Method, system and equipment for detecting abnormity of respiratory mechanical parameters under prone position ventilation

Abstract

The invention is applicable to the technical field of prone position parameter anomaly detection, and particularly relates to a method, a system and equipment for detecting anomaly of respiratory mechanical parameters under prone position ventilation, and the method comprises the following steps: in a prone position ventilation process, a controller continuously collects pressure information in a prone position state at a set frequency through a main sensor, determining a flow waveform based on the dynamic change characteristics of the pressure information; in the prone position ventilation process, the airway pressure in the prone position state is continuously collected through an auxiliary sensor; determining airflow retention characteristics in a plurality of continuous respiratory cycles in the prone position ventilation process according to the flow waveform and the airway pressure; under the condition that the abnormal characteristics of the prone position are determined according to the airflow retention characteristics, parameter abnormal data are determined based on the airflow retention characteristics and the abnormal characteristics. According to the method for detecting the abnormality of the respiratory mechanical parameters under prone position ventilation, airflow retention and the abnormality of the respiratory mechanical parameters of the back side, the ventral side and the trans-region can be accurately determined.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of prone position parameter anomaly detection, and particularly relates to a prone position ventilation under-breathing mechanics parameter anomaly detection method, system and device. BACKGROUND

[0002] Prone position ventilation is a method for improving lung ventilation and oxygenation function by adjusting the body position of a patient (taking a prone position, i.e., the face is downward and the back is upward), which is mainly used for patients with respiratory failure, especially acute respiratory distress syndrome, etc. The core principle is that when the human body is in a supine position, the back (dorsal) alveoli are easily compressed due to factors such as chest pressure and lung tissue weight under the influence of gravity, and the ventilation function is limited; when in a prone position, the direction of gravity changes, which can reduce the compression of the dorsal alveoli, let more alveoli open to participate in ventilation, and is beneficial to the drainage of lung secretions.

[0003] In the related art, the conventional respiratory mechanics parameter detection of prone position ventilation only relies on the monitoring of the airway pressure of the Y-shaped tube of the ventilator, but due to the significant difference in respiratory mechanics between the dorsal and ventral lung regions of the human body (for example, different pressure conduction and airflow distribution), it is impossible to accurately determine the airflow retention and the respiratory mechanics parameter anomaly of the dorsal, ventral and cross regions. SUMMARY

[0004] The application embodiment provides a prone position ventilation under-breathing mechanics parameter anomaly detection method, system and device, which can solve the problem that the respiratory mechanics parameter anomaly cannot be accurately determined due to the difference in respiratory mechanics.

[0005] In a first aspect, the application embodiment provides a prone position ventilation under-breathing mechanics parameter anomaly detection method, comprising: The application is applied to a prone position device, the prone position device comprises a chest pad, a main sensor, a secondary sensor and a controller, the main sensor is arranged on the chest pad, the secondary sensor is arranged in the Y-shaped tube region of the ventilator, and the method comprises: During the prone position ventilation process, the controller continuously acquires pressure information in the prone position state at a set frequency through the main sensor, and determines a flow waveform based on the dynamic change characteristics of the pressure information; wherein the dynamic change characteristics include a pressure change rate, an amplitude and a time distribution; During the prone position ventilation process, the airway pressure in the prone position state is continuously acquired through the secondary sensor; The airflow retention characteristics in a plurality of continuous breathing cycles during the prone position ventilation process are determined according to the flow waveform and the airway pressure; wherein the airflow retention characteristics are used to indicate the situation of insufficient lung gas discharge and abnormal coordination of the dorsal and ventral regions; In a case where an abnormal feature of the prone position is determined according to the airflow retention feature, parameter abnormality data is determined based on the airflow retention feature and the abnormal feature: wherein the abnormal feature includes a dorsal attenuation index, an abdominal stress index, and a cross-zone delay index, the dorsal attenuation index is used to indicate a slope change amount of a pressure-volume curve in an end time of an exhalation phase, the abdominal stress index is used to indicate a frequency of a platform pressure rising over a set threshold in a continuous breathing cycle, and the cross-zone delay feature is used to indicate a time difference between abdominal and dorsal airway pressure peaks; and the parameter abnormality data is used to indicate an abnormal detection result of a respiratory mechanics parameter.

[0006] The prone position ventilation respiratory mechanics parameter abnormality detection method provided in the application, in the prone position ventilation process, the controller continuously acquires pressure information in the prone position state through the main sensor at a set frequency, and determines a flow waveform based on dynamic change features of the pressure information; in the prone position ventilation process, the airway pressure in the prone position state is continuously acquired through the auxiliary sensor, which can be combined with the pressure information acquired by the main sensor, and the prone position ventilation effect can be comprehensively evaluated to provide a reliable basis for adjusting the ventilation parameters; the airflow retention features in a plurality of continuous breathing cycles in the prone position ventilation process are determined according to the flow waveform and the airway pressure; in a case where an abnormal feature of the prone position is determined according to the airflow retention feature, parameter abnormality data is determined based on the airflow retention feature and the abnormal feature: which can improve the accuracy and safety of prone position ventilation treatment. Through in-depth analysis of these parameter abnormality data, the ventilation strategy is adjusted in time, thereby optimizing the treatment effect and reducing the occurrence of complications, which can prevent potential respiratory function damage caused by airflow retention features and accurately determine the airflow retention and the respiratory mechanics parameter abnormalities of the dorsal side, the abdominal side, and the cross-zone.

[0007] In a second aspect, the embodiments of the application provide a prone position ventilation respiratory mechanics parameter abnormality detection system, applied to a prone position device, the prone position device including a chest pad, a main sensor, an auxiliary sensor, and a controller, the main sensor being arranged on the chest pad, and the auxiliary sensor being arranged in a Y-shaped tube region of a ventilator, and the system including: A first acquisition unit is configured to, in the prone position ventilation process, continuously acquire pressure information in the prone position state through the main sensor at a set frequency, and determine a flow waveform based on dynamic change features of the pressure information; wherein the dynamic change features include a pressure change rate, an amplitude, and a time distribution. A second acquisition unit is configured to, in the prone position ventilation process, continuously acquire airway pressure in the prone position state through the auxiliary sensor. determining unit configured to determine, according to the flow waveform and the airway pressure, a gas flow retention feature in a plurality of consecutive breath cycles in the prone position ventilation; wherein the gas flow retention feature is used to indicate a condition of insufficient gas discharge in the lung and abnormal coordination of the dorsal and ventral regions; a result unit configured to determine, in a case where an abnormal feature of the prone position is determined according to the gas flow retention feature, parameter abnormality data based on the gas flow retention feature and the abnormal feature; wherein the abnormal feature includes a dorsal attenuation index, a ventral stress index, and a cross-region delay index, the dorsal attenuation index is used to indicate a slope change amount of the pressure-volume curve at the end of the expiration phase, the ventral stress index is used to indicate a frequency of the platform pressure rising above a set threshold in consecutive breath cycles, and the cross-region delay feature is used to indicate a time difference between the peak values of the ventral and dorsal airway pressures; and the parameter abnormality data is used to indicate an abnormal detection result of a respiratory mechanics parameter.

[0008] In a third aspect, an embodiment of the present application provides a prone position ventilation device, comprising a chest pad, a main sensor, a secondary sensor, and a controller, the main sensor is arranged on the chest pad, the secondary sensor is arranged on a Y-shaped tube region of a ventilator, the controller is in communication connection with the main sensor and the secondary sensor, the controller comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is executed by the processor to implement the method of any one of the above first aspect.

