Calibration method of inspiration and expiration flow sensor and respiratory support equipment

Abstract

The calibration method of the inspiration and expiration flow sensor is applied to respiratory support equipment and comprises the following steps: acquiring flow sensor data detected by the respiratory support equipment; determining that the flow sensor has a null drift risk when the data of the flow sensor meets the null drift initial judgment condition; after it is determined that the flow sensor has the null drift risk, a calibration component of the flow sensor is started, and detection data of the flow sensor are obtained; when the detection data of the flow sensor meet the zero drift secondary judgment condition, it is determined that the zero drift phenomenon exists in the flow sensor; when it is determined that the flow sensor has the null drift phenomenon, null drift calibration is conducted on the flow sensor. According to the method and the device, whether the zero drift phenomenon exists in the flow sensor or not is determined according to a dual judgment mechanism of whether the data of the flow sensor meets the zero drift primary judgment condition or not and whether the detection data of the flow sensor meets the zero drift secondary judgment condition or not, so that the timeliness of zero calibration of the inspiration and expiration flow sensor can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical equipment, and in particular to a calibration method for an inspiratory and expiratory flow sensor and a respiratory support device. Background Art

[0002] Currently, flow sensors in anesthesia machines and ventilators need to monitor patient ventilation (such as tidal volume and minute ventilation) in real time, and clinical standards require that the measurement error is less than or equal to ±2% (refer to ISO

[0003] 80601-2-72 standard). Traditional zeroing methods include: manual zeroing (initiating a calibration command by the operator during device startup or maintenance); periodic zeroing (performing a zeroing at regular intervals (e.g., one hour) after device startup); automatic zeroing during shutdown (suspending airflow in standby mode for zero calibration); and model-based compensation (performing zero-point compensation by establishing a model for the relationship between environmental parameters and drift). However, these zeroing methods have the following drawbacks: First, a forced pause of airflow for 1-3 seconds can affect the continuity of mechanical ventilation; second, they can easily cause interruptions in tidal volume monitoring, potentially triggering ventilator alarms; third, the preset temperature-drift curve becomes ineffective in scenarios with rapid temperature changes (such as ventilation in an operating room with the door open); and third, the model prediction error under condensation interference can reach 1.8% FS.

[0004] Therefore, how to improve the timeliness of zero calibration of respiratory flow sensors is a technical problem that needs to be solved urgently. Summary of the Invention

[0005] The main technical problem solved by the present invention is how to improve the timeliness of zero calibration of an inhalation and exhalation flow sensor.

[0006] According to a first aspect, an embodiment provides a method for calibrating an inspiratory and expiratory flow sensor, which is applied to a respiratory support device, the method comprising:

[0007] Acquiring flow sensor data detected by a respiratory support device; wherein the flow sensor data includes inspiratory flow sensor data or expiratory flow sensor data;

[0008] When the flow sensor data meets the initial zero drift determination condition, determining that the flow sensor has a zero drift risk;

[0009] After determining that the flow sensor has a zero drift risk, activating a calibration component of the flow sensor and acquiring detection data of the flow sensor; wherein the detection data of the flow sensor includes detection data of an inspiratory flow sensor or detection data of an expiratory flow sensor; and the detection data is of a different type from the flow sensor data;

[0010] When the detection data of the flow sensor meets the zero drift secondary determination condition, determining that the flow sensor has a zero drift phenomenon;

[0011] When it is determined that the flow sensor has the zero drift phenomenon, a zero drift calibration is performed on the flow sensor.

[0012] According to a second aspect, an embodiment provides a respiratory support device, the respiratory support device comprising:

[0013] A ventilation module is used to connect to the patient through a ventilation tube to provide respiratory support for the patient;

[0014] a detection system, configured to detect when the ventilation module provides respiratory support to the patient, and obtain flow sensor data and detection data of the flow sensor;

[0015] Human-computer interaction device;

[0016] Controller for:

[0017] When the flow sensor data meets the initial zero drift determination condition, determining that the flow sensor has a zero drift risk;

[0018] After determining that the flow sensor has a zero drift risk, activating a calibration component of the flow sensor; and acquiring detection data of the flow sensor; wherein the detection data of the flow sensor includes detection data of an inspiratory flow sensor or detection data of an expiratory flow sensor; and the detection data is of a different type from the flow sensor data;

[0019] When the detection data of the flow sensor meets the zero drift secondary determination condition, determining that the flow sensor has a zero drift phenomenon;

[0020] When it is determined that the flow sensor has the zero drift phenomenon, a zero drift calibration is performed on the flow sensor.

