Figure 6 This is a schematic diagram of generating a Boolean timing mask in an embodiment of this application.

[0060] Figure 7 This is a schematic diagram of the dynamic filling volume generated by time integration in the embodiments of this application.

[0061] Figure 8 This is a schematic diagram of the effective elastic pressure after multi-stage processing and peeling in the embodiments of this application.

[0062] Figure 9 This is a schematic diagram of transient compliance fixed-point solution and long-term baseline feedback in an embodiment of this application. Detailed Implementation

[0063] The following is in conjunction with the appendix Figures 1-9 A preferred description of the present invention is provided below.

[0064] See attached document Figures 1-9 This invention proposes a dynamic monitoring system for lung compliance based on respiratory loop pressure waveform decoupling, comprising the following modules:

[0065] The sensor drive and registration module is used to receive the mixed raw airway pressure electrical signal input and the gas mass flow sensing value signal, generate a low-frequency pressure signal after preset filtering and bind it, and output the real-time pressure sequence and instantaneous flow sequence.

[0066] The time-series mask stream generation module is used to extract the flow change gradient from the instantaneous flow sequence, combine it with a dynamic adaptive threshold to determine the airflow stability, and generate a Boolean time-series mask sequence.

[0067] The drag factor extraction module calculates a wideband airway drag sequence based on real-time pressure and instantaneous flow sequences, and locks and saves historical values ​​or updates the output real-time airway drag factor according to the logical value of the Boolean time mask sequence.

[0068] The multi-level pressure decoupling module generates an airway resistance pressure drop sequence based on the instantaneous flow sequence and the real-time airway resistance factor, extracts the end-expiratory reference pressure corresponding to the pause node, and synthesizes a temporary elastic pressure signal by removing the airway resistance pressure drop sequence and the end-expiratory reference pressure from the real-time pressure sequence.

[0069] The abnormal data removal module is used to determine the differential change rate of the temporary elastic pressure signal based on the preset physiological tolerance jump threshold, and remove abnormal values ​​that exceed the limit to output the elastic pressure signal.

[0070] The calculation and feedback module is used to integrate the instantaneous flow sequence to generate a dynamic volume signal, divide the dynamic volume signal by the elastic pressure signal to generate a lung compliance sequence, and extract the long-term trend baseline signal as a feedback parameter to modify the configuration of the dynamic adaptive threshold.

[0071] In one embodiment of the present invention, the sensing drive and registration module is configured to perform the following steps:

[0072] Establish a hardware clock reference mapped from the preset motherboard crystal oscillator;

[0073] A hardware clock reference is used to trigger a dual-channel analog-to-digital converter to synchronously quantize the airway pressure electrical signal input stream and the flow sensor value signal.

[0074] The preset fourth-order Butterworth low-pass filter topology is used as the low-pass filter unit to calculate the weighted sum of the current sampled value, historical sampled value, and historical output value of the airway pressure electrical signal input stream. This attenuates broadband mechanical noise that is higher than the physiological respiratory frequency range and outputs a low-frequency pressure signal.

[0075] Specifically, the process of acquiring the respiratory baseline fluctuation parameter data chain is performed first. This process is completed collaboratively by the sensing module and the data acquisition and processing unit deployed at the Y-connection position between the ventilator loop and the patient's endotracheal tube. The sensing module integrates a piezoresistive pressure sensor and a hot-wire or differential pressure flow sensor.

[0076] The data acquisition and processing unit starts up, using a central clock driven by a high-stability quartz crystal oscillator on the motherboard at a uniform sampling frequency. Simultaneous acquisition trigger pulses are sent to the pressure sensor and flow sensor. It should be noted that the sampling frequency of the mixed raw airway pressure electrical signal input and the gas mass flow sensor signal... Based on the Nyquist theorem and the requirement for high fidelity in respiratory waveform details, the Hz frequency is set within a reasonable range, preferably from 100 Hz to 500 Hz, to capture rapid changes and potential abnormal events during respiration.

[0077] In each clock cycle At the rising edge of the signal, the pressure sensor converts the detected gas pressure in the airway into an analog voltage value, forming a mixed original airway pressure electrical signal input stream. Simultaneously, the flow sensor converts the instantaneous gas velocity flowing through its measurement section into another analog voltage value, forming a gas mass flow rate signal. These two analog signals are fed into a dual-channel analog-to-digital converter (ADC) controlled by the same central clock for parallel quantization, ensuring hard alignment of the timestamps at the data source.

[0078] The processor's digital signal processing core retrieves a preset low-pass filter unit to process the digital sequence of the mixed raw airway pressure electrical signal input stream. This filter unit's digital transfer function is designed to have low-pass characteristics, attenuating broadband mechanical noise above the physiological respiratory frequency range generated by the sloshing of condensate in the breathing tubing or the mechanical coupling between the ventilator motor and the casing. After processing by this filter unit, high-frequency abnormal fluctuations are stripped away, retaining signal energy that accurately reflects the elastic deformation of the patient's lung and thoracic soft tissues and the airway's internal obstruction characteristics, thereby generating a low-frequency pressure signal.

