Abnormality detection method for breathing machine

Abstract

The invention discloses a breathing machine anomaly detection method, which relates to the technical field of breathing machine detection, and comprises the following steps: determining whether a breathing machine to be detected is abnormal or not according to the judgment quantity corresponding to two water film states of a liquid water film and a dripping state of the breathing machine to be detected in real-time operation time and the growth coefficients corresponding to bacteria in the two water film states; acquiring real-time bacterial load in the pipeline of the respirator to be detected; judging and generating an abnormal signal according to the real-time bacterial load in the breathing machine pipeline, reflecting pipe wall deformation caused by a water film by monitoring an axial strain value of the inner wall of the breathing machine pipeline in real time, and distinguishing two states of a liquid water film and a dripping water film; the real-time bacterial load is calculated by combining the difference rule of bacterial growth in the two states, and when the real-time bacterial load exceeds a preset early warning threshold value, an abnormal signal is generated to prompt that the breathing machine pipeline has the risk of bacterial exceeding, and potential threats to the health of a patient caused by excessive increase of the real-time bacterial load in the breathing machine pipeline are prevented.

Description

Technical Field

[0001] This invention belongs to the field of ventilator testing technology, specifically a method for detecting abnormalities in ventilators. Background Technology

[0002] Ventilators are crucial devices for maintaining respiratory function in clinical treatment. During operation, moisture in the patient's exhaled breath easily condenses upon cooling, leading to the formation of liquid or dripping water films on the inner walls of the tubing, causing localized accumulation / drips. These water films are not only a significant medium for cross-infection among patients, allowing pathogens such as Pseudomonas aeruginosa to easily multiply, but they can also affect ventilation efficiency due to tubing deformation and even cause equipment malfunctions. Furthermore, the accumulation of condensate inside the tubing increases the risk of bacterial growth, especially under conditions of suitable humidity and temperature, easily leading to cross-infection. However, in existing technologies, the monitoring of condensate is mostly limited to single-point humidity or temperature detection, which cannot accurately determine the physical form of water accumulation in the ventilator tubing, nor can it effectively predict the risk of bacterial growth, resulting in waste of consumables or increased risk of patient infection; based on this, a method for detecting abnormalities in ventilators is proposed. Summary of the Invention

[0003] The purpose of this invention is to provide a method for detecting abnormalities in ventilators, which solves the technical problems of being unable to accurately determine the physical form of water accumulation in ventilator tubing and being unable to effectively predict the risk of bacterial growth, resulting in waste of consumables or increased risk of infection for patients.

[0004] A method for detecting abnormalities in a ventilator includes the following steps: Step 1: Place the two sets of ventilator tubing in two different water film states, namely liquid and dripping. Connect each ventilator tubing in each set to a simulated lung of the same specification. Distribute multiple data acquisition points in a spiral pattern along the direction of the ventilator tubing in each set of ventilator tubing. Collect the axial strain values ​​along each ventilator tubing multiple times. Step 2: Analyze the different axial strain values ​​at each data acquisition distribution point in the ventilator tubing group under the two water film states to obtain the axial strain difference range corresponding to the two water film states. Step 3: Inoculate and culture bacteria on the inner wall of each ventilator tubing in the ventilator tubing group under the two water film conditions, and collect and analyze the bacterial load at each data collection distribution point in different ventilator tubings at a frequency of 30 seconds to obtain the growth coefficient of bacteria under the two water film conditions. Step 4: Acquire the real-time axial strain value in the tubing of the ventilator under test at preset intervals T, and obtain the real-time axial strain difference in the tubing of the ventilator under test during the corresponding test period. After each data acquisition, judge the real-time water film state of the tubing of the ventilator under test during the corresponding test period. Based on the number of judgments corresponding to the liquid water film and dripping water film states during the real-time running time of the ventilator under test, combined with the growth coefficients of bacteria in the two water film states, obtain the real-time bacterial load in the tubing of the ventilator under test. Step 5: Generate an abnormal signal based on the real-time bacterial load in the ventilator tubing.