[0021] According to the above-described embodiment of the method for calibrating an inspiratory and respiratory flow sensor, whether the flow sensor has zero drift is determined by a dual judgment mechanism: whether the flow sensor data satisfies the primary zero drift judgment condition, and whether the flow sensor detection data satisfies the secondary zero drift judgment condition. Thus, when the inspiratory and respiratory flow sensor has zero drift, it can be promptly calibrated for zero drift, thereby improving the timeliness of zero calibration of the inspiratory and respiratory flow sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic flow chart of a method for calibrating an inhalation and exhalation flow sensor provided in an embodiment of the present application;

[0023] Figure 2A schematic flow chart of another method for calibrating an inspiratory and respiratory flow sensor provided in an embodiment of the present application;

[0024] Figure 3 This is a structural block diagram of a respiratory support device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The present invention will be further described in detail below by means of specific embodiments in conjunction with the accompanying drawings. Similar elements in different embodiments are numbered with associated similar elements. In the following embodiments, many detailed descriptions are provided to enable the present application to be better understood. However, those skilled in the art will readily appreciate that some of the features may be omitted in different circumstances, or may be replaced by other elements, materials, or methods. In some cases, some operations related to the present application are not shown or described in the specification. This is to avoid the core portion of the present application being overwhelmed by excessive descriptions, and for those skilled in the art, it is not necessary to describe these related operations in detail. They will fully understand the related operations based on the description in the specification and the general technical knowledge in the art.

[0026] In addition, the features, operations, or characteristics described in the specification may be combined in any appropriate manner to form various embodiments. Furthermore, the steps or actions in the method description may be reordered or adjusted in a manner readily apparent to those skilled in the art. Therefore, the various sequences in the specification and drawings are provided solely for the purpose of clearly describing a particular embodiment and are not intended to be mandatory, unless otherwise specified.

[0027] The serial numbers assigned to components herein, such as "first," "second," etc., are used solely to distinguish the objects being described and do not convey any sequential or technical meaning. References to "connection" and "coupling" herein, unless otherwise specified, include both direct and indirect connections (couplings).

[0028] Flow sensors in traditional anesthesia machines and ventilators monitor patient ventilation (e.g., tidal volume, minute ventilation) in real time. Clinical standards require a measurement error of ≤±2% (see ISO 80601-2-72). Currently, the primary source of measurement error in flow sensors is zero drift (e.g., caused by temperature / humidity changes, condensation accumulation, etc.). Therefore, to minimize the impact of measurement error on flow sensors, zero calibration is essential. Traditional zeroing methods include: manual zeroing (initiated by the operator during device startup or maintenance); periodic zeroing (performed at fixed intervals (e.g., every hour) after device startup); automatic zeroing during shutdown (initiated by pausing airflow in standby mode for zero calibration); and model-based compensation (initiated by modeling the relationship between environmental parameters and drift). However, the above zeroing method has the following disadvantages: on the one hand, the forced suspension of airflow for 1-3 seconds will affect the continuity of mechanical ventilation; on the other hand, it is easy to cause interruption of tidal volume monitoring, and there is a risk of false triggering of ventilator alarm; on the other hand, the timeliness of zeroing cannot be guaranteed, that is, zeroing cannot be performed in time when the flow sensor produces zero drift; on the other hand, the preset temperature-drift curve will fail in rapid temperature change scenarios (such as opening the door for ventilation in the operating room); on the other hand, the model prediction error under condensation water interference can reach 1.8% FS.

[0029] In order to solve the above technical problems, the present application proposes a calibration method for an inhalation and exhalation flow sensor, which determines whether the flow sensor has a zero drift phenomenon based on a dual judgment mechanism of whether the flow sensor data meets the primary zero drift judgment condition and whether the detection data of the flow sensor meets the secondary zero drift judgment condition. In this way, when the inhalation and exhalation flow sensor produces zero drift, it can be calibrated for zero drift in time, thereby improving the timeliness of zero calibration of the inhalation and exhalation flow sensor.

[0030] Please refer to Figure 1 , Figure 1 This is a flow chart of a calibration method for an inspiratory and expiratory flow sensor provided in an embodiment of the present application. The calibration method is applied to respiratory support equipment and specifically includes the following steps S101-S105:

[0031] Step S101: Acquire flow sensor data detected by a respiratory support device; wherein the flow sensor data includes inhalation flow sensor data or expiratory flow sensor data.

[0032] It should be noted that respiratory support equipment refers to equipment used to help patients with respiratory distress maintain normal breathing. For example, the respiratory support equipment may be a miniaturized, portable ventilator or anesthesia machine. Alternatively, it may be a miniaturized, portable home ventilator or anesthesia machine. This application does not impose any limitations on this.

[0033] It should be noted that the flow sensor data may be the gas flow rate output by the respiratory support device to the patient, which includes the flow rate of the gas during the patient's inhalation or exhalation. This application does not impose any restrictions on this.

[0034] It should be noted that the present application performs zero drift determination of the inspiratory flow sensor and the expiratory flow sensor in a time-sharing manner. This is because the patient has no expiratory flow during the inhalation phase. At this time, the flow sensor data (signal) detected by the expiratory flow sensor can be considered as zero-point data (signal), that is, the expiratory zero-point data (signal) can be determined during the inhalation phase of the respiratory cycle. Of course, after the expiratory flow sensor detects the flow sensor data (signal), the data (signal) can be filtered to reduce interference from environmental noise, electromagnetic noise, thermal noise of the respiratory support device itself, etc.

