A reflux cough detection apparatus, method, device and storage medium

Abstract

The application discloses a reflux cough detection device, method, equipment and storage medium, comprising: collecting first data for determining whether a target patient coughs and second data for determining whether gastroesophageal reflux occurs by using a data collection catheter; the data collection catheter is a detection catheter integrated with multiple target sensors; recording the first data and the second data by using a preset data recorder; the preset data recorder establishes a data transmission link with the data collection catheter through a catheter connector of the data collection catheter; analyzing the first data and the second data recorded by the preset data recorder by using preset data analysis software to generate a correlation index; determining whether the correlation index meets a preset condition; when the correlation index meets the preset condition, it is determined that the target patient has reflux cough. The application accurately and timely collects cough and gastroesophageal reflux data by using the data collection catheter, accurately detects reflux cough, and solves the clinical diagnosis problem of reflux cough.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a device, method, apparatus and storage medium for detecting reflux cough. Background Technology

[0002] Currently, GERC (Gastroesophageal Reflux Cough) is a respiratory disease term published in 2018, defined as a clinical manifestation with cough as the prominent symptom caused by the reflux of stomach acid and other gastric contents into the esophagus. It is a common cause of chronic cough. About half of GERC cases present clinically as a standalone chronic cough without typical gastroesophageal reflux symptoms. It lacks specificity compared to chronic cough caused by other reasons, making its diagnosis and treatment somewhat challenging. In practice, diagnostic treatment is often used, meaning that when doctors are unsure of the exact disease, they treat patients based on their past experience to help confirm their diagnosis. Diagnostic treatment should last 1 to 3 months, and in some patients, the cough may even take 2 to 3 months to subside.

[0003] Traditional diagnosis of gastroesophageal reflux cough involves using a simple 24-hour esophageal pH test combined with the patient's handwritten cough logs for analysis by a doctor. The diagnosis is also based on whether the cough subsides after long-term diagnostic treatment. However, to achieve accurate assessment, accurate and timely cough recording is crucial. Relying on patient self-records can lead to inconsistencies in recording time and omissions, ultimately resulting in unreliable analysis. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a reflux cough detection device, method, apparatus, and storage medium that can accurately record cough events, improve the accuracy of reflux cough detection, and thus solve the clinical diagnostic challenge of reflux cough detection. The specific solution is as follows:

[0005] In a first aspect, this application discloses a reflux cough detection device, comprising:

[0006] The data acquisition module is used to acquire first data to determine whether a target patient is coughing and second data to determine whether the target patient is experiencing gastroesophageal reflux using a data acquisition catheter; the data acquisition catheter is a detection catheter integrating multiple target sensors;

[0007] A data recording module is used to record the first data and the second data using a preset data recorder; the preset data recorder establishes a data transmission link with the data acquisition conduit through the conduit connector of the data acquisition conduit;

[0008] The data analysis module is used to analyze the first data and the second data recorded by the preset data recorder through preset data analysis software to generate corresponding correlation indices;

[0009] The condition judgment module is used to determine whether the correlation index meets preset conditions;

[0010] The detection module is used to detect that the target patient has reflux cough when the correlation index meets preset conditions.

[0011] Optionally, the data acquisition module includes:

[0012] The data acquisition unit is used to acquire sound wave data and pressure data to determine whether a target patient is coughing, and to acquire pH data to determine whether the target patient is experiencing gastroesophageal reflux, using a data acquisition catheter that integrates an integrated sound sensor, a pressure sensor, and a pH sensor.

[0013] Optionally, the data recording module includes:

[0014] The data recording unit is used to record the sound wave data, the pressure data, and the pH data in a preset data format using a preset data recorder.

[0015] Optionally, the data analysis module includes:

[0016] The waveform generation submodule is used to analyze the acoustic data, pressure data and pH data recorded by the preset data recorder through preset data analysis software to generate corresponding acoustic waveforms, pressure waveforms and pH waveforms.

[0017] The dataset construction submodule is used to construct a target dataset for calculating the correlation between cough events and gastroesophageal reflux events based on the sound wave waveform, the pressure waveform, and the pH waveform.

[0018] The index determination submodule is used to determine the correlation index between the cough event and the gastroesophageal reflux event based on the target dataset and a preset probability test method.