[0005] As a further aspect of the present invention, the specific method for obtaining the axial strain difference ranges corresponding to the two water film states is as follows: The axial strain values ​​at each data acquisition point in the ventilator tubing assembly under the dripping water film state are obtained at each data acquisition time. The absolute value of the difference between the axial strain values ​​of each data acquisition point at each adjacent data acquisition time is obtained, and the mean value is used as the axial strain difference limit MA under the dripping state. Then the axial strain difference range under the dripping state is VA[MA, +∞]; and the axial strain difference range under the liquid water film state is VB[-∞, MA].

[0006] As a further aspect of the present invention, the specific method for obtaining the growth coefficient of bacteria in the liquid water film state is as follows: First, one ventilator tubing in the ventilator tubing group under liquid water film conditions is selected as the analysis tubing. The average of the absolute value of the difference between two adjacent bacterial load measurements at each data acquisition point in the analysis tubing and the ratio of the difference to 30 seconds is taken as the rate of change of the analysis tubing. Similarly, the bacterial load at each data acquisition point in the remaining ventilator tubing in the ventilator tubing group under liquid water film conditions is analyzed to obtain the rate of change for each ventilator tubing in the ventilator tubing group under liquid water film conditions. The average of the maximum and minimum values ​​of the rate of change is taken as the bacterial growth coefficient K1 under liquid water film conditions.

[0007] As a further aspect of the present invention: the same analytical method is used to analyze each ventilator tubing in the ventilator tubing group under the dripping water film state to obtain the growth coefficient K2 of bacteria under the dripping water film state.

[0008] As a further aspect of the present invention, the specific method for judging the real-time water film status of the ventilator tubing during the corresponding detection period is as follows: Every preset duration T during ventilator operation, the real-time axial strain value in the ventilator tubing under test is acquired. Simultaneously, the absolute value of the difference between the initial value and the real-time axial strain value of the ventilator tubing under test during the corresponding test period is taken as the real-time axial strain difference of the ventilator tubing under test during the corresponding test period. When the real-time axial strain difference falls within the axial strain difference range VA[MA, +∞] of the dripping state, the real-time water film state of the ventilator tubing under test during the corresponding test period is defined as a dripping water film; otherwise, it is defined as a liquid water film. The preset duration T is 5 minutes.

[0009] As a further aspect of the present invention, the specific method for obtaining the real-time bacterial load in the ventilator tubing is as follows: After each data acquisition, the real-time water film status of the ventilator tubing during the corresponding detection period is judged. At the same time, the number of judgments c1 and c2 corresponding to the two water film states of dripping and liquid are obtained during the real-time running time of the ventilator under test. The real-time bacterial load H in the ventilator tubing is calculated by H=K2×c1+K1×c2.

[0010] As a further aspect of the present invention: the specific method for determining the generation of abnormal signals based on the real-time bacterial load in the ventilator tubing is as follows: An abnormal signal is generated when the real-time bacterial load H is greater than the warning value Y; otherwise, no action is taken.

[0011] As a further aspect of the present invention, the specific method for distributing the data collection points is as follows: The radial length L of the ventilator tubing is obtained. Taking the end of the ventilator tubing closest to the ventilator as the distribution starting point, a data acquisition distribution point is set at a distance of 2 cm along the ventilator tubing and at a distance of 60° along the circumferential direction, thereby obtaining multiple data acquisition distribution points.

[0012] Compared with the prior art, the beneficial effects of the present invention are: (1) In this invention, by inoculating typical pathogens under two simulated environments and continuously collecting bacterial load, the growth coefficient of bacteria under different water film conditions can be calculated, which reflects the reproduction rate of bacteria per unit time and per unit water film condition, and provides core parameters for subsequent bacterial load estimation. (1) This invention reflects the deformation of the tube wall caused by the water film by real-time monitoring of the axial strain value of the inner wall of the ventilator tubing, and distinguishes between two states: liquid water film and dripping water film; combined with the difference in bacterial growth between the two states, the real-time bacterial load is calculated. When the real-time bacterial load exceeds the preset warning threshold, an abnormal signal is generated to indicate that there is a risk of bacterial overgrowth in the ventilator tubing, and to prevent excessive bacterial growth in the ventilator tubing from posing a potential threat to the patient's health. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the method framework structure of the present invention; Figure 2 This is a schematic diagram of the structure connecting the working end of the ventilator tubing of the present invention to the simulated lung. Detailed Implementation

