Method and system for estimating autonomous inspiratory effort intensity based on respiratory system model under pressure control ventilation

Abstract

The application provides a method and system for estimating autonomous inspiration effort intensity based on a respiratory system model under pressure control ventilation, which simulates airway pressure waveform data assuming no autonomous inspiration effort by combining airway pressure waveform data existing autonomous inspiration effort under pressure control ventilation with autonomous breathing lung ventilation model, obtains airway resistance and respiratory system compliance to obtain flow waveform according to respiratory mechanics motion equation by using original waveform data measured clinically, and then carries out error analysis with flow waveform existing autonomous inspiration effort measured clinically, finally estimates autonomous inspiration effort intensity by using linear regression curve fitting obtained from error index value of flow waveform data assuming no autonomous inspiration effort and flow waveform data existing autonomous inspiration effort obtained from a number of known gold standard esophageal pressure deviation patients, and realizes autonomous inspiration effort intensity estimation without esophageal pressure.

Description

TECHNICAL FIELD

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

[0002] Pressure-controlled ventilation (PCV) is a ventilation mode of a ventilator, at which time the ventilator completely takes over the autonomous breathing of the patient. Under the PCV mode, the ventilator sends air to the patient's lungs according to the parameter values set by the doctor, mainly including the breathing frequency, the pressure rise time, the inspiratory time, the inspiratory peak pressure, etc. When using the PCV mode ventilation, each inspiration is controlled by the ventilator, that is, after entering the inspiratory time, the ventilator sends air to the patient's airway according to the pre-set pressure value, which will make the airway pressure of the patient reach the set value in a very short time, and then the ventilator will maintain the pressure until the inspiratory time ends.

[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 often cannot fully grasp its internal physiological operation mechanism, and existing detection means are often limited by objective conditions. Therefore, mathematical modeling and simulation have become an important means in the study of physiological systems. The establishment of the autonomous breathing lung ventilation model is based on the classic multi-stage airway model, and the airway classification model proposed by Rideout is referred to, which divides the respiratory system into four parts: the larynx, the trachea, the bronchus and the alveolus. Each part is represented by a linear resistor and a linear capacitor, and 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 relevant driving force part is added by referring to the autonomous breathing driving force model designed by Albanese. The autonomous breathing driving force is derived from the pressure (Pmus) generated by the respiratory muscle, which is transmitted to the trachea, bronchus and alveolus through the chest wall. Considering that the chest wall is an elastic structure, it is assumed that the pressure and volume characteristic curve of the chest wall is a linear curve, and the elastic characteristics of the chest wall can be represented by a linear capacitor.

[0004] Autonomous inspiratory effort refers to any energy-consuming activity of respiratory muscles for driving respiration, including the amplitude and frequency of muscle breathing. During severe respiratory failure, such as acute respiratory distress syndrome (ARDS), patients will take sedatives and completely control their breathing through mechanical ventilation. When patients recover or are at a low sedation level, they exhibit autonomous inspiratory effort, and patients breathe on the basis of supported mechanical ventilation breathing. Retaining autonomous inspiratory effort during mechanical ventilation is a double-edged sword. Retaining autonomous inspiratory effort can improve ventilation of alveoli close to the diaphragm area, improve ventilation-blood flow ratio imbalance, and thus improve oxygenation; maintain diaphragm activity, prevent ventilator-induced diaphragm dysfunction; and at the same time reduce sedation needs, prevent muscle atrophy, and reduce the incidence of ICU-acquired muscle weakness. At the same time, autonomous inspiratory effort introduces uncontrollable variables and asynchrony, which can lead to adverse ventilation results. For non-invasive ventilation patients, excessive autonomous inspiratory effort can cause patient self-inflicted lung injury (P-SILI) through mechanisms such as total lung stress overload, local lung tissue stress overload (pendelluft phenomenon), and asynchrony between man and machine, and thus cause increased lung inflammation, worsened oxygenation function, and increased mortality. In addition, the diaphragm is very sensitive to excessive respiratory load, and strong autonomous inspiratory effort can cause muscle tension to rise, leading to muscle inflammation, proteolysis, myofiber damage, and sarcomere disarray, ultimately resulting in diaphragm weakness.

