Airway airbag pressure detection device and method

Abstract

The invention provides an airway airbag pressure detection device and method. The method comprises the following steps: acquiring thoracic cavity internal pressure information of a target patient and internal pressure information of an airway airbag; identifying airbag pressure fluctuation components of the airway airbag based on the internal pressure information and the thoracic cavity internal pressure information; when the pressure fluctuation amplitude corresponding to the airbag pressure fluctuation component exceeds a preset clinical threshold value, determining a feature correlation map of the internal pressure of the airway airbag on the basis of the pressure value sample of the airway airbag before the respiratory driving interference occurs in combination with the respiratory cycle feature of the target patient; determining a pressure distribution field in the air bag when the airway air bag is inflated, and establishing a pressure compensation model in the airway air bag based on a pressure change gradient in the pressure distribution field in combination with the feature correlation map; and compensating the current pressure detection value according to the pressure compensation model. According to the scheme provided by the invention, the actual pressure in the air pressure air bag can be detected under the influence of the change of the breathing state of the patient and the irregularity of the shape of the air bag.

Description

Technical Field

[0001] This application relates to the field of pressure detection technology, and more specifically, to an airway airbag pressure detection device and method. Background Technology

[0002] Pressure detection technology plays a crucial role in many industrial sectors of modern society. Whether it is pipeline transportation in the petrochemical industry, pipeline systems in medical machinery manufacturing, or engine monitoring in the aerospace field, accurate pressure measurement is the key to ensuring the safe operation of equipment and precise control of processes. With the rapid development of sensor technology, microelectronics technology, and automation control technology, pressure detection technology has ushered in new opportunities for transformation. New types of pressure detection equipment, such as high-precision, intelligent, and wireless transmission equipment, are constantly emerging, providing strong support for the development of industrial automation and intelligence, and promoting technological progress and industrial upgrading in various industries.

[0003] In existing pressure detection methods, pressure signals are mainly converted into electrical signals based on physical effects to achieve accurate measurement. For example, piezoresistive sensors utilize the characteristic that the resistance of semiconductor or metallic materials changes with pressure, determining pressure by measuring the change in resistance. Piezoelectric sensors are based on the characteristic that piezoelectric materials generate charges when subjected to pressure, calculating pressure by measuring the amount of charge. However, in airway cuff pressure detection, the pressure distribution inside the airway cuff is uneven due to changes in the patient's respiratory status and the irregularity of the cuff's shape. This makes it impossible for a single sensor to detect the actual pressure inside the airway cuff. Therefore, how to detect the actual pressure inside the airway cuff under the influence of changes in the patient's respiratory status and the irregularity of the cuff's shape has become a challenge for the industry. Summary of the Invention

[0004] This application provides an airway cuff pressure detection device and method, which can detect the actual pressure inside the air cuff under the influence of changes in the patient's respiratory state and the irregularity of the cuff shape.

[0005] In a first aspect, this application provides an airway cuff pressure compensation method for use in an airway cuff pressure detection device to perform pressure compensation. The method includes the following steps: When monitoring the target patient in the postoperative intensive care unit, obtain the intrathoracic pressure information and the internal pressure information of the airway cuff. Frequency domain coupling analysis was performed on the internal pressure information and the intrathoracic pressure information to identify the components of airway pressure fluctuations caused by respiratory-driven interference. When the pressure fluctuation amplitude corresponding to the airway pressure fluctuation component exceeds a preset clinical threshold, a characteristic correlation map of the internal pressure of the airway airway is determined based on the airway airway pressure value sample before the occurrence of the respiratory driving interference and the respiratory cycle characteristics of the target patient. Determine the pressure distribution field inside the airway airbag when it is inflated, and establish a pressure compensation model inside the airway airbag based on the pressure change gradient in the pressure distribution field and the feature correlation map. The pressure compensation value of the airway cuff is obtained by synchronously compensating the current pressure detection value with the respiratory cycle based on the pressure compensation model inside the airway cuff.

[0006] In some embodiments, frequency domain coupling analysis is performed on the internal pressure information and the intrathoracic pressure information to identify the components of airway pressure fluctuations caused by respiratory-driven disturbances, specifically including: The internal pressure information and the intrathoracic pressure information are preprocessed for time synchronization to obtain synchronized internal pressure information and intrathoracic pressure information; The synchronized internal pressure information and intrathoracic pressure information are divided into short time windows, and the internal pressure change rate sequence and intrathoracic pressure change rate sequence are calculated within each time window; Correlation analysis was performed on the internal pressure change rate sequence and the intrathoracic pressure change rate sequence within each time window to obtain the pressure correlation degree corresponding to each time window. The internal pressure information is detrended based on the pressure correlation degree corresponding to each time window to obtain the detrended internal pressure information. The detrended internal pressure information identified components of air sac pressure fluctuations caused by respiratory-driven disturbances.

[0007] In some embodiments, when the pressure fluctuation amplitude corresponding to the airway pressure fluctuation component exceeds a preset clinical threshold, determining the characteristic correlation map of the airway airway internal pressure based on the airway airway pressure value sample before the occurrence of the respiratory driving interference and the respiratory cycle characteristics of the target patient specifically includes: The steady-state pressure value inside the airway cuff is obtained under the stable breathing state of the target patient, and the interference peak value of the cuff pressure in the pressure fluctuation component is extracted. The pressure fluctuation amplitude corresponding to the airbag pressure fluctuation component is calculated based on the steady-state pressure value and the disturbance peak value. When the pressure fluctuation amplitude exceeds a preset clinical threshold, the respiratory cycle characteristics of the target patient are determined; Based on the respiratory characteristic cycle, extract the pressure value sample of the airway cuff before the occurrence of the respiratory driving interference; By fitting all pressure candidate values ​​in the pressure value sample, a characteristic correlation map of the internal pressure of the airway airbag is obtained.

[0008] In some embodiments, extracting pressure value samples of the airway cuff before the occurrence of respiratory driving interference based on the respiratory characteristic cycle specifically includes: Obtain historical pressure information of the airway cuff before the occurrence of the respiratory drive disturbance; A time sliding window is set according to the respiratory characteristic cycle; According to the time sliding window, all historical pressure values ​​in the historical pressure information are slid and divided in chronological order according to a predetermined sliding step size to obtain multiple pressure sub-information; Based on all the pressure sub-information, the pressure value sample of the airway cuff before the occurrence of the respiratory drive interference was obtained.