[0014] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] Example 1: Please refer to Figures 1-2 This application provides a method for detecting abnormalities in a ventilator, comprising the following steps: Step 1: Set up ventilator tubing groups in both liquid and dripping water film states. Connect the active end of each ventilator tubing in each group to a simulated lung of the same specification. Distribute multiple data acquisition points in a spiral pattern along the ventilator tubing direction within each tubing of each group. Based on these data acquisition points, continuously acquire the axial strain values ​​along each ventilator tubing multiple times at a preset acquisition time interval t. The specific acquisition method is as follows: It should be noted that the specific value of the preset acquisition time interval t is determined by relevant personnel according to actual needs. Here, t = 3 seconds. During the operation of the ventilator, the wet strain sensor converts the deformation of the inner wall of the tubing into an electrical signal. Then, by converting the electrical signal into a digital strain value, the axial strain measurement value corresponding to each data acquisition point at different time points is obtained. It contains the strain information of the inner wall of the tubing at that moment and location. The strain sensor uses a common resistance strain gauge sensor. Its working principle is based on the resistance strain effect. When the inner wall of the tubing deforms, the strain gauge will deform accordingly, causing its resistance value to change. By measuring the change in resistance value, the strain of the tubing can be indirectly obtained. When condensation occurs in the tubing, the weight of the liquid water and its flow state will exert different forces on the tubing wall, causing the tubing wall to undergo slight stretching, compression, or bending deformations. The axial strain value can quantify the degree of this deformation. The data acquisition methods of the sensors used above are all existing and mature technologies. It should be noted that the materials, diameters, lengths, and other parameters of each ventilator tubing in each ventilator tubing group are consistent. The simulated lung can precisely adjust parameters such as respiratory rate and tidal volume to simulate breathing movements under different physiological states, thereby simulating the breathing state of real patients. The temperature, air pressure, and airflow of the simulated environment are also kept consistent. The specific method for distributing data collection points is as follows: The radial length L of the ventilator tubing is obtained. Starting from the end of the tubing closest to the ventilator, a data acquisition distribution point is set within the tubing at 2cm intervals along the radial length and at 60° intervals along the circumference. This yields multiple data acquisition distribution points Da. The radial length between each data acquisition distribution point and the starting point is used as the vertical coordinate, and the rotation angle corresponding to each data acquisition distribution point is used as the horizontal coordinate. This yields the coordinates Da(i60°, a2cm) for each data acquisition distribution point, where i represents different rotation angles (i = 1, 2, ..., e1), and e1 is the total number of rotation angles (e1 = 360° / 60° = 6). a represents different data acquisition distribution points (a = 1, 2, ..., e2), where e2 is the total number of data acquisition distribution points. This achieves full-circumferential, high-density monitoring of the tubing's inner wall, ensuring coverage of all possible condensation areas, including the bottom and sidewall recesses. Step 2: Analyze the different axial strain values ​​at each data acquisition distribution point in the ventilator tubing group under the two water film states, and then obtain the axial strain difference range corresponding to the two water film states respectively. The specific method for obtaining the axial strain difference ranges corresponding to the two water film states is as follows: The axial strain values ​​at each data acquisition point within each ventilator tubing in the dripping water film state are obtained at each data acquisition time. The absolute value of the difference in axial strain values ​​between each data acquisition point at each adjacent data acquisition time is obtained, and the average value is used as the axial strain difference limit MA in the dripping state. The axial strain difference range in the dripping state is VA[MA, +∞]; the axial strain difference range in the liquid water film state is VB[-∞, MA). Because the liquid water film is a thin layer formed by water adhering relatively evenly to the inner wall of the ventilator tubing, the contact area between the water and the tubing inner wall is large, and the water's fluidity is relatively poor, the pressure applied to the tubing inner wall is relatively uniform, preventing drastic deformation of the tubing inner wall. Therefore, the axial strain of the inner wall of the liquid water film tubing is relatively small, and the corresponding axial strain difference is also within a relatively low range. Conversely, water in a dripping water film has a significant tendency to aggregate and flow under the action of gravity, easily forming local water droplets and dripping. This dynamic flow and aggregation of water will generate uneven and large forces on the inner wall of the tubing, leading to local and more obvious deformation of the inner wall of the tubing. Therefore, the dripping water film will cause relatively large axial strain on the inner wall of the tubing, and its corresponding axial strain difference is greater. By calculating the axial strain difference at each sampling point at adjacent time points, an axial strain difference value sequence is obtained. Based on the different characteristics of liquid water film and dripping water film, the strain difference limit range for the two states is calculated respectively. By calculating the axial strain difference and setting the strain difference range for the two water film states respectively, the water film states can be accurately distinguished, and the different deformation characteristics of liquid water film and dripping water film can be effectively distinguished, thus improving the detection sensitivity. By utilizing the change in axial strain difference, the deformation of the inner wall of the pipeline caused by the change in water film state can be detected in a timely manner, providing a clear judgment standard for subsequent real-time detection and avoiding subjective judgment errors.