[0005] For autonomous inspiration patients, esophageal pressure is often needed to measure respiratory effort, respiratory work, and lung mechanics, which is the gold standard for evaluating respiratory effort and respiratory work. When measuring esophageal pressure, the heartbeat of the patient affects the measurement of esophageal pressure to some extent due to the proximity of the balloon of the esophageal pressure catheter to the heart in the esophagus. This interference is usually referred to as a heartbeat artifact or heartbeat notch. The presence of this interference has a certain interference on the esophageal pressure value, and when the amplitude of the heartbeat notch is large, the noise signal brought by the heartbeat notch can also mask the patient's own esophageal pressure signal. Therefore, when the doctor needs to identify and evaluate the patient's autonomous inspiratory effort through the swing of the esophageal pressure, the presence of the heartbeat notch will interfere with the doctor's judgment, making it difficult to evaluate the strength of the patient's autonomous inspiratory effort, and therefore when facing esophageal pressure, attention must be paid to filtering the esophageal pressure. In addition, measuring esophageal pressure is invasive, requires professional knowledge, and can cause discomfort and complications for patients, so it is usually not feasible in daily clinical practice. SUMMARY

[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 deviation from the gold standard esophageal pressure, 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 spontaneous inspiratory effort intensity under pressure-controlled ventilation based on a respiratory system model, comprising:

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

[0010] Extract 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. The airway pressure waveform data is obtained by simulation assuming that there is no spontaneous inspiratory effort.

[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] An error analysis is performed between the estimated flow waveform data assuming the absence of spontaneous inspiratory effort and the acquired flow waveform data. The error index value obtained from the error analysis is substituted into the linear regression curve to obtain an estimated value of the spontaneous inspiratory effort intensity of the patient under 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 the absence of spontaneous inspiratory effort and the error index value of the flow waveform data obtained when spontaneous inspiratory effort exists with 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, inspiration time, positive end-expiratory pressure, ventilator waveform sampling rate, initial inspiratory plateau pressure, initial inspiratory plateau time; wherein the spontaneous breathing lung ventilation model maintains output of the initial inspiratory plateau pressure before the time reaches the initial inspiratory plateau time.

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

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

[0021]

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

[0023] Further, the error analysis uses the mean square error, the root mean square error, and the mean absolute error of the flow waveform data estimated assuming no spontaneous inspiratory effort and the flow waveform data obtained with spontaneous inspiratory effort within the inspiration time as error indicators.

[0024] A spontaneous inspiratory effort strength estimation system based on a respiratory system model under pressure control ventilation, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the spontaneous inspiratory effort strength estimation method based on the respiratory system model under pressure control ventilation.

[0025] A storage medium containing computer executable instructions, which, when executed by a computer processor, implement the spontaneous inspiratory effort strength estimation method based on the respiratory system model under pressure control ventilation.

[0026] A computer program product comprising computer programs / instructions, which, when executed by a processor, implement the steps of the spontaneous inspiratory effort strength estimation method based on the respiratory system model under pressure control ventilation.

[0027] The beneficial effect of the present application is that the change in the flow waveform at the initial appearance of the spontaneous breathing effort under pressure control ventilation is linked to the deviation of the gold standard esophageal pressure, a linear regression relationship between the flow deviation and the esophageal pressure deviation is obtained, and a non-invasive method for estimating the strength of the spontaneous breathing effort is provided. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 A flowchart of the method for estimating the strength of the spontaneous breathing effort based on the respiratory system model under pressure control ventilation.

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

[0030] Figure 3 A simulation control flow of the model of the spontaneous breathing lung ventilation under the condition of containing irregular characteristics.

[0031] Figure 4 A schematic diagram of the linear regression curve of an embodiment. DETAILED DESCRIPTION

[0032] The preferred embodiments of the present application will be described in detail below with reference to the accompanying drawings. It should be understood that the preferred embodiments are only for the purpose of illustrating the present application, and are not intended to limit the protection scope of the present application.