[0019] Optionally, the dataset construction submodule includes:

[0020] The first judgment unit is used to determine, based on the sound waveform, the pressure waveform, and the pH waveform, whether a gastroesophageal reflux event occurs within a preset time before the occurrence of a cough event and a non-cough event;

[0021] The first statistical unit is used to determine that the cough event and the non-cough event are related to the gastroesophageal reflux event when the gastroesophageal reflux event occurs within a preset time before the cough event and the non-cough event occur, and to count the first occurrence number of the cough event related to the gastroesophageal reflux event and the second occurrence number of the non-cough event related to the gastroesophageal reflux event.

[0022] The second statistical unit is used to determine that the cough event and the non-cough event are not related to the gastroesophageal reflux event if the gastroesophageal reflux event has not occurred within a preset time period before the cough event and the non-cough event occur, and to count the third occurrence of the cough event that is not related to the gastroesophageal reflux event and the fourth occurrence of the non-cough event that is not related to the gastroesophageal reflux event.

[0023] The dataset construction unit is used to construct a target dataset for calculating the correlation between the cough event and the gastroesophageal reflux event based on the first occurrence count, the second occurrence count, the third occurrence count, and the fourth occurrence count.

[0024] Optionally, the index determination submodule includes:

[0025] The P-value calculation unit is used to process the target dataset using Fisher's exact test to obtain a P-value characterizing the association between the cough event and the gastroesophageal reflux event;

[0026] An index determination unit is used to determine the correlation index between the cough and the gastroesophageal reflux based on the P value.

[0027] Optionally, the condition judgment module includes:

[0028] The first threshold judgment unit is used to determine whether the P value is not greater than the first preset association threshold.

[0029] Accordingly, the detection module includes:

[0030] The first detection unit is used to detect that the target patient has reflux cough when the P value is not greater than the first preset association threshold.

[0031] Alternatively, a second threshold determination unit is used to determine whether the correlation index is not less than a second preset correlation threshold;

[0032] Accordingly, the detection module includes:

[0033] The second detection unit is used to detect that the target patient has reflux cough when the correlation index is not less than the second preset correlation threshold.

[0034] Secondly, this application discloses a method for detecting reflux cough, including:

[0035] The data acquisition catheter is used to collect first data to determine whether the target patient is coughing and second data to determine whether the target patient is experiencing gastroesophageal reflux; the data acquisition catheter is a detection catheter integrating multiple target sensors.

[0036] The first data and the second data are recorded by a preset data recorder; the preset data recorder establishes a data transmission link with the data acquisition conduit through the conduit connector of the data acquisition conduit;

[0037] The first and second data recorded by the preset data recorder are analyzed by preset data analysis software to generate corresponding correlation indices;

[0038] Determine whether the correlation index meets the preset conditions;

[0039] When the correlation index meets the preset conditions, the target patient is detected to have reflux cough.

[0040] Thirdly, this application discloses an electronic device, including:

[0041] Memory, used to store computer programs;

[0042] A processor is configured to execute the computer program to implement the steps of the aforementioned disclosed reflux cough detection device.

[0043] Fourthly, this application discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the steps of the aforementioned disclosed reflux cough detection device.

[0044] As can be seen, this application provides a reflux cough detection device, comprising: a data acquisition module, used to acquire first data for determining whether a target patient is coughing and second data for determining whether the target patient has gastroesophageal reflux using a data acquisition catheter; the data acquisition catheter is a detection catheter integrating multiple target sensors; a data recording module, used to record the first data and the second data using a preset data recorder; the preset data recorder establishes a data transmission link with the data acquisition catheter through a catheter connector of the data acquisition catheter; a data analysis module, used to analyze the first data and the second data recorded by the preset data recorder using preset data analysis software to generate a corresponding correlation index; a condition judgment module, used to judge whether the correlation index meets a preset condition; and a detection module, used to detect that the target patient has reflux cough when the correlation index meets the preset condition. As can be seen, this application utilizes a data acquisition catheter to collect data for determining whether a target patient is coughing and whether the target patient is experiencing gastroesophageal reflux, thereby accurately and promptly recording the occurrence of cough events and gastroesophageal reflux events. Then, the data is analyzed using preset data analysis software to generate a correlation index, which can then be used to detect whether the target patient has reflux cough. In other words, this application can improve the accuracy of reflux cough detection, thereby solving the clinical diagnostic problem of reflux cough detection. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0046] Figure 1 This is a schematic diagram of the structure of a reflux cough detection device disclosed in this application;

