Respiration compensation monitoring method and system

Abstract

The invention provides a breathing compensation monitoring method and system, and solves the technical problem that the existing breathing monitoring process is lack of compensation phenomenon early recognition capability. The method comprises the following steps: associating a quantitative parameter of a breathing state with breathing muscle potential information to form a potential associated parameter of the breathing state; collecting real-time ventilation data of the patient; according to the data linear trend comparison of the real-time ventilation parameter and the potential correlation parameter, the evaluation of the breathing state is formed; and forming respiration compensation prediction according to an evaluation result. And establishing association between respiratory muscle potential information and ventilatory capacity and flow to form a quantization mechanism of energy conversion in the respiration process. Potential information and ventilatory capacity linear trend of a patient in the course of disease are used for determining the possibility of respiratory decompensation of the patient, and auxiliary reference factors for diagnosing metabolic disorder of the patient are formed. The method has auxiliary utilization value in the process of research and development of physical exercise, health rehabilitation and metabolic therapy equipment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vital sign monitoring, and particularly relates to a respiratory compensation monitoring method and system. BACKGROUND

[0002] The phase, depth and rhythm of respiration of a severe patient often change with the progress of the disease. Changes in respiration often closely relate to the state of oxygenation, ventilation and acid-base balance of the human body. In the prior art, relevant indicators are often judged through peripheral blood oxygen sampling and blood gas analysis reports, but since the judgment is directed to a sign phenomenon derived after the metabolic process, there is bound to be a feedback delay, which is not conducive to timely handling of metabolic acid-base imbalance of the patient.

[0003] The respiratory compensation / decompensation phenomenon of a patient can be understood from the perspective of energy conservation. In the inspiratory phase, the respiratory muscle contracts (mainly relying on the diaphragm) to consume biological energy, lifts the ribs, and lowers the diaphragm, expands the chest cavity to form a negative pressure (-5 to -8 ), at this time the biological energy is converted into the elastic potential energy of the connective tissue of the chest wall. In the expiratory phase, the respiratory muscle relaxes, the chest elasticity retracts to release potential energy, drives the gas to be exhaled at an average flow rate of 0.5-1.0 L / s, and the potential energy is converted into kinetic energy of the gas. The chest cavity expansion and the roughness of the airway surface naturally exist energy loss, basically following the rule of biological energy> chest potential energy> kinetic energy of the gas. In the process of energy conversion, the healthier the body is, the less energy loss is. In pathology, the energy loss increases, the more serious the disease is, and the more loss is. In the pathophysiology of the disease, the respiratory muscle increases the work to offset the energy loss in the energy conversion to meet the needs of the body. When the respiratory muscle increases the work to meet the needs of the body, it belongs to the respiratory compensation stage. When the respiratory muscle increases the work and cannot meet the needs of the body, it belongs to the respiratory decompensation stage. For example: when the respiratory muscle is tired (such as COPD), the absolute or relative value of biological energy output decreases ↓, which cannot meet the kinetic energy required for maintaining ventilation → effective ventilation volume ↓ → respiratory failure. This can be understood as that the activity level of the respiratory muscle is related to the respiratory compensation / decompensation phenomenon. In the above energy conversion chain, airway resistance is a key variable that determines energy demand. According to the fluid dynamics formula, the kinetic energy of the gas E = 1 / 2 * m * v² (where m is the mass and v is the speed). In order to maintain a constant ventilation volume (volume / time), when the cross-sectional area of the airflow passage is halved, the flow rate of the gas must be doubled. At this time, the kinetic energy required to maintain ventilation will be four times that of the original (E ∝ v²), which means that the work required by the respiratory muscle also increases sharply.

[0004] The existing neuromuscular ventilation assistance (NAVA) verification and electrophysiological evidence of respiratory compensation mechanism show that the diaphragmatic potential of the respiratory muscle (mainly the diaphragm) and the respiratory state are in a direct and synchronous causal relationship, and the "presence or absence, strength, frequency" of the diaphragmatic potential directly determines the "phase (inspiration / expiration), depth, rhythm" of the respiration, and the two are completely synchronous and linked, and the causal relationship has high universality under physiological and pathological conditions.

[0005] By utilizing the qualitative correlation between the respiratory muscle potential signal and the respiratory work and the respiratory air flow rate, a new monitoring process of the respiratory compensation phenomenon can be formed. SUMMARY

[0006] In view of the above problems, the embodiment of the present application provides a respiratory compensation monitoring method and system, which solves the technical problem that the existing respiratory monitoring process lacks early identification ability of the compensation phenomenon.

