WO2020037509A9 - 一种气道异常识别方法及装置、通气设备、存储介质 - Google Patents

一种气道异常识别方法及装置、通气设备、存储介质 Download PDF

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WO2020037509A9
WO2020037509A9 PCT/CN2018/101595 CN2018101595W WO2020037509A9 WO 2020037509 A9 WO2020037509 A9 WO 2020037509A9 CN 2018101595 W CN2018101595 W CN 2018101595W WO 2020037509 A9 WO2020037509 A9 WO 2020037509A9
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Prior art keywords
airway
pipeline
pressure
ventilation
expiratory
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PCT/CN2018/101595
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English (en)
French (fr)
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WO2020037509A1 (zh
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徐军
刘京雷
于学忠
付阳阳
邹心茹
周小勇
Original Assignee
深圳迈瑞生物医疗电子股份有限公司
中国医学科学院北京协和医院
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Application filed by 深圳迈瑞生物医疗电子股份有限公司, 中国医学科学院北京协和医院 filed Critical 深圳迈瑞生物医疗电子股份有限公司
Priority to EP18931256.4A priority Critical patent/EP3834720A4/en
Priority to PCT/CN2018/101595 priority patent/WO2020037509A1/zh
Priority to CN201880094565.7A priority patent/CN112770670A/zh
Publication of WO2020037509A1 publication Critical patent/WO2020037509A1/zh
Priority to US17/159,142 priority patent/US20210213217A1/en
Publication of WO2020037509A9 publication Critical patent/WO2020037509A9/zh

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Definitions

  • the embodiments of the present invention relate to the technical field of medical devices, and in particular to an airway abnormality recognition method and device, ventilation equipment, and storage medium.
  • Ventilation equipment such as a ventilator is a device that can effectively replace, control or change a person's normal physiological breathing, increase lung ventilation, improve respiratory function, reduce respiratory consumption, and save heart reserves.
  • some airway abnormal events are prone to occur, including: due to the use of humidifiers in the breathing circuit, it is prone to condensation and accumulation of water vapor in the pipeline to obstruct ventilation; patient airway secretion The accumulation of substances in the airway causes the airway resistance to increase, the airway is blocked, which hinders ventilation; the ventilator is equipped with a bacterial filter at the inhalation and expiration ends to prevent infection.
  • the filter will be breathed by the patient after a long period of use The middle water vapor gets wet, which causes the resistance of the filter to increase, which hinders ventilation.
  • the above-mentioned abnormal airway events can cause the ventilator to fail to ventilate normally, thereby affecting the therapeutic effect.
  • the embodiments of the present invention expect to provide an airway abnormality recognition method and device, ventilation equipment, and storage medium, which can identify specific airway abnormalities and prompt them according to changes in ventilation parameters, which is convenient for operators Deal with abnormal airway events to ensure the normal ventilation of the ventilation equipment, thereby improving the treatment effect.
  • the embodiment of the present invention provides an airway abnormality recognition method, the method includes:
  • Ventilation parameters where the ventilation parameters are airway pressure and/or airway flow rate
  • the abnormal airway event prompt is output.
  • the abnormal airway event includes one or more of the accumulation of water in the pipeline, the accumulation of sputum, and the increase in the resistance of the pipeline.
  • the step of identifying whether there is water accumulation in the pipeline according to the change of the ventilation parameter includes:
  • the step of identifying whether there is water accumulation in the pipeline according to the change of the ventilation parameter includes:
  • the high-frequency component of the ventilation parameter is greater than the preset first threshold, it is determined that there is water in the pipeline.
  • the step of identifying whether there is water accumulation in the pipeline according to the change of the ventilation parameter includes:
  • the step of identifying whether sputum accumulation occurs according to the change of the ventilation parameter includes:
  • the airway pressure includes inspiratory pressure and expiratory pressure
  • the step of identifying whether there is an increase in line resistance according to the change of the ventilation parameter includes:
  • the inspiratory pressure and the expiratory pressure it is determined whether there is an increase in the resistance of the pipeline.
  • the step of judging whether there is an increase in pipeline resistance based on the inspiratory pressure and the expiratory pressure includes:
  • the step of judging whether there is an increase in pipeline resistance based on the inspiratory pressure and the expiratory pressure includes:
  • the airway flow rate includes an inspiratory flow rate and an expiratory flow rate
  • the inspiratory pressure is calculated by the inspiratory flow rate
  • the expiratory pressure is calculated by the expiratory flow rate
  • the embodiment of the present invention provides an airway abnormality recognition device, the device includes:
  • a collection module to collect ventilation parameters, where the ventilation parameters are airway pressure and/or airway flow rate;
  • a recognition module which recognizes whether an abnormal airway event occurs according to the change of the ventilation parameter
  • the output module when recognizing that the abnormal airway event occurs, outputs a prompt of the abnormal airway event.
  • the abnormal airway event includes one or more of water accumulation in the pipeline, accumulation of sputum, and increase in pipeline resistance.
  • the step of the identification module identifying whether there is water accumulation in the pipeline according to the change of the ventilation parameter includes:
  • the step of the identification module identifying whether there is water accumulation in the pipeline according to the change of the ventilation parameter includes:
  • the high-frequency component of the ventilation parameter is greater than the preset first threshold, it is determined that there is water in the pipeline.
  • the step of the identification module identifying whether there is water accumulation in the pipeline according to the change of the ventilation parameter includes:
  • the step of the identification module identifying whether sputum accumulation occurs according to the change of the ventilation parameter includes:
  • the airway pressure includes an inspiratory pressure and an expiratory pressure
  • the step of the identification module to identify whether there is an increase in line resistance according to the change of the ventilation parameter includes:
  • the inspiratory pressure and the expiratory pressure it is determined whether there is an increase in the resistance of the pipeline.
  • the step of determining whether there is an increase in pipeline resistance by the identification module based on the inspiratory pressure and the expiratory pressure includes:
  • the step of determining whether there is an increase in pipeline resistance by the identification module based on the inspiratory pressure and the expiratory pressure includes:
  • the airway flow rate includes an inspiratory flow rate and an expiratory flow rate
  • the inspiratory pressure is calculated by the inspiratory flow rate
  • the expiratory pressure is calculated by the expiratory flow rate
  • the embodiment of the present invention provides a ventilation device including the above-mentioned airway abnormality recognition device, which includes an air source, an inhalation branch, an expiration branch, a display, and a controller;
  • the gas source provides gas during mechanical ventilation
  • the inhalation branch is connected to the air source, and provides an inhalation path during the mechanical ventilation
  • the expiratory branch provides an expiratory path during the mechanical ventilation
  • the airway abnormality recognition device is connected to the inhalation branch, the expiration branch, and the controller;
  • the airway abnormality recognition device recognizes abnormal airway events during the mechanical ventilation
  • the controller is also connected with the air source to control the process of the mechanical ventilation
  • the display is connected to the controller, and displays respiratory waveforms during the mechanical ventilation.
  • An embodiment of the present invention provides a computer-readable storage medium that stores an airway abnormality recognition program, and the airway abnormality recognition program can be executed by a processor to implement the above-mentioned airway abnormality recognition method .
  • the airway abnormality recognition device collects ventilation parameters, the ventilation parameters are airway pressure and/or airway flow rate; according to the changes in ventilation parameters, it can identify whether there is an abnormal airway event; When identifying abnormal airway events, output airway abnormal event prompts. That is to say, the technical solutions provided by the embodiments of the present invention can identify specific abnormal airway events and prompt them according to changes in ventilation parameters, which is convenient for the operator to deal with the abnormal airway events and ensure that the ventilation equipment is normally ventilated. Improve the treatment effect.
  • FIG. 1 is a schematic flowchart of a method for identifying abnormal airway according to an embodiment of the present invention
  • Figure 2 (a) is an exemplary airway pressure-time waveform diagram under normal conditions according to an embodiment of the present invention
  • Figure 2(b) is an exemplary waveform diagram of airway pressure-time when water accumulates in a pipeline according to an embodiment of the present invention
  • Fig. 3(a) is an exemplary waveform diagram of airway flow rate-time under normal conditions according to an embodiment of the present invention
  • Fig. 3(b) is an exemplary waveform diagram of airway flow rate-time when water accumulates in a pipeline according to an embodiment of the present invention
  • Fig. 4 is an exemplary airway pressure fitting curve diagram provided by an embodiment of the present invention.