[0047] Figure 2 This is a schematic diagram of a reflux cough detection system disclosed in this application;

[0048] Figure 3 This is a schematic diagram of a data acquisition conduit structure disclosed in this application;

[0049] Figure 4 A schematic diagram of a target patient wearing a catheter and a recorder as disclosed in this application;

[0050] Figure 5 This is a schematic diagram of an acoustic waveform disclosed in this application;

[0051] Figure 6 This is a schematic diagram of a pressure waveform disclosed in this application;

[0052] Figure 7 This is a schematic diagram of a pH waveform, a pressure waveform, and a sound waveform disclosed in this application;

[0053] Figure 8 This is a schematic diagram illustrating a specific cough event and gastroesophageal reflux event disclosed in this application;

[0054] Figure 9 This is a flowchart of a reflux cough detection device disclosed in this application;

[0055] Figure 10 This is a structural diagram of an electronic device disclosed in this application. Detailed Implementation

[0056] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] Currently, traditional diagnostic methods for gastroesophageal reflux cough are inefficient, requiring long-term observation and diagnostic experience to confirm whether a patient has gastroesophageal reflux cough. However, to achieve accurate assessment, it is essential to ensure accurate and timely recording of coughs. Relying on patients' self-recorded coughs suffers from inconsistencies in recording time and omissions, ultimately leading to unreliable results. Therefore, this application provides a method for detecting reflux cough that can accurately and promptly record cough events, improving the accuracy of reflux cough detection and thus solving the clinical diagnostic challenges of reflux cough.

[0058] This invention discloses a reflux cough detection device, see [link to relevant documentation]. Figure 1 As shown, the device includes:

[0059] Data acquisition module 11 is used to acquire first data for determining whether a target patient is coughing and second data for determining whether the target patient has gastroesophageal reflux using a data acquisition catheter; the data acquisition catheter is a detection catheter integrating multiple target sensors;

[0060] Data recording module 12 is used to record the first data and the second data through a preset data recorder; the preset data recorder establishes a data transmission link with the data acquisition conduit through the conduit connector of the data acquisition conduit;

[0061] Data analysis module 13 is used to analyze the first data and the second data recorded by the preset data recorder through preset data analysis software to generate corresponding correlation indices;

[0062] Condition judgment module 14 is used to determine whether the correlation index meets preset conditions;

[0063] The detection module 15 is used to detect that the target patient has reflux cough when the correlation index meets the preset conditions.

[0064] It should be noted that the data acquisition catheter can accurately record cough events using acoustic detection and pressure measurement technologies, and then use pH detection technology to determine gastroesophageal reflux events. In other words, by combining acoustic detection and pressure measurement technologies, it can distinguish between throat clearing and coughing sounds; for example, the average pressure value for throat clearing is 27 mmHg, while the average pressure value within the esophageal lumen during coughing is 54 mmHg, thereby accurately recording information such as the number, frequency, and intensity of the patient's coughs. Figure 2 As shown, after the data acquisition catheter collects relevant data, a preset data recorder that establishes a data transmission link with the data acquisition catheter records the collected data. Then, the host computer data analysis software analyzes the data recorded by the preset data recorder to automatically record and identify the patient's cough and gastroesophageal reflux, and statistically analyzes the corresponding data. Then, the data analysis software analyzes the data to generate the required correlation index, and then determines whether the target patient has gastroesophageal reflux cough based on the correlation index.

[0065] As can be seen, the embodiments of this application utilize a data acquisition catheter to collect data for determining whether a target patient has coughed and whether the target patient has gastroesophageal reflux, thereby accurately and timely recording the occurrence of cough events and gastroesophageal reflux events. Then, the data is analyzed by preset data analysis software to generate a correlation index, and then the target patient can be detected based on the correlation index to determine whether the target patient has reflux cough. In other words, this application can improve the accuracy of reflux cough detection, thereby solving the clinical diagnostic problem of reflux cough detection.