[0009] In some embodiments, determining the pressure distribution field inside the airway airbag during inflation specifically includes: Miniature pressure sensors are installed at different locations inside the airway airbag, and discrete pressure values ​​at different locations inside the airbag are collected by the miniature pressure sensors when the airway airbag is inflated. Arrange all discrete pressure values ​​according to the arrangement positions of the miniature pressure sensors to obtain a discrete pressure value sequence; The pressure value sequence is interpolated to obtain the pressure distribution field inside the airway airbag when it is inflated.

[0010] In some embodiments, a miniature pressure sensor is used to obtain internal pressure information of the airway airbag.

[0011] In some embodiments, the miniature pressure sensor is an embedded flexible pressure sensor.

[0012] Secondly, this application provides an airway airbag pressure detection device, which includes a pressure compensation unit, the pressure compensation unit comprising: The acquisition module is used to acquire intrathoracic pressure information and internal pressure information of airway cuffs of target patients when monitoring them in the postoperative intensive care unit. The processing module is used to perform frequency domain coupling analysis on the internal pressure information and the intrathoracic pressure information to identify the components of air sac pressure fluctuations caused by respiratory-driven interference. The processing module is also used to determine the feature correlation map of the internal pressure of the airway cuff based on the pressure value sample of the airway cuff before the occurrence of the respiratory driving interference and the respiratory cycle characteristics of the target patient when the pressure fluctuation amplitude corresponding to the airway pressure fluctuation component exceeds the preset clinical threshold. The processing module is also used to determine the pressure distribution field inside the airway airbag when the airway airbag is inflated, and to establish a pressure compensation model inside the airway airbag based on the pressure change gradient in the pressure distribution field and the feature correlation map. The execution module is used to perform respiratory cycle synchronization compensation on the current pressure detection value according to the pressure compensation model inside the airway cuff, so as to obtain the pressure compensation value of the airway cuff.

[0013] Thirdly, this application provides a computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described airway airbag pressure compensation method.

[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described airway airbag pressure compensation method.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: The airway cuff pressure detection device and method provided in this application firstly acquire intrathoracic pressure information and airway cuff internal pressure information of the target patient during monitoring in the postoperative intensive care unit; secondly, frequency domain coupling analysis is performed on the internal pressure information and intrathoracic pressure information to identify cuff pressure fluctuation components caused by respiratory driving interference; furthermore, when the pressure fluctuation amplitude corresponding to the cuff pressure fluctuation component exceeds a preset clinical threshold, a feature correlation map of the airway cuff internal pressure is determined based on the airway cuff pressure value sample before the respiratory driving interference and the respiratory cycle characteristics of the target patient; then, the pressure distribution field inside the airway cuff is determined when the airway cuff is inflated, and a pressure compensation model inside the airway cuff is established based on the pressure change gradient in the pressure distribution field and the feature correlation map; finally, the current pressure detection value is synchronously compensated for with the respiratory cycle according to the pressure compensation model inside the airway cuff to obtain the pressure compensation value of the airway cuff.

[0016] Therefore, this application can detect the actual pressure inside the airway cuff under the influence of changes in the patient's respiratory state and the irregularity of the cuff shape. First, it acquires the intrathoracic pressure information and the internal pressure information of the airway cuff from the target patient, providing a data foundation for subsequent dynamic analysis of the internal pressure changes of the airway cuff. Second, it performs frequency domain coupling analysis on the internal pressure information and intrathoracic pressure information to identify the components of cuff pressure fluctuations caused by respiratory-driven interference, effectively analyzing the interference caused by changes in the patient's respiratory state to the airway cuff. This ensures that the pressure compensation mechanism can accurately correct the pressure inside the cuff, improving the adaptability of the ventilation equipment to different respiratory states, thereby avoiding the limitations of pressure measurement values ​​caused by changes in the patient's respiratory state. Furthermore, when the pressure fluctuation amplitude of the cuff pressure fluctuation components exceeds a preset clinical threshold, it generates a feature correlation map of the airway cuff's internal structure based on the airway cuff pressure value sample before the respiratory-driven interference occurred, reflecting the impact of abnormal factors on the cuff. The system establishes a pressure change benchmark and fits the pressure change trend of the airway cuff under stable breathing conditions. This provides a benchmark for detecting transient pressure interference and compensating for pressure detection values, thereby avoiding deviations in actual pressure monitoring values ​​caused by changes in the patient's respiratory state and the irregularity of the cuff shape. Then, it determines the pressure distribution field inside the airway cuff during inflation and establishes a pressure compensation model based on the pressure change gradient in the pressure distribution field combined with a feature correlation map. This allows the detected pressure value to be extracted through the pressure compensation model to more closely approximate the actual pressure. Finally, it performs respiratory cycle synchronization compensation on the current pressure detection value based on the pressure compensation model inside the airway cuff to obtain the pressure compensation value of the airway cuff, thus enabling the detection of the actual pressure inside the airway cuff. In summary, the technical solution provided in this application can detect the actual pressure inside the airway cuff under the influence of changes in the patient's respiratory state and the irregularity of the cuff shape. Attached Figure Description

[0017] Figure 1 This is an exemplary flowchart of an airway airbag pressure compensation method according to some embodiments of this application; Figure 2 This is a schematic diagram of the structure of an airway inflator according to some embodiments of this application; Figure 3 This is an exemplary flowchart illustrating the determination of airbag pressure fluctuation components according to some embodiments of this application; Figure 4 This is a schematic diagram of the structure of a pressure compensation unit according to some embodiments of this application; Figure 5 This is a schematic diagram of the structure of a computer device for implementing an airway airbag pressure compensation method according to some embodiments of this application. Detailed Implementation

[0018] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] refer to Figure 1 The figure is an exemplary flowchart of an airway airbag pressure compensation method according to some embodiments of this application. The airway airbag pressure compensation method 100 mainly includes the following steps: In step 101, when monitoring the target patient in the postoperative care ward, the intrathoracic pressure information and the internal pressure information of the airway cuff are obtained.

[0020] In practice, firstly, when monitoring the target patient in the postoperative intensive care unit, the pressure value collected by a miniature pressure sensor on the endotracheal tube of the airway cuff is approximated as the intrathoracic pressure value, thus obtaining the target patient's intrathoracic pressure information. This intrathoracic pressure is highly correlated with the pressure at the endotracheal tube site; therefore, the pressure value collected on the endotracheal tube (a catheter inserted into the patient's pleural cavity) is approximated as the intrathoracic pressure value. The obtained intrathoracic pressure information is stored in a time-series format, containing intrathoracic pressure values ​​at multiple time points. This intrathoracic pressure information is obtained indirectly, not directly. Simultaneously, a miniature pressure sensor installed on the inner wall of the airway cuff acquires the internal pressure information of the airway cuff. The acquired internal pressure information is stored in a time series format, including internal pressure values ​​at different time points. The miniature pressure sensor is installed on the endotracheal tube of the airway cuff and on the inner wall of the airway cuff. The miniature pressure sensor is an embedded flexible pressure sensor, which consists of multiple micron-sized piezoresistive sensing units. During data acquisition, a high-precision clock synchronization mechanism is used to ensure time synchronization between the internal pressure information and the intrathoracic pressure information. The internal pressure information and the intrathoracic pressure information are time-calibrated and synchronized, so that the two data are stored and analyzed under the same time reference. In addition, the same sampling frequency is set when using the internal pressure information and the intrathoracic pressure information.