  • FIG. 5 is a schematic diagram of the connection of a ventilation device in an exemplary test process provided by an embodiment of the present invention
  • FIG. 6 is a schematic diagram of the connection of an exemplary ventilation device during ventilation provided by an embodiment of the present invention.
  • FIG. 7 is a schematic structural diagram of an airway abnormality recognition device provided by an embodiment of the present invention.
  • FIG. 8 is a first structural diagram of a ventilation device according to an embodiment of the present invention.
  • Fig. 9 is a second structural diagram of a ventilation device provided by an embodiment of the present invention.
  • FIG. 1 is a schematic flowchart of a method for identifying an abnormality of the airway according to an embodiment of the present invention. As shown in Figure 1, it mainly includes the following steps:
  • the airway abnormality recognition device can collect ventilation parameters in real time during the mechanical ventilation of the ventilation device.
  • the airway parameter is airway pressure and/or airway flow rate.
  • the airway abnormality recognition device is a part of the ventilation equipment, which is a ventilator, an anesthesia machine, or other medical equipment with ventilation function.
  • the specific ventilation device is not limited in the embodiment of the present invention.
  • the airway abnormality recognition device collects ventilation parameters continuously.
  • the airway abnormality recognition device recognizes whether an abnormal airway event occurs according to changes in the ventilation parameters.
  • the abnormal airway event includes one or more of the accumulation of water in the pipeline, the accumulation of sputum, and the increase in the resistance of the pipeline.
  • the ventilation device recognizes whether there is water accumulation in the pipeline according to the change of ventilation parameters. This is because the ventilation device is generally used with a humidifier, and water vapor is easily condensed in the pipeline. Accumulation in the pipeline will hinder ventilation. Ventilation equipment recognizes whether there is sputum accumulation based on changes in ventilation parameters, because the accumulation of sputum in the patient's respiratory tract will lead to an increase in airway resistance, which will hinder ventilation. Ventilation equipment recognizes whether there is an increase in pipeline resistance according to the changes in ventilation parameters. This is because the inhalation and exhalation ends of the ventilation equipment are equipped with bacterial filters. The gas exhaled by the patient is humidified gas, and the filter will be humidified after condensation The resistance of the pipeline is too large, which hinders ventilation.
  • the step of the airway abnormality recognition device identifying whether there is water accumulation in the pipeline according to the change of the ventilation parameters includes: when the ventilation parameters continue to fluctuate, determining that there is water accumulation in the pipeline.
  • the airway abnormality recognition device collects The obtained airway pressure and airway flow rate will always be shaking up and down, and under normal circumstances, the airway pressure and airway flow rate collected by the airway abnormality recognition device will not jitter up and down. Therefore, as long as the ventilation device detects that one of the airway pressure or the airway flow rate is constantly shaking up and down, it can determine that there is water in the pipeline.
  • the airway abnormality recognition device when the value of the airway pressure or the airway flow rate changes up and down at a relatively fast frequency, that is, when jitter occurs, the airway abnormality recognition device can determine that there is water in the pipeline.
  • Fig. 2(a) is an exemplary airway pressure-time waveform diagram under normal conditions provided by an embodiment of the present invention. As shown in Figure 2(a), under normal conditions, the airway pressure does not jitter, and the airway pressure-time waveform is relatively smooth.
  • Fig. 2(b) is a schematic diagram of an exemplary airway pressure-time waveform when water accumulates in a pipeline according to an embodiment of the present invention. As shown in Figure 2(b), when water accumulates in the ventilation equipment pipeline, the airway pressure has been shaking up and down, and the airway pressure-time waveform is not smooth.
  • Fig. 3(a) is an exemplary waveform diagram of airway flow rate-time under normal conditions according to an embodiment of the present invention. As shown in Figure 3(a), under normal conditions, the airway flow rate does not jitter, and the airway flow rate-time waveform is relatively smooth.
  • Fig. 3(b) is an exemplary waveform diagram of gas flow rate versus time when water accumulates in a pipeline according to an embodiment of the present invention. As shown in Figure 3(b), when water accumulates in the ventilating equipment pipeline, the airway flow rate has been jittering up and down, and the airway flow rate-time waveform is not smooth.
  • the ventilation device when the ventilation device is in any ventilation mode, as long as there is water accumulation in the pipeline, the airway pressure and airway flow rate will continue to fluctuate, and the ventilation device only needs to detect the airway pressure and airway flow rate. One of the changes is fine.
  • the step of the airway abnormality recognition device identifying whether there is water accumulation in the pipeline according to the change of the ventilation parameter includes: performing frequency spectrum analysis on the ventilation parameter; if the high frequency component of the ventilation parameter is greater than the preset value The first threshold, it is judged that there is water in the pipeline.
  • the airway abnormality recognition device performs frequency spectrum analysis on the ventilation parameters, that is, the ventilation parameters are transformed from the time domain to the frequency domain for analysis, for example, the collected airway pressure or For the airway flow rate, the data in the real-time domain can be Fourier transformed to obtain the corresponding frequency domain data. Specifically, the high-frequency components of the airway pressure or airway flow rate can be obtained.
  • the specific spectrum analysis method is not limited in the embodiment of the present invention.
  • a component greater than a certain value in a ventilation parameter may be determined as a high frequency component, for example, a component greater than 20 Hz in a ventilation parameter may be determined as a high frequency component.
  • the specific limit value of the high-frequency component is not limited in the embodiment of the present invention.
  • the first threshold may be a value preset by the user autonomously, or may be a value stored by default by the ventilation device.
  • the specific first threshold is not limited in the embodiment of the present invention.
  • the airway abnormality recognition device performs frequency spectrum analysis on the airway pressure to obtain the high-frequency component of the airway pressure, and if the high-frequency component of the airway pressure is greater than the first threshold A, it is determined There is water in the pipeline.
  • the airway abnormality recognition device performs frequency spectrum analysis on the airway flow rate to obtain the high frequency component of the airway flow rate, and if the high frequency component of the airway flow rate is greater than the first threshold B, it is determined There is water in the pipeline.
  • the step of the airway abnormality recognition device identifying whether there is water accumulation in the pipeline according to the change of ventilation parameters includes: curve fitting the ventilation parameters; calculating the error between the fitting curve and the ventilation parameters; If the error is greater than the preset second threshold, it is determined that there is water in the pipeline.
  • the airway abnormality recognition device collects the ventilation parameters, it can also perform curve fitting on the ventilation parameters to obtain a fitting curve, and the fitting curve and the actual ventilation parameters are If there is a certain error, if the calculated error between the fitted curve and the ventilation parameters is large, that is, greater than the preset second threshold, it indicates that the ventilation parameters actually deviate from the fitted curve to a large extent, that is, the ventilation parameters actually continue There was a large degree of up and down shaking, so it was judged that there was water in the pipeline.
  • the second threshold may be a value preset by the user voluntarily, or may be a value stored by the ventilation device by default.
  • the specific second threshold is not limited in the embodiment of the present invention.
  • Fig. 4 is an exemplary airway pressure fitting curve diagram provided by an embodiment of the present invention.
  • the airway abnormality recognition device has performed a curve fitting on the airway pressure, specifically by fitting the numerical points corresponding to the collected airway pressure shown in the figure to obtain curve 1.
  • the error between curve 1 and the numerical point can be calculated. If the error is greater than the preset second threshold, it is determined that there is water in the pipeline.
  • the step of the airway abnormality recognition device identifying whether sputum accumulation occurs according to changes in ventilation parameters includes: calculating airway resistance based on airway pressure and airway flow rate; if the airway resistance rises, then determining There is accumulation of sputum.
  • airway resistance refers to the pressure difference generated by the unit flow rate in the airway. Therefore, the airway abnormality recognition device can calculate the airway resistance based on the collected airway pressure and airway flow rate. .
  • the airway pressure includes inspiratory pressure and expiratory pressure
  • the gas flow rate includes inspiratory flow rate and expiratory flow rate
  • the inspiratory pressure can be calculated from the inspiratory flow rate
  • the expiratory pressure The air pressure can be calculated from the expiratory flow rate, and the inspiratory pressure and the expiratory pressure can also be obtained directly from the relevant sensors on the ventilator.
  • the step of the airway abnormality recognition device identifying whether there is an increase in pipeline resistance according to changes in ventilation parameters includes: judging whether there is an increase in pipeline resistance based on the inspiratory pressure and the expiratory pressure .
  • the airway abnormality recognition device calculates the current pipeline pressure drop through the inspiratory pressure and the expiratory pressure; if the difference between the current pipeline pressure drop and the initial pipeline pressure drop is greater than the preset With the third threshold, it is judged that the pipeline resistance has increased.