[0079] In this embodiment, the low-pass filter unit can specifically adopt a fourth-order Butterworth low-pass filter topology, with a cutoff frequency of The setting is based on the distinction between physiological signals and noise spectra. In one specific embodiment, mechanical vibration noise above 15 Hz is filtered out, while key physiological signals such as the patient's spontaneous breathing effort and cardiogenic oscillations below 10 Hz are fully preserved. The cutoff frequency is then set accordingly. The preferred Hz is 20 Hz.

[0080] Specifically, the filtering process of the low-pass filter unit can be calculated in the discrete-time domain using the following linear constant-coefficient difference equation:

[0081] in, In the first The pressure value output at each sampling time, belonging to the low-frequency pressure signal. In the first The original pressure sample value input at each sampling time, belonging to the mixed original airway pressure electrical signal input stream. These are the forward path coefficients of the filter. These are the feedback path coefficients of the filter. and These represent the filter order, which determines its filtering characteristics and computational complexity. This difference equation filters the signal by weighted summation of the current and historical input signals and the historical output signal. At this point, the filter coefficients... and It is based on the Butterworth filter prototype, combined with the selected order. With cutoff frequency The determined value is calculated using standard digital filter design methods such as bilinear transform. Specifically, in certain application scenarios, such as when the system sets the sampling frequency... At that time, a set of exemplary filter coefficient values ​​obtained after conversion and calculation are: forward path coefficients Feedback path coefficient In practical applications, it can be fixed as the array constant of a digital signal processor.

[0082] Finally, each pressure sample value in the low-frequency pressure signal is paired and bound one-to-one with the flow rate sample value in the gas mass flow rate sensor signal, which was acquired at the same acquisition timestamp and converted to physical units by the calibration curve. This forms a time-series data stream consisting of pressure and flow rate data pairs, i.e., (pressure, flow rate). This is the synchronously registered real-time pressure sequence and instantaneous flow rate sequence, which is continuously output to downstream processing steps. It should be understood that the data unit of the real-time pressure sequence is cmH2O, and the data unit of the instantaneous flow rate sequence is L / min. To facilitate the unification of dimensions in subsequent mathematical operations such as calculus, the flow rate data can also be divided by 60 for second-level dimensionality reduction conversion, so that the data unit of the instantaneous flow rate sequence output downstream is uniformly converted to liters per second, i.e., L / s.

[0083] For example, in one specific embodiment, the system sampling frequency It is 200 Hz, that is ms. At a certain sampling time After ADC acquisition and sensitivity calibration conversion, the original pressure sampling value is obtained. Raw flow sampling value At this point, the digital signal processing core invokes the preset second-order ( The recorded pressure sequence is processed using an IIR low-pass filter. Based on the preset difference equation coefficients ( ) and historical pressure data from preceding moments , and historical output , The system calculates the new filter pressure value according to the formula. Ultimately, the system will calculate the... Acquired synchronously After unit conversion, paired data points are formed. It is transmitted as an element of the real-time pressure sequence and instantaneous flow sequence at that moment.

[0084] In one embodiment of the present invention, the timing mask stream generation module is used to perform the following steps:

[0085] Perform a second-order difference transform operation on the instantaneous flow sequence to generate a sequence composed of the gradient of flow change;

[0086] Two exponentially weighted moving average filters with different time constants are run in parallel.

[0087] The long-term baseline mean of the gradient sequence of flow change is calculated using an exponentially weighted moving average filter with a long time constant.

[0088] The short-term standard deviation of the gradient sequence of flow change is calculated using an exponentially weighted moving average filter with a short time constant.

[0089] The absolute value of the long-term baseline mean is added to the short-term standard deviation multiplied by a preset sensitivity coefficient to generate a dynamic adaptive threshold.

[0090] When the absolute value of the traffic change gradient sequence is less than or equal to the dynamic adaptive threshold, a valid feature label is assigned; when the absolute value is greater than the dynamic adaptive threshold, an abnormal interception label is assigned, and a Boolean time-series mask sequence is generated.

[0091] Specifically, in combination Figures 4-6 As shown, data points are continuously extracted from the instantaneous flow sequence, and a second-order central difference operation is performed on each sampling point along the time axis. This operation calculates the instantaneous second-order time derivative of the flow signal by linearly combining the two adjacent data points before and after the current sampling point with the current data point. This quantifies the rate of change of airflow acceleration and constitutes a flow change gradient sequence that indicates the intensity of the nonlinear behavior of microscopic airflow.

[0092] It should be noted that the second-order center difference transform operation is designed to improve the sensitivity to signal inflection points and can amplify the nonlinear abrupt changes of the waveform more effectively than the first-order difference.

[0093] The gradient sequence of flow change can be calculated using the following formula:

[0094] in, The gradient sequence of flow change is in the th order. The value at each sampling time. , and These are the instantaneous flow sequences at the 1st and 2nd. , and The flow rate value at each sampling time. It is the sampling time interval.

[0095] The system is configured with a dynamic adaptive threshold, which is not a static threshold, but an adaptive boundary maintained by two parallel exponentially weighted moving average filters.

[0096] One of the filters has a long time constant and is used to calculate the long-term baseline mean of the gradient sequence of flow changes to accommodate the slow evolution of the patient’s pathological state.