[0035] Similarly, during the exhalation phase, the patient has no inspiratory flow. At this time, the flow sensor data (signal) detected by the inspiratory flow sensor can be considered as zero-point data (signal). In other words, the inspiratory zero-point data (signal) can be determined during the exhalation phase of the respiratory cycle. Of course, after the inspiratory flow sensor detects the flow sensor data (signal), the data (signal) can be filtered to reduce interference from environmental noise, electromagnetic noise, thermal noise of the respiratory support device itself, and the like.

[0036] Step S102: When the flow sensor data meets the initial zero drift determination condition, it is determined that the flow sensor has a zero drift risk.

[0037] In some embodiments, when the flow sensor data meets the initial zero drift determination condition, determining that the flow sensor has a zero drift risk includes:

[0038] When the absolute value of the inhalation flow sensor data is greater than the first zero drift preliminary judgment threshold, it is determined that the inhalation flow sensor has a zero drift risk.

[0039] It should be noted that the zero drift preliminary judgment formula for the data detected by the inhalation flow sensor is as follows:

[0040] |Fi_0|>T0_Fi

[0041] Wherein, Fi_0 represents the zero-point inspiratory flow detected by the inspiratory flow sensor; T0_Fi represents the first zero drift initial judgment threshold, which is usually set to any value between 0.2L / min and 0.5L / min.

[0042] It should be noted that when the absolute value of the inspiratory flow sensor data is greater than the first zero drift initial judgment threshold, it indicates that the inspiratory flow sensor data may have experienced zero drift, or it may not have experienced zero drift, but rather a gas leak. Further judgment is required for this. For example, when there is a leak in the respiratory system, the pressure in the air circuit during ventilation is higher than the external atmospheric pressure. If the air circuit is not sealed well (for example, there are small holes), then air will leak out. The impact of the airflow leak on the flow sensor will also be different depending on the location of the airflow leak. For example, if the airflow leak is located at the rear end of the inspiratory flow sensor, then the inspiratory flow during the exhalation phase may not be zero, that is, the absolute value of Fi_0 may be greater than the first zero drift initial judgment threshold.

[0043] In practical applications, to eliminate misjudgments caused by other conditions (such as swallowing or coughing), the following conditions can be set: if the absolute value of zero-point inspiratory flow data detected by the inspiratory flow sensor for D consecutive (D = 3) times is greater than the first zero-drift preliminary judgment threshold, or if the absolute value of zero-point inspiratory flow data detected for at least L consecutive (L = 4) times during G consecutive (G = 6) respiratory cycles is greater than the first zero-drift preliminary judgment threshold, a preliminary judgment can be made that the inspiratory flow sensor has zero drift. Otherwise, the inspiratory flow sensor is determined to have no zero drift, and in this case, further zero-point inspiratory flow data can be detected and a preliminary zero-drift judgment can be made.

[0044] In some embodiments, when the flow sensor data meets the initial zero drift determination condition, determining that the flow sensor has a zero drift risk includes:

[0045] When the absolute value of the exhalation flow sensor data is greater than the second zero drift preliminary judgment threshold, it is determined that the exhalation flow sensor has a zero drift risk.

[0046] It should be noted that the zero drift initial judgment formula for the data detected by the exhaled flow sensor is as follows:

[0047] |Fe_0|>T0_Fe

[0048] Where Fe_0 represents the zero-point exhalation flow detected by the exhalation flow sensor; T0_Fe represents the second zero drift initial judgment threshold, which is usually set to any value between 0.2L / min and 0.5L / min.

[0049] It should be noted that when the absolute value of the expiratory flow sensor data is greater than the second zero drift initial judgment threshold, it indicates that the expiratory flow sensor data may have experienced zero drift, or it may not have experienced zero drift, but rather a gas leak. Further judgment is required for this. For example, when there is a leak in the respiratory system, the pressure in the air circuit during ventilation is higher than the external atmospheric pressure. If the air circuit is not sealed well (for example, there are small holes), then air will leak out. The impact of the airflow leak on the flow sensor will also be different depending on the location of the airflow leak. For example, if the airflow leak is located at the rear end of the expiratory flow sensor, then the expiratory flow during the inhalation phase may not be zero, that is, the absolute value of Fe_0 may be greater than the second zero drift initial judgment threshold.

[0050] In practical applications, to eliminate misjudgments caused by other conditions (such as swallowing or coughing), the following conditions can be set: if the absolute value of zero-point expiratory flow data detected by the expiratory flow sensor for D consecutive (D = 3) times is greater than the second zero-drift preliminary judgment threshold, or if the absolute value of zero-point expiratory flow data detected for at least L consecutive (L = 4) times during G consecutive (G = 6) respiratory cycles is greater than the second zero-drift preliminary judgment threshold, a preliminary judgment can be made that the expiratory flow sensor has zero drift. Otherwise, the expiratory flow sensor is determined to have no zero drift, and further zero-point expiratory flow data can be detected and a preliminary zero-drift judgment can be made.