[0021] It should be noted that, in this application, the internal pressure information refers to the real-time pressure data inside the airway cuff, which reflects the dynamic changes in the force on the gas inside the cuff; the intrathoracic pressure information refers to the pressure data inside the thoracic cavity, which is used to monitor the impact of intrathoracic breathing on the pressure inside the cuff. In the pressure detection of the airway cuff, the cuff pressure is usually affected by various factors such as ventilator pressure, airway resistance, and intrathoracic pressure. Therefore, by obtaining the internal pressure information of the airway cuff and the intrathoracic pressure information, a data basis can be provided for subsequent analysis of the dynamic changes in the internal pressure of the airway cuff.

[0022] It should also be noted that, in this application, an airway cuff refers to an inflatable device used to maintain airway patency, provide sealing support, or regulate airway pressure. It is made of flexible material and its inflation degree can be adjusted by inflating or deflating. The airway cuff includes a cuff, an endotracheal tube, an inflation port, etc. Figure 2 As shown in the figure, this is a schematic diagram of the airway airbag structure.

[0023] In step 102, frequency domain coupling analysis is performed on the internal pressure information and the intrathoracic pressure information to identify the components of air sac pressure fluctuations caused by respiratory-driven disturbances.

[0024] It should be noted that, in this application, respiratory-driven interference refers to factors that interfere with the detection of airway cuff pressure due to the patient's breathing movements. When a patient breathes, the intrathoracic pressure changes, and this change is transmitted to the airway cuff through the airway, causing fluctuations in the internal pressure of the cuff, thereby interfering with the accurate detection of cuff pressure. This interference is called respiratory-driven interference.

[0025] In some embodiments, reference Figure 3 As shown in the figure, this is an exemplary flowchart illustrating the determination of airbag pressure fluctuation components according to some embodiments of this application. In this embodiment, frequency domain coupling analysis is performed on the internal pressure information and the intrathoracic pressure information to identify airbag pressure fluctuation components caused by respiratory-driven interference. This can be achieved by the following steps: First, in step 1021, the internal pressure information and the intrathoracic pressure information are preprocessed for time synchronization to obtain synchronized internal pressure information and intrathoracic pressure information; Secondly, in step 1022, the synchronized internal pressure information and intrathoracic pressure information are divided into short time windows, and the internal pressure change rate sequence and intrathoracic pressure change rate sequence are calculated in each time window; Furthermore, in step 1023, within each time window, a correlation analysis is performed on the internal pressure change rate sequence and the intrathoracic pressure change rate sequence to obtain the pressure correlation degree corresponding to each time window; Then, in step 1024, the internal pressure information is detrended based on the pressure correlation degree corresponding to each time window to obtain the detrended internal pressure information. Finally, in step 1025, the air sac pressure fluctuation component caused by respiratory-driven disturbances is identified from the detrended internal pressure information.

[0026] In specific implementation, the internal pressure information and the intrathoracic pressure information are preprocessed for time synchronization to obtain synchronized internal pressure information and intrathoracic pressure information. That is, the internal pressure information and the intrathoracic pressure information are arranged according to timestamps to obtain synchronized internal pressure information and intrathoracic pressure information. The internal pressure information and the intrathoracic pressure information are acquired synchronously, with the same timestamps and the same acquisition frequency.

[0027] In specific implementation, the synchronized internal pressure information and intrathoracic pressure information are divided into short time windows, and the internal pressure change rate sequence and intrathoracic pressure change rate sequence are calculated within each time window. Specifically, a short time window and a window sliding step size are set, and the synchronized internal pressure information and intrathoracic pressure information are synchronously divided according to the window sliding step size, obtaining internal pressure sub-information and external pressure sub-information corresponding to each time window. Within each time window, the internal pressure value of each adjacent internal pressure sub-information corresponding to the time window is calculated using first-order difference, and the result of each calculation is used as the internal pressure change rate. Then, all the calculated internal pressure change rates are arranged in chronological order to obtain the internal pressure change rate sequence corresponding to the time window. Finally, the internal pressure change rate sequence is calculated for each adjacent intrathoracic pressure value of the intrathoracic pressure information corresponding to the time window using first-order difference. The result of each calculation is used as the intrathoracic pressure change rate. All calculated intrathoracic pressure change rates are then arranged in chronological order to obtain the intrathoracic pressure change rate sequence corresponding to each time window. This results in an internal pressure change rate sequence and an intrathoracic pressure change rate sequence for each time window. The short time window can be a fixed time window (e.g., 100ms), and the window sliding step can be set to 10ms. Furthermore, in other embodiments, different time windows and window sliding steps can be set according to actual needs; this is not limited here. It should be noted that the internal pressure change rate sequence represents a set containing multiple internal pressure change rates, where the internal pressure change rate represents the rate of change of pressure inside the airway cuff over time. The intrathoracic pressure change rate sequence represents a set containing multiple intrathoracic pressure change rates, where the intrathoracic pressure change rate represents the rate of change of pressure in the external environment of the airway cuff over time.

[0028] In practice, correlation analysis is performed on the internal pressure change rate sequence and the intrathoracic pressure change rate sequence within each time window to obtain the pressure correlation degree corresponding to each time window. Specifically, within each time window, the Pearson correlation coefficient is used to calculate the internal pressure change rate sequence and the intrathoracic pressure change rate sequence, and the calculation result is used as the pressure correlation degree corresponding to the time window. Thus, the pressure correlation degree corresponding to each time window is obtained. In other words, the internal pressure change rate in the internal pressure change rate sequence and the intrathoracic pressure change rate in the intrathoracic pressure change rate sequence are respectively used as variables for the Pearson correlation coefficient, and the covariance of the two variables and the coefficient of variation of each variable are calculated using the Pearson correlation coefficient. The standard deviation of the two variables is calculated, and the product of the standard deviations of the covariance and the standard deviation is used as the quotient of the product of the covariance and the standard deviation as the result of the Pearson correlation coefficient. The result is used as the pressure correlation degree corresponding to the time window. In addition, in other embodiments, other calculation methods can be used to calculate the pressure correlation degree corresponding to each time window, which is not limited here. In this embodiment, the pressure correlation degree represents the degree of correlation between the rate of change of pressure inside the airway cuff and the rate of change of pressure inside the thoracic cavity. The pressure correlation degree reflects the magnitude of the influence of external environmental pressure on the internal airway cuff pressure. By determining the pressure correlation degree, the interference influence currently experienced by the airway cuff can be effectively analyzed.