[0016] Example 2: As Example 2 of the present invention, in specific implementation, compared with Example 1, the technical solution of this example differs from that of Example 1 only in that this example includes steps 3, 4, and 5: Step 3: In a laboratory simulation environment, bacteria were inoculated and cultured on the inner walls of each ventilator tubing in both liquid and dripping water film states. The bacterial load at each data collection point in different ventilator tubing was collected and analyzed at 30-second intervals to obtain the bacterial growth coefficients over time under the two water film states. The specific method for collecting bacterial load at various data collection points within different ventilator tubing is to use the ATP biofluorescence method for rapid detection, which is an existing and mature technology and therefore will not be described in detail here; the inoculated bacteria can be Pseudomonas aeruginosa, a typical ventilator-associated pathogen. First, one ventilator tubing in the ventilator tubing group under liquid water film conditions is selected as the analysis tubing. The average of the absolute value of the difference between two adjacent bacterial load measurements at each data collection point in the analysis tubing and the ratio of the difference to 30 seconds is taken as the rate of change of the analysis tubing. Similarly, the bacterial load at each data collection point in the remaining ventilator tubing in the ventilator tubing group under liquid water film conditions is analyzed to obtain the rate of change for each ventilator tubing in the ventilator tubing group under liquid water film conditions. The average of the maximum and minimum values ​​of the rate of change is taken as the bacterial growth coefficient K1 under liquid water film conditions over time. At the same time, the same analysis method was used to analyze each ventilator tubing in the ventilator tubing group under the dripping water film state to obtain the bacterial growth coefficient K2 over time under the dripping water film state. By analyzing the changes in bacterial load under two water film conditions, the bacterial growth coefficients under liquid water film and dripping water film were calculated to reflect the growth of bacteria over time under the two conditions. Obtaining the bacterial growth coefficient provides a quantifiable parameter for real-time assessment of bacterial accumulation in ventilator tubing, which helps control the risk of ventilator-associated infections. By inoculating typical pathogens under two simulated environments and continuously collecting bacterial load data, the growth coefficient of bacteria under different water film conditions can be calculated, reflecting the reproduction rate of bacteria per unit time and per unit water film condition, providing core parameters for subsequent bacterial load estimation.