[0097] Another filter with a shorter time constant is used to calculate the short-term standard deviation of the data stream to capture the amplitude of recent respiratory fluctuations;

[0098] These two parameters, together with a preset sensitivity coefficient, determine the dynamic judgment range reflecting the stable state of the airflow. The setting is based on striking a balance between suppressing noise and preserving effective physiological details. Based on statistical analysis of a large amount of clinical data, its value is usually preferably between 4 and 6.

[0099] In this embodiment, the long-term mean in the dynamic adaptive threshold The aim is to track the overall drift of a patient's respiratory mechanics, and its updates employ a smoothing factor. Smaller, preferably This corresponds to an average time window of several minutes. Short-term standard deviation This is used to quantify recent signal volatility, and its update employs a smoothing factor. Larger, preferably This corresponds to an average time window of several seconds.

[0100] Specifically, the safe zone boundary is defined by a dynamically adaptive threshold. Determined by the following formula:

[0101] in, and These are the long-term mean and short-term standard deviation estimates at that moment, calculated recursively using an exponentially weighted moving average. This is the sensitivity coefficient.

[0102] The system compares the absolute value of each data point in the flow rate gradient sequence with the upper boundary of the safe zone determined by a dynamic adaptive threshold at the same time. If the absolute value of the data point does not exceed the boundary, it indicates that the airflow is stable at this moment, and the system assigns a valid feature marker to the sampled data at this time point. In the specific circuit and logic code implementation, this corresponds to a high-level authorized pass-through code or a logic true value of 1. Conversely, if the absolute value of the data point exceeds the boundary, it indicates that a micro-turbulence or vortex caused by secretion disturbance or human-machine interaction has occurred, and the system assigns an abnormal interception marker to this time point, corresponding to a low-level anti-intrusion denial code or a logic false value of 0. By judging the entire instantaneous flow rate sequence point by point, these high and low level codes are concatenated in chronological order to finally form a Boolean time sequence mask sequence with the same synchronization length as the original data stream.

[0103] Boolean time sequence masking sequence The generation logic is as follows:

[0104] in, This represents a high-level authorization pass-through code. This represents a low-level intrusion prevention and denial code.

[0105] Furthermore, the high-level authorization pass-through code corresponds to the logic value 1 or true in the digital circuit implementation, while the low-level intrusion denial code corresponds to the logic value 0 or false. The final generated Boolean timing mask sequence is a binary sequence of the same length as the instantaneous flow sequence.

[0106] Continuing from the above See Hz example scenarios. Figures 4-6 The waveform diagram is shown below, combined with the following specific data, at five consecutive sampling times ( to Obtain and convert the flow sequence value in L / s. The system calculates the result based on the second-order central difference formula. Energy gradient at time Meanwhile, the current long-term average is calculated based on historical data. Short-term standard deviation And in setting the sensitivity coefficient At that moment, the boundary of the safe zone is determined. Due to the absolute value of the gradient This indicates a non-linear change in traffic flow, and the system determines that this moment is abnormal, generating a mask value. Similarly, regarding The gradient value is calculated at time step. It is still above the threshold. Therefore It is also 0. This process continues to determine, and finally outputs a Boolean time mask sequence.

[0107] In one embodiment of the present invention, the resistance factor extraction module is used to perform the following steps:

[0108] Connect the Boolean timing mask sequence to the enable control terminal of the preset controllable data register;

[0109] When the enable control terminal receives a valid feature flag indicating stable airflow, it opens the input port of the controllable data register, writes the calculated wideband airway resistance sequence into and updates it to the real-time airway resistance factor.

[0110] When the enable control terminal receives an abnormal interception flag indicating abnormal airflow, it closes the input port of the controllable data register, locks the last valid historical value written into the controllable data register, and maintains the historical value as the current real-time airway resistance factor.

[0111] The real-time pressure sequence and the instantaneous flow rate sequence are subjected to a first-order differential division operation based on adjacent sampling points. Specifically, at each sampling moment, the processing unit calculates the pressure change between the current moment and the previous moment as the dividend, and simultaneously calculates the instantaneous flow rate change between the same moments as the divisor. Through differential operation, the system can approximately filter out the interference from slowly changing lung elasticity and baseline pressure by leveraging the high-frequency sampling characteristics, thereby separating only the current instantaneous airflow shear resistance. This process is performed continuously over the entire time axis, thereby generating a real-time broadband ventilation drag waveform series that is ungated or unfiltered and includes both linear and nonlinear dissipation effects.

[0112] In this embodiment, the calculation formula for the real-time wideband ventilation resistance waveform series is as follows:

[0113]

[0114] in, In the first Instantaneous ventilation resistance at each sampling moment. and These are the pressure values ​​at the current and previous sampling times, respectively. and These are the flow rates at the current and previous sampling times, respectively. This is a threshold for the absolute value of the flow difference, set to prevent division by zero errors. This threshold... The preferred value is a positive number close to zero, preferably 0.02 L / s. This is based on the measurement accuracy and signal-to-noise ratio of the flow sensor, and aims to avoid meaningless fluctuations in the calculation results due to measurement noise when the breathing phase switches and the flow rate is close to zero.