  • the third threshold may be a value preset by the user voluntarily, or may be a value stored by the ventilation device by default.
  • the specific third threshold is not limited in the embodiment of the present invention.
  • the pressure drop calculation model can be preset to calculate the initial pipeline pressure drop.
  • the pressure drop calculation model is preset The drag coefficient included also needs to be determined according to the actual situation.
  • the resistance coefficient model can be preset, and the resistance coefficient in the preset resistance coefficient model can be determined through related testing methods. After that, the resistance coefficient in the preset pressure drop calculation model can be further calculated according to the resistance coefficient in the preset resistance coefficient model. .
  • the patient port of the patient pipeline in the ventilation device can be closed, and air can be delivered to the inhalation branch according to the preset test flow rate, and then the inspiratory test pressure and the expiratory test pressure can be collected,
  • the test flow rate, inspiratory test pressure, expiration test pressure and the preset resistance coefficient model calculate the resistance coefficient in the preset resistance coefficient model; determine the resistance coefficient in the preset pressure drop calculation model according to the resistance coefficient in the preset resistance coefficient model OK.
  • Fig. 5 is a schematic diagram of the connection of a ventilation device in an exemplary test process provided by an embodiment of the present invention.
  • the patient port used by the patient is closed and blocked, so as to ensure that the ventilation device delivers air through the inhalation branch.
  • the gas flows directly from the inspiratory branch to the expiratory branch and then flows out.
  • a filter 1 an inspiratory flow sensor and an inspiratory pressure sensor are installed on the road, and a filter 2, an expiratory flow sensor, and an expiratory pressure sensor are installed on the expiratory branch.
  • the inspiratory flow sensor is used to obtain the inspiratory flow rate
  • the expiratory flow sensor is used to obtain the expiratory flow rate
  • the inspiratory pressure sensor is used to obtain the inspiratory pressure
  • the expiratory pressure sensor is used to obtain the expiratory pressure.
  • the ventilation device uses three test flow rates F1, F2, and F3 to deliver air to the inspiratory branch.
  • the inspiratory flow rate of the inspiratory branch and the expiratory flow rate of the expiratory branch are both test flow rates, which can be collected separately
  • the exhalation test pressures Pe1, Pe2, and Pe3 these data are substituted into the preset resistance coefficient model for calculation, and the resistance coefficient in the preset resistance coefficient model can be obtained.
  • the preset resistance coefficient model is shown in formula (1):
  • ⁇ Pressure is the difference between the inhalation test pressure and the expiration test pressure
  • Flow is the test flow rate
  • a, b, and c are the resistance coefficients in the preset resistance coefficient model. Therefore, the above F1, Pi1 and Pe1, F2, Pi2 and Pe2, and F3, Pi3 and Pe3 are respectively substituted into formula (1), as shown below:
  • more than three test flow rates may also be included, and the ventilation device can more accurately pass the test of more test flow rates, and obtain the resistance coefficient in the resistance coefficient model through a fitting method.
  • b and c can be simplified adaptively, and only a is retained, and a is calculated through a test flow rate. It is understandable that b and c are simplified, and the prediction of the final resistance coefficient determined by the resistance coefficient model It is assumed that the accuracy of the pressure drop calculation model has a certain impact, but the ventilation device can still recognize the event of excessive filter resistance.
  • the resistance coefficient in the preset pressure drop calculation model can be determined to be 1 of the resistance coefficient in the preset resistance coefficient model. /2, the preset pressure drop calculation model is shown in formula (5):
  • ⁇ Ptube is the target pipeline pressure drop
  • Fi is the inspiratory flow rate
  • Fe is the expiratory flow rate.
  • Fig. 6 is a schematic diagram of the connection of an exemplary ventilation device in a ventilation process provided by an embodiment of the present invention.
  • the ventilation device is connected to the patient through the patient port. If the inspiratory flow rate flowing through the inspiratory branch is Fa, the expiratory flow rate flowing through the expiratory branch is Fb, and Fa and Substituting Fb into formula (5), the initial pipeline pressure drop can be calculated.
  • the ventilation device collects the inspiratory pressure Pia and the expiratory pressure Pib through the inspiratory pressure sensor and the expiratory pressure sensor, and determines the difference between the inspiratory pressure Pia and the expiratory pressure Pib as the current pipeline pressure drop, and compares the current pipeline pressure.
  • the pipeline pressure drop and the initial pipeline pressure drop if the difference between the current pipeline pressure drop and the initial pipeline pressure drop is greater than the preset third threshold, it is determined that the pipeline resistance has increased.
  • the difference between the current pipeline pressure drop and the initial pipeline pressure drop is actually the pressure drop on the filter on the ventilation device. Therefore, if the current pipeline pressure drop is compared with the initial pipeline pressure drop The difference between the pressure drop of the circuit is greater than the preset third threshold, that is, the pressure drop on the filter is too large, indicating that the resistance of the filter is relatively large.
  • the airway abnormality recognition device determines whether there is an increase in the resistance of the pipeline through the inhalation pressure and the expiration pressure, and further includes: calculating the pipeline resistance by the inhalation pressure and the expiration pressure. The pressure drop changes; if the increase in the pressure drop of the pipeline is greater than the preset fourth threshold, it is determined that the pipeline resistance has increased.
  • the fourth threshold may be a value preset by the user autonomously, or may be a value stored by the ventilation device by default.
  • the specific fourth threshold is not limited in the embodiment of the present invention.
  • the airway abnormality recognition device collects inspiratory pressure and expiratory pressure in real time
  • the difference between the inspiratory pressure and the expiratory pressure can be calculated in real time to obtain each The pressure drop of the pipeline at any time, so as to calculate the pressure drop change of the pipeline.
  • the airway abnormality recognition device calculates the difference between the inspiratory pressure and the expiratory pressure as a1 at the first moment, and calculates the difference between the inspiratory pressure and the expiratory pressure at the second moment If it is a2, the pressure drop of the pipeline can be calculated as a2-a1.
  • the pressure drop of the pipeline is basically the same, even if it increases, the pressure drop of the pipeline increases by a small value. , And when the filter is heavily wetted, the pressure drop of the pipeline changes greatly, and the pressure drop increase value of the pipeline is also larger. Therefore, when the pressure drop increase value of the pipeline is greater than the preset value At the fourth threshold, it is judged that the pipeline resistance has increased.
  • the airway abnormality recognition device calculates the pressure drop change of the pipeline through the inspiratory pressure and the expiratory pressure, when the pressure drop increase of the pipeline is obtained as M, and M is greater than the expected value. Set the fourth threshold N, therefore, it is determined that the pipeline resistance is increased.
  • the airway abnormality recognition device outputs an abnormal airway event prompt when the abnormal airway event is recognized after the abnormal airway event occurs according to the change of the ventilation parameter.
  • the airway abnormality recognition device after the airway abnormality recognition device recognizes the abnormal airway event, it may not recognize any abnormal airway event, that is, it does not recognize the accumulation of water, sputum and sputum in the pipeline. For any event in the increase of pipeline resistance, at this time, the airway abnormality recognition device does not need to output an airway abnormal event prompt.
  • outputting an abnormal airway event prompt includes: controlling the prompt light corresponding to the abnormal airway event to flash; or, sending out an abnormal airway event.
  • the first indicator light corresponds to the accumulation of water in the pipeline
  • the second indicator light corresponds to the accumulation of sputum
  • the third indicator light corresponds to the increase in the resistance of the pipeline.
  • the alarm prompt sound corresponding to the water accumulation in the pipeline is a buzzer sound.
  • the airway abnormality recognition device recognizes the occurrence of the water accumulation in the pipeline, the buzzer sounds, and the medical staff hears the sound After the buzzer sounds, the pipeline can be drained in time.
  • the airway abnormal event prompt output by the airway abnormality recognition device may also include other types of prompts.
  • output text prompts related to abnormal airway events and display them on the display of the ventilation device.
  • the specific airway abnormal event prompts that the embodiment of the present invention does not limit it.
  • the embodiment of the present invention provides an airway abnormality recognition method.
  • the airway abnormality recognition device collects ventilation parameters, where the ventilation parameters are airway pressure and/or airway flow rate; identify whether an airway abnormality event occurs according to changes in ventilation parameters; When identifying abnormal airway events, output airway abnormal event prompts. That is to say, the technical solutions provided by the embodiments of the present invention can identify specific abnormal airway events and prompt them according to changes in ventilation parameters, which is convenient for the operator to deal with the abnormal airway events and ensure that the ventilation equipment is normally ventilated. Improve the treatment effect.
