Self-adaptive ventilation pressure control method for breathing machine and breathing machine

Abstract

The invention relates to a breathing machine self-adaptive ventilation pressure control method and a breathing machine. The method comprises the following steps: acquiring a target respiratory flow signal of a current respiratory cycle in a current ventilation treatment stage; predicting and generating expected inspiration time and expected tidal volume of the next respiratory cycle based on the target respiratory flow signal; on the basis of the expected inspiration time, the expected pressure gradient during ventilation in the next respiratory cycle is configured, and on the basis of the expected tidal volume, expected pressure support during ventilation in the next respiratory cycle is configured; based on the expected pressure gradient and the expected pressure support, the ventilation pressure of the next respiratory cycle is configured, so that the pressure gradient during ventilation of the next respiratory cycle is matched with the expected pressure gradient, and the pressure support during ventilation of the next respiratory cycle is matched with the expected pressure support. Self-adaptive ventilation pressure control can be effectively achieved, and ventilation convenience, man-machine synchronism, compliance and treatment effectiveness of a user are improved.

Description

Technical Field

[0001] This invention relates to a control method and a ventilator, and more particularly to an adaptive ventilation pressure control method and a ventilator. Background Technology

[0002] Pressure-controlled ventilation is an important ventilation mode in mechanical ventilation, such as PCV (Pressure Controlled Ventilation), BiPAP (Bilevel or Biphasic Positive Airway Pressure), and PSV (Pressure Support Ventilation). Specifically, during inspiration, pressure support is increased to control the rise in airway pressure and switch to the set inspiratory pressure. During inspiration, pressure support is removed to control the pressure and switch to the set inspiratory pressure.

[0003] During mechanical ventilation, different pressure gradients significantly impact user comfort, especially in home-use non-invasive ventilator therapy, where pressure gradient is a crucial factor influencing user compliance and treatment effectiveness. Studies have found that, under the same pressure support, users with higher tidal volume requirements are better suited to a faster pressure ramp rate, allowing for rapid pressure switching to quickly increase ventilation. Conversely, a slower pressure switch, during the pressure descent phase, helps increase mean pressure, alleviate airway collapse, improve oxygenation, and minimize lung injury from repeated alveolar collapse. Furthermore, at the same pressure gradient, users with higher tidal volume requirements are better suited to higher pressure support to increase ventilation; conversely, users with lower tidal volume requirements are better suited to lower pressure support. Adjusting pressure support in conjunction with the speed of pressure switching is beneficial for improving ventilation comfort and treatment effectiveness.

[0004] Currently, the ventilation parameters such as pressure incline and pressure support of mainstream ventilators on the market are mostly adjusted based on experience values, which involves a high degree of human intervention. Furthermore, fixed pressure incline and pressure support are difficult to cover the individual differences of users, and cannot meet the needs of ventilation comfort and effectiveness. This may lead to poor compliance and even problems of human-machine asynchrony. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a method for adaptive ventilation pressure control of a ventilator and a ventilator, which can effectively achieve adaptive ventilation pressure control and improve the ease of ventilation, human-ventilator synchronization, compliance and treatment effectiveness.

[0006] According to the technical solution provided by the present invention, a method for adaptive ventilation pressure control of a ventilator is provided, the ventilation pressure control method comprising: In bilevel positive airway mode, acquire the target respiratory flow signal of the current respiratory cycle under the current ventilation therapy phase; Based on the target respiratory flow signal, predict and generate the expected inspiratory time and expected tidal volume for the next respiratory cycle. Based on the expected inspiratory time, the expected pressure gradient for the next respiratory cycle is configured, and based on the expected tidal volume, the expected pressure support for the next respiratory cycle is configured, wherein the configured expected pressure gradient corresponds to the expected inspiratory time, and the configured expected pressure support corresponds to the expected tidal volume. Based on the desired pressure gradient and desired pressure support, the ventilation pressure for the next respiratory cycle is configured so that the pressure gradient during ventilation in the next respiratory cycle matches the desired pressure gradient, and the pressure support during ventilation in the next respiratory cycle matches the desired pressure support.

[0007] When predicting the expected inspiratory time and expected tidal volume for the next respiratory cycle based on the target respiratory flow signal, the following are included: Based on the target respiratory flow signal, the respiratory physiological parameters of the current respiratory cycle are calculated and determined. The respiratory physiological parameters include at least the inspiratory time and tidal volume corresponding to the current respiratory cycle. Acquire ventilation treatment stage status information when the ventilator is performing ventilation treatment, wherein the ventilation treatment stage status information includes whether there was a reference ventilation treatment stage before the current ventilation treatment stage or whether there was no reference ventilation treatment stage before the current ventilation treatment stage. Based on the status information of the ventilation therapy stage, statistical information on respiratory physiological parameters during ventilation therapy is generated. The statistical information on respiratory physiological parameters includes the inspiratory time per unit time and the tidal volume per unit time. When both the inspiratory time statistic and the tidal volume statistic satisfy the prior statistical distribution, the prior characteristic equations related to the prior statistical distribution are solved respectively to obtain the prior distribution characteristic parameters of inspiratory time and tidal volume. Based on the prior distribution characteristic parameters of inspiratory time, the expected inspiratory time for the next respiratory cycle is calculated and generated. Based on the prior distribution characteristic parameters of tidal volume, the expected tidal volume for the next respiratory cycle is calculated and generated.

[0008] If the ventilation treatment phase status information is that there is no reference ventilation treatment phase before the current ventilation treatment phase, then the inspiratory time statistic per unit time is the inspiratory time statistic within the current ventilation treatment phase, and the tidal volume statistic per unit time is the tidal volume statistic within the current ventilation treatment phase. In this case, both the inspiratory time statistic and the tidal volume statistic satisfy the prior statistical distribution. If the ventilation therapy phase status information indicates that a reference ventilation therapy phase existed before the current ventilation therapy phase, then: The inspiratory time statistics per unit time include the inspiratory time statistics of the current ventilation treatment phase, the inspiratory time statistics of the reference ventilation treatment phase, and the combined inspiratory time statistics generated based on the inspiratory time statistics of the current ventilation treatment phase and the inspiratory time statistics of the reference ventilation treatment phase. The tidal volume statistics per unit time include the tidal volume statistics of the current ventilation treatment phase, the tidal volume statistics of the reference ventilation treatment phase, and the comprehensive tidal volume statistics generated based on the tidal volume statistics of the current ventilation treatment phase and the tidal volume statistics of the reference ventilation treatment phase.

