Autonomous inspiration effort intensity estimation method and system based on respiratory system model under pressure control ventilation

Abstract

The invention provides an autonomous inspiration effort intensity estimation method and system based on a respiratory system model under pressure control ventilation. Airway pressure waveform data with autonomous inspiration effort under pressure control ventilation is combined with an autonomous respiration lung ventilation model to simulate airway pressure waveform data without autonomous inspiration effort; according to a respiratory mechanics motion equation, airway resistance and respiratory system compliance are obtained by using clinically measured original waveform data to obtain a flow waveform, and error analysis is performed on the flow waveform and a clinically measured flow waveform with autonomous inspiration effort. And finally, estimating the intensity of the autonomous inspiration effort by utilizing a linear regression curve obtained by fitting an error index value of flow waveform data which is obtained by estimating a plurality of patients with known golden standard esophageal pressure deviation and assumed that the autonomous inspiration effort does not exist and the obtained flow waveform data when the autonomous inspiration effort exists with the corresponding esophageal pressure deviation. And the autonomous inspiration effort intensity estimation under the condition of no esophageal pressure is realized.

Description

Technical Field

[0001] The invention belongs to the field of medicine and biomedical engineering, and specifically discloses a method and a system for estimating the intensity of autonomous inspiratory effort based on a respiratory system model under pressure-controlled ventilation. Background Art

[0002] Pressure Controlled Ventilation (PCV) is a ventilation mode of the ventilator, in which the ventilator completely takes over the patient's spontaneous breathing. In PCV mode, the ventilator delivers air to the patient's lungs according to the parameter values ​​set by the doctor, mainly including respiratory rate, pressure rise time, inspiratory time, peak inspiratory pressure, etc. When using PCV mode ventilation, each inhalation is controlled by the ventilator, that is, after entering the inspiratory time, the ventilator delivers air to the patient's airway according to the pre-set pressure value, which will make the patient's airway pressure reach the set value in a very short time, and then the ventilator will maintain this pressure until the end of the inspiratory time.

[0003] In the field of medicine and biomedical engineering, mathematical modeling has received extensive attention. Due to the complexity of the human physiological system, researchers are often unable to fully grasp its internal physiological operating mechanism, and existing detection methods are often limited by objective conditions. Therefore, mathematical modeling and simulation have become important means in the study of physiological systems. The establishment of the spontaneous breathing lung ventilation model is based on the classic multi-level airway model. Referring to the airway classification model proposed by Rideout, the respiratory system is divided into four parts: larynx, trachea, bronchi and alveoli. Each part is represented by a linear resistor and a linear capacitor. The linear resistor represents the resistance characteristics of the respiratory system, and the linear capacitor represents the elastic characteristics of the respiratory system. On the basis of the Rideout model, the design of the autonomous breathing driving force model proposed by Albanese is referred to, and the relevant driving force part is added. The autonomous breathing driving force comes from the pressure (Pmus) generated by the respiratory muscles, which is transmitted to the trachea, bronchi and alveoli through the chest wall. Considering that the chest wall is an elastic structure, assuming that its pressure and volume characteristic curve is a linear curve, the elastic characteristics of the chest wall can be represented by a linear capacitor.

[0004] Spontaneous inspiratory effort refers to any energy-consuming activity performed by the respiratory muscles to drive breathing, including the amplitude and frequency of muscle breathing. During severe respiratory failure, such as acute respiratory distress syndrome (ARDS), patients are sedated and their breathing is fully controlled by mechanical ventilation. When patients recover or are at a low level of sedation, they show spontaneous inspiratory effort, and the patient breathes on the basis of supported mechanical ventilation breathing. Preserving spontaneous inspiratory effort during mechanical ventilation is a double-edged sword. Preserving spontaneous inspiratory effort can improve ventilation of alveoli close to the diaphragm area, improve ventilation-perfusion mismatch, and thus improve oxygenation; maintain diaphragmatic activity and prevent ventilator-induced diaphragmatic dysfunction; at the same time, reduce sedation requirements, prevent muscle atrophy, and reduce the occurrence of ICU-acquired muscle weakness. But at the same time, spontaneous inspiratory effort introduces uncontrolled variables and asynchrony, which may lead to adverse ventilation outcomes. For patients receiving noninvasive ventilation, excessive spontaneous inspiratory effort can cause patient self-inflicted lung injury (P-SILI) through mechanisms such as whole lung stress overload, local lung tissue stress overload (pendelluft phenomenon), and human-machine asynchrony, which in turn causes increased inflammation in the lungs, worsening oxygenation function, and increased mortality. In addition, since the diaphragm is very sensitive to excessive respiratory load, strong spontaneous inspiratory effort can cause increased muscle tension, leading to muscle inflammation, proteolysis, myofibril damage, and sarcolemmal disorder, ultimately leading to diaphragmatic weakness.