  • FIG. 7 is a schematic structural diagram of an airway abnormality recognition device provided by an embodiment of the present invention. As shown in Figure 7, the device includes:
  • the collection module 701 collects ventilation parameters, where the ventilation parameters are airway pressure and/or airway flow rate;
  • the recognition module 702 recognizes whether an abnormal airway event occurs according to the change of the ventilation parameter
  • the output module 703 outputs an airway abnormal event prompt when it is recognized that the airway abnormal event occurs.
  • the abnormal airway event includes one or more of water accumulation in the pipeline, sputum accumulation, and increase in pipeline resistance.
  • the step of the identification module 702 identifying whether there is water accumulation in the pipeline according to the change of the ventilation parameter includes:
  • the step of the identification module 702 identifying whether there is water accumulation in the pipeline according to the change of the ventilation parameter includes:
  • the high-frequency component of the ventilation parameter is greater than the preset first threshold, it is determined that there is water in the pipeline.
  • the step of the identification module 702 identifying whether there is water accumulation in the pipeline according to the change of the ventilation parameter includes:
  • the step of the identification module 702 identifying whether sputum accumulation occurs according to the change of the ventilation parameter includes:
  • the airway pressure includes an inspiratory pressure and an expiratory pressure
  • the step of the identification module 702 identifying whether there is an increase in line resistance according to the change of the ventilation parameter includes:
  • the inspiratory pressure and the expiratory pressure it is determined whether there is an increase in the resistance of the pipeline.
  • the step of the identification module 702 determining whether there is an increase in pipeline resistance based on the inspiratory pressure and the expiratory pressure includes:
  • the step of the identification module 702 determining whether there is an increase in pipeline resistance based on the inspiratory pressure and the expiratory pressure includes:
  • the airway flow rate includes an inspiratory flow rate and an expiratory flow rate
  • the inspiratory pressure is calculated based on the inspiratory flow rate
  • the expiratory pressure is calculated based on the expiratory flow rate
  • the embodiment of the present invention provides an airway abnormality recognition device, which collects ventilation parameters, where the ventilation parameters are airway pressure and/or airway flow rate; recognizes whether an abnormal airway event occurs according to changes in ventilation parameters; when an abnormal airway event occurs when the recognition occurs In the event of an event, the airway abnormal event prompt is output. That is to say, the airway abnormality recognition device provided by the embodiment of the present invention can identify specific airway abnormal events and give prompts according to the changes of ventilation parameters, which is convenient for the operator to deal with the abnormal airway events and ensure that the ventilation equipment is normal. Ventilation, thereby improving the therapeutic effect.
  • FIG. 8 is a structural schematic diagram 1 of a ventilation recognition provided by an embodiment of the present invention.
  • the ventilation device includes: the above-mentioned airway abnormality recognition device 801, and also includes an air source 802, an inhalation branch 803, an expiration branch 804, a display 805, and a controller 806;
  • the gas source 802 provides gas during mechanical ventilation
  • the inspiratory branch 803 is connected to the air source 802 to provide an inspiratory path during the mechanical ventilation;
  • the expiratory branch 804 provides an expiratory path during the mechanical ventilation
  • the airway abnormality recognition device 801 is connected to the inhalation branch 803, the expiration branch 804, and the controller 806;
  • the airway abnormality recognition device 801 recognizes abnormal airway events during the mechanical ventilation
  • the controller 806 is also connected to the air source 802 to control the process of mechanical ventilation