[0009] In a fourth aspect, an embodiment of the present application provides a computer program product, when the computer program product is executed on a prone position ventilation device, the prone position ventilation device executes the prone position ventilation respiratory mechanics parameter abnormality detection method of any one of the above first aspect.

[0010] It can be understood that the beneficial effects of the above-mentioned second aspect to the fourth aspect can be referred to the related description in the above-mentioned first aspect, which will not be repeated here. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0012] Figure 1 is a structural schematic diagram of a ventilator provided by an embodiment of the present application; Figure 2is a structural schematic diagram of a chest pad and a main sensor of a prone position ventilation device provided by an embodiment of the present application; Figure 3 is a flow schematic diagram of a method for detecting abnormality of respiratory mechanics parameters in prone position ventilation provided by an embodiment of the present application; Figure 4 is an implementation flow schematic diagram of step S300 in the method for detecting abnormality of respiratory mechanics parameters in prone position ventilation provided by an embodiment of the present application; Figure 5 is an implementation flow schematic diagram of step S400 in the method for detecting abnormality of respiratory mechanics parameters in prone position ventilation provided by an embodiment of the present application; Figure 6 is an implementation flow schematic diagram of step S400 in the method for detecting abnormality of respiratory mechanics parameters in prone position ventilation provided by an embodiment of the present application; Figure 7 is an implementation flow schematic diagram of step S400 in the method for detecting abnormality of respiratory mechanics parameters in prone position ventilation provided by an embodiment of the present application; Figure 8 is an implementation flow schematic diagram of step S4034 in the method for detecting abnormality of respiratory mechanics parameters in prone position ventilation provided by an embodiment of the present application; Figure 9 is a structural schematic diagram of a system for detecting abnormality of respiratory mechanics parameters in prone position ventilation provided by an embodiment of the present application; Figure 10 is a structural schematic diagram of a controller provided by an embodiment of the present application.

[0013] In the drawings, various specific details are set forth in order to provide a thorough understanding of embodiments of the application. However, embodiments of the application can be practiced without these specific details. In other instances, well-known methods, structures, circuits, and processes have not been described in detail in order to avoid obscuring the application. DETAILED DESCRIPTION

[0014] In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular architectures, techniques, etc., in order to provide a thorough understanding of embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known methods, devices, circuits, and processes are omitted so as not to obscure the description of the present application with unnecessary detail.

[0015] It is to be understood that the terminology "includes", "has", "holds", "contains" and / or "comprising", when used in this specification and in the following claims, indicates the presence of the described features, integers, steps, operations, elements, and / or components but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0016] It should also be understood that the term “and / or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items, and that the term “at least one of’ denotes one, or a plurality of, of the enumerated terms that it precedes, and that the term “per” means are an average over a plurality of repetitions.

[0017] As used in the description of the application and the appended claims, the term “if’ can be interpreted to mean “when” or “once,” or “in response to determining,” or “in response to detecting” depending on the context. Similarly, the phrase “if it is determined” or “if [a described condition or event] is detected” can be interpreted to mean “once it is determined” or “in response to determining,” or “once [the described condition or event] is detected,” or “in response to detecting [the described condition or event],” depending on the context.

[0018] In addition, the terms “first,” “second,” “third,” etc. as used in the description of embodiments herein and throughout the claims (if any) are not used to connote any relative importance but are just used for discrimination in the descriptions.

[0019] As used in the description of the application and the appended claims, the term “if’ can be interpreted to mean “when” or “once,” or “in response to determining,” or “in response to detecting” depending on the context. Similarly, the phrase “if it is determined” or “if [a described condition or event] is detected” can be interpreted to mean “once it is determined” or “in response to determining,” or “once [the described condition or event] is detected,” or “in response to detecting [the described condition or event],” depending on the context.

[0020] In the field of relevant medical technology, there are very significant differences in respiratory mechanics between the dorsal region and the ventral region of the human lung, which are reflected in multiple key aspects. For example, in terms of pressure conduction, due to the influence of factors such as the internal tissue structure of the chest and body gravity, the dorsal lung region will have obvious attenuation and lag in the pressure conduction process, while the ventral lung region is less disturbed and the pressure can be transmitted more directly and quickly; in terms of airflow distribution, the ventral lung region can obtain more sufficient airflow due to the relatively larger chest space and smaller lung tissue expansion resistance, while the dorsal lung region has relatively less and uneven airflow distribution due to compression and other reasons.

[0021] Because of these significant differences, the conventional respiratory mechanics parameter detection has serious limitations if it only relies on the monitoring of the Y-shaped tube airway pressure of the ventilator when prone position ventilation is performed. Because the Y-shaped tube airway pressure monitored is a comprehensive reflection of the whole lung and cannot distinguish the pressure changes of the dorsal and ventral lung regions, it is difficult to accurately determine the airflow retention phenomenon. When the dorsal lung region has airflow retention, due to the particularity of pressure conduction, this retention cannot be reflected by the overall pressure of the Y-shaped tube and is easily ignored. Meanwhile, it is also difficult to accurately identify the abnormal respiratory mechanics parameters of the dorsal lung region, the ventral lung region, and the cross-region by relying only on the monitoring of the Y-shaped tube. It is possible that the dorsal lung region has already appeared parameter abnormalities such as decreased compliance, but the overall pressure data of the Y-shaped tube show normal, thereby misleading the judgment of the medical staff on the ventilation condition of the patient's lung.

[0022] To solve the above problems, the embodiment of the present application provides a prone position ventilation respiratory mechanics parameter abnormality detection method, system and device.

[0023] In the method, during the prone position ventilation process, the controller continuously collects pressure information in the prone position state at a set frequency through the main sensor, and determines a flow waveform based on the dynamic change characteristics of the pressure information. During the prone position ventilation process, the airway pressure in the prone position state is continuously collected through the auxiliary sensor, which can be combined with the pressure information collected by the main sensor, and the prone position ventilation effect can be comprehensively evaluated to provide a reliable basis for adjusting the ventilation parameters. The airflow retention characteristics in a plurality of continuous respiratory cycles in the prone position ventilation process are determined according to the flow waveform and the airway pressure. In the case where the abnormal characteristics of the prone position are determined according to the airflow retention characteristics, the parameter abnormality data are determined based on the airflow retention characteristics and the abnormal characteristics. The accuracy and safety of prone position ventilation treatment can be improved. Through in-depth analysis of these parameter abnormality data, the ventilation strategy is adjusted in time, thereby optimizing the treatment effect and reducing the occurrence of complications, preventing potential respiratory function damage caused by airflow retention characteristics, and accurately determining the airflow retention and the respiratory mechanics parameter abnormalities of the dorsal, ventral and cross-region.

[0024] The prone position ventilation respiratory mechanics parameter abnormality detection method provided by the embodiment of the present application can be applied to a prone position ventilation device. At this time, the prone position ventilation device is the execution subject of the prone position ventilation respiratory mechanics parameter abnormality detection method provided by the embodiment of the present application, and the specific type of the prone position ventilation device is not limited in the embodiment of the present application.

