Device, method and system for detecting pulmonary ventilation function

Abstract

The invention relates to the technical field of pulmonary function detection, in particular to a pulmonary ventilation function detection device, method and system. The tension degree of a subject is quantified through waveform change of an electrocardio curve and amplitude fluctuation of an arm myoelectricity curve to determine the initial tension degree; the initial tension degree is corrected according to the characteristic that tension expressions presented by an electrocardio curve and an arm myoelectricity curve during tension of the subject are consistent, and the corrected tension degree with more accurate tension degree characterization is determined; then the initial pulmonary ventilation volume time sequence curve is corrected in combination with the corrected tension degree, and the problem that the pulmonary ventilation function of the subject is not accurately measured directly according to the pulmonary ventilation volume time sequence curve of the subject in an existing pulmonary ventilation capacity test is solved; the accuracy of lung ventilation function detection according to the corrected lung ventilation volume time sequence curve is higher.

Description

Technical Field

[0001] The present invention relates to the technical field of pulmonary function detection, and particularly to a pulmonary ventilation function detection device, method and system. Background Art

[0002] Pulmonary ventilation function examination mainly refers to forced vital capacity examination, also known as timed vital capacity examination. It is the most commonly used method to judge the airflow limitation of the subject, evaluate their cooperation degree and completion quality, and can measure the respiratory system ability of the subject to a certain extent.

[0003] Existing pulmonary ventilation volume tests usually directly measure the pulmonary ventilation function of the subject according to the time-sequence curve of the pulmonary ventilation volume of the subject. However, during the test, the subject needs to perform corresponding breathing actions according to the instructions. Different subjects have different nervous performances during the test. Subjects who are too nervous will have abnormal breathing states during the test, resulting in inaccurate measurement of the pulmonary ventilation function of the subject by the finally obtained time-sequence curve of the pulmonary ventilation volume, and reducing the accuracy of pulmonary ventilation function detection. Summary of the Invention

[0004] The present application provides a pulmonary ventilation function detection device, method and system. By quantifying the waveform changes of the electrocardiogram curve and the amplitude fluctuations of the arm electromyogram curve to determine the initial tension degree of the subject, and according to the characteristic that the tension performances presented by the electrocardiogram curve and the arm electromyogram curve of the subject when tense will be consistent, the initial tension degree is corrected to determine a corrected tension degree that more accurately represents the tension degree. Then, the initial pulmonary ventilation volume time-sequence curve is corrected in combination with the corrected tension degree, which solves the problem that the existing pulmonary ventilation volume test directly measures the pulmonary ventilation function of the subject according to the time-sequence curve of the pulmonary ventilation volume of the subject inaccurately, and makes the accuracy of pulmonary ventilation function detection based on the corrected pulmonary ventilation volume time-sequence curve higher.

[0005] The first aspect of the present application provides a pulmonary ventilation function detection method, and the method includes: Collect the initial pulmonary ventilation volume time-sequence curve, electrocardiogram curve and arm electromyogram curve during the test period corresponding to the pulmonary ventilation test of the subject; divide the test period into at least two cardiac cycle periods according to the cardiac cycle in the electrocardiogram curve; In the electrocardiogram curve, determine the heart rate mapping tension degree of each cardiac cycle period according to the waveform change situation in each cardiac cycle period; in the arm electromyogram curve, determine the electromyogram mapping tension degree of each cardiac cycle period according to the amplitude fluctuation situation of the electromyogram signal in each cardiac cycle period; Determine the initial tension level of the subject according to the overall magnitude of tension levels in electromyography mapping and the overall magnitude of tension levels in heart rate mapping in all cardiac cycle time periods; determine the reliability of tension level according to the similarity between the trend of tension level change in heart rate mapping and the trend of tension level change in electromyography mapping corresponding to each cardiac cycle time period in time sequence; and modify the initial tension level according to the reliability of tension level to determine the modified tension level of the subject; The initial pulmonary ventilation volume time series curve is corrected according to the corrected tension level to determine a corrected pulmonary ventilation volume time series curve; and pulmonary ventilation function detection is performed according to the corrected pulmonary ventilation volume time series curve.

[0006] Furthermore, the process of acquiring the cardiac cycle time period includes: The electrocardiogram curve is input into a trained convolutional neural network to output each cardiac cycle; and the time period of each cardiac cycle in the test time period is used as the corresponding cardiac cycle time period.

[0007] Furthermore, the process of obtaining the heart rate mapping stress level includes: Inputting the local curve corresponding to each cardiac cycle time period in the electrocardiogram curve into the trained convolutional neural network, and outputting the P wave time period, QRS complex wave time period, ST wave time period and T wave time period in each cardiac cycle time period; The central moment of the P wave time period in each cardiac cycle time period is used as the P wave representative moment of each cardiac cycle time period; the time interval between the P wave representative moment of each cardiac cycle time period and the P wave representative moment of the previous cardiac cycle time period is used as the P wave reference interval of each cardiac cycle time period; Determine the QRS complex wave vibration amplitude of each cardiac cycle time period according to the ECG signal amplitude extreme difference corresponding to the QRS complex wave time period on the ECG curve in each cardiac cycle time period; Determine the ST wave fluctuation degree of each cardiac cycle time period according to the ECG signal amplitude variance at all times in the ST wave time period in each cardiac cycle time period; Performing negative correlation normalization on the mean values of the electrocardiographic signal amplitudes of the T wave in each cardiac cycle time period at all times corresponding to the electrocardiographic curve, and determining the degree of T wave inversion in each cardiac cycle time period; The product of the negative correlation mapping value of the P wave reference interval, the QRS complex wave vibration amplitude, the ST wave fluctuation degree and the T wave inversion degree is normalized to determine the heart rate mapping intensity of each cardiac cycle time period.