  • the display 805 is connected to the controller 806, and displays respiratory waveforms during the mechanical ventilation.
  • Fig. 9 is a second structural diagram of a ventilation device provided by an embodiment of the present invention. As shown in Figure 9, the patient can be connected to a ventilation device through a patient pipeline to achieve mechanical ventilation, wherein the ventilation device includes the above-mentioned airway abnormality recognition device.
  • An embodiment of the present invention provides a computer-readable storage medium that stores an airway abnormality recognition program, and the airway abnormality recognition program can be executed by a processor to implement the above-mentioned airway abnormality recognition method .
  • the computer-readable storage medium may be a volatile memory (volatile memory), such as random-access memory (Random-Access Memory, RAM); or a non-volatile memory (non-volatile memory), such as read-only memory (Read Only Memory). -Only Memory, ROM, flash memory, Hard Disk Drive (HDD) or Solid-State Drive (SSD); it can also be a respective device including one or any combination of the above-mentioned memories, Such as mobile phones, computers, tablet devices, personal digital assistants, etc.
  • the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention may adopt the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may be in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) containing computer-usable program codes.
  • These computer program instructions can also be loaded on a computer or other programmable signal processing equipment, so that a series of operation steps are executed on the computer or other programmable equipment to produce computer-implemented processing, so as to execute on the computer or other programmable equipment.
  • the instructions provide steps for implementing the functions specified in one process or multiple processes in the flowchart and/or one block or multiple blocks in the block diagram.
  • the airway abnormality recognition device collects ventilation parameters, where the ventilation parameters are airway pressure and/or airway flow rate; identify whether an airway abnormal event occurs according to changes in ventilation parameters; when an airway is identified In case of abnormal events, output airway abnormal event prompts. That is to say, the technical solutions provided by the embodiments of the present invention can identify specific abnormal airway events and prompt them according to changes in ventilation parameters, which is convenient for the operator to deal with the abnormal airway events and ensure that the ventilation equipment is normally ventilated. Improve the treatment effect.

Abstract

一种气道异常识别方法，采集通气参数，通气参数为气道压力和/或气道流速（S101）；根据通气参数的变化识别是否出现气道异常事件（S102）；当识别出现气道异常事件时，输出气道异常事件提示（S103）。

Description

一种气道异常识别方法及装置、通气设备、存储介质 技术领域
本发明实施例涉及医疗器械技术领域，尤其涉及一种气道异常识别方法及装置、通气设备、存储介质。
背景技术
呼吸机等通气设备是一种可有效代替、控制或改变人的正常生理呼吸，增加肺通气量，改善呼吸功能，减轻呼吸消耗，节约心脏储备的设备。
在呼吸机实现机械通气的过程中，容易出现一些气道异常事件，包括：呼吸管路由于使用加湿器，容易出现水汽冷凝后积留在管路中对通气产生阻碍的情况；病人气道分泌物聚积在气道中引起气道阻力升高，气道堵塞，对通气产生阻碍；呼吸机送气端和呼气端安装有细菌过滤器以防止感染，过滤器在长时间使用后会被病人呼吸气体中水蒸气打湿，进而导致过滤器阻力升高，对通气产生阻碍。
上述气道异常事件均会导致呼吸机无法正常通气，从而影响治疗效果。
发明内容
为解决上述技术问题，本发明实施例期望提供一种气道异常识别方法及装置、通气设备、存储介质，能够根据通气参数的变化情况识别出具体的气道异常事件并进行提示，便于操作人员针对气道异常事件进行处理，保证通气设备正常通气，从而提高治疗效果。
本发明实施例的技术方案可以如下实现：
本发明实施例提供了一种气道异常识别方法，所述方法包括：
采集通气参数，所述通气参数为气道压力和/或气道流速；
根据所述通气参数的变化识别是否出现气道异常事件；
当识别出现所述气道异常事件时，输出气道异常事件提示。
在上述方案中，所述气道异常事件包括管路积水、积痰、管路阻力增大中的一个或多个。
在上述方案中，所述根据所述通气参数的变化识别是否出现管路积水的步骤包括：
当所述通气参数持续处于抖动时，判断存在管路积水。
在上述方案中，所述根据所述通气参数的变化识别是否出现管路积水的步骤包括：
对所述通气参数进行频谱分析；
如果所述通气参数的高频分量大于预设的第一阈值，则判断存在管路积水。
在上述方案中，所述根据所述通气参数的变化识别是否出现管路积水的步骤包括：
对所述通气参数进行曲线拟合；
计算所述拟合曲线与通气参数的误差；
如果误差大于预设的第二阈值，则判断存在管路积水。
在上述方案中，根据所述通气参数的变化识别是否出现积痰的步骤包括：
通过所述气道压力和气道流速计算气道阻力；
如果气道阻力上升，则判断存在积痰。
在上述方案中，所述气道压力包括吸气压力和呼气压力，根据所述通气参数的变化识别是否出现管路阻力增大的步骤包括：
通过所述吸气压力和呼气压力，判断是否存在管路阻力增大。
在上述方案中，所述通过所述吸气压力和呼气压力，判断是否存在管路阻力增大的步骤包括：
通过所述吸气压力与呼气压力计算当前管路压降；
如果当前管路压降与初始管路压降的差值大于预设的第三阈值，则判断出现管路阻力增大。
在上述方案中，所述通过所述吸气压力和呼气压力，判断是否存在管路阻力增大的步骤包括：
通过所述吸气压力与呼气压力计算管路的压降变化；
如果管路的压降增大值大于预设的第四阈值，则判断出现管路阻力增大。
在上述方案中，所述气道流速包括吸气流速和呼气流速，所述吸气压力通过所述吸气流速计算得到，所述呼气压力通过所述呼气流速计算得到。
本发明实施例提供了一种气道异常识别装置，所述装置包括：
采集模块，采集通气参数，所述通气参数为气道压力和/或气道流速；
识别模块，根据所述通气参数的变化识别是否出现气道异常事件；
输出模块，当识别出现所述气道异常事件时，输出气道异常事件提示。
在上述装置中，所述气道异常事件包括管路积水、积痰、管路阻力增大中的一个或多个。
在上述装置中，所述识别模块根据所述通气参数的变化识别是否出现管路积水的步骤包括：
当所述通气参数持续处于抖动时，判断存在管路积水。