[0025] Exemplarily, Figure 1 The structure of the ventilator is shown, Figure 2A structural diagram of a chest pad in a prone position ventilation device is shown; the prone position ventilation device comprises a chest pad 10, a main sensor 20, a secondary sensor, a controller, and the like. The chest pad 10 is a component that can make the patient comfortable in the prone position while providing a stable mounting position for the main sensor 20. The main sensor 20 is arranged on the chest pad 10 and is used to capture information in real time when the patient is in the prone position. The secondary sensor is arranged in the Y-shaped tube area of the ventilator and is used to monitor the airway pressure to compensate for the limitations of Y-shaped tube monitoring. The controller is in communication connection with the ventilator, the main sensor and the secondary sensor; the controller can be a mobile phone, a tablet computer, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, a desktop computer, a smart screen, a computer, a laptop computer, a handheld communication device, a handheld computing device, and the like. The ventilator comprises a ventilator body, a Y-shaped tube and a breathing mask, and is used for prone position ventilation of the patient, wherein the ventilator body provides the gas pressure and flow required for ventilation, the Y-shaped tube connects the ventilator body and the patient's breathing mask and is used to deliver gas to the patient's body, and the breathing mask can be a nasal mask or a face mask.

[0026] In order to better understand the prone position ventilation respiratory mechanics parameter abnormality detection method provided by the embodiments of the present application, the specific implementation process of the prone position ventilation respiratory mechanics parameter abnormality detection method provided by the embodiments of the present application will be exemplarily introduced below.

[0027] Figure 3 A schematic flowchart of the prone position ventilation respiratory mechanics parameter abnormality detection method provided by the embodiments of the present application is shown, and the prone position ventilation respiratory mechanics parameter abnormality detection method comprises: S100, during the prone position ventilation process, the controller continuously acquires pressure information in the prone position state at a set frequency through the main sensor, and determines a flow waveform based on dynamic change characteristics of the pressure information; wherein the dynamic change characteristics include a pressure change rate, an amplitude and a time distribution.

[0028] It can be understood that the secondary sensor can be installed in a pressure sensor (such as a piezoresistive sensor) in the airway tube of the ventilator for acquiring the pressure signal in the airway. The pressure change rate can be the amount of change of pressure per unit time, reflecting the speed of pressure rise or fall; the amplitude is the difference between the maximum and minimum values of pressure in a single breathing cycle; the time distribution can be the time point at which the pressure peak and valley occur. The flow waveform can be a curve of gas flow change with time, which can be positive flow during inspiration and negative flow during expiration Exemplarily, the controller first sets the sampling frequency to 100 Hz, and the main sensor is installed at the proximal end of the Y-shaped tube connected to the patient by the ventilator. After the controller sends a start instruction to the main sensor, the sensor collects airway pressure data every 20 milliseconds (for example, 0.3 kPa at t1=0 ms, 0.5 kPa at t2=20 ms, and so on), and transmits the data to the controller in the form of an electrical signal.

[0029] According to the dynamic change characteristics of the pressure information, the flow waveform can be determined by the controller receiving the data and calculating the dynamic change characteristics: the pressure change rate, the amplitude, and the time distribution. The pressure change rate is obtained by dividing the pressure difference between two adjacent time points by the time interval (20 ms), such as (0.5 kPa - 0.3 kPa) / 0.02 s = 10 kPa / s. The amplitude is the difference between the maximum pressure (for example, the peak inspiratory pressure 1.5 kPa) and the minimum pressure (for example, the end-expiratory pressure 0.2 kPa) in a single respiratory cycle, which is 1.3 kPa. The time distribution records the time when the pressure peak appears. Then, based on the fluid mechanics principle that the flow changes significantly when the pressure changes rapidly, the controller converts the above characteristics into the flow-time curve, i.e., the flow waveform.

[0030] S200, during the prone position ventilation process, the airway pressure in the prone position state is continuously collected by the auxiliary sensor.

[0031] It can be understood that the sensor for collecting pressure (such as a capacitive pressure sensor) can be installed on the chest pad, in addition to which it can also be installed at the airway interface closer to the patient's mouth and nose (such as the end of the tracheal tube) to supplement the pressure data of the main sensor.

[0032] Exemplarily, the auxiliary sensor is installed at the connection between the chest pad or tracheal tube and the breathing tube, and is connected to the controller through a signal line. After the controller starts the auxiliary sensor, it sets the sampling frequency to 100 Hz (higher than that of the main sensor to improve data density), which can collect airway pressure data every about 16.7 milliseconds and transmit the data to the controller in real time. The controller stores the received pressure data in chronological order to form a continuous airway pressure time sequence (such as an array containing 1000 data points).

[0033] S300, according to the flow waveform and the airway pressure, the airflow retention characteristics in a plurality of continuous respiratory cycles during the prone position ventilation process are determined; wherein the airflow retention characteristics are used to indicate the insufficient lung gas discharge and the abnormal ventilation coordination between the dorsal and ventral regions.

[0034] It can be understood that the continuous multiple breath cycles can be in the range of 2 cycles to 5 cycles, and in the present application can be 3 continuous breath cycles, etc., but are not limited thereto. The breath cycle can be understood as a complete inspiration (flow positive) and expiration (flow negative) process, from the beginning of inspiration to the beginning of the next inspiration as a cycle. Among them, the effective breath cycle is required to meet the breath interval standard deviation <0.3 seconds, and the cross-correlation coefficient of the adjacent cycle flow curve is greater than 0.85.

[0035] Exemplarily, the respiratory cycle is identified, that is, from the flow waveform, the time point of the flow from negative to positive (expiration to inspiration) is taken as the cycle division point, 10 continuous breath cycles (such as the 2nd to the 11th cycle) are selected, the end-expiratory flow is checked for each cycle (for example, the flow is still -2L / min at the end of expiration, indicating that the gas is not exhausted), the difference between the expiratory time and the set expiratory time is calculated (for example, the actual expiratory time is 1 second shorter than the set value, indicating that the discharge is delayed), and then combined with the airway pressure data, the pressure change time of the back side (corresponding to the pressure drop in the expiration stage) and the abdominal side (corresponding to the pressure rise in the inspiration stage) is compared. If the back side pressure drop is more than 0.5 seconds later than the abdominal side, it means coordination abnormality, and the above integration is the air flow retention feature.

[0036] In one possible implementation, please refer to Figure 4 , S300, determining the effective air flow retention feature in the continuous multiple breath cycles according to the flow waveform and the airway pressure, comprising: S310, determining the time corresponding to the expiratory valve opening signal of the ventilator as the time zero point.

[0037] It can be understood that the expiratory valve opening signal can be understood as an electrical signal (such as a high-level pulse) for indicating the start of the expiration stage. The time zero point is used to unify the time reference point (i.e. t=0) of the multiple breath cycle data.

[0038] Exemplarily, the electromagnetic valve control module of the ventilator sends a high-level signal lasting 10ms to the controller when determining the opening of the expiratory valve, and the controller can capture the rising edge (the moment when the low-level turns to high-level) of the signal and record the moment as the time zero point (t=0). For example, the signal appears at 10:00:00.000, at which time t=0, and the subsequent time is based on this reference (for example, 10:00:00.010 corresponds to t=0.01 seconds).

[0039] S320, intercepting a fixed time interval according to the time zero point, and obtaining a sampling flow value according to the fixed time interval at an equal time interval.

[0040] It can be understood that the fixed time interval can be understood as a specific duration (such as 5 seconds) starting from time zero, covering the entire expiratory phase and part of the inspiratory phase. The equal time interval can be understood as dividing the fixed interval into small segments of equal length, ensuring uniform sampling.

[0041] Exemplarily, the sampling flow value can be obtained by taking the time zero (t=0) as the starting point, intercepting the fixed time interval from t=0 to t=5 seconds, dividing the interval by 0.1 seconds, obtaining 50 time points (t=0, 0.2,..., 5.0 seconds), and then extracting the flow value corresponding to each time point from the flow waveform to obtain the sampling flow value.