[0007] The respiratory compensation monitoring method of the embodiment of the present application comprises: Correlate the quantified parameters of the respiratory state with the diaphragmatic potential information to form the potential correlation parameters of the respiratory state; Collect real-time ventilation data of the patient; Form an evaluation of the respiratory state according to the data linear trend comparison between the real-time ventilation parameters and the potential correlation parameters; Form a respiratory compensation prediction according to the evaluation result.

[0008] In an embodiment of the present application, the formation of the potential correlation parameters of the respiratory state comprises: Obtain the potential performance of the respiratory muscle under each respiratory state; Time sequence correlate the quantified parameters under each respiratory state with the potential performance to form the potential correlation parameters.

[0009] In an embodiment of the present application, the collection of the real-time ventilation data of the patient comprises: Obtain the time sequence ventilation data of the patient in the expiratory phase, and the ventilation data comprises the expiratory flow, the expiratory flow rate, the expiratory frequency, the expiratory pressure and the pressure.

[0010] In an embodiment of the present application, the formation of the evaluation of the respiratory state comprises: Establish a time sequence segment of the patient in the ventilation according to the potential correlation parameters and the expiratory ventilation parameters; Quantify the data change trend of the potential correlation parameters and the data change trend of the expiratory ventilation parameters in the time sequence segment; According to the change trend comparison, evaluate the linear convergence characteristics of the biological energy and the ventilation kinetic energy and the disease course characteristics of the respiratory state in a unit of time.

[0011] In an embodiment of the present application, the formation of the respiratory compensation prediction comprises: The development process of respiratory compensation is predicted according to the duration of the linear convergence characteristics; The urgency of respiratory decompensation is predicted according to the duration trend of the linear convergence characteristics.

[0012] The respiratory compensation monitoring system of the embodiment of the application comprises: The parameter establishing device is used for associating the quantized parameters of the respiratory state with the respiratory muscle potential information to form the potential associated parameters of the respiratory state; The data acquisition device is used for acquiring the real-time ventilation data of the patient; The parameter comparison device is used for comparing the data linear trends of the real-time ventilation parameters and the potential associated parameters to form the evaluation of the respiratory state; The state prediction device is used for forming the respiratory compensation prediction according to the evaluation result.

[0013] In an embodiment of the application, the parameter establishing device comprises: The potential receiving module is used for acquiring the potential performance of the respiratory muscle under each respiratory state; The parameter forming module is used for sequentially associating the quantized parameters under each respiratory state with the potential performance to form the potential associated parameters.

[0014] In an embodiment of the application, the data real-time acquisition device comprises: The kinetic energy acquisition module is used for acquiring the sequential ventilation data of the patient in the expiratory phase, and the ventilation data comprises the expiratory flow, the expiratory flow rate, the expiratory frequency, the expiratory pressure and the pressure.

[0015] In an embodiment of the application, the parameter comparison device comprises: The reference establishing module is used for establishing the time sequence segment of the patient in the ventilation according to the potential associated parameters and the ventilation parameters in the expiratory phase; The data processing module is used for quantizing the data change trend of the potential associated parameters and the data change trend of the ventilation parameters in the expiratory phase in the time sequence segment; The trend quantization module is used for comparing and evaluating the linear convergence characteristics of the biological energy and the ventilation kinetic energy and the disease course characteristics of the respiratory state in a unit time according to the change trends.

[0016] In an embodiment of the application, the state prediction device comprises: The process prediction module is used for predicting the development process of respiratory compensation according to the duration of the linear convergence characteristics; The disease course prediction module is used for predicting the urgency of respiratory decompensation according to the duration trend of the linear convergence characteristics.

[0017] The respiratory compensation monitoring method and system of the embodiment of the present application forms a quantitative mechanism of energy conversion in the respiratory process by establishing the correlation between the respiratory muscle potential information and the ventilation flow. The linear trend of the potential information and the ventilation amount in the patient's course is used to qualitatively determine the possibility of respiratory decompensation of the patient, thereby forming an auxiliary reference factor for diagnosing metabolic disorders of the patient. The method has auxiliary utilization value in the development of sports, health rehabilitation and metabolic treatment equipment. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 Fig. 1 shows a flowchart of a respiratory compensation monitoring method according to an embodiment of the present application.