[0005] For patients with spontaneous inhalation, esophageal pressure is often required to measure respiratory effort, work of breathing, and lung mechanics. It is the gold standard for evaluating respiratory effort and work of breathing. When measuring esophageal pressure, since the balloon of the esophageal pressure catheter is located close to the heart in the esophagus, the patient's heartbeat will affect the measurement of esophageal pressure to a certain extent. This interference is usually called heartbeat artifact or heartbeat notch. The existence of this interference has a certain interference on the esophageal pressure value, especially when the patient's heartbeat notch amplitude is large, the noise signal brought by the heartbeat notch may also cover the patient's own esophageal pressure signal. Therefore, when doctors need to identify and evaluate patients' spontaneous inspiratory efforts through the swing of esophageal pressure, the presence of the heartbeat notch will interfere with the doctor's judgment, resulting in difficulty in evaluating the intensity of patients' spontaneous inspiratory efforts. Therefore, when facing esophageal pressure, it is important to pay attention to the filtering of esophageal pressure. In addition, measuring esophageal pressure is invasive, requires professional knowledge, and may cause discomfort and complications to patients, so it is usually not feasible in daily clinical practice. Summary of the invention

[0006] Based on the above problems, the present invention provides a method and system for estimating the intensity of spontaneous inspiratory effort based on a respiratory system model under pressure-controlled ventilation. The model is used to analyze the relationship between spontaneous inspiratory effort and the gold standard esophageal pressure deviation, thereby providing a non-invasive basis for analyzing the intensity of spontaneous inspiratory effort in the absence of an esophageal pressure gold standard.

[0007] The present invention provides the following solutions to solve the above technical problems:

[0008] A method for estimating the intensity of spontaneous inspiratory effort based on a respiratory system model under pressure-controlled ventilation, comprising:

[0009] Acquiring airway pressure and flow waveform data of the patient to be tested under pressure-controlled ventilation, including airway pressure and flow waveform data with spontaneous inspiratory effort and airway pressure and flow waveform data without spontaneous inspiratory effort;

[0010] Extracting waveform features based on airway pressure waveform data with spontaneous inspiratory effort;

[0011] The extracted waveform features are input into the spontaneous breathing lung ventilation model and the respiratory muscle driving force Pmus is set to 0, and the airway pressure waveform data assuming that there is no spontaneous inspiratory effort is obtained by simulation;

[0012] Based on the airway pressure and flow waveform data obtained under pressure-controlled ventilation without spontaneous inspiratory effort, the airway resistance and respiratory system compliance are estimated according to the respiratory mechanics motion equation;

[0013] Based on the simulated airway pressure waveform data assuming no spontaneous inspiratory effort and the estimated airway resistance and respiratory system compliance, the flow waveform data assuming no spontaneous inspiratory effort is estimated according to the respiratory mechanics motion equation;

[0014] The estimated flow waveform data assuming that there is no spontaneous inspiratory effort is subjected to error analysis with the acquired flow waveform data, and the error index value obtained by the error analysis is brought into the linear regression curve to obtain the estimated value of the spontaneous inspiratory effort intensity of the patient under the current pressure-controlled ventilation;

[0015] The linear regression curve is obtained by fitting the flow waveform data estimated from several patients with known gold standard esophageal pressure deviations assuming that there is no spontaneous inspiratory effort and the error index value of the flow waveform data obtained when there is spontaneous inspiratory effort and the corresponding esophageal pressure deviation.

[0016] Furthermore, the airway pressure and flow waveform data are the airway pressure and flow waveform data after abnormal waveforms are filtered out.

[0017] Furthermore, the waveform characteristics include: respiratory rate, peak inspiratory pressure,

[0018] Pressure rise time, inspiratory time, positive end-expiratory pressure, ventilator waveform sampling rate, initial inspiratory steady-state pressure, initial inspiratory steady-state time; among them, the spontaneous breathing lung ventilation model maintains the output initial inspiratory steady-state pressure before the time reaches the initial inspiratory steady-state time.

[0019] Furthermore, the extracted waveform features are input into the spontaneous breathing lung ventilation model and the respiratory muscle driving force Pmus=0 is set, and the proportional integral differential control algorithm is used to control the simulation to obtain the airway pressure waveform data assuming that there is no spontaneous inspiratory effort.