[0115] Simultaneously, each bit of the Boolean timing mask sequence is passed to the enable control terminal of a preset controllable data register. When the mask sequence receives a valid flag during the current clock cycle, the enable control terminal writes a valid signal, authorizing the register to open its input port. The instantaneous impedance value on the real-time wideband ventilation resistance waveform calculated by the arithmetic logic unit at that moment is then fully written and the register's contents are updated. This content is immediately output as the real-time airway resistance factor at that moment. Conversely, when an abnormal interception flag is received, the register's input port remains closed and locked, and the maintained historical value is output as the alternative real-time airway resistance factor.

[0116] The final output is the real-time airway resistance factor. The sequence, whose generation process is rigorously filtered by a Boolean time-series mask sequence, eliminates unreliable drag values ​​calculated during periods of turbulent airflow, thus reflecting only the airway drag characteristics under stable laminar flow conditions. It is specifically generated by the following logic:

[0117] in, It is a Boolean time sequence mask in the th order. The code value at each sampling moment, where 1 represents a high level and 0 represents a low level. This represents the real-time airway resistance factor value output at the previous sampling time.

[0118] In one specific embodiment, at four sampling times of continuous detection ( to In this example, let's take the getter phase. It is known that... Effective resistance output at any time ,and Constant pressure Traffic is The system then received a continuous pressure sequence. for Synchronous traffic sequence for The difference in flow rate is greater than the threshold. The mask sequence received synchronously by the system. The value is [1, 0, 1, 1]. At any moment, because The system obtains the original airway resistance based on differential calculations as the real-time resistance output, i.e. ;exist At any moment, because The system does not accept new calculated values ​​caused by airflow turbulence, but locks them to the previous valid value, meaning the output remains the same. ;exist and At any given time, the mask is restored to 1, and the system again uses the differential method to perform real-time calculations (such as...). time After filtering and locking operations by this masking mechanism, a real-time airway drag factor sequence [15.00, 15.00, 15.00, 15.00...] containing drag information under stable laminar flow conditions was robustly extracted.

[0119] In one embodiment of the present invention, the multi-stage pressure decoupling module is used to perform the following steps:

[0120] Continuously scan the instantaneous flow sequence along the time axis;

[0121] Detect the zero-crossing transition point where the instantaneous flow sequence changes from negative to positive;

[0122] The zero-crossing transition point is defined as the resting point representing the stillness of airflow from the end of expiration to the beginning of inspiration;

[0123] At the resting point, read the current value of the real-time pressure sequence and save it as the end-tidal baseline pressure.

[0124] Specifically, the pressure decoupling computation core retrieves two data streams in parallel: the instantaneous flow rate sequence and the real-time airway drag factor sequence. At each synchronized sampling moment, the computation unit performs a signed multiplication of the real-time airway drag factor value at that moment with the corresponding instantaneous flow rate. By preserving the algebraic sign of the airflow, the directional pressure drop generated during inhalation or exhalation to overcome airway frictional resistance is accurately calculated. This process is executed continuously along the time axis, generating an airway drag pressure drop sequence that reflects only the airway drag energy loss along the airflow path.

[0125] It should be noted that the airway resistance pressure drop sequence corresponds, in a physical sense, to the pressure loss caused by the viscosity and friction between the airflow and the tube wall when the airflow passes through the airway. This loss is directly related to the airway resistance and the flow rate.

[0126] Airway resistance pressure drop sequence The calculation is as follows:

[0127] in, For the real-time airway resistance factor in the first... The value at each sampling time. For the instantaneous flow sequence at the th The actual instantaneous flow rate at each sampling time, with positive and negative respiratory phase characteristics.

[0128] Simultaneously, the instantaneous flow sequence is continuously scanned to locate the pause point in the respiratory cycle. In clinical practice, the pause point is the zero flow point at the end of expiration; the pressure at this point is the baseline pressure experienced by the entire respiratory system when there is no airflow. By detecting the moment when the flow signal crosses zero from a negative value and turns positive, the transition point from end-expiration to early-inspiration is identified, which is the physical state of temporary airflow stillness at the end of a complete mechanical ventilation cycle. Once this pause point is located, the pressure value in the real-time pressure sequence output at the same moment is immediately read and saved as the end-expiratory baseline pressure reflecting the static recoil force of the chest wall. The end-expiratory baseline pressure is clinically known as positive end-expiratory pressure (PEEP), which is also the baseline pressure of background residual offset.

[0129] The end-expiratory baseline pressure, also known as positive end-expiratory pressure (PEEP), is the background pressure applied to maintain alveolar incomplete collapse at the end of expiration. Its determination logic is as follows: continuously scan the instantaneous flow rate sequence and detect the moment when the flow rate signal crosses zero from a negative value and turns positive, i.e., the airflow rate. Furthermore, this indicates that the first derivative of the flow rate is continuously greater than zero in the upward sliding region of the waveform, i.e. The moment at which the exhalation ends is defined as the end-expiratory cessation point. And read the corresponding filter pressure value at that moment. As the reference pressure at the end of expiration .