[0008] Furthermore, the process of obtaining the tension level of electromyography mapping includes: Normalize the range of the vibration amplitude of the myoelectric signals corresponding to each cardiac cycle time period on the myoelectric curve of the arm, and determine the myoelectric mapping tension degree of each cardiac cycle time period.

[0009] Further, the process of obtaining the initial tension degree includes: Take the mean value of the heart rate mapping tension degrees of all cardiac cycle time periods as the overall heart rate tension degree; take the mean value of the myoelectric mapping tension degrees of all cardiac cycle time periods as the overall myoelectric tension degree; take the product of the overall heart rate tension degree and the overall myoelectric tension degree as the initial tension degree of the subject.

[0010] Further, the process of obtaining the credibility of the tension degree includes: After arranging the heart rate mapping tension degrees of all cycle time periods in chronological order, determine the heart rate mapping tension degree time series; after arranging the myoelectric mapping tension degrees of all cycle time periods in chronological order, determine the myoelectric mapping tension degree time series; take the Pearson correlation coefficient between the heart rate mapping tension degree time series and the myoelectric mapping tension degree time series as the credibility of the tension degree.

[0011] Further, the process of obtaining the corrected tension degree includes: Normalize the product between the credibility of the tension degree and the initial tension degree to determine the corrected tension degree of the subject.

[0012] Further, the process of obtaining the corrected pulmonary ventilation volume time series curve includes: Multiply the corrected tension degree by the preset standard gas volume adjustment value to determine the final gas volume adjustment value; the horizontal axis of the initial pulmonary ventilation volume time series curve is time, and the vertical axis is the pulmonary ventilation volume. Obtain the inhalation phase time period and the exhalation phase time period of the initial pulmonary ventilation volume time series curve; take the sum of the pulmonary ventilation volume at each moment in the inhalation phase time period and the final gas volume adjustment value as the corrected gas volume value at each moment in the inhalation phase time period. Take the difference between the pulmonary ventilation volume at each moment in the exhalation phase time period and the final gas volume adjustment value as the corrected gas volume value at each moment in the exhalation phase time period. On the initial pulmonary ventilation volume time series curve, replace the pulmonary ventilation volume corresponding to all moments in the inhalation phase time period and the exhalation phase time period with the corrected gas volume values and perform interpolation processing to determine the corrected pulmonary ventilation volume time series curve.

[0013] In a second aspect, the present application provides a pulmonary ventilation function detection system, and the system includes: A data acquisition and preprocessing module, configured to collect an initial pulmonary ventilation volume time series curve, an electrocardiogram curve, and an arm electromyogram curve during a test time period corresponding to the pulmonary ventilation test of a subject; divide the test time period into at least two cardiac cycle time periods according to the cardiac cycle in the electrocardiogram curve; A first determination module, configured to determine the heart rate mapping tension degree of each cardiac cycle time period according to the waveform change condition in each cardiac cycle time period in the electrocardiogram curve; in the arm electromyogram curve, determine the electromyogram mapping tension degree of each cardiac cycle time period according to the fluctuation condition of the electromyogram signal amplitude in each cardiac cycle time period; A second determination module, configured to determine the initial tension degree of the subject according to the overall magnitude of the electromyogram mapping tension degree and the overall magnitude of the heart rate mapping tension degree of all cardiac cycle time periods; determine the tension degree credibility according to the similarity between the change trends of the heart rate mapping tension degree and the electromyogram mapping tension degree corresponding to each cardiac cycle time period in chronological order; correct the initial tension degree according to the tension degree credibility to determine the corrected tension degree of the subject; A pulmonary ventilation function detection module, configured to correct the initial pulmonary ventilation volume time series curve according to the corrected tension degree to determine a corrected pulmonary ventilation volume time series curve; perform pulmonary ventilation function detection according to the corrected pulmonary ventilation volume time series curve.

[0014] In a third aspect, the present application provides a pulmonary ventilation function detection device, including a memory and a processor. The memory is used to store computer program code, and the processor is used to call and run the computer program code from the memory to execute the method according to the first aspect or any embodiment of the first aspect of the present application.

[0015] In a fourth aspect, the present application provides a computer program product, the computer program product includes computer program code, and when the computer program code is executed, it is used to execute the method according to the first aspect or any embodiment of the first aspect of the present application.

[0016] In a fifth aspect, the present application provides a computer-readable storage medium, the computer-readable storage medium stores computer program code, and when the computer program code is executed, it is used to execute the method according to the first aspect or any embodiment of the first aspect of the present application.

[0017] The present application has the following beneficial effects: First, when a subject is tense, the tension is usually manifested in the waveform changes in each cardiac cycle on the electrocardiogram curve; and considering that the subsequent tension degree needs to be reflected according to the synchronous changes of the electromyogram signal and the electrocardiogram signal, the test time period is first divided into each cardiac cycle to facilitate the analysis of the tension characteristics reflected by the electrocardiogram curve and reduce the calculation complexity.

[0018] When the human body is in a state of tension, the relevant waveforms corresponding to each cardiac cycle will be affected by the tense emotion, resulting in specific changes. Therefore, the degree of tension mapped by the heart rate can be determined according to the waveform changes in each cardiac cycle time period. Similarly, during the pulmonary ventilation volume test, the muscle force application parts of the subject are generally the diaphragm, intercostal muscles, abdominal muscles, etc., and the force application degree of other muscle groups is relatively small. Therefore, the muscles on the arm have a relatively low force application degree, a small muscle contraction strength, and a small potential difference of the myoelectric signal generated by them. Under the influence of tension, the muscles usually show abnormal contraction phenomena, which will cause certain characteristic fluctuations in the amplitude of the myoelectric signal on the corresponding arm myoelectric curve. Therefore, the degree of tension mapped by the myoelectricity can be determined according to the fluctuation of the myoelectric signal amplitude in each cardiac cycle time period.