在上述装置中，所述识别模块根据所述通气参数的变化识别是否出现管路积水的步骤包括：
对所述通气参数进行频谱分析；
如果所述通气参数的高频分量大于预设的第一阈值，则判断存在管路积水。
在上述装置中，所述识别模块根据所述通气参数的变化识别是否出现管路积水的步骤包括：
对所述通气参数进行曲线拟合；
计算所述拟合曲线与通气参数的误差；
如果误差大于预设的第二阈值，则判断存在管路积水。
在上述装置中，所述识别模块根据所述通气参数的变化识别是否出现积痰的步骤包括：
通过所述气道压力和气道流速计算气道阻力；
如果气道阻力上升，则判断存在积痰。
在上述装置中，所述气道压力包括吸气压力和呼气压力，所述识别模块根据所述通气参数的变化识别是否出现管路阻力增大的步骤包括：
通过所述吸气压力和呼气压力，判断是否存在管路阻力增大。
在上述装置中，所述识别模块通过所述吸气压力和呼气压力，判断是否存在管路阻力增大的步骤包括：
通过所述吸气压力与呼气压力计算当前管路压降；
如果当前管路压降与初始管路压降的差值大于预设的第三阈值，则判断出现管路阻力增大。
在上述装置中，所述识别模块通过所述吸气压力和呼气压力，判断是否存在管路阻力增大的步骤包括：
通过所述吸气压力与呼气压力计算管路的压降变化；
如果管路的压降增大值大于预设的第四阈值，则判断出现管路阻力增大。
在上述装置中，所述气道流速包括吸气流速和呼气流速，所述吸气压力通过所述吸气流速计算得到，所述呼气压力通过所述呼气流速计算得到。
本发明实施例提供了一种包含上述气道异常识别装置的通气设备，包括气源、吸气支路、呼气支路、显示器和控制器；
所述气源，在机械通气的过程中提供气体；
所述吸气支路与所述气源连接，在所述机械通气的过程中提供吸气 路径；
所述呼气支路，在所述机械通气的过程中提供呼气路径；
所述气道异常识别装置与所述吸气支路、所述呼气支路和所述控制器连接；
所述气道异常识别装置，在所述机械通气的过程中进行气道异常事件识别；
所述控制器还与所述气源连接，控制所述机械通气的过程；
所述显示器与所述控制器连接，在所述机械通气的过程中显示呼吸波形。
本发明实施例提供了一种计算机可读存储介质，所述计算机可读存储介质存储有气道异常识别程序，所述气道异常识别程序可以被处理器执行，以实现上述气道异常识别方法。
由此可见，在本发明实施例的技术方案中，气道异常识别装置采集通气参数，通气参数为气道压力和/或气道流速；根据通气参数的变化识别是否出现气道异常事件；当识别出现气道异常事件时，输出气道异常事件提示。也就是说，本发明实施例提供的技术方案，能够根据通气参数的变化情况识别出具体的气道异常事件并进行提示，便于操作人员针对气道异常事件进行处理，保证通气设备正常通气，从而提高治疗效果。
附图说明
图1为本发明实施例提供的一种气道异常识别方法的流程示意图；
图2(a)为本发明实施例提供的一种示例性的正常情况时气道压力-时间的波形示意图；
图2(b)为本发明实施例提供的一种示例性的管路积水时气道压力-时间的波形示意图；
图3(a)为本发明实施例提供的一种示例性的正常情况时气道流速-时 间的波形示意图；
图3(b)为本发明实施例提供的一种示例性的管路积水时气道流速-时间的波形示意图；
图4为本发明实施例提供的一种示例性的气道压力的拟合曲线图；
图5为本发明实施例提供的一种示例性的测试过程中通气设备的连接示意图；
图6为本发明实施例提供的一种示例性的通气过程中通气设备的连接示意图；
图7为本发明实施例提供的一种气道异常识别装置的结构示意图；
图8为本发明实施例提供的一种通气设备的结构示意图一；
图9为本发明实施例提供的一种通气设备的结构示意图二。
具体实施方式
为了能够更加详尽地了解本发明实施例的特点与技术内容，下面结合附图对本发明实施例的实现进行详细阐述，所附附图仅供参考说明之用，并非用来限定本发明实施例。
实施例一
本发明实施例提供了一种气道异常识别方法。图1为本发明实施例提供的一种气道异常识别方法的流程示意图。如图1所示，主要包括以下步骤：
S101、采集通气参数，通气参数为气道压力和/或气道流速。
在本发明的实施例中，气道异常识别装置可以在通气设备进行机械通气的过程中，实时采集通气参数。
需要说明的是，在本发明的实施例中，气道参数为气道压力和/或气道流速。
需要说明的是，在本发明的实施例中，气道异常识别装置为通气设备 的一部分，通气设备为呼吸机、麻醉机等具备通气功能的医疗设备。具体的通气设备本发明实施例不作限定。
需要说明的是，在本发明的实施例中，气道异常识别装置采集通气参数是一直持续进行的。
S102、根据通气参数的变化识别是否出现气道异常事件。
在本发明的实施例中，气道异常识别装置在采集通气参数之后，根据通气参数的变化识别是否出现气道异常事件。
需要说明的是，在本发明的实施例中，气道异常事件包括管路积水、积痰、管路阻力增大中的一个或多个。
可以理解的是，在本发明的实施例中，通气设备根据通气参数的变化识别是否出现管路积水，是由于通气设备一般配合加湿器使用，水蒸气容易在管路中冷凝，当大量水积聚在管路中时会对通气产生阻碍。通气设备根据通气参数的变化识别是否出现积痰，是因为病人呼吸道痰液累积之后会导致气道阻力上升，从而会对通气产生阻碍。通气设备根据通气参数的变化识别是否出现管路阻力增大，是因为通气设备的吸气端和呼气端均安装有细菌过滤器，病人呼出的气体为湿润气体，冷凝之后将打湿过滤器导致管路阻力过大，从而对通气产生阻碍。
具体的，在本发明的实施例中，气道异常识别装置根据通气参数的变化识别是否出现管路积水的步骤包括：当通气参数持续处于抖动时，判断存在管路积水。
需要说明的是，在本发明的实施例中，当通气设备出现管路积水时，管路中的积水会随着管路中气体的流动而流动，此时，气道异常识别装置采集到的气道压力和气道流速，均会一直处于上下抖动的情况，而在正常情况下，气道异常识别装置采集到的气道压力和气道流速均不会上下抖动。因此，通气设备只要检测到气道压力或气道流速其中之一，一直处于上下抖动时，就可以判断存在管路积水。
示例性的，在本发明的实施例中，当气道压力或气道流速的数值一直以较快的频率上下变化，即出现抖动时，气道异常识别装置即可判定存在管路积水。
图2(a)为本发明实施例提供的一种示例性的正常情况时气道压力-时间的波形示意图。如图2(a)所示，正常情况时，气道压力未出现抖动，气道压力-时间的波形较为平滑。
图2(b)为本发明实施例提供的一种示例性的管路积水时气道压力-时间的波形示意图。如图2(b)所示，通气设备管路积水时，气道压力一直处于上下抖动的情况，气道压力-时间的波形不平滑。
图3(a)为本发明实施例提供的一种示例性的正常情况时气道流速-时间的波形示意图。如图3(a)所示，正常情况时，气道流速未出现抖动，气道流速-时间的波形较为平滑。
图3(b)为本发明实施例提供的一种示例性的管路积水时气体流速-时间的波形示意图。如图3(b)所示，通气设备管路积水时，气道流速一直处于上下抖动的情况，气道流速-时间的波形不平滑。
需要说明的是，在本发明的实施例中，通气设备处于任何通气模式时，只要出现管路积水，气道压力和气道流速均持续处于抖动，通气设备只需检测气道压力和气道流速其中之一的变化即可。
具体的，在本发明的实施例中，气道异常识别装置根据通气参数的变化识别是否出现管路积水的步骤包括：对通气参数进行频谱分析；如果通气参数的高频分量大于预设的第一阈值，则判断存在管路积水。
需要说明的是，在本发明的实施例中，气道异常识别装置对通气参数进行频谱分析，也就是将通气参数从时域变换至频域加以分析，例如，对采集到的气道压力或气道流速，即时域中的数据进行傅里叶变换，即可获得对应的频域数据，具体可以获得气道压力或气道流速的高频分量。具体的频谱分析方法本发明实施例不作限定。
需要说明的是，在本发明的实施例中，可以将通气参数中大于一定数值的分量确定为高频分量，例如，将通气参数中大于20HZ的确定为高频分量。具体的高频分量的限定值本发明实施例不作限定。
需要说明的是，在本发明的实施例中，在整个机械通气的过程中，通气设备未出现管路积水时，由于气体的流动未持续受到阻碍及震荡，通气参数较为平稳的变化，体现在通气参数频谱分析的结果中高频分量较少，而当通气设备出现管路积水时，由于气体的流动持续受到阻碍及震荡，通气参数的变化剧烈，体现在通气参数频谱分析的结果中高频分量较多，因此，如果通气参数的高频分量大于预设的第一阈值，则判断存在管路积水。
需要说明的是，在本发明的实施例中，第一阈值可以为用户自主预先设置的数值，也可以为通气设备默认存储的数值，具体的第一阈值本发明实施例不作限定。
示例性的，在本发明的实施例中，气道异常识别装置对气道压力进行频谱分析，得到气道压力的高频分量，如果气道压力的高频分量大于第一阈值A，则判断存在管路积水。
示例性的，在本发明的实施例中，气道异常识别装置对气道流速进行频谱分析，得到气道流速的高频分量，如果气道流速的高频分量大于第一阈值B，则判断存在管路积水。
具体的，在本发明的实施例中，气道异常识别装置根据通气参数的变化识别是否出现管路积水的步骤包括：对通气参数进行曲线拟合；计算拟合曲线与通气参数的误差；如果误差大于预设的第二阈值，则判断存在管路积水。
需要说明的是，在本发明的实施例中，气道异常识别装置在采集到通气参数之后，还可以对通气参数进行曲线拟合，获得拟合曲线，而拟合曲线与实际的通气参数是存在一定的误差的，如果计算出拟合曲线与通气参 数的误差较大，即大于预设的第二阈值，表明通气参数实际上较大程度的偏离拟合曲线，也就是实际上通气参数持续出现了较大程度的上下抖动，因此，判断存在管路积水。
需要说明的是，在本发明的实施例中，第二阈值可以为用户自主预先设置的数值，也可以为通气设备默认存储的数值，具体的第二阈值本发明实施例不作限定。
图4为本发明实施例提供的一种示例性的气道压力的拟合曲线图。如图4所示，气道异常识别装置对气道压力进行了曲线拟合，具体是对图中所示的采集到的气道压力对应的数值点进行了拟合，得到了曲线1，之后，可以计算曲线1和数值点之间的误差，如果误差大于预设的第二阈值，则判断存在管路积水。
具体的，在本发明的实施例中，气道异常识别装置根据通气参数的变化识别是否出现积痰的步骤包括：通过气道压力和气道流速计算气道阻力；如果气道阻力上升，则判断存在积痰。
需要说明的是，在本发明的实施例中，当病人呼吸道痰液逐渐累积后会对通气产生阻碍，此时，气道阻力逐渐升高，而在正常情况下，病人呼吸道未累积痰液，气道阻力保持稳定不变。