[0051] Step S103: After determining that the flow sensor has a zero drift risk, start the calibration component of the flow sensor and obtain detection data of the flow sensor; wherein the detection data of the flow sensor includes detection data of the inhalation flow sensor or detection data of the exhalation flow sensor; the detection data and the flow sensor data are of different types.

[0052] In practical applications, the calibration component can be an instrument with a calibration function, such as a zeroing valve. This zeroing valve is installed on a flow sensor and is used to perform zero-drift calibration on flow sensors that exhibit zero-drift. It should be noted that when the calibration component performs zero-drift calibration on the flow sensor, no air is flowing through it. It should be noted that both the flow sensor and the calibration component are installed on the air supply line of the respiratory support device.

[0053] It should be noted that flow sensor data is gas flow data, primarily used for initial zero drift determination. Flow sensor detection data, on the other hand, is sensor data, primarily used for secondary zero drift determination, i.e., final zero drift determination, and subsequent zero drift calibration.

[0054] It should be noted that during the use of respiratory support equipment, the respiratory flow sensor may be affected by various factors, such as temperature and humidity changes, sensor aging, etc., which may lead to measurement errors. Regular calibration using a calibration component (zero valve) can eliminate these errors. This compensates for errors caused by environmental changes or sensor performance drift, thereby ensuring the timeliness of respiratory flow sensor measurements.

[0055] It should be noted that the detection data of the flow sensor can be a digital signal (AD value or differential pressure value) detected when the respiratory flow sensor performs airflow detection on the patient, wherein the differential pressure value is used to represent the pressure change detected by the sensor when the airflow flows through the respiratory flow sensor.

[0056] Step S104: When the detection data of the flow sensor meets the zero drift secondary determination condition, it is determined that the flow sensor has a zero drift phenomenon.

[0057] In some embodiments, when the detection data of the flow sensor meets the zero drift secondary determination condition, determining that the flow sensor has a zero drift phenomenon includes:

[0058] Obtain zero point data of the standard inspiratory flow sensor;

[0059] A first difference is obtained by subtracting the detection data of the inhalation flow sensor from the zero point data of the standard inhalation flow sensor. When the absolute value of the first difference is greater than a third zero drift threshold, it is determined that the inhalation flow sensor has a zero drift phenomenon.

[0060] It should be noted that the zero point data of the standard inhalation flow sensor represents the zero point data detected by the inhalation flow sensor that has been calibrated for zero drift before. For example, it can be the zero point data detected by the inhalation flow sensor that has been calibrated for zero drift last time.

[0061] It should be noted that the zero drift secondary determination formula for the detection data of the inhalation flow sensor is as follows:

[0062] |Yi_1-Yi_0|>T_Yi

[0063] Wherein, Yi_1 represents the detection data of the inhalation flow sensor (such as the zero-point AD value or the zero-point differential pressure value); Yi_0 represents the zero-point data of the standard inhalation flow sensor; and T_Yi represents the third zero drift threshold.

[0064] In actual applications, during the patient's exhalation phase, when it is detected that the inspiratory flow sensor has a zero drift risk, the respiratory system will automatically control the zero calibration valve on the inspiratory flow sensor to open, and wait for a period of time (such as 50ms) until the airflow disturbance generated by the moment the zero calibration valve is opened disappears, and then control the inspiratory flow sensor to continue collecting detection data Yi_1 (such as zero point AD value or zero point differential pressure value). When the detection data Yi_1 meets the zero drift secondary judgment condition, it indicates that the inspiratory flow sensor does have a zero drift, otherwise, it indicates that there is an airflow leak in the respiratory system.

[0065] In some embodiments, when the detection data of the flow sensor meets the zero drift secondary determination condition, determining that the flow sensor has a zero drift phenomenon includes:

[0066] Obtain zero point data of the standard exhalation flow sensor;

[0067] A second difference is obtained by subtracting the detection data of the exhalation flow sensor from the zero point data of the standard exhalation flow sensor. When the absolute value of the second difference is greater than a fourth zero drift threshold, it is determined that the exhalation flow sensor has a zero drift phenomenon.

[0068] It should be noted that the zero point data of the standard expiratory flow sensor represents the zero point data detected by the expiratory flow sensor that has been calibrated for zero drift before. For example, it can be the zero point data detected by the expiratory flow sensor that has been calibrated for zero drift last time.

[0069] It should be noted that the zero drift secondary determination formula for the detection data of the exhalation flow sensor is as follows:

[0070] |Ye_1-Ye_0|>T_Ye

[0071] Wherein, Ye_1 represents the detection data of the exhalation flow sensor (such as the zero-point AD value or the zero-point differential pressure value); Ye_0 represents the zero-point data of the standard exhalation flow sensor; and T_Ye represents the fourth zero drift threshold.