[0020] Furthermore, the respiratory mechanics motion equation is as follows:

[0021]

[0022] Where: Pmus(t) is the esophageal pressure deviation at time t, which is zero in breathing without spontaneous inspiratory effort; Pvent(t) is the pressure obtained at time t under pressure-controlled ventilation; Rrs is the airway resistance; Faw(t) is the flow obtained at time t under pressure-controlled ventilation; V(t) is the volume of the patient under pressure-controlled ventilation at time t, which is equal to the integral of Faw over time; Crs is the compliance of the respiratory system; PEEP total Total positive end-expiratory pressure.

[0023] Furthermore, the error indicators used in the error analysis are the mean square error, root mean square error, and mean absolute error between the flow waveform data estimated during the inspiratory time assuming that there is no spontaneous inspiratory effort and the flow waveform data obtained when there is spontaneous inspiratory effort.

[0024] A system for estimating the intensity of autonomous inspiratory effort based on a respiratory system model under pressure-controlled ventilation comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for estimating the intensity of autonomous inspiratory effort based on a respiratory system model under pressure-controlled ventilation is implemented.

[0025] A storage medium containing computer executable instructions, which, when executed by a computer processor, can implement a method for estimating the intensity of spontaneous inspiratory effort based on a respiratory system model under pressure controlled ventilation.

[0026] A computer program product comprises a computer program / instruction, which, when executed by a processor, implements the steps of a method for estimating the intensity of spontaneous inspiratory effort based on a respiratory system model under pressure-controlled ventilation.

[0027] The beneficial effects of the present invention are as follows: focusing on the changes in the flow waveform when spontaneous breathing effort first appears during inspiration under pressure-controlled ventilation, linking it with the gold standard esophageal pressure deviation, and obtaining a linear regression relationship between the flow deviation and the esophageal pressure deviation, thereby providing a non-invasive method for estimating the intensity of spontaneous inspiratory effort. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 The present invention is a flow chart of a method for estimating the intensity of spontaneous inspiratory effort based on a respiratory system model under pressure-controlled ventilation.

[0029] Figure 2 Schematic diagram of the spontaneous breathing lung ventilation model.

[0030] Figure 3 Simulate the control flow for a spontaneously breathing lung ventilation model that includes unusual characteristics.

[0031] Figure 4 It is a schematic diagram of a linear regression curve of an embodiment. DETAILED DESCRIPTION

[0032] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the preferred embodiments are only for illustrating the present invention, rather than for limiting the protection scope of the present invention.

[0033] The present invention provides a method for estimating the intensity of spontaneous inspiratory effort based on a respiratory system model under pressure-controlled ventilation. The method uses airway pressure waveform data with spontaneous inspiratory effort under pressure-controlled ventilation combined with a spontaneous breathing lung ventilation model to simulate airway pressure waveform data assuming that there is no spontaneous inspiratory effort. Then, according to the respiratory mechanics motion equation, the least squares fitting method of the original waveform data measured clinically is used to obtain airway resistance and respiratory system compliance. Further, a flow waveform assuming that there is no spontaneous inspiratory effort is obtained. Error analysis is performed with the flow waveform with spontaneous inspiratory effort measured clinically. Finally, the flow waveform data assuming that there is no spontaneous inspiratory effort estimated by several patients with known gold standard esophageal pressure deviations and the error index value of the flow waveform data obtained when there is spontaneous inspiratory effort are fitted with the corresponding esophageal pressure deviation to estimate the intensity of spontaneous inspiratory effort. The present invention uses the linear regression curve obtained by fitting the error index value of the flow waveform data obtained when there is spontaneous inspiratory effort and the corresponding esophageal pressure deviation to achieve the estimation of the intensity of spontaneous inspiratory effort in the absence of esophageal pressure. The present invention includes two parts: linear regression curve construction and spontaneous inhalation effort intensity estimation based on the linear regression curve. The present invention will hereinafter elaborate on the two parts in detail.

[0034] Reference Figure 1 The method for estimating the intensity of spontaneous inspiratory effort under pressure-controlled ventilation based on the respiratory system model includes:

[0035] The linear regression curve construction step includes the following sub-steps:

[0036] Step 1.1: Obtain the airway pressure, flow and esophageal pressure waveform data corresponding to several patients under pressure control ventilation with spontaneous inspiratory effort measured clinically;