需要说明的是，在本发明的实施例中，气道阻力是指气道内单位流量所产生的压力差，因此，气道异常识别装置可以通过采集到的气道压力和气道流速计算气道阻力。
需要说明的是，在本发明的实施例中，气道压力包括吸气压力和呼气压力，气体流速包括吸气流速和呼气流速，其中，吸气压力可以通过吸气流速计算得到，呼气压力可以通过呼气流速计算得到，吸气压力和呼气压力也可以直接通过通气设备上的相关传感器获得。
具体的，在本发明的实施例中，气道异常识别装置根据通气参数的变化识别是否出现管路阻力增大的步骤包括：通过吸气压力和呼气压力，判 断是否存在管路阻力增大。
具体的，在本发明的实施例中，气道异常识别装置通过吸气压力与呼气压力计算当前管路压降；如果当前管路压降与初始管路压降的差值大于预设的第三阈值，则判断出现管路阻力增大。
需要说明的是，在本发明的实施例中，第三阈值可以为用户自主预先设置的数值，也可以为通气设备默认存储的数值，具体的第三阈值本发明实施例不作限定。
需要说明的是，在本发明的实施例中，可以预设压降计算模型，用于计算初始管路压降，但是，由于不同的管路其自身的特性不同，预设压降计算模型中包括的阻力系数也需要根据实际情况来确定。具体的，可以预设阻力系数模型，通过相关测试方法来确定预设阻力系数模型中的阻力系数，之后，根据预设阻力系数模型中的阻力系数进一步计算预设压降计算模型中的阻力系数。
具体的，在本发明的实施例中，可以将通气设备中病人管路的病人端口封闭，并按照预设测试流速向吸气支路送气，之后，采集吸气测试压力和呼气测试压力，根据测试流速、吸气测试压力、呼气测试压力和预设阻力系数模型，计算预设阻力系数模型中的阻力系数；根据预设阻力系数模型中的阻力系数确定预设压降计算模型中的阻力系数。
图5为本发明实施例提供的一种示例性的测试过程中通气设备的连接示意图。如图5所示，在测试过程中，将病人使用的病人端口封闭堵塞，这样可以保证通气设备通过吸气支路送气，气体从吸气支路直接流向呼气支路后流出，吸气支路安装有过滤器1、吸气流量传感器和吸气压力传感器，呼气支路安装有过滤器2、呼气流量传感器和呼气压力传感器。其中，吸气流量传感器用于获取吸气流速，呼气流速传感器用于获取呼气流速，吸气压力传感器用于获取吸气压力，呼气压力传感器用于获取呼气压力。在测试过程中，通气设备分别使用F1、F2和F3三个测试流速向吸气支路送气， 吸气支路的吸气流速和呼气支路的呼气流速均为测试流速，可以分别采集到吸气测试压力Pi1、Pi2和Pi3，呼气测试压力Pe1、Pe2和Pe3，将这些数据代入预设阻力系数模型进行计算，可以获得预设阻力系数模型中的阻力系数。其中，预设阻力系数模型如公式(1)所示：
ΔPressure＝a×Flow 2+b×Flow+c    (1)
其中，ΔPressure为吸气测试压力与呼气测试压力之差，Flow为测试流速，a、b和c均为预设阻力系数模型中的阻力系数。因此，将上述F1、Pi1和Pe1，F2、Pi2和Pe2，以及F3、Pi3和Pe3分别代入公式(1)中，如下所示：
Pi1-Pe1＝a×F1 2+b×F1+c     (2)
Pi2-Pe2＝a×F2 2+b×F2+c     (3)
Pi3-Pe3＝a×F3 2+b×F3+c     (4)
根据公式(2)、(3)和(4)联合求解，即可计算出a、b和c的具体数值。
需要说明的是，在本发明的实施例中，还可以包括三个以上的测试流速，通气设备可以更精确的通过更多测试流速的测试，通过拟合方法得到阻力系数模型中的阻力系数。并且，还可以适应性的将b和c简化掉，仅保留a，通过一个测试流速计算出a，可以理解的是，将b和c简化掉，对最终通过阻力系数模型的阻力系数确定的预设压降计算模型的精确度有一定影响，但是通气设备仍然能够实现识别过滤器阻力过大事件。
需要说明的是，在本发明的实施例中，由于呼气支路和吸气支路基本对称，预设压降计算模型中的阻力系数可以确定为预设阻力系数模型中的阻力系数的1/2，预设压降计算模型如公式(5)所示：
Figure PCTCN2018101595-appb-000001
其中，ΔPtube为目标管路压降，Fi为吸气流速，Fe为呼气流速。在采 用上述方法计算出预设阻力系数模型中的阻力系数a、b和c之后，可以将a、b和c的具体数据代入公式(5)，确定出预设压降计算模型中的阻力系数。
图6为本发明实施例提供的一种示例性的通气过程中通气设备的连接示意图。如图6所示，在通气过程中，通气设备与病人通过病人端口连接，若流经吸气支路的吸气流速为Fa，流经呼气支路的呼气流速为Fb，将Fa和Fb代入公式(5)，即可计算出初始管路压降。同时，通气设备通过吸气压力传感器和呼气压力传感器采集到了吸气压力Pia和呼气压力Pib，将吸气压力Pia和呼气压力Pib之差，确定为当前管路压降，比较当前管路压降和初始管路压降，如果当前管路压降与初始管路压降之差大于预设的第三阈值，判断出现管路阻力增大。
可以理解的是，在本发明的实施例中，当前管路压降与初始管路压降之差实际上就是通气设备上过滤器上的压降，因此，如果当前管路压降与初始管路压降之差大于预设的第三阈值，也就是过滤器上的压降过大，说明过滤器的阻力较大。
具体的，在本发明的实施例中，气道异常识别装置通过吸气压力和呼气压力，判断是否存在管路阻力增大的步骤还包括：通过吸气压力和呼气压力计算管路的压降变化；如果管路的压降增大值大于预设的第四阈值，则判断出现管路阻力增大。
需要说明的是，在本发明的实施例中，第四阈值可以为用户自主预先设置的数值，也可以为通气设备默认存储的数值，具体的第四阈值本发明实施例不作限定。
需要说明的是，在本发明的实施例中，由于气道异常识别装置是实时采集吸气压力和呼气压力的，因此，可以实时计算出吸气压力与呼气压力之差，获得每一时刻管路的压降，从而计算管路的压降变化。
示例性的，在本发明的实施例中，气道异常识别装置在第一时刻计算 出吸气压力与呼气压力之差为a1，在第二时刻计算出吸气压力与呼气压力之差为a2，则可计算出管路的压降变化为a2-a1。
可以理解的是，在正常情况下，过滤器未被打湿或被打湿的程度较轻时，管路的压降基本不变，即使增大，管路的压降增大值也较小，而在过滤器被打湿的程度较重时，管路的压降变化较大，管路的压降增大值也较大，因此，当管路的压降增大值大于预设的第四阈值，则判断出现管路阻力增大。
示例性的，在本发明的实施例中，气道异常识别装置通过吸气压力和呼气压力计算管路的压降变化，当得到管路的压降增大值为M，而M大于预设的第四阈值N，因此，判断管路阻力增大。
S103、当识别出气道异常事件时，输出气道异常事件提示。
在本发明的实施例中，气道异常识别装置在根据通气参数的变化是否出现气道异常事件之后，当识别出气道异常事件时，输出气道异常事件提示。
可以理解的是，在本发明的实施例中，气道异常识别装置在进行气道异常事件识别之后，也可能没有识别到任何气道异常事件，即未识别到管路积水、积痰和管路阻力增大中的任一事件，此时，气道异常识别装置不需要输出气道异常事件提示。
具体的，在本发明的实施例中，当气道异常识别装置识别出现气道异常事件时，输出气道异常事件提示包括：控制气道异常事件对应的提示灯闪烁；或者，发出气道异常事件对应的报警提示音。
示例性的，在本发明的实施例中，管路积水对应的为第一提示灯，积痰对应的为第二提示灯，管路阻力增大对应的为第三指示灯。当气道异常识别装置识别出现管路积水时，控制第一指示灯闪烁，医护人员看到第一指示灯闪烁之后，即可及时进行管路排水。当气道异常识别装置识别出现积痰时，控制第二指示灯闪烁，医护人员看到第二指示灯闪烁之后，即可 及时帮助病人进行排痰处理。当气道异常识别装置识别出现管路阻力增大时，控制第三指示灯闪烁，医护人员看到第三指示灯闪烁之后，即可及时更换过滤器。
示例性的，在本发明的实施例中，管路积水对应的报警提示音为蜂鸣音，当气道异常识别装置识别出现管路积水时，发出蜂鸣音，医护人员听到发出的蜂鸣音之后，即可及时进行管路排水。
需要说明的是，在本发明的实施例中，气道异常识别装置输出的气道异常事件提示还可以包括其它类型的提示。例如，输出相关气道异常事件的文字提示，在通气设备的显示器上进行显示。具体的气道异常事件提示本发明实施例不作限定。
本发明实施例提供了一种气道异常识别方法，气道异常识别装置采集通气参数，通气参数为气道压力和/或气道流速；根据通气参数的变化识别是否出现气道异常事件；当识别出现气道异常事件时，输出气道异常事件提示。也就是说，本发明实施例提供的技术方案，能够根据通气参数的变化情况识别出具体的气道异常事件并进行提示，便于操作人员针对气道异常事件进行处理，保证通气设备正常通气，从而提高治疗效果。
实施例二
本发明实施例提供了一种气道异常识别装置，图7为本发明实施例提供的一种气道异常识别装置的结构示意图。如图7所示，所述装置包括：
所述采集模块701，采集通气参数，所述通气参数为气道压力和/或气道流速；
所述识别模块702，根据所述通气参数的变化识别是否出现气道异常事件；
所述输出模块703，当识别出现所述气道异常事件时，输出气道异常事件提示。
可选的，所述气道异常事件包括管路积水、积痰、管路阻力增大中的 一个或多个。
可选的，所述识别模块702根据所述通气参数的变化识别是否出现管路积水的步骤包括：
当所述通气参数持续处于抖动时，判断存在管路积水。
可选的，所述识别模块702根据所述通气参数的变化识别是否出现管路积水的步骤包括：
对所述通气参数进行频谱分析；
如果所述通气参数的高频分量大于预设的第一阈值，则判断存在管路积水。
可选的，所述识别模块702根据所述通气参数的变化识别是否出现管路积水的步骤包括：
对所述通气参数进行曲线拟合；
计算所述拟合曲线与通气参数的误差；
如果误差大于预设的第二阈值，则判断存在管路积水。
可选的，所述识别模块702根据所述通气参数的变化识别是否出现积痰的步骤包括：
通过所述气道压力和气道流速计算气道阻力；
如果气道阻力上升，则判断存在积痰。
可选的，所述气道压力包括吸气压力和呼气压力，所述识别模块702根据所述通气参数的变化识别是否出现管路阻力增大的步骤包括：
通过所述吸气压力和呼气压力，判断是否存在管路阻力增大。
可选的，所述识别模块702通过所述吸气压力和呼气压力，判断是否存在管路阻力增大的步骤包括：
通过所述吸气压力与呼气压力计算当前管路压降；
如果当前管路压降与初始管路压降的差值大于预设的第三阈值，则判断出现管路阻力增大。
可选的，所述识别模块702通过所述吸气压力和呼气压力，判断是否存在管路阻力增大的步骤包括：
