A respiratory health auxiliary monitoring method and system for chronic lung diseases

Abstract

The present invention relates to the technical field of lung respiratory health assistance, and specifically to a respiratory health assistance monitoring method and system for chronic lung diseases, including: obtaining the patient's respiratory data; obtaining the abnormality of each respiratory cycle in the respiratory data; obtaining the exhalation prominence of each exhalation stage in the respiratory data; obtaining the abnormality of the respiratory rhythm of each respiratory cycle in the respiratory data; obtaining the shortness of breath of each respiratory cycle in the respiratory data; obtaining the degree of tremor of each respiratory cycle in the respiratory data; obtaining the probability of a cough event occurring in each respiratory cycle in the respiratory data; eliminating all cough data segments to obtain the patient's final respiratory data; and evaluating the patient's lung health status based on the final respiratory data. The present invention solves the problem of the influence of coughing on the respiratory data when the patient undergoes respiratory health monitoring, and improves the effectiveness of lung respiratory health assistance.

Description

Technical Field

[0001] The present invention relates to the field of lung respiratory health assistance technology, and in particular to a respiratory health assistance monitoring method and system for chronic lung diseases. Background Art

[0002] Respiratory health monitoring is an important branch of the medical and health field. With the aging of the population and the influence of modern lifestyles, the incidence of chronic respiratory diseases has increased year by year. Chronic respiratory diseases, including chronic obstructive pulmonary disease, asthma, occupational lung disease, etc., have become a global public health problem. In addition, symptoms such as chronic cough, chronic sputum, and dyspnea are extremely common in chronic respiratory diseases, which seriously affect the quality of life of patients. Therefore, respiratory health monitoring of chronic lung diseases is very important.

[0003] When monitoring the respiratory health of patients, high-quality normal respiratory data is required, which has a good reference significance for the health status of the lungs. Existing respiratory health monitoring of patients is only carried out through wearable and low-cost intelligent respiratory monitoring sensors, and then the respiratory health of chronic lung diseases is evaluated through the monitored respiratory data. However, patients may cough, which will affect the respiratory data obtained through monitoring, especially when the patient's breathing rhythm is unstable or a more serious cough occurs, which has a greater impact on the respiratory data, making it difficult to monitor the patient's lung respiratory health through normal respiratory data. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a respiratory health auxiliary monitoring method and system for chronic lung diseases.

[0005] The respiratory health auxiliary monitoring method and system for chronic lung diseases of the present invention adopt the following technical solutions:

[0006] An embodiment of the present invention provides a method for assisting respiratory health monitoring of chronic lung diseases, the method comprising the following steps:

[0007] Acquire respiratory data of a patient, wherein the respiratory data includes a plurality of voltage values;

[0008] Obtain the maximum and minimum points of the respiratory data; obtain the respiratory cycle, exhalation phase and inhalation phase of the respiratory data through the maximum and minimum points of the respiratory data;

[0009] According to the change of the duration of the respiratory cycle, the abnormality of each respiratory cycle in the respiratory data is obtained; according to the change of the duration of the exhalation stage in the corresponding respiratory cycle, the exhalation prominence of each exhalation stage in the respiratory data is obtained; according to the abnormality and the exhalation prominence, the abnormality of the respiratory rhythm of each respiratory cycle in the respiratory data is obtained;

[0010] According to the difference between adjacent voltage values ​​in a respiratory cycle, the voltage change rate of each respiratory cycle in the respiratory data is obtained; according to the method for obtaining the voltage change rate of the respiratory cycle, the voltage change rate of each exhalation phase and inhalation phase in the respiratory data is obtained respectively; according to the change of the voltage change rate in the respiratory cycle, the tachypnea of ​​each respiratory cycle in the respiratory data is obtained; according to the expiratory prominence, tachypnea, and the voltage change rate of the exhalation phase and the inhalation phase, the tremor degree of each respiratory cycle in the respiratory data is obtained; according to the abnormality of the respiratory rhythm and the tremor degree, the probability of a cough event occurring in each respiratory cycle in the respiratory data is obtained;

[0011] According to the probability of a cough event occurring in each respiratory cycle in the respiratory data, several cough data segments of the respiratory data are determined; all cough data segments are eliminated to obtain the patient's final respiratory data; and the patient's lung health status is evaluated based on the final respiratory data.