[0019] When the degree of tension mapped by the heart rate and the degree of tension mapped by the myoelectricity of the subject in each cardiac cycle time period are greater, it indicates that the degree of tension of the subject reflected by the electrocardiogram signal and the myoelectric signal is greater. Therefore, the initial degree of tension is determined by combining the overall size of the degree of tension mapped by the myoelectricity and the overall size of the degree of tension mapped by the heart rate in all cardiac cycle time periods. Further, it is necessary to consider that the tense emotion will affect both the electrocardiogram signal and the myoelectric signal at the same time. If the changes in the tense emotions reflected by the electrocardiogram signal and the myoelectric signal are inconsistent, it means that the degree of tension reflected by some electrocardiogram signals or myoelectric signals is not caused by tension. Therefore, further, according to the similarity between the change trends of the degree of tension mapped by the heart rate and the change trends of the degree of tension mapped by the myoelectricity corresponding to each cardiac cycle time period in the time sequence, the credibility of the degree of tension is characterized, and the initial degree of tension is corrected according to the credibility of the degree of tension, so that the corrected degree of tension of the subject obtained is more accurate. Finally, according to the fact that tension will affect the breathing state of the subject, the initial pulmonary ventilation volume time sequence curve is corrected by the corrected degree of tension obtained by quantifying the tense emotion, so that the accuracy of the pulmonary ventilation function detection based on the obtained corrected pulmonary ventilation volume time sequence curve is higher. Description of the Drawings

[0020] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0021] Figure 1 It is a flowchart of a pulmonary ventilation function detection method provided by an embodiment of the present invention; Figure 2 It is an initial pulmonary ventilation volume time sequence curve diagram provided by an embodiment of the present invention; Figure 3 Structural diagram of a pulmonary ventilation function detection system provided by an embodiment of the present invention; Figure 4 Schematic structural diagram of a pulmonary ventilation function detection device provided by an embodiment of the present invention. Specific embodiments

[0022] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following combines the accompanying drawings and preferred embodiments to detail the specific embodiments, structures, features and effects of a pulmonary ventilation function detection device, method and system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment, and specific features, structures or characteristics in one or more embodiments can be combined in any suitable form. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0024] The following specifically describes the specific solutions of a pulmonary ventilation function detection device, method and system provided by the present invention with reference to the accompanying drawings.

[0025] An embodiment of the present application provides a pulmonary ventilation function detection method. Please refer to Figure 1 , which shows a flowchart of a pulmonary ventilation function detection method provided by an embodiment of the present invention. The method includes: Step S101: Collect the initial pulmonary ventilation volume time series curve, electrocardiogram curve and arm electromyogram curve during the test time period corresponding to the pulmonary ventilation test of the subject; divide the test time period into at least two cardiac cycle time periods according to the cardiac cycle in the electrocardiogram curve.

[0026] First, obtain the initial pulmonary ventilation volume time series curve during the entire test time period with the help of a pulmonary function tester according to the pulmonary ventilation test process; please refer to Figure 2 , which shows an initial pulmonary ventilation volume time series curve diagram provided by an embodiment of the present invention. At Figure 2Among them, the initial pulmonary ventilation volume time series curve includes a tidal phase, a maximum inspiration phase, a maximum expiration a phase, and a second maximum inspiration b phase; wherein, in the tidal phase, the subject breathes evenly and calmly; in the maximum inspiration phase, the subject takes a maximum inspiration and inhales deeply to the total lung capacity position TLC; in the maximum expiration a phase, the subject exhales explosively and continuously exhales to the residual volume position RV; the second maximum inspiration b phase is to quickly inhale deeply from the residual volume position RV to the total lung capacity position TLC. In a specific implementation manner of the embodiment of the present invention, the time of the tidal phase is set to 2 min, and the time for testing the maximum voluntary ventilation volume is set to 30 s, both of which can be adjusted according to the specific implementation environment; and during the test, the electrocardiogram curve of the tester is measured by an electrocardiograph, and the arm electromyogram curve on the arm is measured by an electromyograph; and the measurement frequencies of measuring the electrocardiogram curve and the arm electromyogram curve are both set to 200 Hz and can be adjusted by oneself. It should be noted that the test time period is the time period from the start to the end of the subject's pulmonary ventilation test, which depends on the total duration of the entire pulmonary ventilation test process and needs to be obtained according to the specific implementation environment, and will not be further elaborated here.

[0027] First, when the subject is tense, the tension is usually manifested in the waveform changes in each cardiac cycle on the electrocardiogram curve; and considering that the subsequent tension level needs to be reflected according to the synchronous changes of the electromyogram signal and the electrocardiogram signal, therefore, the test time period is first divided into each cardiac cycle to facilitate the analysis of the tension characteristics reflected by the electrocardiogram curve and reduce the calculation complexity.

[0028] Preferably, in some possible implementation manners of the embodiment of the present invention, the process of obtaining the cardiac cycle time period includes: Input the electrocardiogram curve into the trained convolutional neural network, and output each cardiac cycle; take the time period of each cardiac cycle in the test time period as the corresponding cardiac cycle time period; the loss function of the convolutional neural network adopts the cross-entropy loss function. The cardiac cycle specifically refers to the start of one heartbeat to the start of the next heartbeat, showing periodicity; when tense, the tense emotion will affect the heartbeat process, resulting in specific changes in the electrocardiogram signal in each cardiac cycle. Therefore, the tension level of the subject can be further quantified according to the specific changes corresponding to the electrocardiogram signal.