[0130] Finally, the system initiates a continuous subtraction process, using the real-time pressure sequence as the total input and subtracting two calculated components sequentially: subtracting the airway resistance pressure drop sequence point by point, and uniformly subtracting the saved scalar end-expiratory baseline pressure. After two stages of continuous stripping operations, the remaining pressure sequence is the temporary elastic pressure signal, roughly reflecting the work done by lung elastic deformation after removing hysteresis impedance and static baseline shift. In other words, by subtracting these two main inelastic components from the total pressure, the temporary elastic pressure signal should theoretically consist primarily of the elastic pressure required to drive volume changes in the lungs and thoracic cavity.

[0131] The final generated temporary elastic pressure signal The calculation formula is:

[0132] in, The real-time pressure sequence is in the 1st... Pressure values ​​at each sampling time.

[0133] For example, the input sequence continues from the preceding stabilization phase, where the system has locked the end-expiratory reference pressure. Assuming After obtaining the resistance factor sequence and corresponding traffic Then, the system gradually eliminates the pressure drop component according to the formula. At time t, if the resistance is known to be And the traffic is The pressure drop and flow loss were calculated. The total pressure after filtering is acquired synchronously at this moment. Mutation to Then the remaining temporary elastic pressure Following the same calculation logic, subsequent nodes were sequentially stripped, ultimately separating a temporary elastic pressure sequence that excluded pipeline friction and residual baseline interference. (As mentioned above...) to Within a given time segment, the elastic pressure output corresponding to the increase in volume is similar to [4.000, 4.250, 4.500, 4.700...]. .

[0134] In one embodiment of the present invention, the abnormal data removal module is used to perform the following steps:

[0135] At each sampling moment, the difference between the current sampled value of the temporary elastic pressure signal and the sampled value at the previous sampling moment is calculated, and divided by the sampling time interval to generate the first-order differential rate of change.

[0136] The upper limit of the pressure change rate, which characterizes the upper limit of the physical deformation rate of biological tissues, is extracted as the physiological tolerance jump threshold.

[0137] Compare the absolute value of the first-order differential rate of change with the physiological tolerance jump threshold;

[0138] When the absolute value of the first-order differential rate of change does not exceed the physiological tolerance threshold, the value that does not exceed is retained, and the current value of the temporary elastic pressure signal is written into the preset output buffer to generate the elastic pressure signal.

[0139] When the absolute value of the first-order differential rate of change exceeds the physiological tolerance threshold, the current temporary elastic pressure signal value is discarded, and the valid value stored in the output buffer from the previous moment is read to replace the abnormal value that exceeds the threshold as the current output to generate the elastic pressure signal.

[0140] Specifically, after synthesizing the temporary elastic pressure signal, a first-order backward differential operation is first performed on it. That is, at each sampling time, the difference between the current pressure value and the pressure value at the immediately preceding sampling time is calculated. This operation converts the absolute value sequence of the pressure signal into its instantaneous rate of change sequence in real time, thereby generating a first-order differential rate of change characterizing the response speed. Algorithmically, this is represented as a time-series first-order derivative wandering wave. It should be noted that the time-series first-order derivative wandering wave essentially monitors the gradient of the elastic pressure signal, aiming to capture discontinuous transition points in the signal. The calculation method is as follows:

[0141] in, and These are the temporary elastic pressure signals at the current sampling time. and the previous sampling time The pressure value. It is the sampling time interval.

[0142] Next, the system retrieves the physiological tolerance threshold, which is a preset upper limit threshold for the rate of pressure change. This threshold is set based on the physical inertia and tissue viscoelasticity of the lung and thoracic system, meaning that biological tissues cannot undergo deformations exceeding physiological limits within a millisecond timescale.

[0143] Specifically, physiological tolerance threshold This is an empirical constant derived from the analysis of a large amount of clinical respiratory waveform data and simulation using a lung biomechanical model. In a specific application scenario, namely adult mechanical ventilation, the single sampling interval is 5 ms. If the pressure changes by more than 2 cmH2O within a single sampling period, i.e., the rate of change exceeds 400 cmH2O / s, it is considered to exceed the physical capacity of normal lung tissue to expand or contract elastically, and should be regarded as abnormal.

[0144] Subsequently, the absolute value of the time-series first-order derivative wandering wave is compared point by point with the threshold of the physiological tolerance jump threshold. If, at a certain sampling moment, the absolute value of the pressure change rate does not exceed the threshold, the data point in the temporary elastic pressure signal at that moment is determined to be a reasonable change, and its value is written into a preset output buffer as part of the final data. Conversely, if the absolute value of the pressure change rate touches or exceeds the threshold, the data point is determined to be a pathological distortion caused by signal interference or inelastic events such as coughing impacts, and a data rejection operation is directly performed, replacing it with the previous valid value stored in the output buffer.

[0145] See Figure 3 The multi-stage pressure decoupling and outlier data removal process shown in this operational logic ultimately generates an elastic pressure signal. Determined based on the following conditions:

[0146] in, The upper limit of the rate of pressure change set for the physiological tolerance threshold. It is the effective pressure value in the previous moment from the generated elastic pressure signal.