[0066] In one specific implementation, the data acquisition module may include:

[0067] The data acquisition unit is used to acquire sound wave data and pressure data to determine whether a target patient is coughing, and to acquire pH data to determine whether the target patient is experiencing gastroesophageal reflux, using a data acquisition catheter that integrates an integrated sound sensor, a pressure sensor, and a pH sensor.

[0068] In this embodiment, as Figure 3As shown, the data acquisition catheter integrates a sound sensor, pressure sensor 1, pressure sensor 2, and pH sensor to acquire first data for determining whether the target patient is coughing and second data for determining whether the target patient is experiencing gastroesophageal reflux. Figure 4 As shown, when the data acquisition catheter is placed into the esophageal lumen for data acquisition, the sound sensor needs to be placed outside the esophageal lumen, while the pH sensor, pressure sensor 1, and pressure sensor 2 are placed inside the esophageal lumen. The acquisition time can be set according to the actual application requirements, with a typical acquisition time of 24 hours. After the preset data recorder establishes a data transmission link with the data acquisition catheter through the catheter connector, the data acquired by the data acquisition catheter can be recorded in the data recorder.

[0069] In one specific embodiment, the data recording module may include:

[0070] The data recording unit is used to record the sound wave data, the pressure data, and the pH data in a preset data format using a preset data recorder.

[0071] In one specific implementation, the data analysis module may include:

[0072] The waveform generation submodule is used to analyze the acoustic data, pressure data and pH data recorded by the preset data recorder through preset data analysis software to generate corresponding acoustic waveforms, pressure waveforms and pH waveforms.

[0073] The dataset construction submodule is used to construct a target dataset for calculating the correlation between cough events and gastroesophageal reflux events based on the sound wave waveform, the pressure waveform, and the pH waveform.

[0074] The index determination submodule is used to determine the correlation index between the cough event and the gastroesophageal reflux event based on the target dataset and a preset probability test method.

[0075] In this embodiment, the preset data analysis software can generate corresponding sound wave waveforms, pressure waveforms, and pH waveforms based on the sound wave data, pressure data, and pH data recorded by the preset data recorder, such as... Figure 5 As shown, when a cough occurs, the sound wave waveform changes drastically, and as... Figure 6 As shown, when coughing occurs, in addition to the change in the sound wave waveform, the pressure waveform also changes differently than when coughing does not occur; this change is quite significant. Figure 7As shown, when acid reflux occurs, the pH value is lower than the pH value when acid reflux does not occur. When coughing begins, both the sound wave waveform and the pressure waveform change drastically. Therefore, cough events and gastroesophageal reflux events can be identified based on these changes.

[0076] In one specific implementation, the dataset construction submodule may include:

[0077] The first judgment unit is used to determine, based on the sound waveform, the pressure waveform, and the pH waveform, whether a gastroesophageal reflux event occurs within a preset time before the occurrence of a cough event and a non-cough event;

[0078] The first statistical unit is used to determine that the cough event and the non-cough event are related to the gastroesophageal reflux event when the gastroesophageal reflux event occurs within a preset time before the cough event and the non-cough event occur, and to count the first occurrence number of the cough event related to the gastroesophageal reflux event and the second occurrence number of the non-cough event related to the gastroesophageal reflux event.

[0079] The second statistical unit is used to determine that the cough event and the non-cough event are not related to the gastroesophageal reflux event if the gastroesophageal reflux event has not occurred within a preset time period before the cough event and the non-cough event occur, and to count the third occurrence of the cough event that is not related to the gastroesophageal reflux event and the fourth occurrence of the non-cough event that is not related to the gastroesophageal reflux event.

[0080] The dataset construction unit is used to construct a target dataset for calculating the correlation between the cough event and the gastroesophageal reflux event based on the first occurrence count, the second occurrence count, the third occurrence count, and the fourth occurrence count.

[0081] It should be noted that the first two minutes after the onset of gastroesophageal reflux are the time window for defining the relationship between reflux and cough. Therefore, a cough that occurs within two minutes of the onset of gastroesophageal reflux is called "reflux-induced cough"; if a gastroesophageal reflux event occurs within 30 seconds of a cough occurring within two minutes, it is called "precipitating reflux"; and a cough that occurs more than two minutes after a gastroesophageal reflux event is called "independent of reflux".