[0009] The prior statistical distribution is a right-skewed normal distribution; If the ventilation therapy phase status information indicates that a reference ventilation therapy phase existed prior to the current ventilation therapy phase, then when determining the prior statistical distribution characteristics of the inspiratory time statistic, the following should be included: Based on the inspiratory time statistics of the current ventilation therapy phase and the inspiratory time statistics of the reference ventilation therapy phase, the weighted value of the inspiratory time statistics is calculated, and then:

[0010] in, This is a weighted value for the inhalation time statistics. This represents the statistical value of inspiratory time during the current ventilation therapy phase. For reference, the statistical values ​​of inspiratory time during the ventilation therapy phase, For weighted weights; When the aggregated value of inhalation time statistics matches the weighted value of inhalation time statistics, the inhalation time statistic per unit time satisfies the prior statistical distribution; otherwise, the inhalation time statistic per unit time does not satisfy the prior statistical distribution.

[0011] Solving the prior characteristic equation based on inspiratory time statistics includes:

[0012] in, The inspiratory time is the statistical composite value obtained using the 90th percentile statistical method. This is the statistical composite value of inspiratory time obtained using the average statistical method. This represents the mean of the distribution up to the current respiratory cycle. This represents the standard deviation of the distribution up to the current respiratory cycle; After calculating the distribution mean and standard deviation, when calculating the expected inspiratory time for the next respiratory cycle, we have:

[0013] in, The expected inspiratory time for the next respiratory cycle.

[0014] When configuring the desired pressure gradient for the next respiratory cycle based on the expected inspiratory time, we have:

[0015] in, For the desired pressure gradient, This represents the minimum pressure gradient. This represents the maximum pressure gradient. The expected inspiratory time for the next respiratory cycle. This is the lower limit of the expected inspiratory time for the next respiratory cycle. This represents the upper limit of the expected inspiratory time for the next respiratory cycle. It is the exponential coefficient.

[0016] When configuring the expected pressure support for the next respiratory cycle based on the expected tidal volume, then:

[0017] in, To support the expected pressure, To support the minimum pressure, To support the maximum pressure, To adjust the pressure support increment coefficient, which can affect the output of different tidal volume distribution ranges. For the expected tidal volume, This is the median factor for tidal volume.

[0018] When configuring the desired pressure gradient for the next respiratory cycle based on the expected inspiratory time using fuzzy inference methods, the following is included: Construct a fuzzy inference rule table for inhalation-pressure gradient corresponding to expected inhalation time and expected pressure gradient. The fuzzy inference rule table for inhalation-pressure gradient includes several fuzzy rules for inhalation-pressure gradient. Each fuzzy rule for inhalation-pressure gradient includes an inhalation time membership degree that represents the corresponding length of the expected inhalation time and a pressure gradient instance membership degree that represents the corresponding magnitude of the pressure gradient. During fuzzy inference, the inhalation time membership degree within each inhalation-pressure gradient fuzzy rule is calculated based on the expected inhalation time. Subsequently, the corresponding expected pressure gradient is generated based on the centroid method.

[0019] When configuring the expected pressure support for the next respiratory cycle ventilation based on the expected tidal volume using a fuzzy inference method, it includes: Construct a fuzzy inference rule table for tidal volume-pressure support corresponding to expected tidal volume and expected pressure support. The fuzzy inference rule table for tidal volume-pressure support includes several fuzzy rules for tidal volume-pressure support. Each fuzzy rule for tidal volume-pressure support includes a tidal volume membership degree representing the corresponding magnitude of expected tidal volume and a pressure support instance membership degree representing the corresponding magnitude of pressure support. During fuzzy inference, the tidal volume membership degree within each dry tidal volume-pressure support fuzzy rule is calculated based on the expected tidal volume. Subsequently, the corresponding expected pressure support is generated based on the centroid method.

[0020] A ventilator that uses the above-described adaptive ventilation pressure control method for ventilation pressure control.

[0021] The advantages of this invention are as follows: Based on the expected inspiratory time, the desired pressure gradient for ventilation in the next respiratory cycle is configured, and based on the expected tidal volume, the desired pressure support for ventilation in the next respiratory cycle is configured. In the next respiratory cycle, the pressure support during ventilation matches the desired pressure, and the pressure gradient during ventilation matches the desired pressure gradient. This allows the inspiratory time to match the expected inspiratory time, and the tidal volume to match the expected tidal volume. At this time, the ventilation comfort and treatment effectiveness of the therapist can be improved, which is beneficial to improving the therapist's compliance and treatment effect.

[0022] Furthermore, during the current ventilation therapy phase, the expected inspiratory time and expected tidal volume of the next respiratory cycle are predicted and generated based on the target respiratory flow signal of the current respiratory cycle. Subsequently, pressure control during ventilation therapy is performed based on the generated expected pressure gradient and expected pressure support, thereby achieving adaptive pressure control throughout the entire ventilation therapy phase. Attached Figure Description

[0023] Figure 1 This is a flowchart of one embodiment of the adaptive ventilation pressure control of the present invention.

[0024] Figure 2 A schematic diagram of an embodiment of pressure gradient and pressure support during mechanical ventilation.

[0025] Figure 3 A schematic diagram of an embodiment of respiratory flow and treatment pressure during mechanical ventilation.

[0026] Figure 4 This is a schematic diagram of an embodiment of the membership degree of the inhalation time and pressure gradient of the present invention.

[0027] Figure 5 This is a schematic diagram of one embodiment of the tidal volume membership degree and pressure support instance membership degree of the present invention.

[0028] Figure 6 This is a schematic diagram illustrating a nonlinear mapping relationship between the intake time and the control time for the rise and fall of output pressure in this invention.

[0029] Figure 7 This is a schematic diagram illustrating a nonlinear mapping relationship between tidal volume and pressure support according to the present invention. Detailed Implementation

[0030] The present invention will be further described below with reference to specific accompanying drawings and embodiments.

[0031] To effectively achieve adaptive ventilation pressure control and improve user convenience, patient-ventilator synchronization, compliance, and treatment effectiveness, this invention provides a ventilator adaptive ventilation pressure control method. Specifically, the ventilation pressure control method includes: In bilevel positive airway mode, acquire the target respiratory flow signal of the current respiratory cycle under the current ventilation therapy phase; Based on the target respiratory flow signal, predict and generate the expected inspiratory time and expected tidal volume for the next respiratory cycle. Based on the expected inspiratory time, the expected pressure gradient for the next respiratory cycle is configured, and based on the expected tidal volume, the expected pressure support for the next respiratory cycle is configured, wherein the configured expected pressure gradient corresponds to the expected inspiratory time, and the configured expected pressure support corresponds to the expected tidal volume. Based on the desired pressure gradient and desired pressure support, the ventilation pressure for the next respiratory cycle is configured so that the pressure gradient during ventilation in the next respiratory cycle matches the desired pressure gradient, and the pressure support during ventilation in the next respiratory cycle matches the desired pressure support.