[0037] In a specific embodiment, the airway pressure, flow and esophageal pressure waveform data are the airway pressure, flow and esophageal pressure waveform data after abnormal waveforms are filtered out; abnormal waveform filtering includes waveform value abnormalities, waveform morphology abnormalities, etc.; when filtering abnormal waveforms, a rule method can be used to filter out pressure, flow, volume value abnormal waveforms and morphology abnormal waveforms, such as the pressure should be between 0 and 50 cmH2O, the minimum flow should be less than -20 L / min, the maximum volume should be less than 740 mL, and the variance of the last 10 points of pressure should be less than 0.3, etc. In a more specific embodiment, the airway pressure, flow and esophageal pressure waveform data are further subjected to esophageal pressure filtering. Esophageal pressure filtering requires first obtaining the fundamental frequency of the heartbeat notch signal in the esophageal pressure signal, then constructing a reference signal based on the fundamental frequency, then updating the filter parameters of the adaptive filter based on the reference signal, and finally filtering the esophageal pressure with the updated adaptive filter. More specifically, first, one of the cycles of esophageal pressure is selected, the time interval between two adjacent peaks in the cycle is calculated, the inverse of the time interval is used as the fundamental frequency of the cardiac notch signal, and a reference signal is constructed in a sinusoidal manner. The esophageal pressure signal and the reference signal are superimposed as the first input signal, and an adaptive filter is used to filter the first input signal to obtain an output signal. The error signal between the esophageal pressure signal and the output signal is calculated, and the filter parameters of the adaptive filter are updated using an adaptive algorithm based on the error signal. Finally, the updated adaptive filter can be used to filter out the cardiac notch signal in the esophageal pressure.

[0038] In a specific implementation scheme, the linear regression curve is constructed using the airway pressure, flow and esophageal pressure waveform data when there is spontaneous inspiratory effort under pressure-controlled ventilation. After obtaining the data, spontaneous breathing identification is performed to select the waveform containing spontaneous inspiratory effort at the beginning of inspiration. When identifying spontaneous breathing, if the initial inspiratory pressure drop is less than the threshold and the actual respiratory rate is equal to the ventilator setting frequency, it is determined to be passive ventilation, that is, there is no spontaneous breathing.

[0039] Step 1.2: Extracting waveform features based on airway pressure waveform data;

[0040] In the present invention, the waveform characteristics and the spontaneous breathing lung ventilation model used ( Figure 2), generally, including respiratory rate (RR), peak inspiratory pressure (Ppeak), pressure rise time (Trise), inspiratory time (Ti), positive end-expiratory pressure (PEEP), and ventilator waveform sampling rate (Samplerate); in a specific implementation scheme, since the actual pressure waveform often has a stable pressure at the beginning of inhalation, in order to ensure the accuracy of pressure simulation, unconventional features such as initial stable inspiratory pressure (Psteady) and initial stable inspiratory time (Tsteady) are added on the basis of conventional features, that is, the waveform features also include unconventional features: initial stable inspiratory pressure (Psteady) and initial stable inspiratory time (Tsteady).

[0041] Step 1.3: Input the extracted waveform features into the spontaneous breathing lung ventilation model and set the respiratory muscle driving force Pmus = 0, and simulate to obtain the airway pressure waveform data assuming that there is no spontaneous inspiratory effort;

[0042] To simulate the ventilator airway pressure waveform, first send the obtained pressure waveform characteristics to the spontaneous breathing lung ventilation model, then set the respiratory muscle driving force Pmus = 0 for simulation. Figure 3 The following is a simulation control flow of the spontaneous breathing lung ventilation model under the above-mentioned unconventional characteristics, specifically:

[0043] Read in waveform features, including conventional features: respiratory rate (RR), peak inspiratory pressure (Ppeak), pressure rise time (Trise), inspiratory time (Ti), positive end-expiratory pressure (PEEP), ventilator waveform sampling rate (Samplerate) and unconventional features: initial inspiratory steady-state pressure (Psteady), initial inspiratory steady-state time (Tsteady);

[0044] The time step is set based on the sampling rate of the ventilator waveform. At each time step, it is determined whether the pressure rise time (Trise), inspiratory time (Ti) and initial inspiratory steady-state time (Tsteady) are reached. Before the initial inspiratory steady-state time (Tsteady) is reached, the target pressure of the initial inspiratory steady-state time is output, before the pressure rise time (Trise) is reached, the target pressure of the rise time is output, before the pressure rise time (Trise) is reached, the target pressure of the inspiratory phase is output before the inspiratory time (Ti) is reached, and the target pressure of the expiratory phase is output after the inspiratory time (Ti) is reached.

[0045] To ensure that the simulated airway pressure is consistent with the characteristics of the actual clinical airway pressure under the assumption that there is no spontaneous inspiratory effort, a proportional integral differential (PID) control algorithm is used to feedback control the airway pressure waveform of the ventilator during simulation, wherein the parameters of the PID control algorithm, namely the proportional coefficient (Kp), integral coefficient (Ki), and differential coefficient (Kd), are determined in advance by a PID parameter tuning method based on the integral of the absolute error (ITAE) indicator.