[0042] S330, based on the flow waveform, the airway pressure and the sampling flow value, performing centralization processing to obtain a second derivative determination value.

[0043] It can be understood that the centralization processing can be understood as eliminating the influence of the data mean, making the data fluctuate around 0 (each data point minus the average value of the data set), and highlighting the relative change of the data. The second derivative determination value can be understood as the second derivative of the flow with respect to time (reflecting the acceleration of the flow change), which is used to measure the degree of change of the flow (for example, the absolute value of the second derivative indicates that the flow changes quickly).

[0044] Exemplarily, the centralization processing based on the flow waveform, the airway pressure and the sampling flow value can be implemented according to the following example; the centralization processing can be to calculate the average value of the 50 sampling flow values (for example, -3 L / min), subtract the average value from each sampling value to obtain the centralization flow value, and process the airway pressure data (corresponding to the 50 time points) at the same time (subtract the average value of the pressure from the pressure value) to obtain the centralization pressure value, and then calculate the second derivative determination value; the calculation of the second derivative determination value can be to divide the difference between the adjacent two centralization flow values by the time interval (0.1 seconds), and then divide the difference between the adjacent two first derivatives by the time interval, that is, to integrate all the second derivatives into the second derivative determination value.

[0045] S340, determining the effective airflow retention feature according to the second derivative determination value and the continuity verification rule; wherein the continuity verification rule includes verifying the inter-breath interval standard deviation, continuously satisfying a plurality of effective breath periods, and the cross-correlation coefficient of the adjacent period flow curve.

[0046] Exemplarily, the cross-correlation coefficient is an index for measuring the similarity of two flow curves. Determining the effective air flow retention feature can be verifying the standard deviation of the breathing interval, then verifying the continuous effective period and verifying the cross-correlation coefficient, and finally determining the effective feature; specifically, verifying the standard deviation of the breathing interval can be calculating the time lengths of 10 consecutive breathing periods (for example, 3.0, 3.1, 3.0, 3.2 seconds), and the standard deviation is 0.08 seconds, if it is less than the preset threshold of 0.2 seconds, it is verified. Verifying the continuous effective period can be checking whether there are five consecutive breathing periods whose second derivative judgment values are all in the range of -30 to 30, if there are, it is verified. Verifying the cross-correlation coefficient can be calculating the cross-correlation coefficient of the flow curves of the adjacent two periods (for example, 0.85), if it is greater than the preset threshold of 0.7, it is verified. Determining the effective feature can be that after the three verifications are all passed, the corresponding air flow retention feature is effective.

[0047] In this way, the continuity verification rule can filter out abnormal breathing periods (for example, period disorder caused by patient restlessness), improve the stability and reliability of the air flow retention feature, provide high-quality data for subsequent analysis, and reduce the occurrence of false conclusions.

[0048] S400, in the case where the abnormal feature of the prone position is determined according to the air flow retention feature, determining parameter abnormal data based on the air flow retention feature and the abnormal feature: wherein the abnormal feature includes a dorsal decay index, a ventral stress index, and a cross-zone delay index, the dorsal decay index is used to indicate the slope change of the pressure and volume curve in the end time of the exhalation phase, the ventral stress index is used to indicate the frequency of the platform pressure rising above the set threshold in the continuous breathing period, and the cross-zone delay feature is used to indicate the time difference between the ventral and dorsal airway pressure peaks; the parameter abnormal data is used to indicate the abnormal detection result of the respiratory mechanics parameter.

[0049] Exemplarily, in the case where the abnormal feature of the prone position is determined according to the air flow retention feature, the abnormal feature is determined, that is, the dorsal decay index is calculated, the ventral stress index is calculated, and the cross-zone delay index is calculated.

[0050] In this way, the abnormal feature is used to locate the specific ventilation abnormal type and degree, and the parameter abnormal data provides a clear adjustment direction (for example, reducing the platform pressure and prolonging the exhalation time) for medical personnel, which helps to accurately improve the ventilation effect of the prone position and reduce the risk of lung injury.

[0051] In one possible implementation, please refer to Figure 5 S400, the dorsal decay index of the abnormal feature of the prone position is determined according to the air flow retention feature, including: S410, obtain, from the ventilator, flow data and pressure data in an end-expiratory time of one of the plurality of breathing cycles based on the airflow retention feature, wherein the flow data is used to indicate flow values in the end-expiratory time of the one of the plurality of breathing cycles, and the pressure data is used to indicate pressure values.

[0052] Exemplarily, the real-time monitoring data, including the flow time curve and the pressure time curve, are read through a data interface (for example, RS485, Ethernet interface) of the ventilator. According to the flow curve, the flow is positive (gas enters the lung) during inhalation, and the flow is negative (gas is discharged) during exhalation. The interval between the start points of two adjacent inhalations is a breathing cycle. In the exhalation phase of each breathing cycle, a period in which the absolute value of the flow is less than or equal to a preset threshold (for example, 0.1 L / s) and the duration is greater than or equal to 0.3 seconds is screened, and the period is taken as the end-expiratory time. All flow values in the period are taken to form the flow data, and the pressure values at the corresponding time points are taken to form the pressure data.

[0053] S420, obtain a corresponding volume time sequence by numerically integrating the flow data.

[0054] It can be understood that the numerical integration refers to the process of accumulating the discrete flow data according to the time interval to calculate the cumulative gas volume; and the volume time sequence refers to a sequence (unit: L) composed of the gas volumes in the lung at different time points, reflecting the change of the gas volume in the lung over time.

[0055] Exemplarily, the corresponding volume time sequence can be determined by sampling interval (for example, 0.01 seconds / point) of the ventilator data. The starting time of the end-expiratory time is taken as the starting point of volume calculation (the initial volume is set to 0). The flow data is accumulated in time sequence, that is, the volume at the nth time point = the volume at the (n-1)th time point + the flow value at the nth time point multiplied by the sampling interval, so as to obtain the volume corresponding to each time point, and thus the volume time sequence is obtained.

[0056] S430, obtain a pressure-volume curve by combining the pressure data and the volume time sequence.

[0057] Exemplarily, the pressure-volume curve is a curve with airway pressure as the horizontal axis and lung gas volume as the vertical axis, reflecting the dynamic relationship between pressure and volume in the lung during the breathing process, and can reflect the compliance of the lung. The pressure data obtained in step S410 and the volume time sequence obtained in step S420 are aligned in time (the pressure value at the same time corresponds to the volume value one by one), the pressure value is taken as the horizontal coordinate, the corresponding volume value is taken as the vertical coordinate, all data points are marked in the coordinate system, and the points are connected in time sequence to form a continuous pressure-volume curve.

[0058] The correlation between the end-expiratory pressure and the volume can be intuitively displayed, and a visual basis for analyzing the mechanical properties of the lung tissue is provided.

[0059] S440, linear segment fitting is performed on the pressure-volume curve to obtain an average slope corresponding to the pressure-volume curve.

[0060] It can be understood that the linear segment fitting refers to dividing the nonlinear pressure-volume curve into 2-3 approximately linear segments, and each segment can be fitted by using a linear equation (y=kx+b).