[0033] The present application provides a method for estimating the strength of the spontaneous breathing effort based on the respiratory system model under pressure control ventilation, which combines the airway pressure waveform data under pressure control ventilation with the existence of the spontaneous breathing effort with the simulation of the airway pressure waveform data under the assumption of the non-existence of the spontaneous breathing effort by using the model of the spontaneous breathing lung ventilation, then obtains the airway resistance and the respiratory system compliance by using the least square fitting method of the original waveform data measured clinically according to the respiratory mechanics motion equation, further obtains the flow waveform under the assumption of the non-existence of the spontaneous breathing effort, analyzes the error by comparing with the flow waveform measured clinically under the existence of the spontaneous breathing effort, and finally estimates the strength of the spontaneous breathing effort by using the linear regression curve obtained by fitting the error index value of the flow waveform data under the assumption of the non-existence of the spontaneous breathing effort estimated by the known gold standard esophageal pressure deviation patients with the flow waveform data under the existence of the spontaneous breathing effort. The present application realizes the estimation of the strength of the spontaneous breathing effort without esophageal pressure by using the linear regression curve obtained by fitting the error index value of the flow waveform data under the assumption of the non-existence of the spontaneous breathing effort estimated by the known gold standard esophageal pressure deviation patients with the flow waveform data under the existence of the spontaneous breathing effort. The present application includes two parts: the construction of the linear regression curve and the estimation of the strength of the spontaneous breathing effort based on the linear regression curve, which will be described in detail below.

[0034] Referring toFigure 1 The method for estimating the strength of autonomous inspiration effort based on a respiratory system model under pressure control ventilation comprises the following steps:

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

[0036] Step 1.1: Obtain the airway pressure, flow and esophageal pressure waveform data of several patients corresponding to the clinical measurements when autonomous inspiration effort exists under pressure control ventilation;

[0037] In a specific embodiment, the airway pressure, flow and esophageal pressure waveform data are airway pressure, flow and esophageal pressure waveform data after filtering out abnormal waveforms; the filtering out of abnormal waveforms includes waveform numerical abnormality, waveform morphological abnormality and the like; when filtering out abnormal waveforms, a rule method can be used to filter out pressure, flow and volume numerical abnormality waveforms and morphological abnormality waveforms, for example, the pressure should be between 0-50 cmH2O, the minimum value of the flow should be less than-20 L / min, the maximum value of the volume should be less than 740 mL, the variance of the last 10 points of the pressure should be less than 0.3 and the like. In a more specific embodiment, the airway pressure, flow and esophageal pressure waveform data are further filtered by esophageal pressure filtering; the esophageal pressure filtering needs to obtain the fundamental frequency of the heartbeat notch signal in the esophageal pressure signal first, then construct a reference signal according to the fundamental frequency, then update the filtering parameters of the adaptive filter according to the reference signal, and finally filter the esophageal pressure with the updated adaptive filter. More specifically, first, select one period of the esophageal pressure, calculate the time interval between two adjacent wave peaks in the period, take the reciprocal 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 a first input signal, filter the first input signal with an adaptive filter to obtain an output signal, calculate the error signal between the esophageal pressure signal and the output signal, update the filtering parameters of the adaptive filter according to the error signal using an adaptive algorithm, and finally filter out the heartbeat notch signal in the esophageal pressure with the updated adaptive filter.

[0038] In a specific embodiment, the linear regression curve construction needs to use the airway pressure, flow and esophageal pressure waveform data when autonomous inspiration effort exists under pressure control ventilation, and after obtaining the data, autonomous breathing discrimination is needed to select waveforms containing autonomous inspiration effort at the beginning of inspiration. When discriminating autonomous breathing, if the pressure drop at the beginning of inspiration is less than a threshold value and the actual respiratory frequency is equal to the ventilator set frequency, it is judged as passive ventilation, i.e. no autonomous breathing.

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

[0040] In the present application, the waveform features and the autonomous breathing lung ventilation model used are as follows: Figure 2) Correspondingly, in general, the waveform features include 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 embodiment, in order to ensure the accuracy of pressure simulation, the regular features are supplemented with irregular features, i.e., inspiratory plateau pressure (Psteady) and inspiratory plateau time (Tsteady), so that the waveform features also include the irregular features: inspiratory plateau pressure (Psteady) and inspiratory plateau 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 to simulate the airway pressure waveform data assuming no spontaneous inspiratory effort;

[0042] To simulate the ventilator airway pressure waveform, the obtained pressure waveform features are first input into the spontaneous breathing lung ventilation model, and then the respiratory muscle driving force Pmus = 0 is set for simulation, Figure 3 The simulation control flow of the spontaneous breathing lung ventilation model in the case of the above-mentioned regular and irregular features is shown in the figure, and specifically comprises:

[0043] The waveform features are read in, including the regular features: 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), and the irregular features: inspiratory plateau pressure (Psteady) and inspiratory plateau time (Tsteady).