[0147] It should be noted that when the pressure change rate does not exceed the limit, in the software implementation, this corresponds to the true branch of the conditional statement, where the program retains the current data. Conversely, the operation of discarding and blocking abnormally distorted data exceeding the threshold due to interference corresponds to the false branch of the conditional statement, where the data remains unchanged. This mechanism ensures that the final output elastic pressure signal is smooth and continuous in the time domain, accurately reflecting the working properties of the lung sacs.

[0148] In one specific embodiment, combined with Figure 3 Waveform example, sampling interval The preset constant upper limit tolerance threshold is 0.005 s. . Received continuous temporary elastic pressure segments For example, when the calculation reaches... At time t, the current elastic pressure is 4.00, and its first-order rate of change calculated relative to the previous valid data is... The absolute value is within the threshold specification, therefore Treat it as a valid value, save it, and output it stably. In the subsequent... At that moment, the stress data instantly rose to 12.50 due to the patient's coughing disturbance, and its calculated rate of change instantly reached [a certain value]. If the trigger threshold is exceeded, it is judged as an excitation distortion; at this time, the system will perform a rejection action and a smooth replacement operation: that is, the output is forced to maintain at the previous valid level point, i.e. The protective clamping mechanism takes effect at this point, and the final smoothed and reset normal physical tissue pressure sequence is corrected to... This successfully suppressed non-physiological pathological peak penetration caused by conditions such as coughing.

[0149] In one embodiment of the present invention, the calculation and feedback module is used to perform the following steps:

[0150] Starting from the beginning of the respiratory cycle, the current sampled value of the instantaneous flow sequence is multiplied by the sampling time interval and accumulated with the volume value at the previous moment to generate a dynamic volume signal characterizing the changes in lung chamber volume;

[0151] Determine whether the absolute value of the elastic pressure signal is greater than the preset pressure safety desensitization threshold;

[0152] When the absolute value of the elastic pressure signal is greater than the pressure safety desensitization threshold, the dynamic volume signal is used as the numerator and the elastic pressure signal is used as the denominator to perform a division operation, generating a continuously output lung compliance sequence.

[0153] Input the lung compliance sequence into the preset low-pass filter topology;

[0154] By using low-pass filtering topology to filter out fluctuations caused by a single respiration, the long-term trend baseline signal reflecting the long-term pathological evolution process is extracted and separated.

[0155] Transform the long-term trend baseline signal into a reverse adjustment feedback parameter;

[0156] When a downward trend is detected in the long-term trend baseline signal, the reverse adjustment feedback parameter is sent to the parameter tuning interface of the timing mask stream generation module to synchronously increase the sensitivity coefficient corresponding to the generated dynamic adaptive threshold.

[0157] Specifically, time integration is performed on the acquired instantaneous flow rate sequence over the entire time period. Starting from the beginning of each respiratory cycle, the flow rate value at each sampling moment is multiplied by the sampling time interval. The data are then accumulated, and the system calculates the change in intrapulmonary gas volume over time, thereby generating a dynamic volume signal that characterizes the volume change of the lung chambers relative to their end-expiratory functional residual capacity.

[0158] It should be noted that the dynamic volume signal is the curve of tidal volume relative to end-expiratory volume changing over time. The calculation method is discrete-time integration:

[0159] in, The volume value at the previous sampling time, initial value Set to 0 at the start of a respiratory cycle. It is the instantaneous flow rate value. It is the sampling time interval.

[0160] See Figures 7-9 This demonstrates the process of continuous dynamic compliance calculation and pathological trend extraction. The dynamic volume signal is divided point-to-point with the elastic pressure signal synchronously intercepted at the same time base. At each sampling moment, the system uses the volume change value at that moment as the numerator and the elastic pressure value at the same moment as the denominator, and derives a series of instantaneous dynamic compliance values ​​by calculating the ratio between the two. Together, these constitute a lung compliance sequence capable of resisting transient disturbances such as coughing.

[0161] It should be understood that the lung compliance sequence, because its calculation is based on elastic pressure after layers of purification, accurately reflects the lung's expansion capacity at every instant. The corresponding calculation formula is as follows:

[0162] in, This is the elastic pressure value. This is a small positive threshold set to avoid dividing by pressure values ​​close to zero; this pressure threshold is the pressure safety desensitization threshold. Preferably, 1 cmH2O is used to ensure the validity of the calculation.

[0163] Subsequently, this parameter sequence is fed into a Chebyshev Type I low-pass filter. This filter is designed with an extremely low cutoff frequency to filter out compliance fluctuations caused by a single breath and extract a long-term trend baseline signal reflecting changes in tissue stiffness over long time scales.

[0164] Specifically, the Chebyshev low-pass filter topology, due to its equiripple characteristics within the passband, provides a steep roll-off at a finite number of orders, making it suitable for accurately separating extremely low-frequency trend signals. Its cutoff frequency is preferably 0.01 Hz to extract a compliance baseline reflecting pathological changes over several minutes or even longer.

[0165] Long-term trend baseline signal It is generated by the following filtering process:

[0166]

[0167] in, This represents the transfer function of the Chebyshev low-pass filter.