[0029] Step S102: In the electrocardiogram curve, determine the heart rate mapping tension level of each cardiac cycle time period according to the waveform change situation in each cardiac cycle time period; in the arm electromyogram curve, determine the electromyogram mapping tension level of each cardiac cycle time period according to the fluctuation situation of the electromyogram signal amplitude in each cardiac cycle time period.

[0030] When the human body is in a state of tension, the relevant waveforms corresponding to each cardiac cycle will be affected by the tense emotion, resulting in specific changes. Therefore, the degree of heart rate mapping tension can be determined according to the waveform changes in each cardiac cycle time period. Similarly, during the pulmonary ventilation volume test, the muscle force application parts of the subject are generally the diaphragm, intercostal muscles, abdominal muscles, etc., and the force application degree of other muscle groups is relatively small. Therefore, the muscles on the arm have a relatively low force application degree, a small muscle contraction strength, and a small potential difference of the myoelectric signal generated by them. Under the influence of tension, the muscles usually show abnormal contraction phenomena, which will cause certain characteristic fluctuations in the amplitude of the myoelectric signal on the corresponding arm myoelectric curve. Therefore, the degree of myoelectric mapping tension can be determined according to the fluctuation of the myoelectric signal amplitude in each cardiac cycle time period.

[0031] Preferably, in some possible implementation manners of the embodiments of the present invention, the process of obtaining the degree of heart rate mapping tension includes: When the human body is in a state of tension, the sympathetic nervous system will be activated, and the atrium will also contract faster accordingly, which is manifested as an increase in the frequency of the P wave in the electrocardiogram signal. And due to the release of adrenaline, the contractility of the ventricle is enhanced, and the amplitude of the QRS complex wave will increase. The T wave represents ventricular repolarization, and the sympathetic nerve excitation caused by tension may lead to myocardial ischemia and the appearance of T wave inversion. The ST wave is the interval period between ventricular depolarization and repolarization, and it will become irregular due to the increase in heart rate and the increase in myocardial load during tension. Therefore, the degree of tension can be characterized by the P wave, QRS complex wave, T wave, and ST wave in each cardiac cycle time period on the electrocardiogram curve. So first, it is necessary to determine the time periods corresponding to the P wave, QRS complex wave, T wave, and ST wave in each cardiac cycle. In the embodiments of the present invention, the local curve corresponding to each cardiac cycle time period in the electrocardiogram curve is input into the trained convolutional neural network, and the time periods of the P wave time period, QRS complex wave time period, ST wave time period, and T wave time period in each cardiac cycle time period are output. The loss function of the convolutional neural network uses the cross-entropy loss function. It should be noted that the P wave, QRS complex wave, T wave, and ST wave are all common technical terms in electrocardiogram signals and are well-known technical terms to those skilled in the art, and will not be further elaborated here.

[0032] The central moment of the P wave time period in each cardiac cycle time period is used as the representative moment of the P wave in each cardiac cycle time period; the time interval between the representative moment of the P wave in each cardiac cycle time period and the representative moment of the P wave in the previous cardiac cycle time period is used as the reference interval of the P wave in each cardiac cycle time period. Since the frequency of the P wave increases when the human body is in a state of tension, the shorter the interval between the appearances of the P wave, the greater the degree of tension reflected in the dimension of the P wave. Therefore, the P wave reference interval is negatively correlated with the degree of tension.

[0033] Determine the vibration amplitude of the QRS complex wave in each cardiac cycle period according to the amplitude range of the electrocardiogram signals corresponding to the QRS complex wave period on the electrocardiogram curve. Since the amplitude of the QRS complex wave increases when the human body is tense, presenting a larger amplitude drop on the electrocardiogram curve, the greater the amplitude range of the electrocardiogram signals corresponding to the QRS complex wave period on the electrocardiogram curve, the greater the degree of tension reflected by the corresponding cardiac cycle period. That is, the vibration amplitude of the QRS complex wave is positively correlated with the degree of tension.

[0034] Determine the fluctuation degree of the ST segment in each cardiac cycle period according to the variance of the electrocardiogram signals at all times in the ST segment period of each cardiac cycle period. Since the ST segment becomes irregular when the human body is tense, the variance of the electrocardiogram signals, which represents the degree of dispersion, is used to determine the fluctuation degree of the ST segment in each cardiac cycle period, so that the greater the fluctuation degree of the ST segment, the greater the degree of tension reflected by the corresponding cardiac cycle period. That is, the fluctuation degree of the ST segment is positively correlated with the degree of tension.

[0035] Perform negative correlation normalization on the mean value of the electrocardiogram signal amplitudes at all times corresponding to the T wave on the electrocardiogram curve in each cardiac cycle period to determine the inversion degree of the T wave in each cardiac cycle period; since the T wave will be inverted when the human body is tense, and the inversion will cause the electrocardiogram signal to be downward, making the vibration amplitude of the electrocardiogram signal relatively small, so after performing negative correlation normalization on the mean value of the electrocardiogram signal amplitudes at all times corresponding to the T wave on the electrocardiogram curve in each cardiac cycle period, the greater the obtained inversion degree of the T wave, the greater the degree of tension reflected. That is, the inversion degree of the T wave is positively correlated with the degree of tension. It should be noted that all the vibration amplitudes appearing in the embodiments of the present invention are the values taken by the corresponding curves on the vertical axis, and no further elaboration will be made hereinafter.