[0082] In this embodiment, by comparing and analyzing the time of coughing and the time of gastroesophageal reflux, it is determined whether gastroesophageal reflux occurred within 2 minutes before the cough occurred. If gastroesophageal reflux occurred, it indicates that the cough was caused by the gastroesophageal reflux, that is, the cough is related to gastroesophageal reflux.

[0083] In this embodiment, the preset time is set to 2 minutes. That is, based on the sound waveform, the pressure waveform, and the pH waveform, it is determined whether a gastroesophageal reflux event occurs within 2 minutes before the occurrence of a cough event and a non-cough event. Thus, the first occurrence number of cough events related to the gastroesophageal reflux event is counted as a, the second occurrence number of non-cough events related to the gastroesophageal reflux event is counted as b, the third occurrence number of cough events unrelated to the gastroesophageal reflux event is counted as c, and the fourth occurrence number of non-cough events unrelated to the gastroesophageal reflux event is counted as d. Therefore, as shown in Table 1, a target dataset is constructed based on the first occurrence number, the second occurrence number, the third occurrence number, and the fourth occurrence number.

[0084] Table 1

[0085] S + (cough) S - (non-coughing) R+ (Gastroesophageal reflux) a b a+b R - (Non-gastroesophageal reflux) c d c+d a+c b+d n = a + b + c + d

[0086] For example, such as Figure 8 As shown, with 2 minutes as the basic unit, that is, each small square represents two minutes, there were 4 cough events, namely S1, S2, S3 and S4, and 8 non-cough events. In addition, gastroesophageal reflux event R1 occurred within two minutes before cough event S1, gastroesophageal reflux event R1 occurred within two minutes before cough event S2, gastroesophageal reflux events R2 and R3 occurred within two minutes before cough event S3, and no gastroesophageal reflux event occurred within two minutes before cough event S4.

[0087] Therefore, according to the above Figure 8 The first occurrence of the cough event associated with the gastroesophageal reflux event was counted as 3 times, and the third occurrence of the cough event not associated with the gastroesophageal reflux event was counted as 1 time.

[0088] Similarly, gastroesophageal reflux event R1 occurred in a non-coughing event within 2 minutes, and gastroesophageal reflux events R2 and R3 occurred in another non-coughing event within 2 minutes. Thus, the second occurrence of the non-coughing event associated with the gastroesophageal reflux event was counted as 3 times. The remaining fourth occurrence of the non-coughing event not associated with the gastroesophageal reflux event was counted as 5 times, as shown in Table 2. The target dataset was constructed based on the first occurrence with a value of 3, the second occurrence with a value of 3, the third occurrence with a value of 1, and the fourth occurrence with a value of 5.

[0089] Table 2

[0090] S+ S- R+ 3 3 6 R- 1 5 6 4 8 12

[0091] In one specific implementation, the index determination submodule may include:

[0092] The P-value calculation unit is used to process the target dataset using Fisher's exact test to obtain a P-value characterizing the association between the cough event and the gastroesophageal reflux event;

[0093] An index determination unit is used to determine the correlation index between the cough and the gastroesophageal reflux based on the P value.

[0094] It is understandable that the P-value is calculated using the Fisher exact test method. According to Table 1 above, the formula for calculating the P-value is as follows:

[0095]

[0096] Where n represents the sum of the first occurrence count, the second occurrence count, the third occurrence count, and the fourth occurrence count, i.e., n = a + b + c + d; the Fisher exact test method can be used to calculate the correlation between two different types of events.

[0097] In this embodiment, after calculating the P value, the formula for calculating the correlation index SAP based on the P value is: SAP = (1-P) × 100%. Therefore, based on the correlation index SAP, the correlation between the cough event and the gastroesophageal reflux event can be determined, thereby determining whether the target patient has gastroesophageal reflux cough.

[0098] In one specific implementation, the condition judgment module may include:

[0099] The first threshold judgment unit is used to determine whether the P value is not greater than the first preset association threshold.

[0100] Accordingly, the detection module includes:

[0101] The first detection unit is used to detect that the target patient has reflux cough when the P value is not greater than the first preset association threshold.