[0168] Finally, the long-term trend baseline signal is converted into an adjustment voltage or digital control feedback parameter, and then input inversely to the core comparator circuit of the dynamic adaptive threshold via physical or logical connections. This signal directly adjusts the sensitivity coefficient corresponding to the dynamic adaptive threshold. Specifically, the process of converting the long-term trend baseline signal into an inverse adjustment feedback parameter is calculated using the following negative correlation linear mapping model:

[0169]

[0170] in, The converted inverse adjustment feedback parameter is the sensitivity coefficient that needs to be output after the update. , This is the initial reference sensitivity coefficient of the system. The initial baseline value for lung compliance is the value used to reference the patient's condition. This is the currently extracted long-term trend baseline signal. This is a preset adjustment ratio constant. When the extracted long-term trend baseline signal... Indicators of worsening lung compliance, such as Figures 7-9 The trend of the mid-baseline decreasing triggering reverse parameter tuning is shown, that is, when the value decreases, as... As the difference increases, the calculated anomaly detection parameters will be synchronously adjusted upwards according to the above mapping relationship, increasing the system's tolerance to flow fluctuations and thus forming a dual-track interlocking closed loop that adaptively adjusts in real time according to the pathological state. This is mainly because the long-term trend baseline signal serves as a feedback signal; when its value decreases, it indicates that the patient's overall lung tissue compliance has deteriorated, and the respiratory waveform is prone to physiological fluctuations caused by pathological work. At this time, the feedback control logic will adjust the threshold... Sensitivity coefficient in The corresponding increase prompts the safety zone boundary of the dynamically adaptive threshold. By expanding outwards, the system becomes less sensitive to the judgment of "turbulence," avoiding the mistaken filtering out of respiratory pattern changes caused by pathological changes as noise, and enabling the monitoring system to adapt to the progression of the disease.

[0171] For example, the system calculates the instantaneous volume by performing discrete time-domain integration on the flow velocity in real time. If at a certain moment... The dynamic filling volume, obtained from the discrete integral of the flow rate during the mid-inhalation phase, reaches... That is, 200 mL, the true passive elastic net pressure of the synchronously decoupled separation is Under the desensitization condition that the denominator is greater than zero, the fixed-point solution result of dynamic compliance at that instant is obtained. This fixed-point solution process is executed continuously along the time axis to generate a time-series data sequence. The long-term trend baseline signal separated by the Chebyshev filtering unit at the system's backend... As the trend declines, the corresponding inverse mapping model will increase the reset sensitivity parameter, as in the original configuration. Adjusted to based on feedback ratio This adjustment makes the safe zone boundary of the dynamic adaptive threshold... With significant expansion, the system becomes more lenient in its judgment of changes in flow gradient, thereby adapting to more drastic changes in respiratory airflow that may occur due to decreased lung compliance, and completing the entire closed-loop feedback regulation process.

[0172] Each of the modules can be implemented in whole or in part through software, hardware, or a combination thereof. It supports hardware embedded in or independent of the processor in the computer device, and also supports software stored in the memory of the computer device, so that the processor can call and execute the operations corresponding to each of the above modules.

[0173] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A dynamic monitoring system for lung compliance based on decoupling of respiratory loop pressure waveforms, characterized in that, include: The sensor drive and registration module is used to receive the mixed raw airway pressure electrical signal input and the gas mass flow sensing value signal, generate a low-frequency pressure signal after preset filtering and bind it, and output the real-time pressure sequence and instantaneous flow sequence. The time-series mask stream generation module is used to extract the flow change gradient from the instantaneous flow sequence, combine it with a dynamic adaptive threshold to determine the airflow stability, and generate a Boolean time-series mask sequence. The drag factor extraction module calculates a wideband airway drag sequence based on real-time pressure and instantaneous flow sequences, and locks and saves historical values ​​or updates the output real-time airway drag factor according to the logical value of the Boolean time mask sequence. The multi-level pressure decoupling module generates an airway resistance pressure drop sequence based on the instantaneous flow sequence and the real-time airway resistance factor, extracts the end-expiratory reference pressure corresponding to the pause node, and synthesizes a temporary elastic pressure signal by removing the airway resistance pressure drop sequence and the end-expiratory reference pressure from the real-time pressure sequence. The abnormal data removal module is used to determine the differential change rate of the temporary elastic pressure signal based on the preset physiological tolerance jump threshold, and remove abnormal values ​​that exceed the limit to output the elastic pressure signal. The calculation and feedback module is used to integrate the instantaneous flow sequence to generate a dynamic volume signal, divide the dynamic volume signal by the elastic pressure signal to generate a lung compliance sequence, and extract the long-term trend baseline signal as a feedback parameter to modify the configuration of the dynamic adaptive threshold.

2. The lung compliance dynamic monitoring system based on respiratory loop pressure waveform decoupling according to claim 1, characterized in that, The sensor driving and registration module is specifically used for: Establish a hardware clock reference mapped from the preset motherboard crystal oscillator; A hardware clock reference is used to trigger a dual-channel analog-to-digital converter to synchronously quantize the airway pressure electrical signal input stream and the flow sensor value signal. The preset fourth-order Butterworth low-pass filter topology is used as the low-pass filter unit to calculate the weighted sum of the current sampled value, historical sampled value, and historical output value of the airway pressure electrical signal input stream. This attenuates broadband mechanical noise that is higher than the physiological respiratory frequency range and outputs a low-frequency pressure signal.