[0012] Furthermore, the respiratory cycle, exhalation phase and inhalation phase of the respiratory data are obtained through the maximum and minimum points of the respiratory data, and the specific steps include the following:

[0013] The breathing data The maximum point and the The time interval between the maximum value points is taken as a breathing cycle of the breathing data, and all breathing cycles of the breathing data are obtained; the first The time interval between the maximum point and the next adjacent minimum point is taken as an exhalation stage of the breathing data, and all the exhalation stages of the breathing data are obtained; the first The remaining phases except the exhalation phase in a respiratory cycle are taken as an inhalation phase of the respiratory data, and all the inhalation phases of the respiratory data are obtained.

[0014] Furthermore, the step of obtaining the abnormality of each respiratory cycle in the respiratory data according to the change in the duration of the respiratory cycle includes the following specific steps:

[0015]

[0016] In the formula, The breathing data The duration of a respiratory cycle; is the average duration of all respiratory cycles in the respiratory data; is the duration variance of all respiratory cycles in the respiratory data; To take the absolute value; is a linear normalization function; The breathing data The abnormality of a respiratory cycle.

[0017] Furthermore, the exhalation prominence of each exhalation stage in the respiratory data is obtained according to the change in the proportion of the duration of the exhalation stage in the corresponding respiratory cycle, and the specific steps include the following:

[0018]

[0019] In the formula, The breathing data The proportion of the exhalation phase in the corresponding respiratory cycle; It is the average value of the duration of all exhalation phases in the corresponding respiratory cycle in the respiratory data; is the variance of the duration of all exhalation phases in the respiratory data in the corresponding respiratory cycle; is a linear normalization function; The breathing data The expiratory prominence during the exhalation phase.

[0020] Furthermore, the abnormality of the breathing rhythm of each breathing cycle in the breathing data is obtained according to the abnormality and the exhalation protrusion, and the specific steps include the following:

[0021]

[0022] In the formula, The breathing data The degree of abnormality of each respiratory cycle; The breathing data The exhalation prominence during the exhalation phase; The breathing data The degree of abnormality of breathing rhythm during a respiratory cycle.

[0023] Furthermore, the voltage change rate of each respiratory cycle in the respiratory data is obtained according to the difference between adjacent voltage values ​​in the respiratory cycle, and the specific steps include the following:

[0024]

[0025] In the formula, The breathing data The number of voltage values ​​within a breathing cycle; The breathing data In the respiratory cycle Voltage value; The breathing data In the respiratory cycle Voltage value; To take the absolute value; The breathing data The voltage change rate during a breathing cycle.

[0026] Furthermore, the method of obtaining the tachypnea of ​​each respiratory cycle in the respiratory data according to the change of the voltage change rate within the respiratory cycle includes the following specific steps:

[0027]

[0028] In the formula, The breathing data The voltage change rate during a respiratory cycle; is the average value of the voltage change rate of all respiratory cycles in the respiratory data; is the variance of the voltage change rate of all respiratory cycles in the respiratory data; is a linear normalization function; The breathing data The rapidity of breathing during the respiratory cycle.

[0029] Furthermore, the step of obtaining the vibration degree of each respiratory cycle in the respiratory data according to the exhalation protrusion, the tachypnea, the voltage change rate in the exhalation phase and the inhalation phase includes the following specific steps:

[0030]

[0031] In the formula, The breathing data The exhalation prominence during the exhalation phase; The breathing data Shortness of breath during each respiratory cycle; The breathing data The rate of voltage change during the exhalation phase of a respiratory cycle; The breathing data The rate of voltage change during the inspiratory phase of a respiratory cycle; is a linear normalization function; The breathing data The degree of tremor during a respiratory cycle.

[0032] Furthermore, the probability of a cough event occurring in each respiratory cycle in the respiratory data is obtained according to the abnormality of the respiratory rhythm and the degree of tremor, and the specific steps include the following:

[0033]

[0034] In the formula, The breathing data Abnormality of breathing rhythm during each breathing cycle; The breathing data The degree of tremor during each respiratory cycle; is a linear normalization function; The cough event is the first The probability of occurrence within a breathing cycle.

[0035] The present invention also proposes a respiratory health auxiliary monitoring system for chronic lung diseases, comprising a memory and a processor, wherein the processor executes a computer program stored in the memory to implement the steps of the aforementioned method.