[0036] Furthermore, according to the negative correlation between the P wave reference interval and the degree of tension, and the positive correlation between the vibration amplitude of the QRS complex wave, the fluctuation degree of the ST segment and the inversion degree of the T wave and the degree of tension, normalize the product of the negative correlation mapping value of the P wave reference interval, the vibration amplitude of the QRS complex wave, the fluctuation degree of the ST segment and the inversion degree of the T wave to determine the heart rate mapping tension degree in each cardiac cycle period. In a specific implementation manner of the embodiments of the present invention, the process of obtaining the heart rate mapping tension degree is expressed by the formula: ; where is the heart rate mapping tension degree of the th cardiac cycle period; is the time interval between the P wave representative moment of the th cardiac cycle period and the P wave representative moment of the previous cardiac cycle period, that is, the The P-wave reference interval for each cardiac cycle period; is the maximum value of the amplitude of the electrocardiogram signal corresponding to the QRS complex wave period in the th cardiac cycle period on the electrocardiogram curve; is the minimum value of the amplitude of the electrocardiogram signal corresponding to the QRS complex wave period in the th cardiac cycle period on the electrocardiogram curve; that is, the amplitude range of the QRS complex wave; is the variance of the amplitudes of the electrocardiogram signals at all times during the ST wave period in the th cardiac cycle period; that is, the degree of fluctuation of the ST wave in the th cardiac cycle period; is the exponential function with the natural constant as the base. Implementers can adopt other negatively correlated mapping methods according to the specific implementation environment, such as , and the reciprocal. Among them, is the hyperbolic tangent function, which will not be elaborated further here; is the linear normalization function. Implementers can adjust the normalization method according to the specific implementation environment; through normalization, the value of the heart rate mapping tension degree is limited within 0 to 1, which is convenient for subsequent comparative analysis.

[0037] Preferably, in some possible implementation manners of the embodiments of the present invention, the process of obtaining the electromyogram mapping tension degree includes: Normalize the amplitude range of the electromyogram signal corresponding to each cardiac cycle period on the arm electromyogram curve to determine the electromyogram mapping tension degree of each cardiac cycle period. Since under the influence of tension, the muscle usually shows abnormal contraction, the greater the amplitude range of the electromyogram signal corresponding to each cardiac cycle period on the arm electromyogram curve, the more obvious the abnormal contraction of the muscle usually is, and the greater the corresponding tension degree. In a specific implementation manner of the embodiments of the present invention, the process of obtaining the electromyogram mapping tension degree is expressed by the formula: ; where is the electromyogram mapping tension degree of the th cardiac cycle period; The maximum vibration amplitude of the myoelectric signal corresponding to a cardiac cycle time period on the arm myoelectric curve; is the minimum vibration amplitude of the myoelectric signal corresponding to a cardiac cycle time period on the arm myoelectric curve; is the range of the vibration amplitude of the myoelectric signal corresponding to a cardiac cycle time period on the arm myoelectric curve; is a linear normalization function, and the implementer can adjust the normalization method according to the specific implementation environment; through normalization, the value of the myoelectric mapping tension degree is limited within 0 to 1, which is convenient for subsequent comparative analysis.

[0038] Step S103: Determine the initial tension degree of the subject according to the overall magnitude of the myoelectric mapping tension degree and the overall magnitude of the heart rate mapping tension degree of all cardiac cycle time periods; determine the credibility of the tension degree according to the similarity between the change trends of the heart rate mapping tension degree and the myoelectric mapping tension degree corresponding to each cardiac cycle time period in chronological order; and correct the initial tension degree according to the credibility of the tension degree to determine the corrected tension degree of the subject.

[0039] When the heart rate mapping tension degree and the myoelectric mapping tension degree of the subject in each cardiac cycle time period are larger, it indicates that the tension degree of the subject reflected by the electrocardiogram signal and the myoelectric signal is larger. Therefore, the initial tension degree is determined by combining the overall magnitude of the myoelectric mapping tension degree and the overall magnitude of the heart rate mapping tension degree of all cardiac cycle time periods. Further, it is necessary to consider that the tension emotion will affect both the electrocardiogram signal and the myoelectric signal at the same time. If the change trends of the tension emotions reflected by the electrocardiogram signal and the myoelectric signal are inconsistent, it means that the tension degree reflected by some electrocardiogram signals or myoelectric signals is not caused by tension. Therefore, further, the credibility of the tension degree is characterized according to the similarity between the change trends of the heart rate mapping tension degree and the myoelectric mapping tension degree corresponding to each cardiac cycle time period in chronological order, and the initial tension degree is corrected according to the credibility of the tension degree, so that the obtained corrected tension degree of the subject is more accurate.

[0040] Preferably, in some possible implementation manners of the embodiment of the present invention, the process of obtaining the initial tension degree includes: The mean value of the heart rate mapping tension levels over all cardiac cycle time periods is taken as the overall heart rate tension level; the mean value of the electromyogram mapping tension levels over all cardiac cycle time periods is taken as the overall electromyogram tension level; the product of the overall heart rate tension level and the overall electromyogram tension level is taken as the initial tension level of the subject. The mean value can represent the overall magnitude characteristics of a set of data. Therefore, when the greater the overall heart rate tension level obtained through the mean value and the greater the overall electromyogram tension level, it means that the heart rate mapping tension levels and electromyogram mapping tension levels of the subject over each cardiac cycle time period are greater as a whole. That is, the greater the tension level of the subject reflected by the electrocardiogram signal and the electromyogram signal. Therefore, further, the product is used to combine the overall heart rate tension level and the overall electromyogram tension level to measure the tension level of the subject, that is, the initial tension level.

[0041] In a specific implementation manner of the embodiment of the present invention, the process of obtaining the initial tension level is expressed by the formula: ; where is the initial tension level of the subject; is the number of cardiac cycle time periods in the test time period; is the heart rate mapping tension level of the th cardiac cycle time period; is the electromyogram mapping tension level of the th cardiac cycle time period; is the overall heart rate tension level; is the overall electromyogram tension level.