通过所述吸气压力与呼气压力计算管路的压降变化；
如果管路的压降增大值大于预设的第四阈值，则判断出现管路阻力增大。
可选的，所述气道流速包括吸气流速和呼气流速，所述吸气压力通过所述吸气流速计算得到，所述呼气压力通过所述呼气流速计算得到。
本发明实施例提供了一种气道异常识别装置，采集通气参数，通气参数为气道压力和/或气道流速；根据通气参数的变化识别是否出现气道异常事件；当识别出现气道异常事件时，输出气道异常事件提示。也就是说，本发明实施例提供的气道异常识别装置，能够根据通气参数的变化情况识别出具体的气道异常事件并进行提示，便于操作人员针对气道异常事件进行处理，保证通气设备正常通气，从而提高治疗效果。
本发明实施例提供了一种通气设备。图8为本发明实施例提供的一种通气识别的结构示意图一。如图8所示，所述通气设备包括：上述气道异常识别装置801，还包括气源802、吸气支路803、呼气支路804、显示器805和控制器806；
所述气源802，在机械通气的过程中提供气体；
所述吸气支路803与所述气源802连接，在所述机械通气的过程中提供吸气路径；
所述呼气支路804，在所述机械通气的过程中提供呼气路径；
所述气道异常识别装置801与所述吸气支路803、所述呼气支路804和所述控制器806连接；
所述气道异常识别装置801，在所述机械通气的过程中进行气道异常事件识别；
所述控制器806还与所述气源802连接，控制所述机械通气的过程；
所述显示器805与所述控制器806连接，在所述机械通气的过程中显示呼吸波形。
图9为本发明实施例提供的一种通气设备的结构示意图二。如图9所示，病人可以通过病人管路与通气设备连接，从而实现机械通气，其中，通气设备包括上述气道异常识别装置。
本发明实施例提供了一种计算机可读存储介质，所述计算机可读存储介质存储有气道异常识别程序，所述气道异常识别程序可以被处理器执行，以实现上述气道异常识别方法。计算机可读存储介质可以是是易失性存储器(volatile memory)，例如随机存取存储器(Random-Access Memory，RAM)；或者非易失性存储器(non-volatile memory)，例如只读存储器(Read-Only Memory，ROM)，快闪存储器(flash memory)，硬盘(Hard Disk Drive，HDD)或固态硬盘(Solid-State Drive，SSD)；也可以是包括上述存储器之一或任意组合的各自设备，如移动电话、计算机、平板设备、个人数字助理等。
本领域内的技术人员应明白，本发明的实施例可提供为方法、系统、或计算机程序产品。因此，本发明可采用硬件实施例、软件实施例、或结合软件和硬件方面的实施例的形式。而且，本发明可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器和光学存储器等)上实施的计算机程序产品的形式。
本发明是参照根据本发明实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程信号处理设备的处理器以产生一个机器，使得通过计算机或其他可编程信号处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功 能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程信号处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程信号处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
以上所述，仅为本发明的较佳实施例而已，并非用于限定本发明的保护范围。
工业实用性
在本发明实施例的技术方案中，气道异常识别装置采集通气参数，通气参数为气道压力和/或气道流速；根据通气参数的变化识别是否出现气道异常事件；当识别出现气道异常事件时，输出气道异常事件提示。也就是说，本发明实施例提供的技术方案，能够根据通气参数的变化情况识别出具体的气道异常事件并进行提示，便于操作人员针对气道异常事件进行处理，保证通气设备正常通气，从而提高治疗效果。

Claims (22)

  1. 一种气道异常识别方法，其特征在于，所述方法包括：
    采集通气参数，所述通气参数为气道压力和/或气道流速；
    根据所述通气参数的变化识别是否出现气道异常事件；
    当识别出现所述气道异常事件时，输出气道异常事件提示。
  2. 根据权利要求1所述的方法，其特征在于，所述气道异常事件包括管路积水、积痰、管路阻力增大中的一个或多个。
  3. 根据权利要求2所述的方法，其特征在于，所述根据所述通气参数的变化识别是否出现管路积水的步骤包括：
    当所述通气参数持续处于抖动时，判断存在管路积水。
  4. 根据权利要求2所述的方法，其特征在于，所述根据所述通气参数的变化识别是否出现管路积水的步骤包括：
    对所述通气参数进行频谱分析；
    如果所述通气参数的高频分量大于预设的第一阈值，则判断存在管路积水。
  5. 根据权利要求2所述的方法，其特征在于，所述根据所述通气参数的变化识别是否出现管路积水的步骤包括：
    对所述通气参数进行曲线拟合；
    计算所述拟合曲线与通气参数的误差；
    如果误差大于预设的第二阈值，则判断存在管路积水。
  6. 根据权利要求2所述的方法，其特征在于，根据所述通气参数的变化识别是否出现积痰的步骤包括：
    通过所述气道压力和气道流速计算气道阻力；
    如果气道阻力上升，则判断存在积痰。
  7. 根据权利要求2所述的方法，其特征在于，所述气道压力包括吸 气压力和呼气压力，根据所述通气参数的变化识别是否出现管路阻力增大的步骤包括：
    通过所述吸气压力和呼气压力，判断是否存在管路阻力增大。
  8. 根据权利要求7所述的方法，其特征在于，所述通过所述吸气压力和呼气压力，判断是否存在管路阻力增大的步骤包括：
    通过所述吸气压力与呼气压力计算当前管路压降；
    如果当前管路压降与初始管路压降的差值大于预设的第三阈值，则判断出现管路阻力增大。
  9. 根据权利要求7所述的方法，其特征在于，所述通过所述吸气压力和呼气压力，判断是否存在管路阻力增大的步骤包括：
    通过所述吸气压力与呼气压力计算管路的压降变化；
    如果管路的压降增大值大于预设的第四阈值，则判断出现管路阻力增大。
  10. 根据权利要求7所述的方法，其特征在于，所述气道流速包括吸气流速和呼气流速，所述吸气压力通过所述吸气流速计算得到，所述呼气压力通过所述呼气流速计算得到。
  11. 一种气道异常识别装置，其特征在于，所述装置包括：
    采集模块，采集通气参数，所述通气参数为气道压力和/或气道流速；
    识别模块，根据所述通气参数的变化识别是否出现气道异常事件；
    输出模块，当识别出现所述气道异常事件时，输出气道异常事件提示。
  12. 根据权利要求11所述的装置，其特征在于，所述气道异常事件包括管路积水、积痰、管路阻力增大中的一个或多个。
  13. 根据权利要求12所述的装置，其特征在于，所述识别模块根据所述通气参数的变化识别是否出现管路积水的步骤包括：
    当所述通气参数持续处于抖动时，判断存在管路积水。
  14. 根据权利要求12所述的装置，其特征在于，所述识别模块根据所述通气参数的变化识别是否出现管路积水的步骤包括：
    对所述通气参数进行频谱分析；
    如果所述通气参数的高频分量大于预设的第一阈值，则判断存在管路积水。
  15. 根据权利要求12所述的装置，其特征在于，所述识别模块根据所述通气参数的变化识别是否出现管路积水的步骤包括：
    对所述通气参数进行曲线拟合；
    计算所述拟合曲线与通气参数的误差；
    如果误差大于预设的第二阈值，则判断存在管路积水。
  16. 根据权利要求12所述的装置，其特征在于，所述识别模块根据所述通气参数的变化识别是否出现积痰的步骤包括：
    通过所述气道压力和气道流速计算气道阻力；
    如果气道阻力上升，则判断存在积痰。
  17. 根据权利要求12所述的装置，其特征在于，所述气道压力包括吸气压力和呼气压力，所述识别模块根据所述通气参数的变化识别是否出现管路阻力增大的步骤包括：
    通过所述吸气压力和呼气压力，判断是否存在管路阻力增大。
  18. 根据权利要求17所述的装置，其特征在于，所述识别模块通过所述吸气压力和呼气压力，判断是否存在管路阻力增大的步骤包括：
    通过所述吸气压力与呼气压力计算当前管路压降；
    如果当前管路压降与初始管路压降的差值大于预设的第三阈值，则判断出现管路阻力增大。
  19. 根据权利要求17所述的装置，其特征在于，所述识别模块通过所述吸气压力和呼气压力，判断是否存在管路阻力增大的步骤包括：
    通过所述吸气压力与呼气压力计算管路的压降变化；
    如果管路的压降增大值大于预设的第四阈值，则判断出现管路阻力增大。
  20. 根据权利要求17所述的装置，其特征在于，所述气道流速包括吸气流速和呼气流速，所述吸气压力通过所述吸气流速计算得到，所述呼气压力通过所述呼气流速计算得到。
  21. 一种包含权利要求11-20任一项所述气道异常识别装置的通气设备，其特征在于，包括气源、吸气支路、呼气支路、显示器和控制器；
    所述气源，在机械通气的过程中提供气体；
    所述吸气支路与所述气源连接，在所述机械通气的过程中提供吸气路径；
    所述呼气支路，在所述机械通气的过程中提供呼气路径；
    所述气道异常识别装置与所述吸气支路、所述呼气支路和所述控制器连接；
    所述气道异常识别装置，在所述机械通气的过程中进行气道异常事件识别；
    所述控制器还与所述气源连接，控制所述机械通气的过程；
    所述显示器与所述控制器连接，在所述机械通气的过程中显示呼吸波形。
  22. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质存储有气道异常识别程序，所述气道异常识别程序可以被处理器执行，以实现权利要求1-10任一项所述的气道异常识别方法。
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