[0029] In specific implementation, the internal pressure information is detrended based on the pressure correlation degree corresponding to each time window to obtain detrended internal pressure information. That is, the pressure correlation degree corresponding to each time window is compared with the pressure correlation degree threshold, all time windows with pressure correlation degrees greater than the pressure correlation degree threshold are extracted, and the low-frequency components of the internal pressure sub-information within each extracted time window are removed by wavelet decomposition. The internal pressure sub-information after removing the low-frequency components replaces the corresponding original internal pressure sub-information in the internal pressure information, thereby obtaining the detrended internal pressure information. The pressure correlation degree threshold is set in advance by analyzing a large amount of experimental data. For example, when setting the pressure correlation degree threshold, the cross-validation method is used to divide the experimental data into multiple subsets, and the threshold is set and the model is trained on different subsets. By comparing the recognition accuracy, recall rate and other indicators of the model on the test set under different thresholds, the optimal pressure correlation degree threshold is selected. There is no limitation here. In this embodiment, the detrended internal pressure information represents the pressure information after removing the long-term trend component from the internal pressure information of the airway and airbag. It is mainly used to highlight the short-term dynamic change characteristics, especially the part related to the airbag pressure fluctuation component.

[0030] In practice, the cuff pressure fluctuation component caused by respiratory-driven disturbances is identified from the detrended internal pressure information. Specifically, a short-time Fourier transform is used to extract the high-frequency signal of the detrended internal pressure information. The transient energy value at each time point in the high-frequency signal is compared with a preset threshold. All time points with transient energy values ​​greater than the preset threshold are extracted, and consecutive time points form the time intervals in which respiratory-driven disturbances occur. The internal pressure signal corresponding to these time intervals is then used as the cuff pressure fluctuation component. The preset threshold can be set by analyzing a large amount of experimental data, for example, based on the patient's individual characteristics and real-time monitoring data. The preset threshold is dynamically adjusted by combining factors such as the patient's age, disease severity, and recent respiratory stability, and using machine learning algorithms (such as support vector machines) to establish a threshold prediction model to set the preset threshold. The transient energy value represents the instantaneous change in energy within a specific time range in a high-frequency signal, used to characterize the energy characteristics of short-term sudden interference or dynamic fluctuations. The transient energy value reflects the intensity change of the signal in a short time and can be obtained by short-time energy extraction, which will not be elaborated here. Furthermore, in other embodiments, other calculation methods can be used to identify the airway pressure fluctuation components caused by respiratory-driven interference, which are not limited here.

[0031] It should be noted that, in this application, the airbag pressure fluctuation component refers to the pressure fluctuation signal inside the airway cuff. Specifically, the airbag pressure fluctuation component refers to the sudden, high-frequency pressure fluctuation signal that occurs within a short period of time during airway cuff pressure detection due to dynamic changes in the airway and external environmental interference. The airbag pressure fluctuation component includes a segment of internal pressure sub-signal that has abruptly changed within the internal pressure information. The internal pressure sub-signal consists of multiple airbag pressure amplitudes, and the airbag pressure amplitude is the internal pressure that has abruptly changed. In airway cuff pressure detection, the presence of the airbag pressure fluctuation component will significantly affect the accuracy and stability of pressure monitoring. Therefore, accurately determining the airbag pressure fluctuation component is crucial for optimizing the detection system, which can effectively ensure that the pressure compensation mechanism can accurately correct the pressure inside the cuff and improve the adaptability of the ventilation equipment to different breathing states.

[0032] It should also be noted that, in this application, frequency domain coupling analysis refers to an analysis method that performs frequency domain coupling processing on multiple related signals to extract useful information, enhance signal quality, or suppress noise. Specifically, performing frequency domain coupling analysis on the internal pressure information and the intrathoracic pressure information involves: performing time synchronization preprocessing on the internal pressure information and the intrathoracic pressure information to obtain synchronized internal pressure information and intrathoracic pressure information; dividing the synchronized internal pressure information and intrathoracic pressure information into short time windows; and calculating the internal pressure change rate sequence and intrathoracic pressure change rate within each time window. The sequence is analyzed; within each time window, the correlation between the internal pressure change rate sequence and the intrathoracic pressure change rate sequence is performed to obtain the pressure correlation degree corresponding to each time window; based on the pressure correlation degree corresponding to each time window, the internal pressure information is detrended to obtain detrended internal pressure information; the airbag pressure fluctuation component caused by respiratory driving interference is identified from the detrended internal pressure information, and the extracted airbag pressure fluctuation component is used as the analysis result of frequency domain coupling analysis, thus completing the frequency domain coupling analysis of the internal pressure information and the intrathoracic pressure information.

[0033] In step 103, when the pressure fluctuation amplitude corresponding to the airway pressure fluctuation component exceeds a preset clinical threshold, a characteristic correlation map of the internal pressure of the airway airway is determined based on the airway airway pressure value sample before the occurrence of the respiratory driving interference and the respiratory cycle characteristics of the target patient.

[0034] In some embodiments, when the pressure fluctuation amplitude corresponding to the airway pressure fluctuation component exceeds a preset clinical threshold, the characteristic correlation map of the internal pressure of the airway airway can be determined based on the airway airway pressure value sample before the occurrence of the respiratory driving interference and the respiratory cycle characteristics of the target patient, which can be achieved by the following steps: The steady-state pressure value inside the airway cuff is obtained under the stable breathing state of the target patient, and the interference peak value of the cuff pressure in the pressure fluctuation component is extracted. The pressure fluctuation amplitude corresponding to the airbag pressure fluctuation component is calculated based on the steady-state pressure value and the disturbance peak value. When the pressure fluctuation amplitude exceeds a preset clinical threshold, the respiratory cycle characteristics of the target patient are determined; Based on the respiratory characteristic cycle, extract the pressure value sample of the airway cuff before the occurrence of the respiratory driving interference; By fitting all pressure candidate values ​​in the pressure value sample, a characteristic correlation map of the internal pressure of the airway airbag is obtained.