[0046] Step 1.4: Based on the acquired airway pressure, flow and esophageal pressure waveform data under pressure-controlled ventilation, i.e., using the pressure, flow and esophageal pressure waveform data measured clinically, the airway resistance and respiratory system compliance are estimated according to the respiratory mechanics motion equation;

[0047] In this step, the pressure, flow, and esophageal pressure waveform data measured clinically are used to replace the respiratory muscle driving force Pmus with the esophageal pressure deviation ΔPes according to the respiratory mechanics motion equation, and then the airway resistance and respiratory system compliance are estimated by the least squares fitting method. The expression of the respiratory mechanics motion equation is as follows:

[0048]

[0049] Wherein: Pmus is the pressure applied by the respiratory muscles, which is the driving force of the respiratory muscles. In the present invention, the esophageal pressure deviation ΔPes is used instead. Pmus(t) is the calculated esophageal pressure deviation at time t; Pvent is the pressure applied by the ventilator, which is equal to the pressure at the airway opening, that is, the measured airway pressure. Pvent(t) is the pressure obtained at time t under pressure-controlled ventilation; Rrs is the airway resistance; Faw is the measured flow rate; Faw(t) is the flow rate obtained at time t under pressure-controlled ventilation; V is the measured capacity, V(t) is the capacity of the patient to be tested under pressure-controlled ventilation at time t, which is equal to the integral of Faw over time; Crs is the compliance of the respiratory system; PEEP total It is the total positive end-expiratory pressure, which includes intrinsic positive end-expiratory pressure and exogenous positive end-expiratory pressure. The influence of intrinsic positive end-expiratory pressure can be excluded.

[0050] Step 1.5: Based on the airway pressure waveform data obtained by simulation in step 1.3 assuming that there is no spontaneous inspiratory effort and the airway resistance and respiratory system compliance estimated in step 1.4, the flow waveform data assuming that there is no spontaneous inspiratory effort is estimated according to the respiratory mechanics motion equation;

[0051] This step obtains the flow waveform assuming that there is no spontaneous inspiratory effort. Based on the airway pressure waveform simulated in step 1.3 assuming that there is no spontaneous inspiratory effort and the airway resistance and respiratory system compliance estimated by least squares fitting in step 1.4, the respiratory mechanics motion equation is used again. At this time, the respiratory muscle driving force Pmus is set to 0. The flow waveform assuming that there is no spontaneous inspiratory effort is obtained based on the numerical solution method. The formula is as follows:

[0052]

[0053] Wherein, since the respiratory muscle driving force Pmus is set to 0, Pvent(t) is the airway pressure waveform data obtained by simulation based on step 1.3 assuming that there is no spontaneous inspiratory effort.

[0054] Step 1.6: Perform error analysis on the flow waveform obtained in step 1.5 and the flow waveform with spontaneous inspiratory effort measured clinically, and then perform linear regression analysis with the gold standard to obtain a linear regression curve.

[0055] The error analysis mainly analyzes and estimates the error between the flow waveform data assuming that there is no spontaneous inspiratory effort and the acquired flow waveform data. The error index can be estimated during the inspiratory time to obtain the mean square error, root mean square error, mean absolute error, etc. between the flow waveform data assuming that there is no spontaneous inspiratory effort and the acquired flow waveform data when there is spontaneous inspiratory effort.

[0056] Using the linear regression curve, the intensity of voluntary inspiratory effort can be estimated without the presence of esophageal pressure.

[0057] The steps for estimating the intensity of spontaneous inspiratory effort based on the linear regression curve include:

[0058] Step 2.1: Obtain airway pressure and flow waveform data of the patient under pressure control ventilation;

[0059] Step 2.2: Extract waveform features based on airway pressure waveform data in the same way as step 1.2;

[0060] Step 2.3: Input the extracted waveform features into the spontaneous breathing lung ventilation model and set the respiratory muscle driving force Pmus = 0, and simulate to obtain the airway pressure waveform data assuming that there is no spontaneous inspiratory effort;

[0061] Step 2.4: Based on the acquired airway pressure and flow waveform data under pressure-controlled ventilation without spontaneous inspiratory effort, the airway resistance and respiratory system compliance are estimated according to the respiratory mechanics motion equation to replace the airway resistance and respiratory system compliance when there is spontaneous inspiratory effort;

[0062] Step 2.5: Based on the simulated airway pressure waveform data assuming no spontaneous inspiratory effort and the estimated airway resistance and respiratory system compliance, the flow waveform data assuming no spontaneous inspiratory effort is estimated according to the respiratory mechanics motion equation;

[0063] Step 2.6: Perform error analysis on the estimated flow waveform data assuming that there is no spontaneous inspiratory effort and the acquired flow waveform data, and substitute the error index value obtained by the error analysis into the linear regression curve to obtain the estimated value of the spontaneous inspiratory effort intensity of the patient under the current pressure-controlled ventilation.