[0032] It should be noted that the ventilation pressure control of this invention is mainly for ventilators that can operate in bilevel positive pressure ventilation mode. Therefore, when the ventilator cannot operate in bilevel positive pressure ventilation mode, the adaptive ventilation pressure control method of this invention cannot be used for ventilation pressure control. This invention controls ventilation pressure primarily by adjusting the pressure support and pressure slope within each inspiratory cycle. Pressure support (PS) refers to the relative value of the inspiratory pressure (positive end-inspiratory pressure); pressure slope (Tslope), also generally called pressure rise time or fall time, is the time it takes for the delivered pressure to rise to a preset value (pressure rise time) or for the inspiratory high-pressure order to fall to the baseline value (pressure fall time) in constant-pressure ventilation mode. Figure 2 The diagram illustrates an embodiment of pressure gradient and pressure support during ventilator-assisted ventilation.

[0033] Depend on Figure 1It is understood that when controlling ventilation pressure, the target respiratory flow signal for the current respiratory cycle within the current ventilation treatment phase should be acquired. The current ventilation treatment phase refers to the current stage of mechanical ventilation. It is also understood that the respiratory cycle should be related to the patient receiving mechanical ventilation; the patient is the person receiving mechanical ventilation, and one breathing process of the patient constitutes one respiratory cycle. The method of mechanical ventilation can be consistent with existing technologies. During ventilation, respiratory flow signals can be acquired using flow sensors or similar methods, and the acquired respiratory flow signals can be preprocessed to generate the corresponding target respiratory flow signal.

[0034] When preprocessing the acquired respiratory flow signal, at least filtering is required. Generally, the spectral range of respiratory flow signals is 0.1Hz to 0.5Hz. Therefore, a 0.5Hz low-pass filter or a 0.1Hz to 0.5Hz band-pass filter can be used. Filtering is not limited to FIR or IIR filters; the specific filtering method can be selected as needed, and will not be elaborated here. Of course, other preprocessing methods can also be used, and specific selections can be made as needed, which will not be listed here.

[0035] Studies have found that in bilevel positive pressure ventilation (BPP) mode, the comfort of ventilators is closely related to pressure gradient and pressure support. Different pressure support and pressure gradients have a significant impact on ventilator comfort and treatment efficacy. For example, ventilators with high tidal volume and fast respiratory rate (fast inspiratory time) have a better experience with faster pressure switching (pressure rise and fall) and higher pressure support, and their compliance with ventilator treatment is relatively higher. On the other hand, ventilators with low tidal volume and low respiratory rate have a better experience with slower pressure switching and lower pressure support, and their compliance with ventilator treatment is relatively higher. Therefore, pressure gradient is generally highly correlated with inspiratory time, while pressure support is highly correlated with tidal volume.

[0036] To achieve the aforementioned pressure gradient and pressure support control, after acquiring the target respiratory flow signal, the expected inspiratory time and corresponding expected tidal volume for the next respiratory cycle should be predicted. Subsequently, based on the expected inspiratory time and expected tidal volume for the next respiratory cycle, the pressure gradient and pressure support for the next respiratory cycle are configured. Specifically, inspiratory time (TI) refers to the duration of the inspiratory phase of a respiratory cycle, and tidal volume (Tv) refers to the volume of air inhaled or exhaled with each breath. The method for predicting the expected inspiratory time and expected tidal volume for the next respiratory cycle can be found in the following explanation.

[0037] Since pressure gradient is related to inspiratory time, after predicting the expected inspiratory time for the next respiratory cycle, the desired pressure gradient for ventilation in the next respiratory cycle can be configured. In this case, the configured desired pressure gradient corresponds directly to the expected inspiratory time. Specifically, the expected inspiratory time can be achieved under the desired pressure gradient. Similarly, the desired pressure support for ventilation in the next respiratory cycle should be configured based on the expected tidal volume. In this case, the configured desired pressure support corresponds directly to the expected tidal volume. Specifically, the expected tidal volume can be achieved under the desired pressure support.

[0038] Depend on Figure 1 As can be seen, after generating the desired pressure gradient and desired pressure support, commonly used techniques in this field can be employed to control the ventilation pressure of the ventilator in the next respiratory cycle. It is understood that controlling the ventilation pressure in the next respiratory cycle primarily involves matching the pressure gradient during ventilation with the desired pressure gradient, and matching the pressure support during ventilation with the desired pressure support. Specifically, matching the pressure gradient with the desired pressure gradient means that the pressure gradient is consistent with the desired pressure gradient, or that the difference between the two is within an allowable numerical range. Furthermore, matching pressure support with the desired pressure support can represent the same meaning; please refer to the corresponding explanation here for details.

[0039] Understandably, if the pressure support and pressure gradient during ventilation match the expected pressure in the next respiratory cycle, then the inspiratory time and tidal volume will match the expected inspiratory time and tidal volume. This improves the comfort and effectiveness of ventilation for the therapist, thus enhancing patient compliance and treatment outcomes. Furthermore, within the current ventilation therapy phase, the expected inspiratory time and tidal volume for the next respiratory cycle are predicted and generated based on the target respiratory flow signal of the current respiratory cycle. Subsequently, pressure control during ventilation therapy is performed based on the generated expected pressure gradient and expected pressure support, thereby achieving adaptive pressure control throughout the entire ventilation therapy phase.

[0040] In one embodiment of the present invention, predicting and generating the expected inspiratory time and expected tidal volume for the next respiratory cycle based on a target respiratory flow signal includes: Based on the target respiratory flow signal, the respiratory physiological parameters of the current respiratory cycle are calculated and determined. The respiratory physiological parameters include at least the inspiratory time and tidal volume corresponding to the current respiratory cycle. Acquire ventilation treatment stage status information when the ventilator is performing ventilation treatment, wherein the ventilation treatment stage status information includes whether there was a reference ventilation treatment stage before the current ventilation treatment stage or whether there was no reference ventilation treatment stage before the current ventilation treatment stage. Based on the status information of the ventilation therapy stage, statistical information on respiratory physiological parameters during ventilation therapy is generated. The statistical information on respiratory physiological parameters includes the inspiratory time per unit time and the tidal volume per unit time. When both the inspiratory time statistic and the tidal volume statistic satisfy the prior statistical distribution, the prior characteristic equations related to the prior statistical distribution are solved respectively to obtain the prior distribution characteristic parameters of inspiratory time and tidal volume. Based on the prior distribution characteristic parameters of inspiratory time, the expected inspiratory time for the next respiratory cycle is calculated and generated. Based on the prior distribution characteristic parameters of tidal volume, the expected tidal volume for the next respiratory cycle is calculated and generated.