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

[0045] To ensure that the airway pressure waveform obtained by simulation is consistent with the characteristics of the airway pressure waveform in the clinical situation where there is no autonomous inspiratory effort, a proportional-integral-derivative (PID) control algorithm is used to perform feedback control on the airway pressure waveform of the ventilator during simulation, wherein the proportional coefficient (Kp), integral coefficient (Ki), and derivative coefficient (Kd) of the PID control algorithm are determined in advance by a PID parameter tuning method based on the integral of the absolute error (ITAE) index.

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

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

[0048]

[0049] Wherein: Pmus is the pressure applied by the respiratory muscle, which is the driving force of the respiratory muscle, and in the present application, the esophageal pressure deviation ΔPes is used to replace Pmus(t), which 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, i.e., the measured airway pressure, Pvent(t) is the pressure obtained at time t under pressure control ventilation; Rrs is the airway resistance; Faw is the measured flow; Faw(t) is the flow obtained at time t under pressure control ventilation; V is the measured volume, V(t) is the volume of the patient under pressure control ventilation at time t, which is equal to the integral of Faw over time; Crs is the respiratory system compliance; PEEP total is the total positive end-expiratory pressure, including the endogenous positive end-expiratory pressure and the exogenous positive end-expiratory pressure, wherein the influence of the endogenous positive end-expiratory pressure can be selected to be excluded.

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

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

[0052]

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

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

[0055] The error analysis mainly analyzes the error between the estimated flow waveform data assuming no spontaneous inspiratory effort and the obtained flow waveform data, and the error indicators can include the mean square error, root mean square error, and mean absolute error of the estimated flow waveform data assuming no spontaneous inspiratory effort and the obtained flow waveform data with spontaneous inspiratory effort within the inspiratory time.

[0056] The strength of the spontaneous inspiratory effort can be estimated without esophageal pressure by using the linear regression curve.

[0057] The spontaneous inspiratory effort strength estimation step based on the linear regression curve includes:

[0058] Step 2.1: obtaining airway pressure and flow waveform data of a patient to be tested under pressure control ventilation;

[0059] Step 2.2: extracting waveform features based on the airway pressure waveform data according to the same method as step 1.2;

[0060] Step 2.3: inputting the extracted waveform features into the spontaneous breathing lung ventilation model and setting the respiratory muscle driving force Pmus to 0 to simulate the airway pressure waveform data assuming no spontaneous inspiratory effort;

[0061] Step 2.4: based on the obtained airway pressure and flow waveform data without spontaneous inspiratory effort under pressure control ventilation, the airway resistance and respiratory system compliance are estimated according to the equation of motion of respiratory mechanics to replace the airway resistance and respiratory system compliance with spontaneous inspiratory effort;

[0062] Step 2.5: Based on the airway pressure waveform data obtained by simulation under the assumption that there is no autonomous inspiration effort and the airway resistance and respiratory system compliance obtained by estimation, the flow waveform data under the assumption that there is no autonomous inspiration effort is estimated according to the respiratory mechanics motion equation;

[0063] Step 2.6: The error analysis is performed on the flow waveform data under the assumption that there is no autonomous inspiration effort and the obtained flow waveform data, and the error index value obtained by the error analysis is brought into the linear regression curve to obtain the autonomous inspiration effort strength estimation value of the patient under the current pressure control ventilation.

[0064] The effects of the present application are further described below in combination with a specific embodiment:

[0065] The autonomous inspiration effort strength estimation method based on a respiratory system model under pressure control ventilation of the present embodiment comprises the following steps:

[0066] Step 1.1: Obtain the airway pressure, flow and esophageal pressure waveform data of a plurality of patients corresponding to the pressure control ventilation under the assumption that there is autonomous inspiration effort, which are measured clinically.