[0064] The effect of the present invention is further described below in conjunction with a specific embodiment:

[0065] The method for estimating the intensity of spontaneous inspiratory effort based on a respiratory system model under pressure controlled ventilation of this embodiment comprises the following steps:

[0066] Step 1.1 Obtain airway pressure, flow and esophageal pressure waveform data corresponding to several patients under pressure control ventilation with spontaneous inspiratory effort measured clinically;

[0067] When filtering the esophageal pressure, first select one of the cycles of the esophageal pressure, calculate the time interval between two adjacent peaks in the cycle, use the inverse of the time interval as the fundamental frequency of the heartbeat notch signal, construct a reference signal in the form of a sine wave, superimpose the esophageal pressure signal and the reference signal as the first input signal, use an adaptive filter to filter the first input signal, obtain an output signal, calculate the error signal between the esophageal pressure signal and the output signal, and use an adaptive algorithm to update the filter parameters of the adaptive filter according to the error signal. Finally, use the updated adaptive filter to filter out the heartbeat notch signal in the esophageal pressure. When filtering abnormal waveforms, the rule method is used to filter out abnormal waveforms of pressure, flow, and volume values ​​and abnormal waveforms of morphology. For example, the pressure should be between 0 and 50 cmH2O, the minimum flow should be less than -20 L / min, the maximum volume should be less than 740 mL, and the variance of the last 10 points of pressure should be less than 0.3. When judging spontaneous breathing, if the initial inspiratory pressure drop is less than 0.3 cmH2O and the actual respiratory rate is equal to the ventilator setting frequency, it is judged as passive ventilation, that is, there is no spontaneous breathing.

[0068] Step 1.2: Obtain the waveform characteristics of the pressure when there is spontaneous inspiratory effort under clinically measured pressure-controlled ventilation. When obtaining the pressure characteristics, since the actual pressure waveform often has a stable pressure at the beginning of inspiration, in order to ensure the accuracy of the pressure simulation, the unconventional characteristics such as the initial stable pressure (Psteady) and the initial stable time (Tsteady) of inspiration are added on the basis of the conventional characteristics.

[0069] Step 1.3: The pressure waveform characteristics obtained in step 1 are sent to the spontaneous breathing lung ventilation model ( Figure 2 ), and simulate the airway pressure waveform of the ventilator. Table 1 gives the respiratory system parameter values ​​used in the models constructed for different types of people, which can be selected according to actual needs.

[0070] Table 1: Respiratory system parameter values

[0071]

[0072] Where R l , R t , R b , R A They are laryngeal resistance, tracheal resistance, bronchial resistance, and alveolar resistance. l , C t , C b , C A , C cw They are laryngeal compliance, tracheal compliance, bronchial compliance, alveolar compliance, and chest wall compliance.

[0073] The parameter proportional coefficient (Kp), integral coefficient (Ki), and differential coefficient (Kd) of the PID control algorithm used in the simulation control are set in the range of [0,1.5], [0,1], and [0,1], and the test step sizes are 0.05, 0.01, and 0.002, respectively. The parameters are determined by the integral of the absolute error (ITAE) indicator when there are no glitches in the simulation results. Since the waveform is unstable at the beginning of the simulation, the second waveform of the simulation is taken as the result of the airway pressure simulation.

[0074] Step 1.4: Use the clinically measured pressure, flow, and esophageal pressure waveform data to estimate airway resistance and respiratory system compliance according to the respiratory mechanics motion equation. First, calculate the baseline value of the previous esophageal pressure, and then take the time point when Pmus is greater than 0 at the beginning of the current inspiration for the least squares fitting of airway resistance and respiratory system compliance. The Pmus at each time point is replaced by the esophageal pressure deviation at the current time. The expression is as follows:

[0075] Pmus(k)=Pes baseline -Pes(k)

[0076] Where Pmus is the respiratory muscle driving pressure; Pes baseline is the baseline value of the previous esophageal pressure; Pes is the esophageal pressure; k is the moment when Pmus is greater than 0 at the beginning of inspiration of the current breath.

[0077] At the same time, in order to eliminate the influence of endogenous positive end-expiratory pressure in the respiratory mechanics motion equation, the selected waveform should satisfy the requirement that the flow rate at the end of the previous breath is greater than -3L / min and the difference between the current initial breathing pressure and the previous end-breath pressure is less than 2cmH2O.

[0078] Step 1.5: Based on the airway pressure waveform simulated in step 1.3 and the airway resistance and respiratory system compliance estimated in step 1.4, the flow waveform is obtained according to the respiratory mechanics motion equation. In order to ensure the reliability of the airway resistance and respiratory system compliance obtained by the least squares fitting, the breath with the least squares fitting goodness greater than 0.6 is selected.