[0072] In actual applications, during the patient's inhalation phase, when the risk of zero drift in the expiratory flow sensor is detected, the respiratory system will automatically control the zero calibration valve on the expiratory flow sensor to open, and wait for a period of time (such as 50ms) until the airflow disturbance generated by the moment the zero calibration valve is opened disappears, and then control the expiratory flow sensor to continue collecting detection data Ye_1 (such as zero point AD value or zero point differential pressure value). When the detection data Ye_1 meets the zero drift secondary judgment condition, it indicates that the expiratory flow sensor does have zero drift, otherwise, it indicates that there is airflow leakage in the respiratory system.

[0073] Step S105: When it is determined that the flow sensor has a zero drift phenomenon, perform zero drift calibration on the flow sensor.

[0074] In some embodiments, when it is determined that the flow sensor has a zero drift phenomenon, performing zero drift calibration on the flow sensor includes:

[0075] Obtaining a first calibration data table corresponding to the inspiratory flow sensor;

[0076] Adding the first detection data and the first difference in the first calibration data table to obtain calibrated first detection data;

[0077] The calibrated first detection data is updated as the zero point data of the standard inspiratory flow sensor.

[0078] It should be noted that the first calibration data table is a data table after calibration of the inhalation flow rate sensor. This first calibration data table can be the original calibration data table of the inhalation flow rate sensor when it leaves the factory, or a new calibration table after calibration of the original calibration table. This data table includes multiple sets of original inhalation flow rate sensor data and first detection data, wherein one set of original inhalation flow rate sensor data corresponds to one set of first detection data. The first detection data can be a first AD value or a first differential pressure value.

[0079] In actual use, when zero drift is detected in the inspiratory flow sensor during the exhalation phase of the patient's respiratory cycle, zero drift calibration is required for the first detection data of the inspiratory flow sensor. Specifically, the zero offset (the difference between the current inspiratory flow sensor detection data Yi_1 and the zero point data Yi_0 of the standard inspiratory flow sensor) is added to each first detection data in the first calibration data table to achieve zero drift calibration of the inspiratory flow sensor. This improves the accuracy of the inspiratory flow sensor data.

[0080] It should be noted that when performing zero drift calibration on the first detection data of the inspiratory flow sensor, there is no need to suspend the airflow, so that the impact of the zero drift calibration process on ventilation can be reduced.

[0081] It should be noted that after calibrating the zero drift of the above-mentioned inhalation flow sensor, it is necessary to update the calibrated first detection data to the zero point data of the standard inhalation flow sensor, thereby completing this zero point calibration.

[0082] In some embodiments, when it is determined that the flow sensor has a zero drift phenomenon, performing zero drift calibration on the flow sensor includes:

[0083] Obtaining a second calibration data table corresponding to the expiratory flow sensor;

[0084] Sum the second detection data and the second difference in the second calibration data table to obtain calibrated second detection data;

[0085] The calibrated second detection data is updated as the zero point data of the standard exhalation flow sensor.

[0086] It should be noted that the second calibration data table is a data table after calibration of the expiratory flow sensor. This second calibration data table can be the original calibration data table of the expiratory flow sensor when it leaves the factory, or a new calibration table after calibration of the original calibration table. This data table includes multiple sets of original expiratory flow sensor data and second detection data, where each piece of original expiratory flow sensor data corresponds to one piece of second detection data. The second detection data can be a second AD value or a second differential pressure value.

[0087] In actual use, when zero drift is detected in the expiratory flow sensor during the inhalation phase of the patient's respiratory cycle, zero drift calibration is required for the second detection data of the expiratory flow sensor. Specifically, the zero offset (the difference between the current expiratory flow sensor detection data Ye_1 and the zero data Ye_0 of the standard expiratory flow sensor) is added to each second detection data in the second calibration data table to achieve zero drift calibration of the expiratory flow sensor, thereby improving the accuracy of the expiratory flow sensor data.

[0088] It should be noted that when performing zero drift calibration on the second detection data of the expiratory flow sensor, there is no need to suspend the airflow, thereby reducing the impact of the zero drift calibration process on ventilation.

[0089] It should be noted that after calibrating the zero drift of the above-mentioned exhalation flow sensor, the calibrated second detection data needs to be updated to the zero point data of the standard exhalation flow sensor, thereby completing this zero point calibration.

[0090] In some embodiments, when the detection data of the flow sensor does not meet the zero drift secondary determination condition, it is determined that the flow sensor has a non-zero drift phenomenon.

[0091] After determining that the flow sensor has a non-zero drift phenomenon, the method further includes:

[0092] Acquire inspiratory flow sensor data for at least three respiratory cycles before the current moment;

[0093] A third difference is obtained by subtracting an average of the plurality of inhalation flow sensor data from the inhalation flow sensor data at the current moment. When an absolute value of the third difference is greater than a fifth zero drift adjustment threshold, it is determined that the inhalation flow sensor has a zero drift risk.