[0102] In this embodiment, the P-value calculated by processing the target dataset using Fisher's exact test can also determine the correlation between the cough event and the gastroesophageal reflux event, thereby determining whether the target patient suffers from gastroesophageal reflux cough.

[0103] In one specific implementation, the condition judgment module may include:

[0104] The second threshold judgment unit is used to determine whether the correlation index is not less than the second preset correlation threshold.

[0105] Accordingly, the detection module includes:

[0106] The second detection unit is used to detect that the target patient has reflux cough when the correlation index is not less than the second preset correlation threshold.

[0107] In this embodiment, the second preset correlation threshold can be determined to be 95%. It is determined whether the correlation index is greater than or equal to 95%, and when the correlation index is greater than or equal to 95%, the target patient is determined to have gastroesophageal reflux cough.

[0108] Further, see Figure 9 As shown in the embodiments of this application, a method for detecting reflux cough is also disclosed, including:

[0109] Step S11: Use a data acquisition catheter to acquire first data for determining whether the target patient is coughing and second data for determining whether the target patient is experiencing gastroesophageal reflux; the data acquisition catheter is a detection catheter integrating multiple target sensors.

[0110] Step S12: Record the first data and the second data using a preset data logger; the preset data logger establishes a data transmission link with the data acquisition conduit through the conduit connector of the data acquisition conduit.

[0111] Step S13: Analyze the first data and the second data recorded by the preset data recorder using preset data analysis software to generate corresponding correlation indices.

[0112] Step S14: Determine whether the correlation index meets the preset conditions.

[0113] Step S15: When the correlation index meets the preset conditions, the target patient is detected to have reflux cough.

[0114] In one specific embodiment, the method of using a data acquisition catheter to acquire first data for determining whether a target patient is coughing and second data for determining whether the target patient has gastroesophageal reflux may specifically include: using a data acquisition catheter integrating a sound sensor, a pressure sensor, and a pH sensor to acquire sound wave data and pressure data for determining whether a target patient is coughing and pH data for determining whether the target patient has gastroesophageal reflux.

[0115] In one specific embodiment, the step of recording the first data and the second data using a preset data logger may specifically include: recording the sound wave data, the pressure data, and the pH data using a preset data logger according to a preset data format.

[0116] In one specific embodiment, the step of analyzing the first data and the second data recorded by the preset data recorder using preset data analysis software to generate a corresponding correlation index may specifically include: analyzing the sound wave data, the pressure data, and the pH data recorded by the preset data recorder using preset data analysis software to generate corresponding sound wave waveforms, pressure waveforms, and pH waveforms; constructing a target dataset for calculating the correlation between cough events and gastroesophageal reflux events based on the sound wave waveforms, the pressure waveforms, and the pH waveforms; and determining the correlation index between the cough events and the gastroesophageal reflux events based on the target dataset and a preset probability test method.

[0117] In one specific embodiment, constructing a target dataset for calculating the correlation between cough events and gastroesophageal reflux events based on the sound waveform, pressure waveform, and pH waveform may specifically include: determining whether a gastroesophageal reflux event occurred within a preset time period prior to the occurrence of a cough event and a non-cough event based on the sound waveform, pressure waveform, and pH waveform; if a gastroesophageal reflux event occurs within the preset time period prior to the occurrence of a cough event and a non-cough event, then determining that the cough event and the non-cough event are related to the gastroesophageal reflux event, and counting the first occurrence frequency of the cough event related to the gastroesophageal reflux event, as well as counting the occurrence frequency of the cough event related to the gastroesophageal reflux event. The second occurrence count of the non-coughing event related to the gastroesophageal reflux event is determined; if the gastroesophageal reflux event does not occur within a preset time period before the occurrence of the coughing event and the non-coughing event, it is determined that the coughing event and the non-coughing event are not related to the gastroesophageal reflux event, and the third occurrence count of the coughing event and the fourth occurrence count of the non-coughing event are counted; based on the first occurrence count, the second occurrence count, the third occurrence count, and the fourth occurrence count, a target dataset for calculating the correlation between the coughing event and the gastroesophageal reflux event is constructed.