[0041] In practice, when predicting the expected inspiratory time and expected tidal volume for the next respiratory cycle, the respiratory physiological parameters of the current respiratory cycle should first be calculated based on the target respiratory flow signal. Generally, the respiratory physiological parameters of the current respiratory cycle should at least include the inspiratory time and the corresponding tidal volume. The following is an example illustrating the calculation method for inspiratory time and tidal volume. Specifically, one feasible method is as follows: Figure 3 The figure above illustrates one embodiment of the relationship between the target respiratory flow signal and the sampling point. After obtaining the target respiratory flow signal, phase identification should be performed on the target respiratory flow signal. Respiratory phase identification can be based on a threshold triggering method of flow rate or pressure. Specifically, when the detected respiratory signal amplitude is greater than the inspiratory trigger threshold, it is determined to be inspiratory trigger, and the inspiratory phase is entered, with the pressure switching from the inspiratory phase pressure to the inspiratory phase pressure; when the detected respiratory signal amplitude is less than the inspiratory trigger threshold, it is determined to be inspiratory trigger, and the inspiratory phase is entered, with the pressure switching from the inspiratory phase to the inspiratory phase. The phase identification method based on the threshold triggering of flow rate or pressure can be consistent with the existing technology and will not be elaborated here.

[0042] It is understandable that each respiratory cycle includes an inspiratory phase and a corresponding inspiratory time. In practice, the inspiratory time (TI) can be obtained by counting the duration of the inspiratory phase, and similarly, the inspiratory time (Te) can be obtained by counting the duration of the inspiratory phase. The sum of the inspiratory and inspiratory times of the current respiratory cycle is the duration of the current respiratory cycle, and thus the respiratory rate can be obtained. Therefore:

[0043] in, Respiratory rate, Sampling frequency, This is the respiratory cycle.

[0044] The tidal volume for the current respiratory cycle can generally be calculated by integrating the amplitude of the target respiratory flow signal accumulated throughout the entire inspiratory phase, and the result of the integration can be used as the tidal volume for the inspiratory phase. Similarly, the tidal volume for the corresponding respiratory phase can be calculated using the same method. In practice, either the tidal volume of the inspiratory phase or the tidal volume of the inspiratory phase can be selected as the tidal volume for the current respiratory cycle.

[0045] In one embodiment of the present invention, when predicting the expected inspiratory time and expected tidal volume of the next respiratory cycle, ventilation treatment phase status information during ventilator-assisted ventilation should also be obtained. This ventilation treatment phase status information includes whether a reference ventilation treatment phase existed before the current ventilation treatment phase or whether no reference ventilation treatment phase existed before the current ventilation treatment phase. For example, if the therapist is using the current ventilator for ventilation treatment for the first time, the ventilation treatment phase status information should be that no reference ventilation treatment phase existed before the current ventilation treatment phase. Alternatively, even if it is not the first time using the current ventilator for ventilation treatment, but previous ventilation treatment phases are no longer relevant, it should also be considered that no reference ventilation treatment phase existed before the current ventilation treatment phase. Otherwise, the ventilation treatment phase status information should indicate that a reference ventilation treatment phase existed before the current ventilation treatment phase.

[0046] In practice, the reference ventilation treatment phase can be determined based on the clinical situation. It should be noted that the reference ventilation treatment phase should be a ventilation treatment phase immediately adjacent to the current ventilation treatment phase. Preferably, the reference ventilation treatment phase includes a complete treatment cycle, such as ventilation treatment phases during sleep and wakefulness.

[0047] After obtaining the status information based on the ventilation therapy phase, statistical information on respiratory physiological parameters during ventilation therapy can be generated. This includes statistics on inspiratory time and tidal volume per unit time, the size of which can be selected as needed. It is understandable that the presence of the ventilation therapy phase will affect the generated statistical information on respiratory physiological parameters; the following explanations will address these factors based on the specific circumstances of the ventilation therapy phase status information.

[0048] If the ventilation treatment phase status information indicates that there is no reference ventilation treatment phase prior to the current ventilation treatment phase, then the inspiratory time statistic per unit time is the inspiratory time statistic within the current ventilation treatment phase, and the tidal volume statistic per unit time is the tidal volume statistic within the current ventilation treatment phase. In practice, the method for obtaining the inspiratory time and tidal volume statistics can be consistent with existing technologies, such as using an average statistical method or a percentile statistical method. For example, within the current ventilation treatment phase, one unit time is one respiratory cycle. During this period, the ventilation treatment has lasted for 100 respiratory cycles. At this point, the inspiratory time and tidal volume corresponding to these 100 cycles can be obtained. Using an average statistical method, the inspiratory time of the 100 respiratory cycles is arithmetically averaged to obtain the corresponding inspiratory time statistical value. Alternatively, using a percentile statistical method, the 90th percentile inspiratory time can be selected as the inspiratory time statistical value for the current ventilation treatment phase. Similarly, the tidal volume statistical value for the current ventilation treatment phase can be obtained using either an average statistical method or a percentile statistical method. Of course, other statistical methods can also be used, depending on the specific needs, prioritizing the method that effectively obtains statistical information on respiratory time and tidal volume. Examples of these methods will not be elaborated upon here.

[0049] If the ventilation therapy phase status information indicates that a reference ventilation therapy phase existed before the current ventilation therapy phase, then: The inspiratory time statistics per unit time include the inspiratory time statistics of the current ventilation treatment phase, the inspiratory time statistics of the reference ventilation treatment phase, and the combined inspiratory time statistics generated based on the inspiratory time statistics of the current ventilation treatment phase and the inspiratory time statistics of the reference ventilation treatment phase. The tidal volume statistics per unit time include the tidal volume statistics of the current ventilation treatment phase, the tidal volume statistics of the reference ventilation treatment phase, and the comprehensive tidal volume statistics generated based on the tidal volume statistics of the current ventilation treatment phase and the tidal volume statistics of the reference ventilation treatment phase.

[0050] Specifically, the inspiratory time statistics for the current ventilation treatment phase and the reference ventilation treatment phase can be generated using the statistical methods described above, and will not be repeated here. The comprehensive inspiratory time statistics can be generated by arithmetically averaging the inspiratory time statistics for the current ventilation treatment phase and the reference ventilation treatment phase, or by using a weighted average. The weights for the weighted average can be selected as needed, based on what best represents the statistical status of inspiratory time.