[0061] Exemplarily, according to the morphological characteristics of the pressure-volume curve, the segments are divided according to the volume range (for example, a low volume segment, a medium volume segment, and a high volume segment), the least square method is used for linear fitting for each segment, the sum of squared errors of all points in the segment and the fitted straight line is calculated, the slope k and the intercept b are adjusted to minimize the sum of squared errors, the slope of the segment is obtained, and the arithmetic mean of the slopes of all segments is calculated, which can be used as the average slope of the pressure-volume curve. The error caused by the nonlinearity of the curve is reduced by segment fitting, and the average slope can quantitatively reflect the overall mechanical properties of the lung, and provide a unified index for comparative analysis of different breathing cycles. S450, determining a slope change amount based on the average slope, and determining the slope change amount as the dorsal attenuation index; wherein the slope change amount is the absolute average value of the slope difference between adjacent cycles.

[0062] It can be understood that the slope change amount is the arithmetic mean of the absolute values of the slope difference between adjacent cycles in the average slopes of the continuous multiple breathing cycles.

[0063] Exemplarily, the average slopes (S1, S2,..., S n ) of continuous N breathing cycles (for example, 10) are obtained, the difference between adjacent cycles is calculated, that is, , and the arithmetic mean of the difference is calculated: the slope change amount = , and the slope change amount is determined as the dorsal attenuation index.

[0064] In this way, the dynamic change of the mechanical properties of the dorsal lung tissue is quantified, and the possible collapse or reduced elasticity of the dorsal alveoli is sensitively reflected, thereby providing a basis for early identification of dorsal lung injury.

[0065] In one possible implementation, please refer to Figure 6 S400, determining an abdominal stress index of the abnormal feature in the prone position according to the airflow retention feature, including: S410A, extracting plateau pressure data in continuous multiple breathing cycles based on the airflow retention feature; wherein the plateau pressure data is used to indicate the stable value of the airway pressure at the end of inspiration pause time in each breathing cycle.

[0066] It can be understood that the plateau pressure refers to the value after the airway pressure stabilizes when ventilation is paused at the end of inspiration (end-inspiratory pause) (reflecting the static pressure in the alveoli); the end-inspiratory pause time is the pause time set by the ventilator after the end of inspiration without air supply or exhalation (usually 0.5-1 second, to facilitate pressure stabilization); the plateau pressure data is a sequence of plateau pressures over multiple respiratory cycles.

[0067] Exemplarily, the end-inspiratory pause time setting (e.g., 0.8 seconds) is read through the ventilator parameters. In each respiratory cycle, the end of inspiration is located (the moment when the flow rate suddenly changes from a positive value to 0). The subsequent 0.2-0.8 second period is the pressure stabilization period (excluding the initial fluctuation). The pressure value within the stabilization period is extracted, and its average value is calculated as the plateau pressure of the cycle. The plateau pressure of multiple consecutive cycles is collected to form the plateau pressure data.

[0068] S420A, calculating a sequence of differences in the plateau pressures of adjacent respiratory cycles based on the plateau pressure data.

[0069] For example, the plateau pressure data are arranged in the order of respiratory cycle (P1, P2, ..., P n ), calculate the adjacent differences: D = P-P1, D = P3-P2, ..., D n-1 =P n -P n-1 , and then the sequence composed of these differences in order is the difference sequence.

[0070] S430A: Count the frequency of increases in the difference sequence exceeding a preset threshold, and use the increase frequency as a ventral stress indicator.

[0071] It is understood that the threshold is set based on the critical value set in the clinical safety range (e.g. , exceeding this value indicates an abnormal increase in plateau pressure); the rising frequency refers to the number of times the rising value in the difference sequence exceeds the X threshold; the ventral stress index is an indicator that reflects the frequency of abnormal increase in the pressure load on the ventral lung tissue.

[0072] For example, a threshold value (e.g. ), by traversing the difference sequence, all rising values ​​greater than 0 and with values ​​greater than the set threshold are screened out, and the number of screened rising values ​​is counted, that is, the rising frequency, which is defined as the ventral stress index.

[0073] This setting quantifies the frequency of abnormal increase in ventral lung tissue pressure load. A higher frequency indicates a greater risk of ventral alveolar overinflation, providing a basis for timely adjustment of ventilation parameters.

[0074] In one possible implementation, see Figure 7S400, determining the cross-zone delay indicator of the prone position according to the airflow retention feature, including: S410B, identifying the airway pressure peak time of each breathing cycle based on the airflow retention feature and the pressure data, and determining the airway pressure peak time as the correlation peak time of the abdominal stress indicator.

[0075] It can be understood that the airway pressure peak time refers to the time when the airway pressure reaches the maximum value in each breathing cycle; the correlation peak time is the time when the pressure peak is associated with the change of the abdominal stress (the time when the abdominal alveoli are subjected to the maximum stress).

[0076] Exemplarily, in the pressure data of each breathing cycle, the time point with the maximum pressure value is located (determined by comparing the sizes of all pressure values), which is the airway pressure peak time. Since the abdominal stress is closely related to the airway pressure peak, this time point is defined as the correlation peak time of the abdominal stress indicator.

[0077] S420B, identifying the expiratory phase flow peak time of each breathing cycle as the dorsal representative peak time.

[0078] Exemplarily, in the expiratory phase flow data of each breathing cycle, the time point with the maximum absolute value of flow is screened out, which can be determined by comparing the absolute values of all expiratory flow values, and then the time point is the expiratory phase flow peak time, which is defined as the dorsal representative peak time.

[0079] S430B, calculating the difference between the correlation peak time and the dorsal representative peak time in each breathing cycle.

[0080] Exemplarily, the difference between the correlation peak time and the dorsal representative peak time in each breathing cycle is to subtract the value of the dorsal representative peak time from the value of the correlation peak time (in seconds) for each breathing cycle, to obtain the difference value (which can be positive, indicating that the abdominal peak is later than the dorsal peak; which can be negative, indicating that the abdominal peak is earlier than the dorsal peak).

[0081] S440B, averaging the differences of a plurality of consecutive breathing cycles to obtain a time difference average, and determining the time difference average as the cross-zone delay indicator.

[0082] Exemplarily, the cross-zone delay indicator is a comprehensive indicator reflecting the delay degree of the function response of the abdominal and dorsal lung tissues (the larger the absolute value, the more obvious the delay). The time difference average can be obtained by collecting the time differences of N consecutive breathing cycles, and then calculating the arithmetic average to obtain the time difference average, and then defining the average as the cross-zone delay indicator, which comprehensively reflects the time sequence differences of multiple breathing cycles, more stably reflects the functional coordination abnormalities of the cross-zone in the lung, and provides a basis for evaluating the overall ventilation efficiency of the lung.

[0083] In a possible implementation, S400, determining the parameter abnormality data based on the airflow retention feature and the abnormality feature comprises: S401, extracting a retention parameter from the airflow retention feature; wherein the retention parameter comprises a retention average value and a coefficient of variation in a plurality of consecutive breath cycles.

[0084] It can be understood that the retention parameter is a quantitative index describing the degree of gas retention; the retention average value is the arithmetic mean of the gas retention amount in a plurality of consecutive breath cycles (reflecting the average retention level); and the coefficient of variation is the ratio of the standard deviation of the retention amount to the average value (reflecting the fluctuation degree of the retention amount).

[0085] Exemplarily, the gas retention amount (such as the difference between the end-tidal volume and the preset normal end-tidal volume) of each breath cycle is extracted from the airflow retention feature, the arithmetic mean of the retention amount in a plurality of consecutive cycles is calculated to obtain the retention average value, and the standard deviation of the plurality of retention amounts is calculated and divided by the retention average value to obtain the coefficient of variation, so that the two can be determined as the retention parameter.

[0086] S402, determining a retention intensity coefficient according to the retention average value and the coefficient of variation; wherein the retention intensity coefficient is used to represent the severity of the gas retention in the expiratory phase.