[0118] In one specific embodiment, determining the association index between the cough event and the gastroesophageal reflux event based on the target dataset and a preset probability test method may specifically include: processing the target dataset using the Fisher exact test to obtain a P-value characterizing the association between the cough event and the gastroesophageal reflux event; and determining the association index between the cough and the gastroesophageal reflux based on the P-value.

[0119] In one specific embodiment, determining whether the correlation index meets a preset condition, and detecting that the target patient has reflux cough when the correlation index meets the preset condition, may specifically include: determining whether the P value is not greater than a first preset correlation threshold; and detecting that the target patient has reflux cough when the P value is not greater than the first preset correlation threshold.

[0120] In one specific embodiment, determining whether the correlation index meets a preset condition, and detecting that the target patient has reflux cough when the correlation index meets the preset condition, may specifically include: determining whether the correlation index is not less than a second preset correlation threshold; and detecting that the target patient has reflux cough when the correlation index is not less than the second preset correlation threshold.

[0121] As can be seen, this application utilizes a data acquisition catheter to collect data for determining whether a target patient is coughing and whether the target patient is experiencing gastroesophageal reflux, thereby accurately and promptly recording the occurrence of cough events and gastroesophageal reflux events. Then, the data is analyzed using preset data analysis software to generate a correlation index, which can then be used to detect whether the target patient has reflux cough. In other words, this application can improve the accuracy of reflux cough detection, thereby solving the clinical diagnostic problem of reflux cough detection.

[0122] Furthermore, embodiments of this application also provide an electronic device. Figure 10 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0123] Figure 10 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the reflux cough detection device disclosed in any of the foregoing embodiments. Alternatively, the electronic device 20 in this embodiment may specifically be a computer.

[0124] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0125] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0126] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the reflux cough detection device executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include a computer program capable of performing other specific tasks.

[0127] Furthermore, this application also discloses a storage medium storing a computer program, which, when loaded and executed by a processor, implements the steps of the reflux cough detection device disclosed in any of the foregoing embodiments.

[0128] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0129] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0130] The above provides a detailed description of the reflux cough detection device, method, equipment, and storage medium provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A device for detecting reflux cough, characterized in that, include: The data acquisition module is used to acquire first data for determining whether a target patient is coughing and second data for determining whether the target patient is experiencing gastroesophageal reflux using a data acquisition catheter. The data acquisition conduit is a detection conduit that integrates multiple target sensors; A data recording module is used to record the first data and the second data using a preset data recorder; the preset data recorder establishes a data transmission link with the data acquisition conduit through the conduit connector of the data acquisition conduit; The data analysis module is used to analyze the first data and the second data recorded by the preset data recorder through preset data analysis software to generate corresponding correlation indices; The condition judgment module is used to determine whether the correlation index meets preset conditions; The detection module is used to detect that the target patient has reflux cough when the correlation index meets preset conditions; The data acquisition module includes a data acquisition unit, which uses a data acquisition catheter integrating a sound sensor, a pressure sensor, and a pH sensor to acquire sound wave data and pressure data for determining whether the target patient is coughing, and to acquire pH data for determining whether the target patient is experiencing gastroesophageal reflux. The data recording module includes: a data recording unit, used to record the sound wave data, the pressure data and the pH data according to a preset data format using a preset data recorder; The data analysis module includes: a waveform generation submodule, used to analyze the sound wave data, pressure data, and pH data recorded by the preset data recorder using preset data analysis software to generate corresponding sound wave waveforms, pressure waveforms, and pH waveforms; a dataset construction submodule, used to construct a target dataset for calculating the correlation between cough events and gastroesophageal reflux events based on the sound wave waveforms, pressure waveforms, and pH waveforms; and an index determination submodule, used to determine the correlation index between cough events and gastroesophageal reflux events based on the target dataset and a preset probability test method. The dataset construction submodule includes: The first judgment unit is used to determine, based on the sound waveform, the pressure waveform, and the pH waveform, whether a gastroesophageal reflux event occurs within a preset time before the occurrence of a cough event and a non-cough event; The first statistical unit is used to determine that the cough event and the non-cough event are related to the gastroesophageal reflux event when the gastroesophageal reflux event occurs within a preset time before the cough event and the non-cough event occur, and to count the first occurrence number of the cough event related to the gastroesophageal reflux event and the second occurrence number of the non-cough event related to the gastroesophageal reflux event. The second statistical unit is used to determine that the cough event and the non-cough event are not related to the gastroesophageal reflux event if the gastroesophageal reflux event has not occurred within a preset time period before the cough event and the non-cough event occur, and to count the third occurrence of the cough event that is not related to the gastroesophageal reflux event and the fourth occurrence of the non-cough event that is not related to the gastroesophageal reflux event. A dataset construction unit is used to construct a target dataset for calculating the correlation between the cough event and the gastroesophageal reflux event based on the first occurrence count, the second occurrence count, the third occurrence count, and the fourth occurrence count. The index determination submodule includes: The P-value calculation unit is used to process the target dataset using Fisher's exact test to obtain a P-value characterizing the association between the cough event and the gastroesophageal reflux event; wherein, the formula for calculating the P-value is: Where, a is the first occurrence count; b is the second occurrence count; c is the third occurrence count; d is the fourth occurrence count; and n is the sum of the four occurrence counts. An index determination unit is used to determine the correlation index between the cough and the gastroesophageal reflux based on the P value.