[0051] It is understandable that, referring to the above explanation, one can obtain the tidal volume statistics for the current ventilation treatment stage, the tidal volume statistics for the reference ventilation treatment stage, and the comprehensive tidal volume statistics generated based on the tidal volume statistics for the current ventilation treatment stage and the tidal volume statistics for the reference ventilation treatment stage.

[0052] Although there are a few high values ​​for respiratory physiological parameters (tidal volume, inspiratory time, and respiratory rate), in the general population, respiratory physiological parameters usually conform to a right-skewed normal distribution (log-normal). Therefore, the prior statistical distribution mentioned above is a right-skewed normal distribution. After obtaining the inspiratory time and tidal volume statistics, it should be determined whether they both satisfy the prior statistical distribution. It can be understood that when the ventilation treatment stage status information is such that there is no reference ventilation treatment stage before the current ventilation treatment stage, the inspiratory time and tidal volume statistics must satisfy the prior statistical distribution.

[0053] In one embodiment of the present invention, if the ventilation treatment stage status information indicates that a reference ventilation treatment stage existed before the current ventilation treatment stage, then when performing prior statistical distribution characteristic judgment on the inspiratory time statistic, the following is included: Based on the inspiratory time statistics of the current ventilation therapy phase and the inspiratory time statistics of the reference ventilation therapy phase, the weighted value of the inspiratory time statistics is calculated, and then:

[0054] in, This is a weighted value for the inhalation time statistics. This represents the statistical value of inspiratory time during the current ventilation therapy phase. For reference, the statistical values ​​of inspiratory time during the ventilation therapy phase, For weighted weights; When the aggregated value of inhalation time statistics matches the weighted value of inhalation time statistics, the inhalation time statistic per unit time satisfies the prior statistical distribution; otherwise, the inhalation time statistic per unit time does not satisfy the prior statistical distribution.

[0055] It is understandable that if a reference ventilation therapy stage exists, the inspiratory time statistic should be judged by a priori statistical distribution characteristics. Of course, the tidal volume statistic should also be judged by a priori statistical distribution characteristics. The methods used for judging the a priori statistical distribution characteristics of the two can be consistent. Here, we will take the judgment of the a priori statistical distribution characteristics of the inspiratory time statistic as an example for explanation.

[0056] For the inspiratory time statistics of the reference ventilation treatment phase, since the effectiveness feature presented includes the respiratory information of the therapist in a sleep state, while the comfort feature presented by the inspiratory time statistics of the current ventilation treatment phase is based on the respiratory information statistics of the therapist in a waking state, the therapist perceives comfort in a waking state, while the statistical data in a sleep state presents the treatment effectiveness feature. That is, the current ventilation treatment phase is closer to the therapist's current respiratory characteristics. Therefore, in one embodiment of the present invention, the weighting weight can be set to 0.6-0.7.

[0057] Matching the statistical composite value of inspiratory time with the statistical weighted value of inspiratory time means that the statistical composite value of inspiratory time is equal to the statistical weighted value of inspiratory time, or the difference between the two is within an allowable range. The allowable range can be selected as needed to meet the needs of ventilation therapy.

[0058] Referring to the above explanation, the method and process for judging the prior statistical distribution characteristics of tidal volume statistics can be obtained. For example, the corresponding tidal volume statistical weighted value should be calculated. Then, the comprehensive value of tidal volume statistics is compared with the tidal volume statistical weighted value to determine whether they match, and thus whether the prior statistical distribution is satisfied.

[0059] It should be noted that the inspiratory time statistics for the current ventilation treatment phase and the reference ventilation treatment phase mentioned above are all generated using the same statistical method. For example, if they are all generated using an average statistical method or a percentile statistical method, the statistical method corresponding to the composite inspiratory time statistics will also correspond to the statistical method corresponding to the individual inspiratory time statistics. Furthermore, the tidal volume statistics can be found here and will not be elaborated further.

[0060] As explained above, when the inspiratory time statistic satisfies the prior statistical distribution, the corresponding prior characteristic equation should be solved. In one embodiment of the present invention, solving the prior characteristic equation based on the inspiratory time statistic includes:

[0061] in, The inspiratory time is the statistical composite value obtained using the 90th percentile statistical method. This is the statistical composite value of inspiratory time obtained using the average statistical method. This represents the mean of the distribution up to the current respiratory cycle. This represents the standard deviation of the distribution up to the current respiratory cycle; After calculating the distribution mean and standard deviation, when calculating the expected inspiratory time for the next respiratory cycle, we have:

[0062] in, The expected inspiratory time for the next respiratory cycle.

[0063] It is understandable that the aforementioned prior characteristic equation is mainly related to the right-skewed normal distribution used, and the 1.28155 mentioned above is the 90th quantile of the standard normal distribution. Solving the prior characteristic equation primarily involves calculating and determining the distribution mean. and distribution standard deviation , here Specifically, it refers to the sequence number of the current respiratory cycle within the current ventilation therapy phase, that is, the distribution mean determined based on the current respiratory cycle. and distribution standard deviation When solving the prior characteristic equation, the statistical composite value of inspiratory time obtained using the 90th percentile statistical method is used. The statistical composite value of inspiratory time obtained by the average statistical method The above method can be used to calculate the value. Subsequently, the distribution mean can be obtained using commonly used existing calculation methods. and distribution standard deviation Furthermore, when calculating the expected inspiratory time for the next respiratory cycle, the distribution mean can be used. .

[0064] In practical implementation, refer to the above explanation of solving the prior characteristic equation based on inspiratory time statistics. You can perform the calculation of the prior characteristic equation related to tidal volume based on tidal volume statistics. However, unlike the prior characteristic equation for inspiratory time statistics, the prior characteristic equation for tidal volume statistics should utilize both the tidal volume statistical composite value obtained using the 90th percentile method and the tidal volume statistical composite value obtained using the average method. After calculating the corresponding distribution mean and standard deviation, the expected tidal volume for the next respiratory cycle can be: = ,in, This is the expected tidal volume for the next respiratory cycle. To solve for the distribution mean obtained by the prior characteristic equation.

[0065] Clinical trials have shown a strong correlation between the comfort of ventilation therapy and pressure gradient and pressure support ventilation parameters. Specifically, longer inspiratory time, slower respiratory rate, and a gentler controlled pressure gradient result in better comfort, while shorter inspiratory time, faster respiratory rate, and a steeper controlled pressure gradient also lead to better comfort. However, excessively high pressure support settings (typically above 6 cmH2O) decrease comfort. For some patients with chronic lung diseases, the effectiveness of ventilation therapy is strongly correlated with pressure support ventilation parameters; higher pressure support settings increase the patient's tidal volume to meet treatment needs.