[0042] Preferably, in some possible implementation manners of the embodiment of the present invention, the process of obtaining the tension level credibility includes: After arranging the heart rate mapping tension levels of all cycle time periods in chronological order, a heart rate mapping tension level time series is determined; after arranging the electromyogram mapping tension levels of all cycle time periods in chronological order, an electromyogram mapping tension level time series is determined; the Pearson correlation coefficient between the heart rate mapping tension level time series and the electromyogram mapping tension level time series is taken as the tension level credibility. Since the tense emotion will affect both the electrocardiogram signal and the electromyogram signal simultaneously, ideally, the heart rate mapping tension level and the electromyogram mapping tension level should show highly correlated changes in time sequence to determine that the initial tension level is completely caused by the tense emotion. Therefore, in the dimension of correlation, the Pearson correlation coefficient is used to measure the tension level credibility, and thus the initial tension level is corrected by means of the tension level credibility, making the quantified tension level after correction more accurate.

[0043] Preferably, in some possible implementation manners of the embodiment of the present invention, the process of obtaining the corrected tension level includes: Since the greater the credibility of the tension level, the more accurate the quantification of the initial tension level for the tension emotion, the product of the credibility of the tension level and the initial tension level is normalized to determine the corrected tension level of the subject. In a specific implementation manner of the embodiment of the present invention, the process of obtaining the corrected tension level is expressed by the formula: ; where is the corrected tension level of the subject; is the initial tension level of the subject; is the Pearson correlation coefficient between the heart rate mapped tension level time series and the electromyogram mapped tension level time series, that is, the credibility of the tension level. The greater the corresponding corrected tension level, the more nervous the subject is, and then the greater the impact of the initial pulmonary ventilation volume time series curve by the tension.

[0044] Step S104: Correct the initial pulmonary ventilation volume time series curve according to the corrected tension level to determine the corrected pulmonary ventilation volume time series curve; perform pulmonary ventilation function detection according to the corrected pulmonary ventilation volume time series curve.

[0045] Finally, since tension will affect the respiratory state of the subject, the initial pulmonary ventilation volume time series curve is corrected by the corrected tension level obtained by quantifying the tension emotion, so that the accuracy of the pulmonary ventilation function detection according to the obtained corrected pulmonary ventilation volume time series curve is higher.

[0046] Preferably, in some possible implementation manners of the embodiment of the present invention, the process of obtaining the corrected pulmonary ventilation volume time series curve includes: Multiply the corrected tension level by the preset standard gas volume adjustment value to determine the final gas volume adjustment value; the horizontal axis of the initial pulmonary ventilation volume time series curve is time, and the vertical axis is the pulmonary ventilation volume; obtain the inhalation phase time period and the exhalation phase time period of the initial pulmonary ventilation volume time series curve; use the sum of the pulmonary ventilation volume at each moment in the inhalation phase time period and the final gas volume adjustment value as the corrected gas volume value at each moment in the inhalation phase time period; use the difference between the pulmonary ventilation volume at each moment in the exhalation phase time period and the final gas volume adjustment value as the corrected gas volume value at each moment in the exhalation phase time period. First, objectively, the impact of tension on the subject is mainly reflected in the inability to reach the maximum inhalation degree during the inhalation phase and the inability to reach the maximum exhalation degree during the exhalation phase; therefore, in the inhalation phase of the initial pulmonary ventilation volume time series curve, that is, the maximum inhalation phase and the second maximum inhalation phase b of the embodiments of the present invention, it needs to be adjusted more according to the corrected tension level; conversely, in the exhalation phase, that is, the maximum exhalation phase a, it needs to be adjusted less according to the corrected tension level; therefore, different correction methods need to be combined for correction in the inhalation phase time period and the exhalation phase time period. In a specific implementation manner of the embodiments of the present invention, the preset standard gas volume adjustment value is set to half of the maximum gas volume value in the tidal phase, which can be adjusted according to the specific implementation environment. In essence, it is used for correction, and the tidal phase can reflect the breathing habits of the subject to a certain extent.

[0047] In a specific implementation manner of the embodiments of the present invention, the process of obtaining the corrected gas volume value in the inhalation phase time period is expressed by the formula: ; where is the corrected gas volume value at time in the inhalation phase time period; is the pulmonary ventilation volume at time in the initial pulmonary ventilation volume time series curve; is the corrected tension level of the subject; is the preset standard gas volume adjustment value; is the final gas volume adjustment value. In a specific implementation manner of the embodiments of the present invention, the process of obtaining the corrected gas volume value in the exhalation phase time period is expressed by the formula: ; where is the corrected gas volume value at time in the exhalation phase time period; is the pulmonary ventilation volume at time in the initial pulmonary ventilation volume time series curve; is the corrected tension level of the subject; is the preset standard gas volume adjustment value; is the final gas volume adjustment value.

[0048] After considering the correction of the lung ventilation volume in the inhalation phase and the exhalation phase, the corresponding corrected volume values may not be continuous in time series. Therefore, further interpolation processing is performed on the initial lung ventilation volume time series curve by replacing the corrected air volume values at all moments corresponding to the inhalation phase time period and the exhalation phase time period with the corresponding lung ventilation volumes to determine the corrected lung ventilation volume time series curve; the interpolation processing is used to avoid the appearance of a piecewise function, making the corrected lung ventilation volume time series curve smoother. After determining the corrected lung ventilation volume time series curve, the lung ventilation function is detected according to the conventional method in the lung ventilation test in combination with the corrected lung ventilation volume time series curve. It should be noted that the lung ventilation test based on the lung ventilation volume time series curve is a well-known technical means for those skilled in the art and will not be further elaborated here.