[0036] The beneficial effects of the technical solution of the present invention are as follows: according to the present invention, when a patient has a more serious cough phenomenon, the abnormality of the patient's respiratory cycle, the prominence of exhalation in the exhalation stage, the abnormality of the respiratory rhythm in the respiratory cycle, the shortness of breath in the respiratory cycle and the probability of a cough event occurring in each respiratory cycle in the respiratory data can be accurately analyzed, and several cough data segments of the respiratory data can be determined based on the analyzed characteristics, thereby obtaining the patient's final respiratory data; and the patient's lung health status can be evaluated according to the final respiratory data. When determining the abnormality of the respiratory cycle, the abnormality is determined by analyzing the change in the duration of the respiratory cycle, thereby improving the scientificity, accuracy and objectivity of the abnormality. When determining the prominence of exhalation in the exhalation stage, the prominence of exhalation is determined by analyzing the change in the duration of the exhalation stage in the corresponding respiratory cycle, thereby accurately describing the changing relationship of the exhalation stage in the respiratory cycle, thereby improving the accuracy, scientificity and objectivity of the prominence of exhalation. When determining the abnormality of the respiratory rhythm in the respiratory cycle, the abnormality of the respiratory rhythm is determined by combining the abnormality and the prominence of exhalation, thereby improving the accuracy, scientificity and objectivity of the abnormality of the respiratory rhythm. When determining the probability of a cough event occurring in each respiratory cycle in the respiratory data, the patient's exhalation prominence, shortness of breath, voltage change rate during the exhalation and inhalation phases, and respiratory rhythm abnormality are fully considered, which improves the accuracy of determining the cough data segment in the respiratory data, and then obtains the patient's final respiratory data; the patient's lung health status is evaluated based on the final respiratory data. The impact of coughing on respiratory data during respiratory health monitoring of patients is resolved, and the effectiveness of lung respiratory health assistance is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1A flowchart of the steps of a respiratory health auxiliary monitoring method for chronic lung diseases provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the following is a detailed description of the respiratory health auxiliary monitoring method and system for chronic lung diseases proposed by the present invention, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0040] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0041] The following is a detailed description of a method and system for assisting in monitoring respiratory health for chronic lung diseases provided by the present invention in conjunction with the accompanying drawings.

[0042] See also Figure 1 , which shows a flowchart of a method for assisting respiratory health monitoring of chronic lung diseases provided by an embodiment of the present invention, the method comprising the following steps:

[0043] Step S001: Acquire the patient's respiratory data, wherein the respiratory data includes a number of voltage values.

[0044] It should be noted that the main purpose of this embodiment is to optimize the patient's respiratory data and eliminate the influence of the patient's cough on the respiratory data, so as to better evaluate the patient's lung health through the optimized respiratory data. Before starting the analysis, first obtain the patient's respiratory data.

[0045] Specifically, the patient's breathing data is obtained, and the breathing data includes several voltage values, as follows:

[0046] The patient wears a respiratory monitoring device on his chest. The respiratory monitoring device has a built-in piezoelectric respiratory sensor, which is a chest strap fixed respiratory wave sensor that can detect the patient's respiratory movement waveform without damage. Its output is The analog voltage signal is about mV, and the frequency response range is Hz.

[0047] The patient's respiratory data is collected by the respiratory monitoring device. The respiratory data is essentially voltage data. The data collection frequency is set to Hz.

[0048] At this point, the patient's breathing data is obtained.

[0049] Step S002, obtaining the maximum and minimum points of the respiratory data; obtaining the respiratory cycle, exhalation phase and inhalation phase of the respiratory data through the maximum and minimum points of the respiratory data.

[0050] It should be noted that when a patient breathes, there is a good periodic performance in the respiratory data. If the patient is uncomfortable and coughs, it will have a certain impact on the respiratory data, which is not conducive to the patient's lung respiratory health monitoring. Therefore, it is necessary to remove the coughing data segment to obtain better respiratory data, so as to better realize lung respiratory health monitoring. When a patient coughs, the breathing rhythm will change greatly, so the abnormal breathing rhythm of each breathing cycle in the respiratory data is first analyzed.