[0066] Normally, a healthy person's respiratory rate is between 10 and 20 bpm, inspiratory time is between 1.0 and 1.5 seconds, and tidal volume is between 300 and 600 mL. Therefore, based on the statistical regularities of relevant clinical data, a nonlinear expression for inspiratory time and pressure gradient can be fitted. Specifically, according to the distribution patterns of respiratory rate and inspiratory time in clinical data, fast and slow inspiratory times are in the minority, while moderate inspiratory times are in the majority. Therefore, the fitted nonlinear relationship yields different increments in pressure rise and fall time across different inspiratory time intervals. Generally, in intervals with short and moderate inspiratory times, the output pressure rise and fall time increments are faster, while in intervals with long inspiratory times, the output pressure rise and fall time increments are slower. Figure 6 The diagram shows an embodiment of the control time for the intake time and the rise and fall of the output pressure.

[0067] Based on the above fitting description, in one embodiment of the present invention, when configuring the desired pressure gradient for the next respiratory cycle based on the expected inspiratory time, the following is true:

[0068] in, For the desired pressure gradient, This represents the minimum pressure gradient. This represents the maximum pressure gradient. The expected inspiratory time for the next respiratory cycle. This is the lower limit of the expected inspiratory time for the next respiratory cycle. This represents the upper limit of the expected inspiratory time for the next respiratory cycle. It is the exponential coefficient.

[0069] In practice, the minimum pressure gradient is generally the minimum pressure gradient configured for ventilation therapy using the ventilator. This minimum pressure gradient can be pre-set within the ventilator as needed, and is typically set to 100 ms. Similarly, the maximum pressure gradient is the maximum pressure gradient configured for ventilation therapy using the ventilator, typically 900 ms. The lower limit of the expected inspiratory time for the next respiratory cycle can generally be selected based on actual needs, such as 0.3 s, while the upper limit is typically 2 s.

[0070] Furthermore, if you want the output pressure rise and fall time increments to be fast in the range of short and moderate inhalation time, and slow in the range of long inhalation time, then the exponent coefficient should be less than 1, such as 0.5. You can choose according to the instructions here, and I will not give examples here.

[0071] In practice, based on the distribution patterns of tidal volume in clinical data, low and high tidal volumes are in the minority, while moderate tidal volumes are in the majority. Since the tidal volume distribution range is relatively wide, a nonlinear relationship between tidal volume and pressure support can be fitted. Unlike the nonlinear expressions for fitting inspiratory time and pressure gradient mentioned above, when fitting the nonlinear relationship between tidal volume and pressure support, the increment of output control pressure support is small in the low and high tidal volume distribution ranges, while the increment of output control pressure support is slightly larger in the moderate tidal volume distribution range. Figure 7 The diagram shows an embodiment of tidal volume and pressure support.

[0072] Based on the above fitting description, in one embodiment of the present invention, when configuring the expected pressure support for the next respiratory cycle ventilation based on the expected tidal volume, then:

[0073] in, To support the expected pressure, To support the minimum pressure, To support the maximum pressure, To adjust the pressure support increment coefficient, which can affect the output of different tidal volume distribution ranges. For the expected tidal volume, This is the median factor for tidal volume.

[0074] Specifically, the minimum pressure support is the minimum pressure support that the ventilator can provide during ventilation therapy. For example, the minimum pressure support can be 1 cmH2O (1 cm of water column), and the maximum pressure support can be 6 cmH2O. In addition, the pressure support increment coefficient can be 0.02. The tidal volume median factor is usually linked to the median tidal volume, and the tidal volume median factor can be 400.

[0075] The above describes the generation of the desired pressure gradient and desired pressure support for the next respiratory cycle using methods such as nonlinear fitting. To further improve the accuracy of ventilation control pressure, fuzzy inference methods can also be employed. The following section details the methods and processes used in fuzzy inference.

[0076] In one embodiment of the present invention, when configuring the desired pressure gradient for ventilation in the next respiratory cycle based on the expected inspiratory time using a fuzzy inference method, the method includes: Construct a fuzzy inference rule table for inhalation-pressure gradient corresponding to expected inhalation time and expected pressure gradient. The fuzzy inference rule table for inhalation-pressure gradient includes several fuzzy rules for inhalation-pressure gradient. Each fuzzy rule for inhalation-pressure gradient includes an inhalation time membership degree that represents the corresponding length of the expected inhalation time and a pressure gradient instance membership degree that represents the corresponding magnitude of the pressure gradient. During fuzzy inference, the inhalation time membership degree within each inhalation-pressure gradient fuzzy rule is calculated based on the expected inhalation time. Subsequently, the corresponding expected pressure gradient is generated based on the centroid method.

[0077] As explained above, inhalation time can be categorized into three types: short inhalation time, moderate inhalation time, and long inhalation time. Therefore, when constructing the inhalation-pressure gradient fuzzy rule table, the membership degree of inhalation time for these three types should be determined. Figure 4 The text illustrates the membership of inhalation time in three scenarios: short inhalation time, moderate inhalation time, and long inhalation time. It also provides examples of membership for corresponding pressure gradients. The following section combines these examples with... Figure 4 The membership degree is explained in detail.

[0078] In practice, when the inhalation time is short, the corresponding inhalation time membership degree can be:

[0079] in, This represents the membership degree of the inspiratory time when the expected inspiratory time is short.

[0080] When the inhalation time is long, the corresponding inhalation time membership degree can be:

[0081] in, This represents the membership degree of the inhalation time when the expected inhalation time is longer.

[0082] When the inhalation time is moderate, the corresponding inhalation time membership degree can be:

[0083] in, This represents the membership degree of inspiratory time when the expected inspiratory time is moderate. Furthermore, the inspiratory time mentioned here is measured in seconds (s).

[0084] As explained above, pressure gradient can be categorized into three types: rapid pressure rise and fall times, slow pressure rise and fall times, and moderate pressure rise and fall times. Therefore, combining... Figure 4 The membership degree of the corresponding pressure gradient instance can be obtained, specifically: When the pressure gradient results in rapid pressure rise and fall times, the corresponding membership degree of the pressure gradient instance can be:

[0085] in, The desired pressure gradient is the membership degree of the pressure gradient instance when the pressure rises and falls rapidly.

[0086] When the pressure gradient results in slow pressure rise and fall times, the corresponding membership degree of the pressure gradient instance can be:

[0087] in, The membership degree of the pressure gradient instance is the pressure gradient that corresponds to the slow rise and fall time of the pressure gradient.