[0067] When filtering the esophageal pressure, first, select one period of the esophageal pressure, calculate the time interval between two adjacent wave crests in the period, take the reciprocal 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 a first input signal, filter the first input signal using an adaptive filter to obtain an output signal, calculate the error signal between the esophageal pressure signal and the output signal, update the filter parameters of the adaptive filter using an adaptive algorithm according to the error signal, and finally filter out the heartbeat notch signal in the esophageal pressure using the updated adaptive filter. When filtering abnormal waveforms, the rule method is used to filter abnormal numerical and morphological waveforms of pressure, flow and volume, such as pressure should be between 0-50 cmH2O, minimum flow should be less than-20 L / min, maximum volume should be less than 740 mL, and variance of the last 10 points of pressure should be less than 0.3, etc. When distinguishing autonomous breathing, if the initial inspiration pressure decreases by less than 0.3 cmH2O and the actual respiratory frequency is equal to the ventilator set frequency, it is determined to be passive ventilation, i.e. no autonomous breathing.

[0068] Step 1.2: Obtain the pressure waveform characteristics under pressure control ventilation with autonomous inspiration effort, which are measured clinically. 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 regular characteristics are added with the irregular characteristics such as the steady-state pressure at the beginning of inspiration (Psteady) and the steady-state time at the beginning of inspiration (Tsteady).

[0069] Step 1.3: The pressure waveform characteristics obtained in step 1 are fed into the spontaneous breathing lung ventilation model ( Figure 2 ), simulating the airway pressure waveform of the ventilator. Table 1 shows 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, alveolar resistance, C 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 parameters of the PID control algorithm used in the simulation control, including the proportional coefficient (Kp), integral coefficient (Ki), and differential coefficient (Kd), are set in the ranges of [0, 1.5], [0, 1], and [0, 1], with test steps of 0.05, 0.01, and 0.002, respectively. The integral of the absolute error (ITAE) indicator is used to determine the parameters 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: Estimate airway resistance and respiratory system compliance using clinically measured pressure, flow, and esophageal pressure waveform data according to the respiratory mechanics equation of motion. First, calculate the baseline value of the previous esophageal pressure. Then, use the moment when Pmus is greater than 0 at the beginning of the current inspiration to fit airway resistance and respiratory system compliance using the least squares method. Pmus at each moment is replaced by the esophageal pressure deviation at the current moment. 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 exclude the influence of endogenous positive end-expiratory pressure in the equation of respiratory mechanics, the selected waveform should meet the condition that the flow rate at the end of the previous breath is greater than -3 L / min, and the difference between the current respiratory initial pressure and the pressure at the end of the previous breath is less than 2 cmH2O.

[0078] Step 1.5: According to the airway pressure waveform obtained by simulation 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 equation of respiratory mechanics. In order to ensure the reliability of the airway resistance and respiratory system compliance obtained by least square fitting, the breath with a least square fitting goodness greater than 0.6 is selected.

[0079] Step 1.6: The flow waveform obtained in step 1.5 is subjected to error analysis with the flow waveform measured clinically with the presence of spontaneous inspiratory effort, then linear regression analysis is performed with the gold standard, and finally the strength of spontaneous inspiratory effort without the gold standard of esophageal pressure is evaluated according to the results of linear regression. The error analysis index is the mean square error (MSE) of the two in the inspiratory time, and the selected range is [tk_min, Ti_index], tk_min is the starting time of the inspiratory initial flow greater than 0, and Ti_index is the terminal time of the inspiratory time obtained in step 1. The linear regression curve result is shown in FIG. 2. Figure 4

[0080] Further, the strength of spontaneous inspiratory effort can be estimated without esophageal pressure by using the linear regression curve.

[0081] The spontaneous inspiratory effort strength estimation step based on the linear regression curve comprises:

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

[0083] Step 2.2: Based on the airway pressure waveform data with spontaneous inspiratory effort, the waveform features are extracted according to the same method as step 1.2;

[0084] Step 2.3: 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 airway pressure waveform data assuming no spontaneous inspiratory effort is simulated;

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

[0086] ​Step 2.5: Based on the airway pressure waveform data obtained from the simulation assuming no autonomous inspiratory effort, and the airway resistance and respiratory system compliance obtained from the estimation, the flow waveform data assuming no autonomous inspiratory effort is estimated according to the equation of respiratory mechanics motion.