[0051] It should be further explained that when a patient coughs, the breathing rhythm will change greatly, and there will be certain abnormalities in the breathing cycle. Consider analyzing the changes in the duration of the breathing cycle to obtain the abnormality of each breathing cycle in the breathing data. First, it is necessary to obtain the breathing cycle, as well as the exhalation phase and the inhalation phase from the breathing data, and then determine the abnormality of the breathing cycle.

[0052] Specifically, the maximum and minimum points of the respiratory data are obtained as follows:

[0053] The upper envelope and the lower envelope of the breathing data are obtained; the coincidence point of the upper envelope and the breathing data is used as the initial maximum point of the breathing data; the coincidence point of the lower envelope and the breathing data is used as the initial minimum point of the breathing data; if there are multiple initial maximum points or multiple initial minimum points in the extreme point of the envelope within a preset neighborhood range, the point closest to the extreme point in the envelope is selected as the extreme point of the breathing data, otherwise, it is directly used as the extreme point of the breathing data; several maximum points and several minimum points of the breathing data are obtained, wherein the preset neighborhood range is set to 10 in this embodiment.

[0054] It should be noted that obtaining the upper envelope and the lower envelope of the respiratory data is an existing method, which will not be described in detail in this embodiment.

[0055] It should be noted that the maximum and minimum points of the respiratory data are obtained above. Through the positions of the maximum and minimum points in the respiratory data, the respiratory cycle, exhalation phase and inhalation phase can be determined, so as to subsequently analyze the abnormalities of the respiratory cycle and the abnormalities of the respiratory rhythm.

[0056] Furthermore, the respiratory cycle, exhalation phase, and inhalation phase of the respiratory data are obtained through the maximum and minimum points of the respiratory data, as follows:

[0057] The breathing data The maximum point and the The time interval between the maximum value points is taken as a breathing cycle of the breathing data, and all breathing cycles of the breathing data are obtained; the first The time interval between the maximum point and the next adjacent minimum point is taken as an exhalation stage of the breathing data, and all the exhalation stages of the breathing data are obtained; the first The remaining phases except the exhalation phase in a respiratory cycle are taken as an inhalation phase of the respiratory data, and all the inhalation phases of the respiratory data are obtained.

[0058] It should be noted that the last maximum point in the respiratory data has no adjacent maximum point and cannot constitute a respiratory cycle, so it is discarded.

[0059] Step S003, according to the change of the breathing cycle duration, obtain the abnormality of each breathing cycle in the breathing data; according to the change of the proportion of the exhalation stage in the corresponding breathing cycle, obtain the exhalation prominence of each exhalation stage in the breathing data; according to the abnormality and the exhalation prominence, obtain the abnormality of the breathing rhythm of each breathing cycle in the breathing data.

[0060] It should be noted that the respiratory cycle, exhalation phase and inhalation phase of the respiratory data are determined above, and then the abnormality of each respiratory cycle in the respiratory data is obtained by analyzing the change in the duration of the respiratory cycle.

[0061] Specifically, according to the change in the duration of the respiratory cycle, the abnormality of each respiratory cycle in the respiratory data is obtained, as follows:

[0062]

[0063] In the formula, The breathing data The duration of a respiratory cycle; is the average duration of all respiratory cycles in the respiratory data; is the duration variance of all respiratory cycles in the respiratory data; To take the absolute value; is a linear normalization function; The breathing data The abnormality of a respiratory cycle.

[0064] It should be noted that Indicates The difference between the duration of each respiratory cycle and the average value, represents the standard duration difference of the respiratory cycle. If The bigger, the The greater the difference from the standard duration of the respiratory cycle, the more likely it is abnormal. The more abnormal the breathing cycle.

[0065] It should be noted that in addition to the abnormal breathing cycle when the patient coughs, the exhalation phase in the breathing cycle accounts for a larger proportion of time due to the need to accumulate strength for coughing, that is, the proportion of the exhalation phase in the breathing cycle will also increase. In order to better analyze the abnormal breathing rhythm of the breathing cycle, the exhalation prominence of the exhalation phase is obtained by analyzing the changes in the proportion of the duration of the exhalation phase in the corresponding breathing cycle.

[0066] Specifically, according to the change in the proportion of the exhalation phase in the corresponding respiratory cycle, the exhalation prominence of each exhalation phase in the respiratory data is obtained, as follows:

[0067]

[0068] In the formula, The breathing data The proportion of the exhalation phase in the corresponding respiratory cycle; It is the average value of the duration of all exhalation phases in the corresponding respiratory cycle in the respiratory data; is the variance of the duration of all exhalation phases in the respiratory data in the corresponding respiratory cycle; is a linear normalization function; The breathing data The expiratory prominence during the exhalation phase.