[0087] Exemplarily, the weight of the retention average value (such as 0.7) and the weight of the coefficient of variation (such as 0.3) are set, the retention average value is multiplied by its weight, and the sum of the coefficient of variation multiplied by its weight is added to obtain the retention intensity coefficient, such as the retention intensity coefficient is calculated by 0.7 times the retention average value plus 0.3 times the coefficient of variation.

[0088] S403, generating the parameter abnormality data according to a first combination relationship between the backside attenuation index and the retention intensity coefficient, a second combination relationship between the abdominal side stress index and the cross-zone delay index, and a third combination relationship between the cross-zone delay index and the backside attenuation index.

[0089] Exemplarily, the combination relationship refers to the association operation between different indexes, that is, the first combination relationship is obtained by associating the backside attenuation index with the retention intensity coefficient, the second combination relationship is obtained by associating the abdominal side stress index with the cross-zone delay index, and the third combination relationship is obtained by associating the cross-zone delay index with the backside attenuation index. The parameter abnormality data can be obtained by multiplying the product of the backside attenuation index and the retention intensity coefficient (reflecting the synergistic effect of the backside attenuation and the gas retention) by the first combination relationship, multiplying the ratio of the abdominal side stress index to the cross-zone delay index (reflecting the relative relationship between the abdominal side stress and the regional delay) by the second combination relationship, and multiplying the difference between the cross-zone delay index and the backside attenuation index (reflecting the gap between the regional delay and the backside attenuation) by the third combination relationship, and then adding the results of the three combination relationships according to the preset weight (for example, each 1 / 3) to obtain the parameter abnormality data.

[0090] In this way, the correlation of the comprehensive multi-dimensional indicators is set, the limitations of single indicators are avoided, and the overall state of lung function abnormalities is comprehensively reflected.

[0091] In a possible implementation, S403, according to the first combination relationship between the backside attenuation index and the retention intensity coefficient, the second combination relationship between the abdominal side stress index and the cross-zone delay index, and the third combination relationship between the cross-zone delay index and the backside attenuation index, the parameter abnormal data is generated, including: S4031, the backside attenuation index and the retention intensity coefficient are jointly operated to generate a backside coordination factor; wherein the backside coordination factor is used to reflect the coordination of backside alveolar collapse and gas residue.

[0092] For example, the value of the backside attenuation index is multiplied by the value of the retention intensity coefficient to obtain a product, and the product is the backside coordination factor. For example, the controller performs product calculation on the obtained backside attenuation index and the obtained retention intensity coefficient to obtain a calculation result, and the calculation result is the backside coordination factor.

[0093] S4032, the abdominal side stress index and the cross-zone delay index are dynamically coupled and analyzed to generate an abdominal side overload factor; the abdominal side overload factor is used to indicate the correlation between abdominal side alveolar overexpansion and cross-zone conduction delay.

[0094] It can be understood that the dynamic coupling analysis refers to the dynamic correlation (for example, the correlation coefficient in the sliding window) of the two indicators in continuous multiple periods; the greater the value of the abdominal side overload factor, the stronger the correlation between the abdominal side overexpansion and the regional conduction delay.

[0095] For example, the abdominal side stress index and the cross-zone delay index of continuous multiple breathing periods (which can be 5, 3, or 8, etc.) are taken to calculate the Pearson correlation coefficient of the two, and the Pearson correlation coefficient reflects the degree of linear correlation. The sliding window (moving 1 period each time) is repeatedly calculated to obtain multiple correlation coefficients, and the average value of the multiple correlation coefficients is determined as the abdominal side overload factor.

[0096] S4033, the cross-zone delay index and the backside attenuation index are time series aligned and analyzed to generate an asynchronous distribution factor; wherein the asynchronous distribution factor is used to indicate the time correlation between pressure conduction delay and backside mechanical attenuation.

[0097] For example, cross-correlation analysis is performed on the time series of the cross-zone delay index and the backside attenuation index, the correlation coefficients under different time offsets are calculated, the time offset corresponding to the maximum correlation coefficient is found from the correlation coefficients, and the absolute value of the time offset is determined as the asynchronous distribution factor.

[0098] S4034, determining the parameter abnormality data according to the dorsal coordination factor, the ventral overload factor and the asynchronous distribution factor.

[0099] Exemplarily, the parameter abnormality data can be determined by setting the weights of the dorsal coordination factor, the ventral overload factor and the asynchronous distribution factor as 0.4, 0.3 and 0.3 respectively, multiplying the numerical value of each factor by the corresponding weight and summing up the results to obtain the total sum.

[0100] In this way, through the abnormal information of the three dimensions, the overall level of lung function abnormality can be comprehensively and objectively reflected. The dorsal coordination factor effectively reveals the coordination of dorsal alveolar collapse and gas retention, which helps to identify potential damage to the dorsal lung. The ventral overload factor quantifies the correlation between ventral alveolar overexpansion and cross-regional conduction delay through dynamic coupling analysis, providing a strong basis for assessing ventral lung pressure load. The asynchronous distribution factor further reveals the time correlation between pressure conduction delay and dorsal mechanical attenuation, providing a new perspective for understanding the functional coordination abnormalities between different regions in the lung. This not only improves the sensitivity and specificity of diagnosis, but also helps to optimize the prone position ventilation strategy and reduce complications.

[0101] In one possible implementation, please refer to Figure 8 S4034, determining the parameter abnormality data according to the dorsal coordination factor, the ventral overload factor and the asynchronous distribution factor includes: S40341, adding the dorsal coordination factor and the asynchronous distribution factor to obtain a preliminary imbalance value.

[0102] Exemplarily, the preliminary imbalance value is directly obtained by adding the numerical value of the dorsal coordination factor and the numerical value of the asynchronous distribution factor.

[0103] S40342, calculating the preliminary imbalance value with a preset weight coefficient to obtain a regional imbalance index.

[0104] Exemplarily, the regional imbalance index is a quantitative indicator reflecting the degree of functional imbalance in different regions of the lung. Specifically, the regional imbalance index is obtained by multiplying the numerical value of the preliminary imbalance value by the preset weight coefficient (for example, K=1.2) to obtain the product.

[0105] S40343, extracting the decimal part of the ventral overload factor and determining a risk increase ratio according to the decimal part.

[0106] It can be understood that the decimal part refers to the numerical value obtained by subtracting the integer part of the ventral overload factor (for example, the decimal part of 2.3 is 0.3); the risk increase ratio reflects the proportion of the overall risk increase caused by the ventral abnormality (the value of the decimal part is the increase ratio, for example, 0.3 represents a 30% increase in risk).

[0107] Exemplarily, the risk increase ratio is determined according to the decimal part, that is, the integer and the decimal part of the ventral overload factor are separated (for example, a modulo operation is used), the decimal part is extracted, and the decimal part is taken as the risk increase ratio.

[0108] S40344, determining a parameter abnormality level according to the regional imbalance index and the risk increase ratio; wherein the parameter abnormality level includes a first abnormality parameter, a second abnormality parameter, and a third abnormality parameter, the first abnormality parameter is greater than the second abnormality parameter, and the second abnormality parameter is greater than the third abnormality parameter.

[0109] Exemplarily, the classification threshold can be set, that is, the regional imbalance index is greater than 5 and the risk increase ratio is greater than 0.5, which is a first abnormality; the regional imbalance index is 3-5 and the risk increase ratio is 0.3-0.5, which is a second abnormality; and the rest is a third abnormality; and the level meeting the condition is determined as the parameter abnormality level. The above specific values of the regional imbalance index and the risk increase ratio are exemplary and are not specifically limited.