[0035] In specific implementation, the steady-state pressure value inside the airway cuff under stable breathing conditions of the target patient is obtained, and the interference peak value of the cuff pressure in the cuff pressure fluctuation component is extracted. That is, pressure monitoring data of the target patient under stable breathing conditions is obtained through an airway cuff pressure monitoring database. The pressure monitoring data contains multiple pressure monitoring values. The average value of all pressure monitoring values ​​in the pressure monitoring data is calculated, and the average calculation result is used as the steady-state pressure value inside the airway cuff. The maximum cuff pressure amplitude in the cuff pressure fluctuation component is extracted as the interference peak value. In this embodiment, the pressure monitoring value represents the pressure value monitored inside the airway cuff, and the steady-state pressure value represents the pressure inside the airway cuff under stable breathing conditions of the target patient. The average pressure value that remains relatively stable over a longer period of time represents the baseline pressure level of the airway cuff under normal physiological conditions. It can be used as a reference benchmark for judging transient pressure interference and performing pressure compensation. In this embodiment, the interference peak value represents the maximum airway pressure amplitude in the airway pressure fluctuation component. The interference peak value reflects the degree of influence of transient interference on pressure measurement. In this embodiment, the airway cuff pressure monitoring database refers to an intelligent database used to store and manage all data related to airway cuff pressure monitoring. It includes real-time data collected from the pressure sensor inside the airway cuff as well as data after processing and analysis. This data is usually used to support tasks such as clinical monitoring, equipment adjustment, pressure compensation, fault detection, and patient physiological status analysis.

[0036] In specific implementation, the pressure fluctuation amplitude corresponding to the airbag pressure fluctuation component is calculated based on the steady-state pressure value and the interference peak value. That is, the absolute difference between the steady-state pressure value and the interference peak value is calculated, and the result of the absolute difference calculation is used as the pressure fluctuation amplitude corresponding to the airbag pressure fluctuation component. In addition, in other embodiments, other calculation methods can be used to calculate the pressure fluctuation amplitude of the steady-state interference signal. This is not limited here. In this application, the pressure fluctuation amplitude represents the degree of change of the pressure value at the moment of maximum interference in the airbag pressure fluctuation component relative to the steady-state pressure. The pressure fluctuation amplitude is used to describe the magnitude of the pressure fluctuation. In the pressure detection and compensation of the airway airbag, the pressure fluctuation amplitude of transient pressure interference is a key parameter and can be used to trigger the pressure compensation mechanism to improve the detection accuracy.

[0037] In specific implementation, when the pressure fluctuation amplitude exceeds a preset clinical threshold, the respiratory cycle characteristics of the target patient are determined. Specifically, when the pressure fluctuation amplitude exceeds the preset clinical threshold, historical pressure signals of the airway cuff are extracted from the airway cuff pressure monitoring database. All zero-intersection points in the historical pressure signals are extracted using the first derivative, and these zero-intersection points are used as local extrema. This yields all local extrema, including local maxima and local minima. The time length between each adjacent local minima and local maxima is extracted, resulting in multiple time lengths. The average of all time lengths is calculated, and the average is then calculated. The result serves as the respiratory cycle characteristic of the target patient. In addition, in other embodiments, other calculation methods can be used to calculate the respiratory cycle characteristic of the target patient, which is not limited here. In this embodiment, the respiratory cycle characteristic represents a parameter reflecting the periodic changes in the patient's respiratory state, used to describe the regularity and physiological state of pressure changes in a respiratory cycle. By determining the respiratory cycle characteristic, the respiratory health status of the target patient can be fully understood, the pressure control of the airway cuff can be optimized, personalized treatment can be provided, abnormal breathing patterns can be monitored and intervened in real time, disease early warning can be assisted, and a basis for real-time adjustment of airway pressure can be provided to improve patient comfort and the intelligence level of the equipment.

[0038] It should be noted that the preset clinical threshold in this application represents a specific pressure fluctuation amplitude standard determined in clinical practice based on medical experience and relevant research. When the pressure fluctuation amplitude corresponding to the airway cuff pressure fluctuation component exceeds this standard, the cuff pressure needs to be compensated to ensure patient safety and treatment effectiveness.

[0039] In some embodiments, extracting the pressure value sample of the airway cuff before the occurrence of respiratory driving interference based on the respiratory characteristic cycle can be achieved by the following steps: Obtain historical pressure information of the airway cuff before the occurrence of the respiratory drive disturbance; A time sliding window is set according to the respiratory characteristic cycle; According to the time sliding window, all historical pressure values ​​in the historical pressure information are slid and divided in chronological order according to a predetermined sliding step size to obtain multiple pressure sub-information; Based on all the pressure sub-information, the pressure value sample of the airway cuff before the occurrence of the respiratory drive interference was obtained.

[0040] In specific implementation, firstly, historical pressure information of the airway cuff before the occurrence of the respiratory-driven disturbance is obtained through an airway cuff pressure monitoring database. This historical pressure information includes multiple historical pressure values, which are the pressure detection values ​​inside the airway cuff during the historical period. Secondly, a time sliding window is set according to the respiratory characteristic cycle; that is, the time sliding window is set based on the size of the respiratory characteristic cycle. For example, if the respiratory characteristic cycle is 5 seconds, then a time sliding window of size 5 is set, representing a fixed-size length interval. Then, according to the time sliding window, all historical pressure values ​​in the historical pressure information are chronologically segmented according to a predetermined sliding step size. Each sliding step uses the historical pressure information within the sliding time window as a pressure sub-indicator. The system obtains multiple pressure sub-information values. The preset sliding step size is set to 1 second, but it can also be set according to actual needs. In this embodiment, the pressure sub-information represents a segment of pressure information extracted from historical pressure information. Finally, based on all the pressure sub-information values, the pressure value sample of the airway cuff before the occurrence of respiratory driving interference is extracted. That is, for each pressure sub-information value, the mean of all historical pressure values ​​in the pressure sub-information is calculated, and the calculated mean is used as a pressure candidate value. Then, all pressure candidate values ​​are obtained. All pressure candidate values ​​are arranged and combined in chronological order to obtain the pressure value sample of the airway cuff before the occurrence of the cuff pressure fluctuation component. In this embodiment, the pressure candidate value represents a pre-selected historical pressure value.

[0041] It should be noted that the pressure value sample in this application represents a set of multiple candidate pressure values. Specifically, the pressure sample in the application refers to a set of multiple discrete pressure measurements extracted from the historical pressure information of the airway cuff before the occurrence of airway cuff pressure fluctuation components. These pressure value samples reflect the pressure state of the airway cuff in different time periods. By determining the pressure sample values, the pressure state of the airway cuff can be accurately monitored, the accuracy of the data can be improved, and the trend of airway cuff pressure changes can be identified, so as to optimize the control logic of the intelligent breathing device and provide personalized breathing support.