[0069] It should be noted that Indicates The difference between the proportion of the duration of the exhalation phase in the corresponding respiratory cycle and the average value, Indicates the difference in the standard duration of the exhalation phase in the corresponding respiratory cycle. If The bigger, the The greater the difference in the proportion of the exhalation phase relative to the standard duration of the corresponding respiratory cycle, the more prominent it is. The larger the proportion of the exhalation phase, the greater the prominence of the exhalation.

[0070] It should be noted that the above analysis analyzes the abnormality and the exhalation prominence separately, and the abnormality of the breathing rhythm of each breathing cycle in the breathing data is comprehensively determined by combining the two.

[0071] Specifically, according to the abnormality and the exhalation prominence, the abnormality of the breathing rhythm of each breathing cycle in the breathing data is obtained, as follows:

[0072]

[0073] In the formula, The breathing data The degree of abnormality of each respiratory cycle; The breathing data The exhalation prominence during the exhalation phase; The breathing data The degree of abnormality of breathing rhythm during a respiratory cycle.

[0074] It should be noted that The larger the The greater the abnormality of the first respiratory cycle, the The greater the expiratory prominence of the first expiratory phase, the more likely the patient The worse the breathing rhythm of a respiratory cycle, the greater the abnormality of the breathing rhythm.

[0075] At this point, the abnormality of the breathing rhythm of each breathing cycle in the breathing data is obtained.

[0076] Step S004, according to the difference between adjacent voltage values ​​in the respiratory cycle, obtain the voltage change rate of each respiratory cycle in the respiratory data; according to the method for obtaining the voltage change rate of the respiratory cycle, respectively obtain the voltage change rate of each exhalation stage and inhalation stage in the respiratory data; according to the change of the voltage change rate in the respiratory cycle, obtain the shortness of breath of each respiratory cycle in the respiratory data; according to the expiratory prominence, shortness of breath, and the voltage change rate of the exhalation stage and the inhalation stage, obtain the vibration degree of each respiratory cycle in the respiratory data; according to the abnormality of the respiratory rhythm and the vibration degree, obtain the probability of a cough event occurring in each respiratory cycle in the respiratory data.

[0077] It should be noted that when the patient breathes normally, the breathing rhythm is relatively stable and the breathing process is relatively uniform, so the data changes slowly; when the patient coughs, the rise and fall of the chest will change rapidly, so it is manifested as a rapid change in voltage data in the respiratory data. At the same time, since coughing is usually continuous, and continuous coughing will produce chain data changes, which will cause shortness of breath. Therefore, the voltage changes of the respiratory cycle and the shortness of breath of the respiratory cycle in the respiratory data are analyzed to determine the degree of vibration of the respiratory cycle later. The degree of vibration can reflect whether the patient has a cough.

[0078] Specifically, the voltage change rate of each breathing cycle in the breathing data is obtained according to the difference between adjacent voltage values ​​in the breathing cycle, as follows:

[0079]

[0080] In the formula, The breathing data The number of voltage values ​​within a breathing cycle; The breathing data In the respiratory cycle Voltage value; The breathing data In the respiratory cycle Voltage value; To take the absolute value; The breathing data The voltage change rate during a breathing cycle.

[0081] Furthermore, according to the method for obtaining the voltage change rate of the respiratory cycle, the voltage change rate of each exhalation phase and inhalation phase in the respiratory data is obtained respectively.

[0082] It should be noted that the voltage change rate of the respiratory cycle has been obtained above, and the voltage change rate of the exhalation stage and the inhalation stage is calculated in the same way as the voltage change rate of the respiratory cycle, which will not be repeated in this embodiment.

[0083] Furthermore, according to the change of the voltage change rate within the respiratory cycle, the tachypnea of ​​each respiratory cycle in the respiratory data is obtained, as follows:

[0084]

[0085] In the formula, The breathing data The voltage change rate during a respiratory cycle; is the average value of the voltage change rate of all respiratory cycles in the respiratory data; is the variance of the voltage change rate of all respiratory cycles in the respiratory data; is a linear normalization function; The breathing data The rapidity of breathing during the respiratory cycle.