[0088] When the pressure gradient results in rapid pressure rise and fall times, the corresponding membership degree of the pressure gradient instance can be:

[0089] in, This represents the membership degree of the pressure gradient instance when the desired pressure gradient is moderate in terms of pressure rise and fall times. Furthermore, the unit for pressure rise and fall times here is milliseconds (ms).

[0090] Table 1 below shows an example of an inhalation-pressure gradient fuzzy inference rule table, where R1, R2, and R3 are three inhalation-pressure gradient fuzzy rules.

[0091] In one embodiment of the present invention, when configuring the expected pressure support for the next respiratory cycle ventilation using a fuzzy inference method based on the expected tidal volume, the method includes: Construct a fuzzy inference rule table for tidal volume-pressure support corresponding to expected tidal volume and expected pressure support. The fuzzy inference rule table for tidal volume-pressure support includes several fuzzy rules for tidal volume-pressure support. Each fuzzy rule for tidal volume-pressure support includes a tidal volume membership degree representing the corresponding magnitude of expected tidal volume and a pressure support membership degree representing the corresponding magnitude of pressure support. During fuzzy inference, the tidal volume membership degree within each dry tidal volume-pressure support fuzzy rule is calculated based on the expected tidal volume. Subsequently, the corresponding expected pressure support is generated based on the centroid method.

[0092] It is understandable that Table 1 also shows an embodiment of the tidal volume-pressure support fuzzy inference rule table, that is, Table 1 is an example of a combination of the inspiratory-pressure slope fuzzy inference rule table and the tidal volume-pressure support fuzzy inference rule table. As can be seen from Table 1, the tidal volume-pressure support fuzzy inference rule table can include three tidal volume-pressure support fuzzy rules. In this case, tidal volume can be divided into three cases: low tidal volume, moderate tidal volume, and high tidal volume. The following will be combined with Figure 5 The specific details regarding the constructed tidal volume membership and pressure support membership are explained.

[0093] When the tidal volume is low, the corresponding tidal volume membership degree is:

[0094] in, This represents the tidal volume membership degree corresponding to the expected tidal volume being low.

[0095] When the tidal volume is low, the corresponding tidal volume membership degree is:

[0096] in, This represents the tidal volume membership degree corresponding to a high tidal volume.

[0097] When the tidal volume is low, the corresponding tidal volume membership degree is:

[0098] in, This represents the tidal volume membership degree corresponding to the expected tidal volume being moderate. Furthermore, the unit for tidal volume here is L / min.

[0099] When there are three tidal volume-pressure support fuzzy rules, there are three corresponding pressure support membership degrees, specifically: When the pressure support is low, the corresponding pressure support membership degree is:

[0100] in, The expected pressure support is the pressure support membership degree corresponding to a low pressure support level.

[0101] When the pressure support is high, the corresponding pressure support membership degree is:

[0102] in, The expected pressure support is the pressure support membership degree corresponding to a high pressure support level.

[0103] When the pressure support is low, the corresponding pressure support membership degree is:

[0104] in, This represents the membership degree of the pressure support when the desired pressure support is moderate. Furthermore, the unit of measurement for pressure support here is cmH2O.

[0105] Table 1

[0106] It is understandable that the methods for determining the desired pressure gradient and the desired pressure support are consistent during fuzzy inference. The following uses fuzzy inference to determine the desired pressure gradient as an example to specifically illustrate the fuzzy inference method and process of this invention. In one embodiment, the following is an example: Determine the expected inhalation time. For example, if the expected inhalation time determined by the above method is 1.4 seconds, then substituting it into the membership degree of the expected inhalation time mentioned above, we have: , , Subsequently, the fuzzy set truncation of the corresponding desired pressure gradient is calculated using the inhalation-pressure gradient fuzzy rule table, and then the corresponding aggregated output activation intensity is calculated. Specifically: When the pressure rises and falls rapidly, then: When the pressure rise and fall times are moderate, then: When the pressure rises and falls slowly, then: This allows us to determine the activation intensity corresponding to each inspiratory-pressure gradient fuzzy rule. For example, for rule R2, when At that time, Figure 4 Within the membership function of the downforce gradient, draw a horizontal line with a value of 0.33. This will then be related to... Figure 4 The triangle in the diagram has two intersection points (Medium). The intersection point with the larger value can be used as the corresponding output, which is how we obtain the result. The value of R2 can then be used to determine the activation intensity corresponding to the rule. Activation intensity under other rules The method for determining this can be found here.

[0107] When calculating the desired pressure gradient using the centroid method, we have: , Where N is the number of fuzzy rules for the inhalation-pressure slope, and N should be 3 here; For the first The output value of the fuzzy rule for the inhalation-pressure gradient (as shown in the example above for rule R2). value), Indicates the first Activation intensity of the fuzzy rule for inhalation-pressure gradient; To determine the desired pressure gradient, such as when the expected inhalation time is 1.4 seconds, the pressure gradient can be calculated using this method. It takes 626ms.

[0108] Based on the above description, a ventilator can be obtained. In one embodiment of the present invention, the ventilator uses the above-described adaptive ventilation pressure control method for ventilation pressure control.

[0109] Specifically, the ventilator can adopt the commonly used forms, such as being able to operate in bilevel positive pressure ventilation mode and perform the adaptive pressure control mentioned above. The specific details of the ventilator will not be listed here.

Claims

1. A method for adaptive ventilation pressure control in a ventilator, characterized in that, The ventilation pressure control method includes: In bilevel positive airway mode, acquire the target respiratory flow signal of the current respiratory cycle under the current ventilation therapy phase; Based on the target respiratory flow signal, predict and generate the expected inspiratory time and expected tidal volume for the next respiratory cycle. Based on the expected inspiratory time, the expected pressure gradient for the next respiratory cycle is configured, and based on the expected tidal volume, the expected pressure support for the next respiratory cycle is configured, wherein the configured expected pressure gradient corresponds to the expected inspiratory time, and the configured expected pressure support corresponds to the expected tidal volume. Based on the desired pressure gradient and desired pressure support, the ventilation pressure for the next respiratory cycle is configured so that the pressure gradient during ventilation in the next respiratory cycle matches the desired pressure gradient, and the pressure support during ventilation in the next respiratory cycle matches the desired pressure support.