[0049] In summary, a method for detecting lung ventilation function quantifies the initial tension level of a subject by the waveform changes of the electrocardiogram curve and the amplitude fluctuations of the arm electromyogram curve, and corrects the initial tension level according to the consistent characteristics of the tension manifestations presented by the electrocardiogram curve and the arm electromyogram curve when the subject is tense, so as to determine a corrected tension level that more accurately represents the tension level; then, the initial lung ventilation volume time series curve is corrected in combination with the corrected tension level, which solves the problem that the existing lung ventilation volume test directly measures the lung ventilation function of the subject inaccurately according to the lung ventilation volume time series curve of the subject, and makes the accuracy of detecting the lung ventilation function according to the corrected lung ventilation volume time series curve higher.

[0050] This application also provides a lung ventilation function detection system. Please refer to Figure 3 FIG. 9, which shows a structural diagram of a lung ventilation function detection system provided by an embodiment of the present invention. The system includes: a data acquisition and preprocessing module 301, a first determination module 302, a second determination module 303, and a lung ventilation function detection module 304.

[0051] The data acquisition and preprocessing module 301 is configured to acquire the initial lung ventilation volume time series curve, the electrocardiogram curve, and the arm electromyogram curve during the test time period corresponding to the lung ventilation test of the subject; divide the test time period into at least two cardiac cycle time periods according to the cardiac cycle in the electrocardiogram curve; The first determination module 302 is configured to determine the heart rate mapped tension level of each cardiac cycle time period according to the waveform change situation in each cardiac cycle time period in the electrocardiogram curve; determine the electromyogram mapped tension level of each cardiac cycle time period according to the amplitude fluctuation situation of the electromyogram signal in each cardiac cycle time period in the arm electromyogram curve; A second determination module 303, configured to determine the initial tension level of the subject according to the overall magnitude of the myoelectric mapping tension level and the overall magnitude of the heart rate mapping tension level in all cardiac cycle time periods; determine the tension level credibility according to the similarity between the change trends of the heart rate mapping tension level and the myoelectric mapping tension level corresponding to each cardiac cycle time period in chronological order; correct the initial tension level according to the tension level credibility to determine the corrected tension level of the subject; A pulmonary ventilation function detection module 304, configured to correct the initial pulmonary ventilation volume time series curve according to the corrected tension level to determine a corrected pulmonary ventilation volume time series curve; perform pulmonary ventilation function detection according to the corrected pulmonary ventilation volume time series curve.

[0052] It should be noted that the system provided in the above embodiments is only illustrated by dividing the above-mentioned functional modules. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, a pulmonary ventilation function detection system and a pulmonary ventilation function detection method embodiment provided in the above embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0053] An embodiment of the present application further provides a pulmonary ventilation function detection device. Please refer to Figure 4 , which shows a schematic structural diagram of a pulmonary ventilation function detection device provided by an embodiment of the present invention. The device includes a memory 401, a processor 402, and a computer program 403 stored in the memory 401 and running on the processor 402. When the processor 402 executes the computer program 403, the device can execute any one of the pulmonary ventilation function detection methods described above.

[0054] An embodiment of the present application further provides a computer program product. When the computer program product runs on a computer device, the computer device can execute any one of the pulmonary ventilation function detection methods described above.

[0055] An embodiment of the present application further provides a computer-readable storage medium. Computer program code is stored in the computer-readable storage medium. When the computer program code runs on a computer device, the computer device can execute any one of the pulmonary ventilation function detection methods described above.

[0056] In the embodiments provided in the present application, it should be understood that the provided computer device, computer program product, and computer-readable storage medium are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the methods provided above, which will not be repeated here.

[0057] It should be noted that: the above order of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0058] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments.

Claims

1. A method for detecting pulmonary ventilation function, characterized in that, The method includes: Collecting the initial pulmonary ventilation volume time series curve, electrocardiogram curve, and arm electromyogram curve during the test time period corresponding to the pulmonary ventilation test of the subject; dividing the test time period into at least two cardiac cycle time periods according to the cardiac cycle in the electrocardiogram curve; In the electrocardiogram curve, determining the heart rate mapping tension degree of each cardiac cycle time period according to the waveform change situation in each cardiac cycle time period; in the arm electromyogram curve, determining the electromyogram mapping tension degree of each cardiac cycle time period according to the fluctuation situation of the electromyogram signal amplitude in each cardiac cycle time period; Determining the initial tension degree of the subject according to the overall magnitude of the electromyogram mapping tension degree and the overall magnitude of the heart rate mapping tension degree of all cardiac cycle time periods; determining the tension degree credibility according to the similarity between the change trends of the heart rate mapping tension degree and the electromyogram mapping tension degree corresponding to each cardiac cycle time period in chronological order; correcting the initial tension degree according to the tension degree credibility to determine the corrected tension degree of the subject; Correcting the initial pulmonary ventilation volume time series curve according to the corrected tension degree to determine the corrected pulmonary ventilation volume time series curve; performing pulmonary ventilation function detection according to the corrected pulmonary ventilation volume time series curve.

2. The method for detecting pulmonary ventilation function according to claim 1, wherein The obtaining process of the cardiac cycle time period includes: Inputting the electrocardiogram curve into the trained convolutional neural network to output each cardiac cycle; taking the time period where each cardiac cycle is located in the test time period as the corresponding cardiac cycle time period.