[0079] Step 1.6: Perform error analysis on the flow waveform obtained in step 1.5 and the flow waveform with spontaneous inspiratory effort measured clinically, and then perform linear regression analysis with the gold standard. Finally, evaluate the intensity of spontaneous inspiratory effort in the absence of esophageal pressure gold standard based on the results of linear regression. The indicator of error analysis is the mean square error (MSE) of the two during the inspiratory time, and the selected range is [tk_min, Ti_index], where tk_min is the starting time when the initial inspiratory flow is greater than 0, and Ti_index is the end time of the inspiratory time obtained in step 1. The linear regression curve results are as follows: Figure 4 shown.

[0080] Furthermore, the linear regression curve can be used to estimate the intensity of spontaneous inspiratory effort without esophageal pressure.

[0081] The steps for estimating the intensity of spontaneous inspiratory effort based on the linear regression curve include:

[0082] Step 2.1: Obtain airway pressure and flow waveform data of the patient to be tested under pressure-controlled ventilation, including airway pressure and flow waveform data with spontaneous inspiratory effort and airway pressure and flow waveform data without spontaneous inspiratory effort;

[0083] Step 2.2: Extract waveform features based on the airway pressure waveform data with spontaneous inspiratory effort in the same way as step 1.2;

[0084] Step 2.3: Input the extracted waveform features into the spontaneous breathing lung ventilation model and set the respiratory muscle driving force Pmus = 0, and simulate to obtain the airway pressure waveform data assuming that there is no spontaneous inspiratory effort;

[0085] Step 2.4: Based on the acquired airway pressure and flow waveform data without spontaneous inspiratory effort under pressure-controlled ventilation, the airway resistance and respiratory system compliance are estimated according to the respiratory mechanics motion equation to replace the airway resistance and respiratory system compliance when there is spontaneous inspiratory effort;

[0086] Step 2.5: Based on the simulated airway pressure waveform data assuming no spontaneous inspiratory effort and the estimated airway resistance and respiratory system compliance, the flow waveform data assuming no spontaneous inspiratory effort is estimated according to the respiratory mechanics motion equation;

[0087] Step 2.6: Perform error analysis on the estimated flow waveform data assuming that there is no spontaneous inspiratory effort and the acquired flow waveform data, and bring the error index value obtained by the error analysis into the linear regression curve to obtain the spontaneous inspiratory effort intensity of the patient under the current pressure-controlled ventilation. In order to illustrate the performance of the invention, the coefficient of determination (R-squared) is selected as the model prediction index of the linear regression of the MSE in the inspiratory time and the gold standard esophageal pressure deviation ΔPes. The results are shown in Figure 2. Figure 4 As shown, it is shown that the method of the present invention has good accuracy.

[0088] Corresponding to the aforementioned embodiment of a method for estimating the intensity of autonomous inspiratory effort based on a respiratory system model under pressure-controlled ventilation, the present invention also provides an embodiment of a system for estimating the intensity of autonomous inspiratory effort based on a respiratory system model under pressure-controlled ventilation.

[0089] An embodiment of the present invention provides a system for estimating the intensity of autonomous inspiratory effort based on a respiratory system model under pressure-controlled ventilation, comprising one or more processors for implementing a method for estimating the intensity of autonomous inspiratory effort based on a respiratory system model under pressure-controlled ventilation in the above-mentioned embodiment.

[0090] An embodiment of a system for estimating the intensity of spontaneous inspiratory effort based on a respiratory system model under pressure-controlled ventilation of the present invention can be applied to any device with data processing capability, which can be a device or apparatus such as a computer.

[0091] The device embodiment can be implemented by software, hardware or a combination of software and hardware. Taking software implementation as an example, as a device in a logical sense, it is formed by the processor of any device with data processing capability in which it is located to read the corresponding computer program instructions in the non-volatile memory into the memory for execution. From the hardware level, it includes a processor, memory, network interface, and non-volatile memory. In addition, any device with data processing capability in which the device in the embodiment is located can also include other hardware according to the actual function of the device with data processing capability, which will not be described in detail.

[0092] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0093] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of the present invention. Ordinary technicians in this field can understand and implement it without paying creative work.

[0094] An embodiment of the present invention further provides a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, a method for estimating the intensity of autonomous inspiratory effort based on a respiratory system model under pressure-controlled ventilation in the above embodiment is implemented.

[0095] The computer-readable storage medium may be an internal storage unit of any device with data processing capability described in any of the aforementioned embodiments, such as a hard disk or a memory. The computer-readable storage medium may also be any device with data processing capability, such as a plug-in hard disk, a smart media card (SMC), an SD card, a flash card, etc. equipped on the device. Furthermore, the computer-readable storage medium may also include both an internal storage unit of any device with data processing capability and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capability, and may also be used to temporarily store data that has been output or is to be output.