[0094] In practical applications, an initial zero-drift determination is performed on the inspiratory flow sensor during each respiratory cycle. To minimize the impact of opening the calibration component (zeroing valve) on data monitoring during each respiratory cycle, the zeroing valve is typically not opened during the initial zero-drift determination phase. Instead, the zeroing valve is opened for secondary confirmation only after an initial determination that the inspiratory flow sensor may have zero drift. However, opening the zeroing valve can affect ventilation data monitoring. Therefore, when the secondary zero-drift determination is based on airflow leakage in the respiratory system, rather than zero drift in the inspiratory flow sensor, dynamic adjustment of the initial zero-drift determination conditions is necessary to avoid frequent triggering of the zeroing valve opening during the secondary zero-drift determination.

[0095] It should be noted that the initial judgment adjustment formula for the zero drift initial judgment condition of the inhalation flow sensor is as follows:

[0096] |Fi_0-μ_i|>T0_Fi

[0097] Wherein, Fi_0 represents the zero-point inspiratory flow detected by the inspiratory flow sensor; μ_i represents the average value of multiple inspiratory flow sensor data detected by the inspiratory flow sensor; T0_Fi represents the fifth zero drift initial judgment threshold, which is usually set to any value between 0.2L / min and 0.5L / min.

[0098] It should be noted that the fifth zero drift initial judgment threshold may be the same as the first zero drift threshold.

[0099] It should be noted that when calculating μ_i, the inspiratory flow sensor data from at least three respiratory cycles prior to the moment when airflow leakage is detected in the respiratory system may be selected and averaged. The respiratory cycle may be three consecutive respiratory cycles, four consecutive respiratory cycles, or five consecutive respiratory cycles, and this application does not impose any limitation on this.

[0100] In some embodiments, when the detection data of the flow sensor does not meet the zero drift secondary determination condition, it is determined that the flow sensor has a non-zero drift phenomenon.

[0101] After determining that the flow sensor has a non-zero drift phenomenon, the method further includes:

[0102] Acquire expiratory flow sensor data for at least three respiratory cycles before the current moment;

[0103] A fourth difference is obtained by subtracting an average of the plurality of expiratory flow sensor data from the expiratory flow sensor data at the current moment. When an absolute value of the fourth difference is greater than a sixth zero drift adjustment threshold, it is determined that the expiratory flow sensor has a zero drift risk.

[0104] In practical applications, an initial zero-drift determination is performed on the expiratory flow sensor during each respiratory cycle. To minimize the impact of opening the calibration component (zeroing valve) on data monitoring during each respiratory cycle, the zeroing valve is typically not opened during the initial zero-drift determination phase. Instead, the zeroing valve is opened for secondary confirmation only after the initial determination that the expiratory flow sensor may have zero drift. However, opening the zeroing valve can affect ventilation data monitoring. Therefore, when the secondary zero-drift determination indicates an airflow leak in the respiratory system, rather than a zero-drift in the expiratory flow sensor, dynamic adjustment of the initial zero-drift determination conditions is necessary to avoid frequent triggering of the zeroing valve opening during the secondary zero-drift determination.

[0105] It should be noted that the initial adjustment formula for the zero drift initial judgment condition of the expiratory flow sensor is as follows:

[0106] |Fe_0-μ_e|>T0_Fe

[0107] Wherein, Fe_0 represents the zero-point expiratory flow detected by the expiratory flow sensor; μ_e represents the average value of multiple expiratory flow sensor data detected by the expiratory flow sensor; T0 Fe represents the sixth zero drift initial judgment threshold, which is usually set to any value between 0.2L / min and 0.5L / min.

[0108] It should be noted that the sixth zero drift preliminary judgment threshold may be the same as the second zero drift threshold.

[0109] It should be noted that when calculating μ_e, the expiratory flow sensor data from at least three respiratory cycles prior to the moment when airflow leakage is detected in the respiratory system may be selected to calculate the average value. The respiratory cycle may be three consecutive respiratory cycles, four consecutive respiratory cycles, or five consecutive respiratory cycles, and this application does not impose any limitation on this.

[0110] Please note that, please refer to Figure 2 , Figure 2 A flow chart of another method for calibrating an inhalation and exhalation flow sensor provided in an embodiment of the present application. First, the signal detected by the flow sensor (flow sensor data) is preprocessed (filtered), and then a preliminary determination of the risk of zero drift is performed. When it is determined that the flow sensor has a risk of zero drift, a secondary determination of zero drift is performed. Specifically, the calibration component of the flow sensor is turned on, and a zero drift determination is performed on the detection data of the flow sensor. If the detection data has zero drift, the flow sensor is zero-drift calibrated. Otherwise, the zero-drift initial determination threshold formula is dynamically adjusted, and the zero-drift risk of subsequent detection signals (flow sensor data) is determined using the adjusted zero-drift initial determination threshold formula.