3. The method for detecting pulmonary ventilation function according to claim 1, wherein, The obtaining process of the heart rate mapping tension degree includes: Inputting the local curve corresponding to each cardiac cycle time period in the electrocardiogram curve into the trained convolutional neural network to output the P wave time period, QRS complex wave time period, ST wave time period, and T wave time period in each cardiac cycle time period; Taking the central moment of the P wave time period in each cardiac cycle time period as the P wave representative moment of each cardiac cycle time period; taking the time interval between the P wave representative moment of each cardiac cycle time period and the P wave representative moment of the previous cardiac cycle time period as the P wave reference interval of each cardiac cycle time period; Determining the QRS complex wave vibration amplitude of each cardiac cycle time period according to the amplitude range of the electrocardiogram signal corresponding to the QRS complex wave time period in each cardiac cycle time period on the electrocardiogram curve; Determining the ST wave fluctuation degree of each cardiac cycle time period according to the variance of the electrocardiogram signal amplitudes at all moments in the ST wave time period of each cardiac cycle time period; Performing negative correlation normalization on the mean value of the electrocardiogram signal amplitudes at all moments corresponding to the T wave in the electrocardiogram curve of each cardiac cycle time period to determine the T wave inversion degree of each cardiac cycle time period; Normalizing the product between the negative correlation mapping value of the P wave reference interval, the QRS complex wave vibration amplitude, the ST wave fluctuation degree, and the T wave inversion degree to determine the heart rate mapping tension degree of each cardiac cycle time period.

4. A method for detecting pulmonary ventilation function according to claim 1, characterized in that, The obtaining process of the electromyogram mapping tension degree includes: Normalize the range of the vibration amplitude of the myoelectric signals corresponding to each cardiac cycle time period on the myoelectric curve of the arm, and determine the myoelectric mapping tension degree of each cardiac cycle time period.

5. A method for detecting pulmonary ventilation function according to claim 1, characterized in that, The process of obtaining the initial tension degree includes: Take the average value of the heart rate mapping tension degrees of all cardiac cycle time periods as the overall heart rate tension degree; take the average value of the myoelectric mapping tension degrees of all cardiac cycle time periods as the overall myoelectric tension degree; take the product of the overall heart rate tension degree and the overall myoelectric tension degree as the initial tension degree of the subject.

6. The method for detecting pulmonary ventilation function according to claim 1, wherein The process of obtaining the credibility of the tension degree includes: After arranging the heart rate mapping tension degrees of all cycle time periods in chronological order, determine the heart rate mapping tension degree time series; after arranging the myoelectric mapping tension degrees of all cycle time periods in chronological order, determine the myoelectric mapping tension degree time series; take the Pearson correlation coefficient between the heart rate mapping tension degree time series and the myoelectric mapping tension degree time series as the credibility of the tension degree.

7. A method for detecting pulmonary ventilation function according to claim 1, characterized in that, The process of obtaining the corrected tension degree includes: Normalize the product between the credibility of the tension degree and the initial tension degree to determine the corrected tension degree of the subject.

8. A method for detecting pulmonary ventilation function according to claim 1, characterized in that, The process of obtaining the corrected pulmonary ventilation volume time series curve includes: Multiply the corrected tension degree by the preset standard gas volume adjustment value to determine the final gas volume adjustment value; the horizontal axis of the initial pulmonary ventilation volume time series curve is time, and the vertical axis is the pulmonary ventilation volume. Obtain the inhalation phase time period and the exhalation phase time period of the initial pulmonary ventilation volume time series curve; take the sum of the pulmonary ventilation volume at each moment in the inhalation phase time period and the final gas volume adjustment value as the corrected gas volume value at each moment in the inhalation phase time period. Take the difference between the pulmonary ventilation volume at each moment in the exhalation phase time period and the final gas volume adjustment value as the corrected gas volume value at each moment in the exhalation phase time period. On the initial pulmonary ventilation volume time series curve, replace the pulmonary ventilation volume corresponding to all moments in the inhalation phase time period and the exhalation phase time period with the corrected gas volume values, and then perform interpolation processing to determine the corrected pulmonary ventilation volume time series curve.

9. A pulmonary ventilation function detection system, characterized in that, The system includes: A data acquisition and preprocessing module, which is used to collect the initial pulmonary ventilation volume time series curve, electrocardiogram curve, and arm myoelectric curve during the test time period corresponding to the pulmonary ventilation test of the subject; divide the test time period into at least two cardiac cycle time periods according to the cardiac cycle in the electrocardiogram curve. A first determination module, which is used to determine the heart rate mapping tension degree of each cardiac cycle time period according to the waveform change situation in each cardiac cycle time period in the electrocardiogram curve; determine the myoelectric mapping tension degree of each cardiac cycle time period according to the myoelectric signal amplitude fluctuation situation in each cardiac cycle time period in the arm myoelectric curve. A second determination module, configured to determine the initial tension level of the subject according to the overall magnitude of the myoelectric mapping tension level and the overall magnitude of the heart rate mapping tension level in all cardiac cycle time periods; determine the tension level credibility according to the similarity between the change trends of the heart rate mapping tension level and the myoelectric mapping tension level corresponding to each cardiac cycle time period in chronological order; and correct the initial tension level according to the tension level credibility to determine the corrected tension level of the subject. A pulmonary ventilation function detection module, configured to correct the initial pulmonary ventilation volume time sequence curve according to the corrected tension level to determine a corrected pulmonary ventilation volume time sequence curve; and perform pulmonary ventilation function detection according to the corrected pulmonary ventilation volume time sequence curve.

10. A lung ventilation function detection device, characterized in that, It includes a memory and a processor; wherein, the memory is used to store computer program codes that can be run on the processor; the processor is used to call and run the computer program codes from the memory to implement the steps of a pulmonary ventilation function detection method according to any one of claims 1-8.

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