[0096] The contents described in the embodiments of this specification are merely enumerations of implementation forms of the inventive concept and are for illustrative purposes only. The protection scope of the present invention should not be considered to be limited to the specific forms described in this embodiment, and the protection scope of the present invention also extends to equivalent technical means that can be thought of by ordinary technicians in this field based on the inventive concept.

Claims

1. A method for estimating the intensity of spontaneous inspiratory effort based on a respiratory system model under pressure-controlled ventilation, characterized in that: include: Acquiring airway pressure and flow waveform data of the patient to be tested under pressure-controlled ventilation, including airway pressure and flow waveform data with spontaneous inspiratory effort and airway pressure and flow waveform data without spontaneous inspiratory effort; Extracting waveform features based on airway pressure waveform data with spontaneous inspiratory effort; The extracted waveform features are input into the spontaneous breathing lung ventilation model and the respiratory muscle driving force Pmus is set to 0, and the airway pressure waveform data assuming that there is no spontaneous inspiratory effort is obtained by simulation; Based on the airway pressure and flow waveform data obtained under pressure-controlled ventilation without spontaneous inspiratory effort, the airway resistance and respiratory system compliance are estimated according to the respiratory mechanics motion equation; Based on the simulated airway pressure waveform data assuming no spontaneous inspiratory effort and the estimated airway resistance and respiratory system compliance, the flow waveform data assuming no spontaneous inspiratory effort is estimated according to the respiratory mechanics motion equation; The estimated flow waveform data assuming that there is no spontaneous inspiratory effort is subjected to error analysis with the acquired flow waveform data, and the error index value obtained by the error analysis is brought into the linear regression curve to obtain the estimated value of the spontaneous inspiratory effort intensity of the patient under the current pressure-controlled ventilation; The linear regression curve is obtained by fitting the flow waveform data estimated from several patients with known gold standard esophageal pressure deviations assuming that there is no spontaneous inspiratory effort and the error index value of the flow waveform data obtained when there is spontaneous inspiratory effort and the corresponding esophageal pressure deviation.

2. The method according to claim 1, characterized in that The airway pressure and flow waveform data are the airway pressure and flow waveform data after abnormal waveforms are filtered out.

3. The method according to claim 1, characterized in that The waveform characteristics include: respiratory rate, peak inspiratory pressure, pressure rise time, inspiratory time, positive end-expiratory pressure, ventilator waveform sampling rate, initial inspiratory steady-state pressure, and initial inspiratory steady-state time; among them, the spontaneous breathing lung ventilation model maintains the output initial inspiratory steady-state pressure before the time reaches the initial inspiratory steady-state time.

4. The method according to claim 1, characterized in that: The extracted waveform features are input into the spontaneous breathing pulmonary ventilation model and the respiratory muscle driving force Pmus=0 is set. The proportional integral differential control algorithm is used to control the simulation to obtain the airway pressure waveform data assuming that there is no spontaneous inspiratory effort.

5. The method according to claim 1, characterized in that The respiratory mechanics motion equation is as follows: Where: Pmus(t) is the esophageal pressure deviation at time t, which is zero in breathing without spontaneous inspiratory effort; Pvent(t) is the pressure obtained at time t under pressure-controlled ventilation; Rrs is the airway resistance; Faw(t) is the flow obtained at time t under pressure-controlled ventilation; V(t) is the volume of the patient under pressure-controlled ventilation at time t, which is equal to the integral of Faw over time; Crs is the compliance of the respiratory system; PEEP total Total positive end-expiratory pressure.

6. The method according to claim 1, characterized in that The error indicators used in the error analysis are the mean square error, root mean square error, and mean absolute error between the flow waveform data estimated during the inspiratory time assuming that there is no spontaneous inspiratory effort and the flow waveform data obtained when there is spontaneous inspiratory effort.

7. A system for estimating the intensity of spontaneous inspiratory effort based on a respiratory system model under pressure-controlled ventilation, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for estimating the intensity of autonomous inspiratory effort based on a respiratory system model under pressure controlled ventilation as described in any one of claims 1 to 6 is implemented.

8. A storage medium comprising computer executable instructions, wherein when the computer executable instructions are executed by a computer processor, the method for estimating the intensity of spontaneous inspiratory effort based on a respiratory system model under pressure control ventilation as described in any one of claims 1 to 6 is implemented.

9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of a method for estimating the intensity of spontaneous inspiratory effort based on a respiratory system model under pressure controlled ventilation as described in any one of claims 1 to 6 are implemented.

Images