[0042] In specific implementation, all pressure candidate values ​​in the pressure value sample are fitted to obtain a feature correlation map of the internal pressure of the airway cuff. That is, each pressure candidate value in the pressure value sample is assigned a sequential label in chronological order, and the sequential label of each pressure candidate value is used as the element of the horizontal axis, and each pressure candidate value is used as the element of the vertical axis. Least square fitting is performed on all pressure candidate values, and the fitted curve is used as the feature correlation map of the internal pressure of the airway cuff. In addition, in other embodiments, other fitting methods can be used to fit all pressure candidate values ​​in the pressure value sample, which is not limited here.

[0043] It should be noted that the feature correlation map in this application represents a map associated with pressure detection values ​​under stable breathing conditions. Specifically, in this embodiment, the feature correlation map is a map obtained by fitting pressure value samples under stable breathing conditions. This feature correlation map can describe the normal pressure change trend inside the airway cuff. The feature correlation map reflects the pressure change benchmark of the cuff when it is not affected by sudden interference or abnormal factors. The purpose of determining the feature correlation map is to provide benchmark support for the detection and compensation of pressure interference by fitting the pressure change trend of the airway cuff under stable breathing conditions. Compared with traditional single-point pressure measurement or simple time window smoothing methods, this provides a more reliable and reliable method. This innovative technology constructs a dynamic reference model, enabling the pressure detection system to accurately distinguish between normal pressure fluctuations and short-term disturbances. In practical applications, traditional methods often struggle to effectively address short-term pressure anomalies caused by changes in airway compliance, cuff inflation / deflation fluctuations, or changes in patient breathing patterns, potentially leading to misjudgments or measurement errors. However, the feature correlation map, through fitting and trend modeling of historical pressure data, can continuously update the airway cuff pressure baseline, giving it adaptive capabilities. Even with slight changes in respiratory rhythm, it can still provide a stable and reliable pressure reference, effectively improving the robustness and intelligence of the pressure monitoring system.

[0044] In step 104, the pressure distribution field inside the airway airbag is determined when the airway airbag is inflated, and a pressure compensation model inside the airway airbag is established based on the pressure change gradient in the pressure distribution field and the feature correlation map.

[0045] In some implementations, determining the pressure distribution field inside the airway airbag during inflation can be achieved using the following steps: Miniature pressure sensors are installed at different locations inside the airway airbag, and discrete pressure values ​​at different locations inside the airbag are collected by the miniature pressure sensors when the airway airbag is inflated. Arrange all discrete pressure values ​​according to the arrangement positions of the miniature pressure sensors to obtain a discrete pressure value sequence; The pressure value sequence is interpolated to obtain the pressure distribution field inside the airway airbag when it is inflated.

[0046] In practice, all discrete pressure values ​​are arranged according to the arrangement of the micro pressure sensors to obtain a discrete pressure value sequence. That is, the discrete pressure values ​​collected by the micro pressure sensor closest to the inflation port of the airway bag are arranged from front to back to obtain a discrete pressure value sequence. The discrete pressure value sequence contains multiple discrete pressure values, which represent the pressure values ​​collected at different locations inside the airway bag.

[0047] It should be noted that the miniature pressure sensor in this embodiment is an embedded flexible pressure sensor, which is composed of multiple micron-level piezoresistive sensing units. In addition, other types of sensors may be used in other embodiments, which are not limited here.

[0048] In specific implementation, the pressure value sequence is interpolated to obtain the pressure distribution field inside the airway airbag when it is inflated. Specifically, bilinear interpolation is used to interpolate the pressure value sequence to obtain the pressure distribution field inside the airway airbag when it is inflated. Each discrete pressure value in the pressure value sequence is arranged in a two-dimensional grid, with each grid point corresponding to a discrete pressure value. For any unmeasured point in the grid, bilinear interpolation calculates the average pressure of the four surrounding known points as the discrete pressure value of that point. In this way, bilinear interpolation can smoothly fill the gaps in the two-dimensional grid and generate the pressure distribution field inside the airway airbag when it is inflated. In addition, in other embodiments, other interpolation methods can be used to process the pressure value sequence, which are not limited here.

[0049] It should be noted that, in this application, the pressure distribution field represents the spatial distribution map formed by the pressure value inside the airway cuff changing with position. The pressure distribution field reflects the pressure state at various points in this area. The pressure distribution field can not only describe the pressure magnitude at different locations inside the airway cuff, but also reveal the gradient, trend and law of pressure change. By determining the pressure distribution field, the pressure distribution of the airway cuff under different environmental conditions can be effectively analyzed, thereby providing favorable conditions for subsequent pressure compensation models.

[0050] In some embodiments, establishing a pressure compensation model inside the airway cuff based on the pressure change gradient in the pressure distribution field and the feature correlation map can be achieved by the following steps: Extract all pressure change gradients in the pressure distribution field, and determine the pressure change gradient characteristics of the airway cuff based on all pressure change gradients; Each discrete pressure value in the pressure distribution field is compared with the characteristic correlation map to obtain all pressure measurement deviations; The measurement deviation characteristics of the airway cuff were determined by measuring all pressure measurement deviations. The pressure compensation coefficient inside the airway inflator is determined based on the pressure change gradient characteristics and the measurement deviation characteristics. A pressure compensation model for the airway airbag is constructed based on the pressure compensation coefficient.

[0051] In specific implementation, all pressure change gradients in the pressure distribution field are extracted, and the pressure change gradient characteristics of the airway cuff are determined based on all pressure change gradients. That is, the absolute difference of each adjacent discrete pressure value in the pressure distribution field is calculated, and the result of the absolute difference calculation is used as the pressure change gradient, thereby obtaining all pressure change gradients. Then, the variance of all pressure change gradients is calculated, and the variance calculation result is used as the pressure change gradient characteristics of the airway cuff. In addition, in other embodiments, other calculation methods can be used to calculate the pressure change gradient characteristics of the airway cuff, which is not limited here. In this embodiment, the pressure change gradient represents the degree of pressure change with position in the airway cuff. The pressure change gradient describes the difference in pressure values ​​between different positions. The pressure change gradient effectively characterizes the pressure change trend at different positions inside the airway cuff. In this embodiment, the pressure change gradient characteristic represents an index for measuring the change trend of the pressure change gradient. The pressure change gradient characteristic can effectively measure the regularity of pressure changes inside the airway cuff.