2. The reflux cough detection device according to claim 1, characterized in that, The condition judgment module includes: The first threshold judgment unit is used to determine whether the P value is not greater than the first preset association threshold. Accordingly, the detection module includes: The first detection unit is used to detect that the target patient has reflux cough when the P value is not greater than the first preset association threshold. Alternatively, a second threshold determination unit is used to determine whether the correlation index is not less than a second preset correlation threshold; Accordingly, the detection module includes: The second detection unit is used to detect that the target patient has reflux cough when the correlation index is not less than the second preset correlation threshold.

3. A method for detecting reflux cough, characterized in that, include: A data acquisition catheter integrating an integrated sound sensor, pressure sensor, and pH sensor is used to acquire sound wave data and pressure data to determine whether the target patient is coughing, and to acquire pH data to determine whether the target patient is experiencing gastroesophageal reflux. The acoustic data, pressure data, and pH data are recorded using a preset data logger according to a preset data format. The preset data recorder establishes a data transmission link with the data acquisition conduit through the conduit connector of the data acquisition conduit; The acoustic wave data, pressure data, and pH data recorded by the preset data recorder are analyzed by preset data analysis software to generate corresponding acoustic wave waveforms, pressure waveforms, and pH waveforms. Based on the sound waveform, the pressure waveform, and the pH waveform, determine whether a gastroesophageal reflux event occurs within a preset time before a cough event or a non-cough event occurs; If the gastroesophageal reflux event occurs within a preset time period prior to the occurrence of the cough event and the non-cough event, then the cough event and the non-cough event are determined to be related to the gastroesophageal reflux event, and the first occurrence number of the cough event related to the gastroesophageal reflux event and the second occurrence number of the non-cough event related to the gastroesophageal reflux event are counted. If the gastroesophageal reflux event does not occur within a preset time period prior to the occurrence of the cough event and the non-cough event, it is determined that the cough event and the non-cough event are not related to the gastroesophageal reflux event, and the third occurrence of the cough event that is not related to the gastroesophageal reflux event and the fourth occurrence of the non-cough event that is not related to the gastroesophageal reflux event are counted. A target dataset for calculating the correlation between the cough event and the gastroesophageal reflux event is constructed based on the first occurrence count, the second occurrence count, the third occurrence count, and the fourth occurrence count. The target dataset was processed using Fisher's exact test to obtain a p-value characterizing the association between the cough event and the gastroesophageal reflux event; wherein the p-value was calculated using the following formula: Where, a is the first occurrence count; b is the second occurrence count; c is the third occurrence count; d is the fourth occurrence count; and n is the sum of the four occurrence counts. Used to determine the correlation index between the cough and the gastroesophageal reflux based on the P value; Determine whether the correlation index meets the preset conditions; When the correlation index meets the preset conditions, the target patient is detected to have reflux cough.

4. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the steps of the reflux cough detection method as described in claim 3.

5. A computer-readable storage medium, characterized in that, Used to store a computer program; wherein, when the computer program is executed by a processor, it implements the steps of the reflux cough detection method as described in claim 3.

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