[0086] It should be noted that Indicates the first The difference between the voltage change rate of each breathing cycle and the average value, represents the difference in the standard voltage change rate of all respiratory cycles in the respiratory data, The bigger, the The greater the difference in the rate of change of voltage relative to the standard rate of change of all respiratory cycles in the respiratory data, the more likely the patient is to The faster the breathing during each respiratory cycle.

[0087] It should be noted that the above analysis analyzed the expiratory prominence and shortness of breath in the expiratory phase, and obtained the voltage change rate of the expiratory phase and the inhalation phase in the respiratory cycle. The higher the patient's expiratory prominence and the more rapid the breathing, the faster the voltage change rate in the expiratory phase relative to the inhalation phase, indicating that the patient coughs and has a high degree of tremor during the respiratory cycle.

[0088] Specifically, the vibration degree of each respiratory cycle in the respiratory data is obtained according to the exhalation protrusion, the tachypnea, the voltage change rate during the exhalation phase and the inhalation phase, as follows:

[0089]

[0090] In the formula, The breathing data The exhalation prominence during the exhalation phase; The breathing data Shortness of breath during each respiratory cycle; The breathing data The rate of voltage change during the exhalation phase of a respiratory cycle; The breathing data The rate of voltage change during the inspiratory phase of a respiratory cycle; is a linear normalization function; The breathing data The degree of tremor during a respiratory cycle.

[0091] It should be noted that It indicates the patient's exhalation prominence during the exhalation phase. The greater the exhalation prominence, the more abnormal the patient's breathing rhythm is, the more likely the patient is to cough, and the greater the degree of tremor; Indicates the patient's shortness of breath during the respiratory cycle. The more rapid the patient's breathing, the more likely he or she is to cough and the greater the degree of tremor. It indicates the difference in voltage change rate during the exhalation phase relative to the inhalation phase. The larger the value, the more likely the patient is coughing during the respiratory cycle and the greater the degree of tremor.

[0092] It should be noted that the above analysis includes the abnormality and tremor of the patient's breathing rhythm. The greater the abnormality of the patient's breathing rhythm in a breathing cycle, the greater the tremor of the breathing cycle, which means that the patient is more likely to cough.

[0093] Specifically, according to the abnormality of the breathing rhythm and the degree of tremor, the probability of a cough event occurring in each breathing cycle in the breathing data is obtained, as follows:

[0094]

[0095] In the formula, The breathing data Abnormality of breathing rhythm during each breathing cycle; The breathing data The degree of tremor during each respiratory cycle; is a linear normalization function; The cough event is the first The probability of occurrence within a breathing cycle.

[0096] It should be noted that The larger it is, the more abnormal the patient's breathing rhythm is. At the same time, the greater the degree of tremor in the breathing cycle is, the more likely coughing is to occur.

[0097] At this point, the probability of a cough event occurring in each respiratory cycle in the respiratory data is obtained.

[0098] Step S005: determining a number of cough data segments of the respiratory data according to the probability of a cough event occurring in each respiratory cycle in the respiratory data; removing all cough data segments to obtain the patient's final respiratory data; and evaluating the patient's lung health status according to the final respiratory data.

[0099] It should be noted that the above determines the probability of a cough event occurring in each respiratory cycle in the respiratory data, and determines the cough data segment of the respiratory data by setting a suitable probability threshold.

[0100] Specifically, according to the probability of a cough event occurring in each respiratory cycle in the respiratory data, several cough data segments of the respiratory data are determined, as follows:

[0101] The probability of a cough event occurring in each respiratory cycle in the respiratory data is obtained, and the respiratory cycle with a probability greater than a preset first threshold is used as a cough data segment of the respiratory data. This embodiment is described with the first threshold being 0.6.

[0102] Furthermore, all cough data segments are removed to obtain the patient's final breathing data.

[0103] Furthermore, the patient's lung health status is evaluated based on the final respiratory data, as follows:

[0104] The final respiratory data is mapped to the actual exhaled volume through a neural network training model to obtain the exhaled volume of each respiratory cycle in the final respiratory data. The average exhaled volume in the last minute is used as the patient's exhaled flow rate. The patient's lung health status is evaluated based on the exhaled flow rate.