[0017] Step 4: In the anomaly detection of the ventilator, the real-time axial strain value in the tubing of the ventilator under test is acquired every preset time interval T, and the real-time axial strain difference in the tubing of the ventilator under test during the corresponding detection period is obtained. Based on the real-time axial strain difference, the real-time water film state of the tubing of the ventilator under test during the corresponding detection period is judged after each data acquisition. Simultaneously, based on the number of judgments corresponding to the liquid water film and dripping water film states during the real-time operation of the ventilator under test, the real-time bacterial load in the tubing of the ventilator under test is obtained. The specific method is as follows: The specific method for judging the real-time water film status of the ventilator tubing during the corresponding detection period is as follows: Every preset duration T of the ventilator operation, the real-time axial strain value in the ventilator tubing under test is acquired. Simultaneously, based on the difference between the initial value and the real-time axial strain value of the ventilator tubing under test during the corresponding test period, i.e., the change value of the axial strain value of the ventilator tubing under test during the corresponding test period, the real-time axial strain difference of the ventilator tubing under test during the corresponding test period is obtained. When the real-time axial strain difference belongs to the axial strain difference range VA[MA, +∞] of the dripping state, the real-time water film state of the ventilator tubing under test during the corresponding test period is considered to be a dripping water film; otherwise, it is considered a liquid water film. The same analysis method is used to determine the real-time water film state of the ventilator tubing under test during the corresponding test period after each data acquisition. The preset duration T is 5 minutes. The absolute value of the difference between the initial value and the real-time axial strain value of the ventilator tubing under test during the corresponding test period is taken as the real-time axial strain difference of the ventilator tubing under test during the corresponding test period. The initial axial strain value of the ventilator tubing under test is 0 by default. The number of judgments c1 and c2 corresponding to the two water film states of dripping and liquid are obtained during the real-time running time of the ventilator under test. The real-time bacterial load H in the ventilator tubing is calculated by H=K2×c1+K1×c2. By monitoring changes in real-time strain difference, the current water film status of the ventilator tubing can be accurately determined, providing real-time data for subsequent bacterial load assessment. Dynamic assessment of bacterial load: Dynamic estimation of bacterial load can reflect the impact of water film status on bacterial growth in real time, allowing for early detection of the risk of excessive bacterial growth and achieving the goal of infection prevention. Step 5: Generate an abnormal signal based on the real-time bacterial load in the ventilator tubing. An abnormal signal is generated when the real-time bacterial load H is greater than the warning value Y; otherwise, no action is taken. The specific method for determining the warning value Y will be formulated by relevant personnel based on actual needs. By monitoring the axial strain value of the ventilator tubing in real time to reflect the tubing wall deformation caused by the water film, the system distinguishes between two states: liquid water film and dripping water film. Combining the differences in bacterial growth between the two states, the system calculates the real-time bacterial load and generates an abnormal signal when the bacterial load exceeds the safety threshold. This enables early warning of the risk of condensate accumulation and microbial contamination in the tubing, reducing the risk of infection for patients and improving the safety of ventilator use.

[0018] When the real-time bacterial load exceeds the preset warning threshold, an abnormal signal is generated, indicating that there is a risk of excessive bacteria in the ventilator tubing, preventing excessive bacterial growth in the ventilator tubing from posing a potential threat to the patient's health.

[0019] Example 3: As Example 3 of the present invention, in specific implementation, compared with Example 1 and Example 2, the technical solution of this example is to combine the solutions of Example 1 and Example 2.

[0020] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0021] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for detecting abnormalities in a ventilator, characterized in that, Includes the following steps: Step 1: Place the two sets of ventilator tubing in two different water film states, namely liquid and dripping. Connect each ventilator tubing in each set to a simulated lung of the same specification. Distribute multiple data acquisition points in a spiral pattern along the direction of the ventilator tubing in each set of ventilator tubing. Collect the axial strain values ​​along each ventilator tubing multiple times. Step 2: Analyze the different axial strain values ​​at each data acquisition distribution point in the ventilator tubing group under the two water film states to obtain the axial strain difference range corresponding to the two water film states. Step 3: Inoculate and culture bacteria on the inner wall of each ventilator tubing in the ventilator tubing group under the two water film conditions, and collect and analyze the bacterial load at each data collection distribution point in different ventilator tubings at a frequency of 30 seconds to obtain the growth coefficient of bacteria under the two water film conditions. Step 4: Acquire the real-time axial strain value in the tubing of the ventilator under test at preset time intervals T, and obtain the real-time axial strain difference in the tubing of the ventilator under test during the corresponding test period. After each data acquisition, judge the real-time water film state of the tubing of the ventilator under test during the corresponding test period. Based on the number of judgments corresponding to the liquid water film and dripping water film states during the real-time running time of the ventilator under test, combined with the growth coefficients of bacteria in the two water film states, obtain the real-time bacterial load in the tubing of the ventilator under test. Step 5: Generate an abnormal signal based on the real-time bacterial load in the ventilator tubing.