[0052] In specific implementation, each discrete pressure value in the pressure distribution field is compared with the feature correlation map to obtain all pressure measurement deviations. That is, a discrete pressure value in the pressure distribution field is selected as the selected discrete pressure value. The absolute difference between the selected discrete pressure value and the minimum pressure reference value and the maximum pressure reference value in the feature correlation map is calculated respectively. The mean of all the calculated absolute difference results is calculated to obtain the pressure measurement deviation corresponding to the selected discrete pressure value. The pressure measurement deviations corresponding to the remaining discrete pressure values ​​are then determined, and thus all pressure measurement deviations are obtained. In this implementation, the pressure measurement deviation represents the deviation between the discrete pressure value and the pressure reference value.

[0053] In specific implementation, the measurement deviation characteristics of the airway airbag are determined by all pressure measurement deviations. That is, the variance of all pressure measurement deviations is calculated, and the variance calculation result is used as the measurement deviation characteristics of the airway airbag. In addition, in other embodiments, other calculation methods can be used to calculate the measurement deviation characteristics of the airway airbag. This is not limited here. In this embodiment, the measurement deviation characteristics represent an index that measures the changing trend of pressure measurement deviation within the airway airbag space.

[0054] In specific implementation, the pressure compensation coefficient inside the airway cuff is determined based on the pressure change gradient characteristics and the measurement deviation characteristics. That is, the pressure change gradient characteristics and the measurement deviation characteristics are weighted and summed, and the weighted sum is used as the pressure compensation coefficient inside the airway cuff. The weight values ​​of the pressure change gradient characteristics and the measurement deviation characteristics can be set between 0 and 1 using a large amount of clinical trial data and machine learning algorithms (such as neural networks, genetic algorithms, etc.). For example, in this embodiment, the weight values ​​of the pressure change gradient characteristics and the measurement deviation characteristics are set to 0.63 and 0.37, respectively. In addition, other weights can be set in other embodiments, which are not limited here. In this embodiment, the pressure compensation coefficient represents a numerical factor used to adjust the measured pressure value. It is used to quantify the pressure deviation caused by environmental factors, equipment errors, or structural inhomogeneities. By compensating the real-time monitored pressure value through the pressure compensation coefficient, the compensated pressure value can be closer to the true value.

[0055] In a specific implementation, a pressure compensation model inside the airway airbag is constructed based on the pressure compensation coefficient. The expression of the pressure compensation model is: pressure compensation value = pressure measurement value to be compensated + pressure compensation coefficient. In addition, in other embodiments, other forms of pressure compensation models can be established, which are not limited here.

[0056] It should be noted that the pressure compensation model in this application represents a mathematical model used to correct the measured pressure value. It aims to reduce measurement deviations caused by factors such as sensor error, uneven pressure inside the airway cuff, and pressure fluctuation components in the cuff, so that the final pressure value is closer to the actual physiological pressure distribution. The pressure compensation model determined in the final version of this application achieves accurate compensation for the measured pressure inside the airway cuff through comprehensive calculation of feature correlation spectrum, pressure change gradient, and pressure compensation coefficient. The core advantage of this model is that it integrates time-varying pressure characteristics and dynamically adjusts compensation parameters, making the pressure detection value more consistent with the actual physiological condition of the target patient. Compared with traditional static compensation methods (such as fixed error correction or single-point calibration), this model can respond in real time to transient pressure interference, non-uniform pressure distribution, and individual physiological differences, ensuring that the compensated pressure value is more in line with the physiological needs of the human airway.

[0057] In step 105, the current pressure detection value is synchronously compensated for during the respiratory cycle based on the pressure compensation model inside the airway cuff to obtain the pressure compensation value of the airway cuff.

[0058] In practice, the current pressure detection value is synchronously compensated for during the respiratory cycle based on the pressure compensation model inside the airway cuff to obtain the pressure compensation value of the airway cuff. That is, the current pressure detection value is obtained, the pressure detection value is input into the pressure compensation model as the pressure measurement value that needs to be compensated, and the pressure compensation model outputs the pressure compensation value of the airway cuff.

[0059] It should be noted that by using the pressure compensation model inside the airway cuff to synchronize the respiratory cycle with the current pressure measurement value, a pressure measurement value closer to the actual value can be obtained, thereby effectively improving the patient's experience.

[0060] It should also be noted that, in this application, respiratory cycle synchronization compensation refers to the process of adjusting the airway cuff pressure detection value according to the patient's respiratory cycle characteristics. Specifically, considering the cuff pressure fluctuations caused by respiratory driving interference, and combining the characteristics of different stages of the respiratory cycle, the established pressure compensation model is used to perform targeted compensation calculations on the currently detected airway cuff pressure value, so that the final pressure compensation value can more accurately reflect the true pressure state of the cuff, eliminate or reduce the influence of respiratory driving interference on the pressure detection results, and achieve more accurate cuff pressure compensation that matches the patient's respiratory cycle.

[0061] In another aspect, in some embodiments, this application provides an airway airbag pressure detection device, which includes a pressure compensation unit, as referenced. Figure 4 The figure is a schematic diagram of the structure of a pressure compensation unit according to some embodiments of this application. The pressure compensation unit 200 includes: an acquisition module 201, a processing module 202, and an execution module 203, which are described below: The acquisition module 201 in this application is mainly used to acquire the intrathoracic pressure information and the internal pressure information of the airway cuff when monitoring the target patient in the postoperative intensive care unit. Processing module 202, in this application, is mainly used to perform frequency domain coupling analysis on the internal pressure information and the intrathoracic pressure information to identify the components of air sac pressure fluctuation caused by respiratory-driven interference; The processing module 202 is further configured to determine the feature correlation map of the internal pressure of the airway cuff based on the pressure value sample of the airway cuff before the occurrence of the respiratory driving interference and the respiratory cycle characteristics of the target patient when the pressure fluctuation amplitude corresponding to the airway pressure fluctuation component exceeds a preset clinical threshold. In addition, the processing module 202 is also used to determine the pressure distribution field inside the airway airbag when the airway airbag is inflated, and to establish a pressure compensation model inside the airway airbag based on the pressure change gradient in the pressure distribution field and the feature correlation map. The execution module 203 in this application is mainly used to perform respiratory cycle synchronization compensation on the current pressure detection value according to the pressure compensation model inside the airway cuff, so as to obtain the pressure compensation value of the airway cuff.

[0062] In addition, this application also provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described airway airbag pressure compensation method.

[0063] In some embodiments, reference Figure 5 The figure is a schematic diagram of the structure of a computer device for implementing an airway airbag pressure compensation method according to some embodiments of this application. The airway airbag pressure compensation method in the above embodiments can be achieved through... Figure 5 The computer device shown is used to implement this, and the computer device 300 includes at least one processor 301, a communication bus 302, a memory 303, and at least one communication interface 304.