3. The lung compliance dynamic monitoring system based on respiratory loop pressure waveform decoupling according to claim 1, characterized in that, The timing mask stream generation module is specifically used for: Perform a second-order difference transform operation on the instantaneous flow sequence to generate a sequence composed of the gradient of flow change; Two exponentially weighted moving average filters with different time constants are run in parallel. The long-term baseline mean of the gradient sequence of flow change is calculated using an exponentially weighted moving average filter with a long time constant. The short-term standard deviation of the gradient sequence of flow change is calculated using an exponentially weighted moving average filter with a short time constant. The absolute value of the long-term baseline mean is added to the short-term standard deviation multiplied by a preset sensitivity coefficient to generate a dynamic adaptive threshold. When the absolute value of the traffic change gradient sequence is less than or equal to the dynamic adaptive threshold, a valid feature label is assigned; when the absolute value is greater than the dynamic adaptive threshold, an abnormal interception label is assigned, and a Boolean time-series mask sequence is generated.

4. The lung compliance dynamic monitoring system based on respiratory loop pressure waveform decoupling according to claim 1, characterized in that, The resistance factor extraction module is specifically used for: Connect the Boolean timing mask sequence to the enable control terminal of the preset controllable data register; When the enable control terminal receives a valid feature flag indicating stable airflow, it opens the input port of the controllable data register, writes the calculated wideband airway resistance sequence into and updates it to the real-time airway resistance factor. When the enable control terminal receives an abnormal interception flag indicating abnormal airflow, it closes the input port of the controllable data register, locks the last valid historical value written into the controllable data register, and maintains the historical value as the current real-time airway resistance factor.

5. The lung compliance dynamic monitoring system based on respiratory loop pressure waveform decoupling according to claim 1, characterized in that, The multi-stage pressure decoupling module is specifically used for: Continuously scan the instantaneous flow sequence along the time axis; Detect the zero-crossing transition point where the instantaneous flow sequence changes from negative to positive; The zero-crossing transition point is defined as the resting point representing the stillness of airflow from the end of expiration to the beginning of inspiration; At the resting point, read the current value of the real-time pressure sequence and save it as the end-tidal baseline pressure.

6. The lung compliance dynamic monitoring system based on respiratory loop pressure waveform decoupling according to claim 1, characterized in that, The abnormal data removal module is specifically used for: At each sampling moment, the difference between the current sampled value of the temporary elastic pressure signal and the sampled value at the previous sampling moment is calculated, and divided by the sampling time interval to generate the first-order differential rate of change. The upper limit of the pressure change rate, which characterizes the upper limit of the physical deformation rate of biological tissues, is extracted as the physiological tolerance jump threshold. Compare the absolute value of the first-order differential rate of change with the physiological tolerance jump threshold.

7. The lung compliance dynamic monitoring system based on respiratory loop pressure waveform decoupling according to claim 6, characterized in that, When the absolute value of the first-order differential rate of change does not exceed the physiological tolerance threshold, the value that does not exceed the threshold is retained, and the current value of the temporary elastic pressure signal is written into the preset output buffer to generate the elastic pressure signal.

8. The lung compliance dynamic monitoring system based on respiratory loop pressure waveform decoupling according to claim 6, characterized in that, When the absolute value of the first-order differential rate of change exceeds the physiological tolerance threshold, the current temporary elastic pressure signal value is discarded, and the valid value stored in the output buffer from the previous moment is read to replace the abnormal value that exceeds the threshold as the current output to generate the elastic pressure signal.

9. The lung compliance dynamic monitoring system based on respiratory loop pressure waveform decoupling according to claim 1, characterized in that, The solution and feedback module is specifically used for: Starting from the beginning of the respiratory cycle, the current sampled value of the instantaneous flow sequence is multiplied by the sampling time interval and accumulated with the volume value at the previous moment to generate a dynamic volume signal characterizing the changes in lung chamber volume; Determine whether the absolute value of the elastic pressure signal is greater than the preset pressure safety desensitization threshold; When the absolute value of the elastic pressure signal is greater than the pressure safety desensitization threshold, the dynamic volume signal is used as the numerator and the elastic pressure signal is used as the denominator to perform a division operation, generating a continuously output lung compliance sequence.

10. The lung compliance dynamic monitoring system based on respiratory loop pressure waveform decoupling according to claim 1, characterized in that, The calculation and feedback module extracts the long-term trend baseline signal as a feedback parameter to modify the configuration of the dynamic adaptive threshold in reverse. Specifically, it is used for: Input the lung compliance sequence into the preset low-pass filter topology; By using low-pass filtering topology to filter out fluctuations caused by a single respiration, the long-term trend baseline signal reflecting the long-term pathological evolution process is extracted and separated. Transform the long-term trend baseline signal into a reverse adjustment feedback parameter; When a downward trend is detected in the long-term trend baseline signal, the reverse adjustment feedback parameter is sent to the parameter tuning interface of the timing mask stream generation module to synchronously increase the sensitivity coefficient corresponding to the generated dynamic adaptive threshold.

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