[0105] It should be noted that mapping the final respiratory data with the actual exhaled volume through a neural network training model to obtain the exhaled volume of each respiratory cycle in the final respiratory data; and evaluating the patient's lung health status based on the exhaled flow rate are existing methods, which will not be repeated in this embodiment.

[0106] Through the above steps, a respiratory health auxiliary monitoring method for chronic lung diseases is completed.

[0107] Another embodiment of the present invention provides a respiratory health auxiliary monitoring system for chronic lung diseases, the system comprising a memory and a processor, and when the processor executes a computer program stored in the memory, the processor performs the following operations:

[0108] Acquire the patient's respiratory data, wherein the respiratory data includes a number of voltage values; acquire the maximum and minimum points of the respiratory data; acquire the respiratory cycle, exhalation phase, and inhalation phase of the respiratory data through the maximum and minimum points of the respiratory data; acquire the abnormality of each respiratory cycle in the respiratory data according to the change in the duration of the respiratory cycle; acquire the exhalation prominence of each exhalation phase in the respiratory data according to the change in the duration of the exhalation phase in the corresponding respiratory cycle; acquire the abnormality of the respiratory rhythm of each respiratory cycle in the respiratory data according to the abnormality and the exhalation prominence; acquire the voltage change rate of each respiratory cycle in the respiratory data according to the difference between adjacent voltage values ​​within the respiratory cycle; and according to the method for acquiring the voltage change rate of the respiratory cycle, respectively The voltage change rate of each exhalation phase and inhalation phase in the respiratory data is obtained; the tachypnea of ​​each respiratory cycle in the respiratory data is obtained according to the change of the voltage change rate within the respiratory cycle; the degree of tremor of each respiratory cycle in the respiratory data is obtained according to the expiratory prominence, tachypnea, and the voltage change rate of the exhalation phase and the inhalation phase; the probability of a cough event occurring in each respiratory cycle in the respiratory data is obtained according to the abnormality of the respiratory rhythm and the degree of tremor; a number of cough data segments of the respiratory data are determined according to the probability of a cough event occurring in each respiratory cycle in the respiratory data; all cough data segments are eliminated to obtain the patient's final respiratory data; and the patient's lung health status is evaluated according to the final respiratory data.

[0109] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A respiratory health auxiliary monitoring method for chronic lung diseases, characterized in that: The method comprises the following steps: Acquire respiratory data of a patient, wherein the respiratory data includes a plurality of voltage values; Obtain the maximum and minimum points of the respiratory data; obtain the respiratory cycle, exhalation phase and inhalation phase of the respiratory data through the maximum and minimum points of the respiratory data; According to the change of the duration of the respiratory cycle, the abnormality of each respiratory cycle in the respiratory data is obtained; according to the change of the duration of the exhalation stage in the corresponding respiratory cycle, the exhalation prominence of each exhalation stage in the respiratory data is obtained; according to the abnormality and the exhalation prominence, the abnormality of the respiratory rhythm of each respiratory cycle in the respiratory data is obtained; According to the difference between adjacent voltage values ​​in a respiratory cycle, the voltage change rate of each respiratory cycle in the respiratory data is obtained; according to the method for obtaining the voltage change rate of the respiratory cycle, the voltage change rate of each exhalation phase and inhalation phase in the respiratory data is obtained respectively; according to the change of the voltage change rate in the respiratory cycle, the tachypnea of ​​each respiratory cycle in the respiratory data is obtained; according to the expiratory prominence, tachypnea, and the voltage change rate of the exhalation phase and the inhalation phase, the tremor degree of each respiratory cycle in the respiratory data is obtained; according to the abnormality of the respiratory rhythm and the tremor degree, the probability of a cough event occurring in each respiratory cycle in the respiratory data is obtained; According to the probability of a cough event occurring in each respiratory cycle in the respiratory data, several cough data segments of the respiratory data are determined; all cough data segments are eliminated to obtain the patient's final respiratory data; and the patient's lung health status is evaluated based on the final respiratory data.

2. A respiratory health auxiliary monitoring method for chronic lung disease according to claim 1, characterized in that: The specific steps of obtaining the respiratory cycle, exhalation phase and inhalation phase of the respiratory data through the maximum and minimum points of the respiratory data are as follows: The breathing data The maximum point and the The time interval between the maximum value points is taken as a breathing cycle of the breathing data, and all breathing cycles of the breathing data are obtained; the first The time interval between the maximum point and the next adjacent minimum point is taken as an exhalation stage of the breathing data, and all the exhalation stages of the breathing data are obtained; the first The remaining phases except the exhalation phase in a respiratory cycle are taken as an inhalation phase of the respiratory data, and all the inhalation phases of the respiratory data are obtained.