[0064] The processor 301 may be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more devices used to control the execution of the airway airbag pressure compensation method in this application.

[0065] The communication bus 302 can be used to transmit information between the aforementioned components.

[0066] The memory 303 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CDROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), a magnetic disk or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 303 may exist independently and be connected to the processor 301 via a communication bus 302. The memory 303 may also be integrated with the processor 301.

[0067] The memory 303 stores program code for executing the scheme of this application, and its execution is controlled by the processor 301. The processor 301 executes the program code stored in the memory 303. The program code may include one or more software modules. In the above embodiments, the determination of the airway airbag pressure compensation method can be implemented by the processor 301 and one or more software modules in the program code in the memory 303.

[0068] Communication interface 304 uses any transceiver-like device to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0069] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single CPU) processor or a multi-core (multi CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0070] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0071] In addition, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described airway airbag pressure compensation method.

[0072] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0073] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A method for compensating airway cuff pressure, used in an airway cuff pressure detection device for pressure compensation, characterized in that, The method includes the following steps: When monitoring the target patient in the postoperative intensive care unit, obtain the intrathoracic pressure information and the internal pressure information of the airway cuff. Frequency domain coupling analysis was performed on the internal pressure information and the intrathoracic pressure information to identify the components of airway pressure fluctuations caused by respiratory-driven interference. When the pressure fluctuation amplitude corresponding to the airway pressure fluctuation component exceeds a preset clinical threshold, a characteristic correlation map of the internal pressure of the airway airway is determined based on the airway airway pressure value sample before the occurrence of the respiratory driving interference and the respiratory cycle characteristics of the target patient. Determine the pressure distribution field inside the airway airbag when it is inflated, and establish a pressure compensation model inside the airway airbag based on the pressure change gradient in the pressure distribution field and the feature correlation map. The pressure compensation value of the airway cuff is obtained by synchronously compensating the current pressure detection value with the respiratory cycle based on the pressure compensation model inside the airway cuff.

2. The method as described in claim 1, characterized in that, Frequency domain coupling analysis was performed on the internal pressure information and the intrathoracic pressure information to identify the components of cuff pressure fluctuations caused by respiratory-driven disturbances, specifically including: The internal pressure information and the intrathoracic pressure information are preprocessed for time synchronization to obtain synchronized internal pressure information and intrathoracic pressure information; The synchronized internal pressure information and intrathoracic pressure information are divided into short time windows, and the internal pressure change rate sequence and intrathoracic pressure change rate sequence are calculated within each time window; Correlation analysis was performed on the internal pressure change rate sequence and the intrathoracic pressure change rate sequence within each time window to obtain the pressure correlation degree corresponding to each time window. The internal pressure information is detrended based on the pressure correlation degree corresponding to each time window to obtain the detrended internal pressure information. The detrended internal pressure information identified components of airway pressure fluctuations caused by respiratory-driven disturbances.

3. The method as described in claim 1, characterized in that, When the pressure fluctuation amplitude corresponding to the airway pressure fluctuation component exceeds a preset clinical threshold, the characteristic correlation map of the internal pressure of the airway airway is determined based on the airway airway pressure value sample before the occurrence of the respiratory driving interference and the respiratory cycle characteristics of the target patient. Specifically, this includes: The steady-state pressure value inside the airway cuff is obtained under the stable breathing state of the target patient, and the interference peak value of the cuff pressure in the pressure fluctuation component is extracted. The pressure fluctuation amplitude corresponding to the airbag pressure fluctuation component is calculated based on the steady-state pressure value and the disturbance peak value. When the pressure fluctuation amplitude exceeds a preset clinical threshold, the respiratory cycle characteristics of the target patient are determined; Based on the respiratory characteristic cycle, extract the pressure value sample of the airway cuff before the occurrence of the respiratory driving interference; By fitting all pressure candidate values ​​in the pressure value sample, a characteristic correlation map of the internal pressure of the airway airbag is obtained.

4. The method as described in claim 3, characterized in that, Extracting the airway cuff pressure value sample before the occurrence of respiratory driving interference based on the respiratory characteristic cycle specifically includes: Obtain historical pressure information of the airway cuff before the occurrence of the respiratory drive disturbance; A time sliding window is set according to the respiratory characteristic cycle; According to the time sliding window, all historical pressure values ​​in the historical pressure information are slid and divided in chronological order according to a predetermined sliding step size to obtain multiple pressure sub-information; Based on all the pressure sub-information, the pressure value sample of the airway cuff before the occurrence of the respiratory drive interference was obtained.

5. The method as described in claim 1, characterized in that, Determining the pressure distribution field inside the airway airbag during inflation specifically includes: Miniature pressure sensors are installed at different locations inside the airway airbag, and discrete pressure values ​​at different locations inside the airbag are collected by the miniature pressure sensors when the airway airbag is inflated. Arrange all discrete pressure values ​​according to the arrangement positions of the miniature pressure sensors to obtain a discrete pressure value sequence; The pressure value sequence is interpolated to obtain the pressure distribution field inside the airway airbag when it is inflated.

6. The method as described in claim 1, characterized in that, A miniature pressure sensor is used to obtain the internal pressure information of the airway airbag.

7. The method as described in claim 6, characterized in that, The miniature pressure sensor is an embedded flexible pressure sensor.

8. An airway airbag pressure detection device, comprising a pressure compensation unit, characterized in that, The pressure compensation unit includes: The acquisition module is used to acquire intrathoracic pressure information and internal pressure information of airway cuffs of target patients when monitoring them in the postoperative intensive care unit. The processing module is used to perform frequency domain coupling analysis on the internal pressure information and the intrathoracic pressure information to identify the components of air sac pressure fluctuations caused by respiratory-driven interference. The processing module is also used to determine the characteristic correlation map of the internal pressure of the airway cuff based on the pressure value sample of the airway cuff before the occurrence of the respiratory driving interference and the respiratory cycle characteristics of the target patient when the pressure fluctuation amplitude corresponding to the airway pressure fluctuation component exceeds the preset clinical threshold. The processing module is also used to determine the pressure distribution field inside the airway airbag when the airway airbag is inflated, and to establish a pressure compensation model inside the airway airbag based on the pressure change gradient in the pressure distribution field and the feature correlation map. The execution module is used to perform respiratory cycle synchronization compensation on the current pressure detection value according to the pressure compensation model inside the airway cuff, so as to obtain the pressure compensation value of the airway cuff.

9. A computer device, characterized in that, The computer device includes a memory and a processor, the memory storing code, and the processor being configured to retrieve the code and execute the airway airbag pressure compensation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the airway airbag pressure compensation method as described in any one of claims 1 to 7.

Images