2. The adaptive ventilation pressure control method for ventilators according to claim 1, characterized in that, When predicting the expected inspiratory time and expected tidal volume for the next respiratory cycle based on the target respiratory flow signal, the following are included: Based on the target respiratory flow signal, the respiratory physiological parameters of the current respiratory cycle are calculated and determined. The respiratory physiological parameters include at least the inspiratory time and tidal volume corresponding to the current respiratory cycle. Acquire ventilation treatment stage status information when the ventilator is performing ventilation treatment, wherein the ventilation treatment stage status information includes whether there was a reference ventilation treatment stage before the current ventilation treatment stage or whether there was no reference ventilation treatment stage before the current ventilation treatment stage. Based on the status information of the ventilation therapy stage, statistical information on respiratory physiological parameters during ventilation therapy is generated. The statistical information on respiratory physiological parameters includes the inspiratory time per unit time and the tidal volume per unit time. When both the inspiratory time statistic and the tidal volume statistic satisfy the prior statistical distribution, the prior characteristic equations related to the prior statistical distribution are solved respectively to obtain the prior distribution characteristic parameters of inspiratory time and tidal volume. Based on the prior distribution characteristic parameters of inspiratory time, the expected inspiratory time for the next respiratory cycle is calculated and generated. Based on the prior distribution characteristic parameters of tidal volume, the expected tidal volume for the next respiratory cycle is calculated and generated.

3. The adaptive ventilation pressure control method for ventilators according to claim 2, characterized in that, If the ventilation treatment phase status information is that there is no reference ventilation treatment phase before the current ventilation treatment phase, then the inspiratory time statistic per unit time is the inspiratory time statistic within the current ventilation treatment phase, and the tidal volume statistic per unit time is the tidal volume statistic within the current ventilation treatment phase. In this case, both the inspiratory time statistic and the tidal volume statistic satisfy the prior statistical distribution. If the ventilation therapy phase status information indicates that a reference ventilation therapy phase existed before the current ventilation therapy phase, then: The inspiratory time statistics per unit time include the inspiratory time statistics of the current ventilation treatment phase, the inspiratory time statistics of the reference ventilation treatment phase, and the combined inspiratory time statistics generated based on the inspiratory time statistics of the current ventilation treatment phase and the inspiratory time statistics of the reference ventilation treatment phase. The tidal volume statistics per unit time include the tidal volume statistics of the current ventilation treatment phase, the tidal volume statistics of the reference ventilation treatment phase, and the comprehensive tidal volume statistics generated based on the tidal volume statistics of the current ventilation treatment phase and the tidal volume statistics of the reference ventilation treatment phase.

4. The adaptive ventilation pressure control method for ventilators according to claim 3, characterized in that, The prior statistical distribution is a right-skewed normal distribution; If the ventilation therapy phase status information indicates that a reference ventilation therapy phase existed prior to the current ventilation therapy phase, then when determining the prior statistical distribution characteristics of the inspiratory time statistic, the following should be included: Based on the inspiratory time statistics of the current ventilation therapy phase and the inspiratory time statistics of the reference ventilation therapy phase, the weighted value of the inspiratory time statistics is calculated, and then: in, This is a weighted value for the inhalation time statistics. This represents the statistical value of inspiratory time during the current ventilation therapy phase. For reference, the statistical values ​​of inspiratory time during the ventilation therapy phase, For weighted weights; When the aggregated value of inhalation time statistics matches the weighted value of inhalation time statistics, the inhalation time statistic per unit time satisfies the prior statistical distribution; otherwise, the inhalation time statistic per unit time does not satisfy the prior statistical distribution.

5. The adaptive ventilation pressure control method for ventilators according to claim 4, characterized in that, Solving the prior characteristic equation based on inspiratory time statistics includes: in, The composite inspiratory time is obtained using the 90th percentile statistical method. This is the statistical composite value of inspiratory time obtained using the average statistical method. This represents the mean of the distribution up to the current respiratory cycle. This represents the standard deviation of the distribution up to the current respiratory cycle; After calculating the distribution mean and standard deviation, when calculating the expected inspiratory time for the next respiratory cycle, we have: in, The expected inspiratory time for the next respiratory cycle.

6. The adaptive ventilation pressure control method for ventilators according to any one of claims 1 to 5, characterized in that, When configuring the desired pressure gradient for the next respiratory cycle based on the expected inspiratory time, we have: in, For the desired pressure gradient, This represents the minimum pressure gradient. This represents the maximum pressure gradient. The expected inspiratory time for the next respiratory cycle. This is the lower limit of the expected inspiratory time for the next respiratory cycle. This represents the upper limit of the expected inspiratory time for the next respiratory cycle. It is the exponential coefficient.

7. The adaptive ventilation pressure control method for ventilators according to any one of claims 1 to 5, characterized in that, When configuring the expected pressure support for the next respiratory cycle based on the expected tidal volume, then: in, To support the expected pressure, To support the minimum pressure, To support the maximum pressure, To adjust the pressure support increment coefficient, which can affect the output of different tidal volume distribution ranges. For the expected tidal volume, This is the median factor for tidal volume.

8. The adaptive ventilation pressure control method for a ventilator according to any one of claims 1 to 5, characterized in that, When configuring the desired pressure gradient for the next respiratory cycle based on the expected inspiratory time using fuzzy inference methods, the following is included: Construct a fuzzy inference rule table for inhalation-pressure gradient corresponding to expected inhalation time and expected pressure gradient. The fuzzy inference rule table for inhalation-pressure gradient includes several fuzzy rules for inhalation-pressure gradient. Each fuzzy rule for inhalation-pressure gradient includes an inhalation time membership degree that represents the corresponding length of the expected inhalation time and a pressure gradient instance membership degree that represents the corresponding magnitude of the pressure gradient. During fuzzy inference, the inhalation time membership degree within each inhalation-pressure gradient fuzzy rule is calculated based on the expected inhalation time. Subsequently, the corresponding expected pressure gradient is generated based on the centroid method.

9. The adaptive ventilation pressure control method for a ventilator according to any one of claims 1 to 5, characterized in that, When configuring the expected pressure support for the next respiratory cycle ventilation based on the expected tidal volume using a fuzzy inference method, it includes: Construct a fuzzy inference rule table for tidal volume-pressure support corresponding to expected tidal volume and expected pressure support. The fuzzy inference rule table for tidal volume-pressure support includes several fuzzy rules for tidal volume-pressure support. Each fuzzy rule for tidal volume-pressure support includes a tidal volume membership degree representing the corresponding magnitude of expected tidal volume and a pressure support membership degree representing the corresponding magnitude of pressure support. During fuzzy inference, the tidal volume membership degree within each dry tidal volume-pressure support fuzzy rule is calculated based on the expected tidal volume. Subsequently, the corresponding expected pressure support is generated based on the centroid method.

10. A ventilator, characterized in that, The ventilator uses the adaptive ventilation pressure control method of any one of claims 1 to 9 to control the ventilation pressure.

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