[0087] Step 2.6: The error analysis is performed on the flow waveform data assuming no autonomous inspiratory effort and the obtained flow waveform data, and the error index value obtained from the error analysis is brought into the linear regression curve to obtain the autonomous inspiratory effort strength of the patient under the current pressure control ventilation. In order to illustrate the performance of the application, the determination coefficient (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, and the results are as shown in Figure 4 , which illustrates that the method of the application has good accuracy.

[0088] Corresponding to the embodiment of the method for estimating the autonomous inspiratory effort strength under pressure control ventilation based on the respiratory system model, the application also provides an embodiment of a system for estimating the autonomous inspiratory effort strength under pressure control ventilation based on the respiratory system model.

[0089] The system for estimating the autonomous inspiratory effort strength under pressure control ventilation based on the respiratory system model provided by the embodiment of the application comprises one or more processors for implementing the method for estimating the autonomous inspiratory effort strength under pressure control ventilation based on the respiratory system model.

[0090] The embodiment of the system for estimating the autonomous inspiratory effort strength under pressure control ventilation based on the respiratory system model 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, or by hardware or a combination of software and hardware. Taking the software implementation as an example, as a logically meaningful device, it is formed by the processor of the device with data processing capability reading the corresponding computer program instructions in the non-volatile memory into the memory for execution from the hardware level, and includes the processor, the memory, the network interface, and the non-volatile memory. In addition, the device with data processing capability in the embodiment usually includes other hardware according to the actual functions of the device with data processing capability, and details are not described herein.

[0092] The implementation process of the functions and roles of each unit in the above device is specifically described in the implementation process of the corresponding steps in the above method, and is not described herein.

[0093] For the device embodiment, since it basically corresponds to the method embodiment, the relevant part can be seen from the part of the method embodiment. The device embodiment described above is only illustrative, wherein the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the present application according to actual needs. Those skilled in the art can understand and implement it without creative labor.

[0094] The embodiment of the present application also provides a computer readable storage medium, which stores a program, and the program is executed by a processor to realize the method for estimating self-inhalation effort intensity based on a respiratory system model under pressure control ventilation in one of the above embodiments.

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

[0096] The embodiments of the present application are only enumerations of the implementation forms of the inventive concept, and are only used for the purpose of illustration. The protection scope of the present application should not be regarded as limited to the specific forms described in the embodiments, and the protection scope of the present application also includes equivalent technical means that can be thought of by those skilled in the art according to the inventive concept.

Claims

1. A method for estimating spontaneous inspiratory effort intensity based on a respiratory system model under pressure-controlled ventilation, characterized in that: include: Acquiring airway pressure and flow waveform data of the patient under pressure-controlled ventilation, including airway pressure and flow waveform data with and without spontaneous inspiratory effort; Extract 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. The airway pressure waveform data is obtained by simulation assuming that there is no spontaneous inspiratory effort. 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; An error analysis is performed between the estimated flow waveform data assuming the absence of spontaneous inspiratory effort and the acquired flow waveform data. The error index value obtained from the error analysis is substituted into the linear regression curve to obtain an estimated value of the spontaneous inspiratory effort intensity of the patient under 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 the absence of spontaneous inspiratory effort and the error index value of the flow waveform data obtained when spontaneous inspiratory effort exists with 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 plateau pressure, and initial inspiratory plateau time; among them, the spontaneous breathing lung ventilation model maintains the output initial inspiratory plateau pressure before the time reaches the initial inspiratory plateau time.

4. The method according to claim 1, wherein The extracted waveform features are input into the spontaneous breathing lung ventilation model and the respiratory muscle driving force Pmus is set to 0. 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, wherein 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 time integral of Faw; Crs is the respiratory system compliance; 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 the absence of spontaneous inspiratory effort and the flow waveform data obtained when spontaneous inspiratory effort exists.

7. A system for estimating spontaneous inspiratory effort intensity under pressure-controlled ventilation based on a respiratory system model, 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 spontaneous inspiratory effort based on a respiratory system model under pressure control ventilation according to any one of claims 1 to 6 is implemented.

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

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 the method for estimating the intensity of spontaneous inspiratory effort based on a respiratory system model under pressure control ventilation according to any one of claims 1 to 6 are implemented.

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