[0111] The calibration method of the respiratory flow sensor provided in the present application does not require manual operation by the user, which can effectively reduce the complexity of the operation; moreover, the timeliness of zero calibration can be guaranteed, that is, zero calibration can be completed within a few respiratory cycles when zero drift occurs; in addition, zero calibration will not be performed when it is not necessary (airflow leakage occurs in the respiratory system), thereby avoiding ventilation monitoring interruptions caused by periodic zero calibration; there is no need to establish an environmental parameter-drift relationship model, thereby avoiding the impact of sudden environmental changes.

[0112] The present embodiment provides a method for calibrating an inspiratory and expiratory flow sensor, which is applied to a respiratory support device, comprising: obtaining flow sensor data detected by the respiratory support device; wherein the flow sensor data includes inspiratory flow sensor data or expiratory flow sensor data; when the flow sensor data meets a primary zero drift determination condition, determining that the flow sensor has a zero drift risk; after determining that the flow sensor has a zero drift risk, turning on a calibration component of the flow sensor and obtaining detection data of the flow sensor; wherein the detection data of the flow sensor includes detection data of the inspiratory flow sensor or detection data of the expiratory flow sensor; the detection data and the flow sensor data are of different types; when the detection data of the flow sensor meets a secondary zero drift determination condition, determining that the flow sensor has a zero drift phenomenon; when determining that the flow sensor has a zero drift phenomenon, performing zero drift calibration on the flow sensor. The present application determines whether the flow sensor has a zero drift phenomenon based on a dual judgment mechanism of whether the flow sensor data meets the primary zero drift determination condition and whether the detection data of the flow sensor meets the secondary zero drift determination condition. In this way, when the inspiratory and expiratory flow sensor has a zero drift, it can be zero-drift calibrated in a timely manner, thereby improving the timeliness of zeroing the inspiratory and expiratory flow sensor.

[0113] Please refer to Figure 3 , Figure 3 This is a structural block diagram of a respiratory support device provided in an embodiment of the present application. The respiratory support device 20 includes:

[0114] The ventilation module 201 is used to connect to the patient through a ventilation circuit to provide respiratory support for the patient.

[0115] The detection system 202 is used to detect when the ventilation module provides respiratory support to the patient, and obtain flow sensor data and flow sensor detection data, that is, to execute the above step S101.

[0116] Human-computer interaction device 203.

[0117] The controller 204 is configured to:

[0118] When the flow sensor data meets the initial zero drift determination condition, the flow sensor is determined to have a zero drift risk; after determining that the flow sensor has a zero drift risk, the flow sensor calibration component is turned on; and the flow sensor detection data is obtained; wherein the flow sensor detection data includes the detection data of the inhalation flow sensor or the detection data of the exhalation flow sensor; the detection data and the flow sensor data are of different types; when the flow sensor detection data meets the secondary zero drift determination condition, the flow sensor is determined to have a zero drift phenomenon; when it is determined that the flow sensor has a zero drift phenomenon, the flow sensor is zero-drift calibrated. That is, it is used to execute the above-mentioned steps S102-S105. The specific functions of the controller 204 have been explained in detail in steps S102-S105 and will not be repeated here.

[0119] The present embodiment provides a respiratory support device, comprising: a ventilation module, which is used to connect to a patient through a ventilation line to provide respiratory support for the patient. A detection system, which is used to perform detection when the ventilation module provides respiratory support for the patient, and obtain flow sensor data and flow sensor detection data. A human-computer interaction device. A controller, which is used to: determine that the flow sensor has a zero drift risk when the flow sensor data meets the zero drift primary judgment condition; after determining that the flow sensor has a zero drift risk, turn on the calibration component of the flow sensor; and obtain the detection data of the flow sensor; wherein the detection data of the flow sensor includes the detection data of the inhalation flow sensor or the detection data of the exhalation flow sensor; the detection data is of a different type from the flow sensor data; when the detection data of the flow sensor meets the zero drift secondary judgment condition, determine that the flow sensor has a zero drift phenomenon; when it is determined that the flow sensor has a zero drift phenomenon, perform zero drift calibration on the flow sensor. The present application determines whether the flow sensor has zero drift based on a dual judgment mechanism of whether the flow sensor data meets the primary zero drift judgment condition and whether the detection data of the flow sensor meets the secondary zero drift judgment condition. In this way, when the respiratory flow sensor produces zero drift, it can be calibrated for zero drift in time, thereby improving the timeliness of zero calibration of the respiratory flow sensor.

[0120] The above examples are used to illustrate the present invention, which are only used to help understand the present invention and are not intended to limit the present invention. Those skilled in the art can make several simple deductions, modifications or substitutions based on the concept of the present invention.