[0110] S40345, determining the parameter abnormality level as parameter abnormality data.

[0111] Exemplarily, the parameter abnormality level (first abnormality, second abnormality, and third abnormality) is taken as the final parameter abnormality data, directly reflecting the severity of lung function abnormality.

[0112] In summary, the regional abnormality specific to the prone position is accurately captured, the influence of the prone position interference on the respiratory data is effectively avoided, the synergistic effect of these composite abnormalities can be captured, the limitation of the traditional single index that can only reflect isolated abnormalities is broken through, the interference of the respiratory cycle fluctuation caused by the prone position on the abnormality detection result is avoided, and then the airflow retention and the respiratory mechanics parameter abnormality of the dorsal side, the ventral side, and the cross region can be accurately determined.

[0113] It should be understood that the size of the serial number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0114] Corresponding to the prone position ventilation respiratory mechanics parameter abnormality detection method described in the above embodiment, the present embodiment also provides a prone position ventilation respiratory mechanics parameter abnormality detection system. Each unit of the system can realize each step of the prone position ventilation respiratory mechanics parameter abnormality detection method. Figure 9 The structure block diagram of the prone position ventilation respiratory mechanics parameter abnormality detection system provided by the present embodiment is shown, and only the part related to the present embodiment is shown for the convenience of description.

[0115] Reference Figure 9The prone position ventilation under-breathing mechanics parameter abnormality detection system comprises: The first acquisition unit is configured to, during the prone position ventilation, control the controller to continuously acquire pressure information in the prone position state through the main sensor at a set frequency, and determine a flow waveform based on dynamic change characteristics of the pressure information, wherein the dynamic change characteristics include a pressure change rate, an amplitude, and a time distribution. The second acquisition unit is configured to, during the prone position ventilation, continuously acquire airway pressure in the prone position state through the auxiliary sensor. The determination unit is configured to determine airflow retention characteristics in a plurality of continuous breathing cycles during the prone position ventilation according to the flow waveform and the airway pressure, wherein the airflow retention characteristics are used to indicate situations of insufficient lung gas discharge and abnormal coordination of the dorsal and ventral regions. The result unit is configured to, in a case where the abnormal characteristics of the prone position are determined according to the airflow retention characteristics, determine parameter abnormality data based on the airflow retention characteristics and the abnormal characteristics, wherein the abnormal characteristics include a dorsal attenuation index, a ventral stress index, and a cross-zone delay index, the dorsal attenuation index is used to indicate a slope change amount of a pressure-volume curve in a tail time of an expiration phase, the ventral stress index is used to indicate a frequency of a platform pressure rising over a set threshold in a continuous breathing cycle, and the cross-zone delay index is used to indicate a time difference between airway pressure peaks of the ventral and dorsal regions; and the parameter abnormality data is used to indicate an abnormal detection result of a respiratory mechanics parameter.

[0116] It should be noted that the information interaction and execution process between the above system / units are based on the same concept as the method embodiments of the present application, and the specific functions and technical effects thereof can be referred to the method embodiments part, which will not be described here.

[0117] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The above integrated unit can be realized in the form of hardware or software. In addition, the specific names of each functional unit and module are only for easy distinction, and do not limit the protection scope of the present application. The specific working process of the units and modules in the system can be referred to the corresponding process in the foregoing method embodiments, which will not be described here.

[0118] The present application also provides a controller, Figure 10This is a schematic diagram of the structure of a controller provided in one embodiment of the present application. Figure 10 As shown, the controller 6 of this embodiment includes: at least one processor 60 ( Figure 10 Only one is shown), at least one memory 61 ( Figure 10 Only one is shown in the figure) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, the controller 6 implements the steps of any of the above-mentioned embodiments of the method for detecting abnormal respiratory mechanics parameters under prone ventilation, or implements the functions of each module / unit in the above-mentioned system embodiments.

[0119] For example, the computer program 62 may be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to implement the present application. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program 62 in the controller 6.

[0120] The controller 6 may be a computing device such as a desktop computer, and the controller may include, but is not limited to, a processor 60 and a memory 61. It will be understood by those skilled in the art that Figure 10 This is only an example of the controller 6 and does not constitute a limitation on the controller 6. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, buses, etc.

[0121] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0122] In some embodiments, the memory 61 may be an internal storage unit of the controller 6, such as a hard drive or memory of the controller 6. In other embodiments, the memory 61 may also be an external storage device of the controller 6, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. equipped on the controller 6. Furthermore, the memory 61 may include both the internal storage unit of the controller 6 and an external storage device. The memory 61 is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of the computer program. The memory 61 may also be used to temporarily store data that has been output or is about to be output.

[0123] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0124] An embodiment of the present application provides a computer program product. When the computer program product is run on a controller, the controller is enabled to implement the steps of any of the above method embodiments.

[0125] If the integrated unit is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process steps in the above-mentioned method embodiments by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to the controller, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium. Examples include a USB flash drive, a removable hard drive, a magnetic disk, or an optical disk.

[0126] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0127] Those skilled in the art can understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0128] In the embodiments provided in the present application, it should be understood that the disclosed prone position ventilation under respiratory mechanics parameter abnormality detection system, controller and method can be implemented in other ways. For example, the prone position ventilation under respiratory mechanics parameter abnormality detection system and controller embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, another division manner can be used. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units or components shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0129] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e. can be located in one place or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0130] The above-described embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method of detecting abnormality in respiratory mechanics parameters under prone position ventilation, characterized by, The application is applied to a prone position device, the prone position device comprises a chest pad, a main sensor, a secondary sensor and a controller, the main sensor is arranged on the chest pad, the secondary sensor is arranged on a Y-shaped tube area of a ventilator, and the method comprises: During the prone position ventilation process, the controller continuously acquires pressure information in the prone position state through the main sensor at a set frequency, and determines a flow waveform based on dynamic change characteristics of the pressure information; wherein the dynamic change characteristics comprise a pressure change rate, an amplitude and a time distribution; During the prone position ventilation process, the secondary sensor continuously acquires airway pressure in the prone position state; According to the flow waveform and the airway pressure, air flow retention characteristics in a plurality of continuous breathing cycles during the prone position ventilation process are determined; wherein the air flow retention characteristics are used to indicate that the lung gas is not fully discharged and the back and abdominal regions are ventilated abnormally; In the case where abnormal characteristics of the prone position are determined according to the air flow retention characteristics, parameter abnormal data are determined based on the air flow retention characteristics and the abnormal characteristics; wherein the abnormal characteristics comprise a backside attenuation index, an abdominal stress index and a cross-zone delay index, the backside attenuation index is used to indicate a slope change amount of a pressure-volume curve in the end time of the exhalation phase, the abdominal stress index is used to indicate a frequency of the platform pressure rising above a set threshold in the continuous breathing cycle, and the cross-zone delay index is used to indicate a time difference between the abdominal and back airway pressure peaks; the parameter abnormal data are used to indicate abnormal detection results of respiratory mechanics parameters.