2. The method for detecting abnormalities in a ventilator according to claim 1, characterized in that, The specific method for obtaining the axial strain difference ranges corresponding to the two water film states is as follows: The axial strain values ​​at each data acquisition point in the ventilator tubing assembly under the dripping water film state are obtained at each data acquisition time. The absolute value of the difference between the axial strain values ​​of each data acquisition point at each adjacent data acquisition time is obtained, and the mean value is used as the axial strain difference limit MA under the dripping state. Then the axial strain difference range under the dripping state is VA[MA, +∞]; and the axial strain difference range under the liquid water film state is VB[-∞, MA].

3. The method for detecting abnormalities in a ventilator according to claim 2, characterized in that, The specific method for obtaining the growth coefficient of bacteria in the liquid water film state is as follows: First, one ventilator tubing in the ventilator tubing group under liquid water film conditions is selected as the analysis tubing. The average of the absolute value of the difference between two adjacent bacterial load measurements at each data acquisition point in the analysis tubing and the ratio of the difference to 30 seconds is taken as the rate of change of the analysis tubing. Similarly, the bacterial load at each data acquisition point in the remaining ventilator tubing in the ventilator tubing group under liquid water film conditions is analyzed to obtain the rate of change for each ventilator tubing in the ventilator tubing group under liquid water film conditions. The average of the maximum and minimum values ​​of the rate of change is taken as the bacterial growth coefficient K1 under liquid water film conditions.

4. The method for detecting abnormalities in a ventilator according to claim 3, characterized in that, At the same time, the same analytical method was used to analyze each ventilator tubing in the ventilator tubing group under the dripping water film state to obtain the growth coefficient K2 of bacteria under the dripping water film state.

5. The method for detecting abnormalities in a ventilator according to claim 4, characterized in that, The specific method for judging the real-time water film status of the ventilator tubing during the corresponding detection period is as follows: Every preset duration T during ventilator operation, the real-time axial strain value in the ventilator tubing under test is acquired. Simultaneously, the absolute value of the difference between the initial value and the real-time axial strain value of the ventilator tubing under test during the corresponding test period is taken as the real-time axial strain difference of the ventilator tubing under test during the corresponding test period. When the real-time axial strain difference falls within the axial strain difference range VA[MA, +∞] of the dripping state, the real-time water film state of the ventilator tubing under test during the corresponding test period is defined as a dripping water film; otherwise, it is defined as a liquid water film. The preset duration T is 5 minutes.

6. The method for detecting abnormalities in a ventilator according to claim 5, characterized in that, The specific method for obtaining the real-time bacterial load in the ventilator tubing is as follows: After each data acquisition, the real-time water film status of the ventilator tubing during the corresponding detection period is judged. At the same time, the number of judgments c1 and c2 corresponding to the two water film states of dripping and liquid are obtained during the real-time running time of the ventilator under test. The real-time bacterial load H in the ventilator tubing is calculated by H=K2×c1+K1×c2.

7. The method for detecting abnormalities in a ventilator according to claim 6, characterized in that, The specific method for determining the generation of abnormal signals based on the real-time bacterial load in the ventilator tubing is as follows: An abnormal signal is generated when the real-time bacterial load H is greater than the warning value Y; otherwise, no action is taken.

8. The method for detecting abnormalities in a ventilator according to claim 1, characterized in that, The specific method for distributing data collection points is as follows: The radial length L of the ventilator tubing is obtained. Taking the end of the ventilator tubing closest to the ventilator as the distribution starting point, a data acquisition distribution point is set at a distance of 2 cm along the ventilator tubing and at a distance of 60° along the circumferential direction, thereby obtaining multiple data acquisition distribution points.

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