3. The respiratory health auxiliary monitoring method for chronic lung disease according to claim 1, characterized in that: The specific steps of obtaining the abnormality of each respiratory cycle in the respiratory data according to the change of the respiratory cycle duration are as follows: In the formula, The breathing data The duration of a respiratory cycle; is the average duration of all respiratory cycles in the respiratory data; is the duration variance of all respiratory cycles in the respiratory data; To take the absolute value; is a linear normalization function; The breathing data The abnormality of a respiratory cycle.

4. The respiratory health auxiliary monitoring method for chronic lung disease according to claim 1, characterized in that: The specific steps of obtaining the exhalation prominence of each exhalation stage in the respiratory data according to the change in the proportion of the exhalation stage in the corresponding respiratory cycle are as follows: In the formula, The breathing data The proportion of the exhalation phase in the corresponding respiratory cycle; It is the average value of the duration of all exhalation phases in the corresponding respiratory cycle in the respiratory data; is the variance of the duration of all exhalation phases in the respiratory data in the corresponding respiratory cycle; is a linear normalization function; The breathing data The expiratory prominence during the exhalation phase.

5. The respiratory health auxiliary monitoring method for chronic lung disease according to claim 1, characterized in that: The specific steps of obtaining the abnormality of the respiratory rhythm of each respiratory cycle in the respiratory data according to the abnormality and the exhalation prominence are as follows: In the formula, The breathing data The degree of abnormality of each respiratory cycle; The breathing data The exhalation prominence during the exhalation phase; The breathing data The degree of abnormality of breathing rhythm during a respiratory cycle.

6. The respiratory health auxiliary monitoring method for chronic lung disease according to claim 1, characterized in that: The specific steps of obtaining the voltage change rate of each respiratory cycle in the respiratory data according to the difference between adjacent voltage values ​​in the respiratory cycle are as follows: In the formula, The breathing data The number of voltage values ​​within a breathing cycle; The breathing data In the respiratory cycle Voltage value; The breathing data In the respiratory cycle Voltage value; To take the absolute value; The breathing data The voltage change rate during a breathing cycle.

7. The respiratory health auxiliary monitoring method for chronic lung disease according to claim 1, characterized in that: The method of obtaining the tachypnea of ​​each respiratory cycle in the respiratory data according to the change of the voltage change rate in the respiratory cycle includes the following specific steps: In the formula, The breathing data The voltage change rate during a respiratory cycle; is the average value of the voltage change rate of all respiratory cycles in the respiratory data; is the variance of the voltage change rate of all respiratory cycles in the respiratory data; is a linear normalization function; The breathing data The rapidity of breathing during the respiratory cycle.

8. The respiratory health auxiliary monitoring method for chronic lung disease according to claim 1, characterized in that: The method of obtaining the vibration degree of each respiratory cycle in the respiratory data according to the exhalation protrusion, the tachypnea, the voltage change rate in the exhalation phase and the inhalation phase includes the following specific steps: In the formula, The breathing data The exhalation prominence during the exhalation phase; The breathing data Shortness of breath during each respiratory cycle; The breathing data The rate of voltage change during the exhalation phase of a respiratory cycle; The breathing data The rate of voltage change during the inspiratory phase of a respiratory cycle; is a linear normalization function; The breathing data The degree of tremor during a respiratory cycle.

9. The respiratory health auxiliary monitoring method for chronic lung disease according to claim 1, characterized in that: The method of obtaining the probability of a cough event occurring in each respiratory cycle in the respiratory data according to the abnormality of the respiratory rhythm and the degree of tremor includes the following specific steps: In the formula, The breathing data Abnormality of breathing rhythm during each breathing cycle; The breathing data The degree of tremor during each respiratory cycle; is a linear normalization function; The cough event is the first The probability of occurrence within a breathing cycle.

10. A respiratory health auxiliary monitoring system for chronic lung diseases, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is executed by a processor, the steps of a respiratory health auxiliary monitoring method for chronic lung diseases as described in any one of claims 1 to 9 are implemented.

Images