Claims

1. A method for calibrating an inspiratory and expiratory flow sensor, applied to a respiratory support device, characterized in that: The method comprises: Acquiring flow sensor data detected by a respiratory support device; wherein the flow sensor data includes inspiratory flow sensor data or expiratory flow sensor data; When the flow sensor data meets the initial zero drift determination condition, determining that the flow sensor has a zero drift risk; After determining that the flow sensor has a zero drift risk, activating a calibration component of the flow sensor and acquiring detection data of the flow sensor; wherein the detection data of the flow sensor includes detection data of an inspiratory flow sensor or detection data of an expiratory flow sensor; and the detection data is of a different type from the flow sensor data; When the detection data of the flow sensor meets the zero drift secondary determination condition, determining that the flow sensor has a zero drift phenomenon; When it is determined that the flow sensor has the zero drift phenomenon, a zero drift calibration is performed on the flow sensor.

2. The method according to claim 1, wherein When the flow sensor data meets the initial zero drift determination condition, determining that the flow sensor has a zero drift risk includes: When the absolute value of the inhalation flow sensor data is greater than a first zero drift preliminary judgment threshold, it is determined that the inhalation flow sensor has a zero drift risk.

3. The method according to claim 1, wherein When the flow sensor data meets the initial zero drift determination condition, determining that the flow sensor has a zero drift risk includes: When the absolute value of the exhalation flow sensor data is greater than the second zero drift preliminary judgment threshold, it is determined that the exhalation flow sensor has a zero drift risk.

4. The method according to claim 1 or 2, wherein: When the detection data of the flow sensor meets the zero drift secondary determination condition, determining that the flow sensor has a zero drift phenomenon includes: Obtain zero point data of the standard inspiratory flow sensor; A first difference is obtained by subtracting the detection data of the inhalation flow sensor from the zero point data of the standard inhalation flow sensor. When the absolute value of the first difference is greater than a third zero drift threshold, it is determined that the inhalation flow sensor has a zero drift phenomenon.

5. The method according to claim 1 or 3, wherein: When the detection data of the flow sensor meets the zero drift secondary determination condition, determining that the flow sensor has a zero drift phenomenon includes: Obtain zero point data of the standard exhalation flow sensor; A second difference is obtained by subtracting the detection data of the exhalation flow sensor from the zero point data of the standard exhalation flow sensor. When the absolute value of the second difference is greater than a fourth zero drift threshold, it is determined that the exhalation flow sensor has a zero drift phenomenon.

6. The method according to claim 4, wherein When it is determined that the flow sensor has the zero drift phenomenon, performing zero drift calibration on the flow sensor includes: Obtaining a first calibration data table corresponding to the inspiratory flow sensor; Adding the first detection data in the first calibration data table and the first difference to obtain calibrated first detection data; The calibrated first detection data is updated as zero point data of the standard inspiratory flow sensor.

7. The method according to claim 5, wherein When it is determined that the flow sensor has the zero drift phenomenon, performing zero drift calibration on the flow sensor includes: Obtaining a second calibration data table corresponding to the expiratory flow sensor; Adding the second detection data in the second calibration data table and the second difference to obtain calibrated second detection data; The calibrated second detection data is updated as the zero point data of the standard exhalation flow sensor.

8. The method according to claim 6, wherein When the detection data of the flow sensor does not meet the zero drift secondary determination condition, determining that the flow sensor has a non-zero drift phenomenon; After determining that the flow sensor has a non-zero drift phenomenon, the method further includes: Acquire inspiratory flow sensor data for at least three respiratory cycles before the current moment; A third difference is obtained by subtracting an average of the plurality of inhalation flow sensor data from the inhalation flow sensor data at the current moment. When an absolute value of the third difference is greater than a fifth zero drift adjustment threshold, it is determined that the inhalation flow sensor has a zero drift risk.

9. The method according to claim 7, wherein When the detection data of the flow sensor does not meet the zero drift secondary determination condition, determining that the flow sensor has a non-zero drift phenomenon; After determining that the flow sensor has a non-zero drift phenomenon, the method further includes: Acquire expiratory flow sensor data for at least three respiratory cycles before the current moment; A fourth difference is obtained by subtracting an average of the plurality of expiratory flow sensor data from the expiratory flow sensor data at a current moment. When an absolute value of the fourth difference is greater than a sixth zero drift adjustment threshold, it is determined that the expiratory flow sensor has a zero drift risk.

10. A respiratory support device, characterized in that The respiratory support device comprises: A ventilation module is used to connect to the patient through a ventilation tube to provide respiratory support for the patient; a detection system, configured to detect when the ventilation module provides respiratory support to the patient, and obtain flow sensor data and detection data of the flow sensor; Human-computer interaction device; Controller for: When the flow sensor data meets the initial zero drift determination condition, determining that the flow sensor has a zero drift risk; After determining that the flow sensor has a zero drift risk, activating a calibration component of the flow sensor; and acquiring detection data of the flow sensor; wherein the detection data of the flow sensor includes detection data of an inspiratory flow sensor or detection data of an expiratory flow sensor; and the detection data is of a different type from the flow sensor data; When the detection data of the flow sensor meets the zero drift secondary determination condition, determining that the flow sensor has a zero drift phenomenon; When it is determined that the flow sensor has the zero drift phenomenon, a zero drift calibration is performed on the flow sensor.

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