2. The prone position ventilation lower respiratory mechanics parameter abnormality detection method of claim 1, wherein, According to the flow waveform and the airway pressure, the effective air flow retention characteristics in a plurality of continuous breathing cycles during the prone position ventilation process are determined, which comprises: The time corresponding to the exhalation valve opening signal of the ventilator is determined as a time zero point; A fixed time interval is intercepted according to the time zero point, and sampling flow values are obtained at equal time intervals in the fixed time interval; Centralized processing is performed based on the flow waveform, the airway pressure and the sampling flow values to obtain a second derivative determination value; The effective air flow retention characteristics are determined according to the second derivative determination value and a continuity verification rule; wherein the continuity verification rule comprises a verification breathing interval standard deviation, a plurality of valid breathing cycles and a correlation coefficient of adjacent cycle flow curves.

3. The method of detecting abnormality in lower respiratory mechanics parameters in prone position ventilation according to claim 1, wherein, The backside attenuation index of the abnormal characteristics of the prone position determined according to the air flow retention characteristics comprises: Based on the air flow retention characteristics, flow data and pressure data in the end time of the exhalation phase of one of the plurality of breathing cycles are obtained from the ventilator; wherein the flow data are used to indicate flow values in the end time of the exhalation phase of one of the plurality of breathing cycles, and the pressure data are used to indicate pressure values; Numerical integration is performed on the flow data to obtain a corresponding volume time sequence; In combination with the pressure data and the volume time sequence, a pressure-volume curve is obtained; Linear piecewise fitting is performed on the pressure-volume curve to obtain an average slope corresponding to the pressure-volume curve; Determine a slope change amount based on the average slope, and determine the slope change amount as the back-side decay index; wherein the slope change amount is an absolute average of slope difference values of adjacent periods.

4. The prone position ventilation lower respiratory mechanics parameter abnormality detection method of claim 1, wherein, The abdominal-side stress index of the abnormal feature of the prone position is determined according to the airflow retention feature, and includes: Based on the airflow retention feature, platform pressure data in a plurality of continuous breathing periods is extracted; wherein the platform pressure data is used to indicate an airway pressure stable value detected at an end-inspiratory pause time of each breathing period; A difference sequence of platform pressures of adjacent breathing periods is calculated according to the platform pressure data; An ascending frequency of an ascending value exceeding a set threshold in the difference sequence is counted, and the ascending frequency is taken as the abdominal-side stress index.

5. The prone position ventilation lower respiratory mechanics parameter abnormality detection method of claim 3, wherein, The cross-zone delay index of the abnormal feature of the prone position is determined according to the airflow retention feature, and includes: Based on the airflow retention feature and the pressure data, an airway pressure peak time of each breathing period is identified, and the airway pressure peak time is determined as a related peak time of the abdominal-side stress index; An expiratory phase flow peak time of each breathing period is identified as a back-side representative peak time; A difference value between the related peak time and the back-side representative peak time in each breathing period is calculated; The difference values of a plurality of continuous breathing periods are averaged to obtain a time difference average, and the time difference average is determined as the cross-zone delay index.

6. The prone position ventilation lower respiratory mechanics parameter abnormality detection method of claim 3, wherein, The parameter abnormal data is determined based on the airflow retention feature and the abnormal feature, and includes: Retention parameters are extracted from the airflow retention feature; wherein the retention parameters include a retention average and a coefficient of variation in a plurality of continuous breathing periods; A retention intensity coefficient is determined according to the retention average and the coefficient of variation; wherein the retention intensity coefficient is used to represent a severity of expiratory phase gas retention; Parameter abnormal data is generated according to a first combination relationship between the back-side decay index and the retention intensity coefficient, a second combination relationship between the abdominal-side stress index and the cross-zone delay index, and a third combination relationship between the cross-zone delay index and the back-side decay index.

7. The prone position ventilation lower respiratory mechanics parameter abnormality detection method of claim 6, wherein, The parameter abnormal data is generated according to the first combination relationship between the back-side decay index and the retention intensity coefficient, the second combination relationship between the abdominal-side stress index and the cross-zone delay index, and the third combination relationship between the cross-zone delay index and the back-side decay index, and includes: The back-side decay index and the retention intensity coefficient are jointly operated to generate a back-side coordination factor; wherein the back-side coordination factor is used to reflect a coordination situation of back-side alveolar collapse and gas retention; The abdominal-side stress index and the cross-zone delay index are dynamically coupled and analyzed to generate an abdominal-side overload factor; the abdominal-side overload factor is used to indicate an association relationship between abdominal-side alveolar overexpansion and cross-zone conduction delay; The cross-zone delay index and the back-side decay index are time-series aligned and analyzed to generate an asynchronous distribution factor; wherein the asynchronous distribution factor is used to indicate a time correlation between pressure conduction delay and back-side mechanical decay. Determine parameter abnormality data according to the dorsally coordinated factor, the ventrally overloaded factor and the asynchronous distribution factor.

8. The prone position ventilation lower respiratory mechanics parameter abnormality detection method of claim 7, wherein, The determining parameter abnormality data according to the dorsally coordinated factor, the ventrally overloaded factor and the asynchronous distribution factor comprises: Adding the dorsally coordinated factor and the asynchronous distribution factor to obtain a preliminary imbalance value; Calculating the preliminary imbalance value and a preset weight coefficient to obtain a regional imbalance index; Extracting a decimal part of the ventrally overloaded factor and determining a risk amplification ratio according to the decimal part; Determining a parameter abnormality level according to the regional imbalance index and the risk amplification ratio; wherein the parameter abnormality level comprises a first abnormality parameter, a second abnormality parameter and a third abnormality parameter, the first abnormality parameter is greater than the second abnormality parameter, and the second abnormality parameter is greater than the third abnormality parameter; Determining the parameter abnormality level as the parameter abnormality data.

9. A prone position ventilation lower airway mechanics parameter abnormality detection system, characterized by, The application is applied to a prone position device, the prone position device comprises a chest pad, a main sensor, a secondary sensor and a controller, the main sensor is arranged on the chest pad, the secondary sensor is arranged on a Y-shaped tube region of a breathing machine, and the system comprises: A first acquisition unit is configured to acquire pressure information in a prone position state at a set frequency by the main sensor during a prone position ventilation process, and determine a flow waveform based on dynamic change characteristics of the pressure information; wherein the dynamic change characteristics comprise a pressure change rate, an amplitude and a time distribution. A second acquisition unit is configured to continuously acquire airway pressure in the prone position state by the secondary sensor during the prone position ventilation process. A determination unit is configured to determine airflow retention characteristics in a plurality of continuous breathing cycles during the prone position ventilation process according to the flow waveform and the airway pressure; wherein the airflow retention characteristics are used to indicate lung gas discharge insufficiency and abnormal ventilation coordination in dorsal and ventral regions. A result unit is configured to determine parameter abnormality data based on the airflow retention characteristics and abnormal characteristics in a case where the prone position abnormal characteristics are determined according to the airflow retention characteristics; wherein the abnormal characteristics comprise a dorsal attenuation index, a ventral stress index and a cross-region delay index, the dorsal attenuation index is used to indicate a slope change amount of a pressure and volume curve in a tail time of an exhalation phase, the ventral stress index is used to indicate a frequency of platform pressure rising above a set threshold in continuous breathing cycles, and the cross-region delay index is used to indicate a time difference between ventral and dorsal airway pressure peaks; and the parameter abnormality data is used to indicate abnormal detection results of respiratory mechanics parameters.

10. A prone position ventilation device, characterized in that, The application relates to a prone position device, the prone position device comprises a chest pad, a main sensor, a secondary sensor and a controller, the main sensor is arranged on the chest pad, the secondary sensor is arranged on a Y-shaped tube region of a breathing machine, and the controller is in communication connection with the main sensor and the secondary sensor, the controller comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the method according to any one of claims 1 to 8 when executing the computer program.

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