[0019] Figure 2 Fig. 2 shows an architecture diagram of a respiratory compensation monitoring system according to an embodiment of the present application. DETAILED DESCRIPTION

[0020] To make the objectives, technical solutions and advantages of the present application clearer and more apparent, the present application is further described below in combination with the drawings and specific embodiments. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0021] A respiratory compensation monitoring method according to an embodiment of the present application is shown in Fig. 1. Figure 1 In the embodiment, the method comprises the following steps. Figure 1 Step 100: Correlate the quantitative parameters of the respiratory state with the respiratory muscle potential information to form potential correlation parameters of the respiratory state.

[0022] Those skilled in the art can understand that the respiratory state has time sequence quantitative parameters accompanied by the respiratory phase, depth and rhythm. The quantitative parameters include but are not limited to the flow, pressure and speed of ventilation, and the time sequence data of the quantitative parameters can form a trend curve of a certain dimension of the quantitative respiratory state.

[0023] In an embodiment of the present application, in the coordinate space of the trend curve, the time node or the cycle time can be used as the horizontal coordinate axis. The numerical value distribution range of the time sequence data of the quantitative parameters can be used as the vertical coordinate axis. The numerical value difference distribution range of the time sequence data of the quantitative parameters can be used as the vertical coordinate axis. The change trend (for example, increasing or decreasing) between the numerical values of the time sequence data of the quantitative parameters can be used as the vertical coordinate axis. The normalized numerical value range of the physical quantity values of the time sequence data of the quantitative parameters can be used as the vertical coordinate axis.

[0024] ​The respiratory muscle potential information is associated with the existing quantitative parameters through a time coordinate axis to form potential correlation parameters corresponding to the existing respiratory states and the quantitative parameters.

[0025] The potential correlation parameters and the quantitative parameters are affected by the physical quantity difference, and the correlation between them can reflect quantitative correlation and qualitative correlation. By comparing the potential correlation parameters with other quantitative parameters, a linear trend between the parameters can be observed.

[0026] Step 200: Collect real-time ventilation data of the patient.

[0027] In view of the difficulty of sensor setting and the difficulty of interference factor elimination, the measurement of ventilation preferably converts the chest potential energy into the kinetic energy of exhaled gas. For example, by using an open oxygen mask, the sensor can be fixed by using the mask frame to form the collection of exhaled gas flow, and then the time sequence data of the quantitative parameters of the respiratory state such as respiratory frequency and respiratory depth can be obtained according to the ventilation flow.

[0028] Step 300: Form an evaluation of the respiratory state according to the linear trend comparison between the real-time ventilation parameters and the potential correlation parameters.

[0029] The real-time ventilation data quantifies the ventilation kinetic energy after resistance consumption in the respiratory muscle work function conversion process, and maps the final ventilation effect of the respiratory muscle work. The potential correlation parameters mainly form the definite qualitative indicators and the quantitative indicators that can be referred to. By comparing the linear trend between the real-time ventilation data and the potential correlation parameters in the patient's respiratory cycle, data evaluation of various respiratory states can be formed.

[0030] Step 400: Form a respiratory compensation prediction according to the evaluation result.

[0031] As can be understood by those skilled in the art, once respiratory decompensation occurs, it means that metabolic acidosis, metabolic alkalosis or blood oxygen concentration imbalance is likely to occur. Mapping the evaluation results of different respiratory states with the probability of respiratory compensation / decompensation can constitute an early state prediction with clinical reference significance.

[0032] The respiratory compensation monitoring method of the embodiment of the present application forms a quantitative mechanism of energy conversion in the respiratory process by establishing the correlation between the respiratory muscle potential information and the ventilation flow. The linear trend of the potential information and the ventilation flow in the patient's course of disease is used to qualitatively predict the possibility of respiratory decompensation of the patient, and an auxiliary reference factor for diagnosing metabolic disorders of the patient is formed. The method has auxiliary utilization value in the development and research of sports, health rehabilitation and metabolic treatment equipment.

[0033] As Figure 1 shown in the embodiment of the present application, step 100 comprises: Step 110: Obtain the potential performance of the respiratory muscle in each respiratory state.

[0034] The presence, strength and frequency of diaphragm potential directly affect the phase (inhalation / exhalation), depth and rhythm of respiration. The diaphragm potential adjusts with the switching of the respiratory state, mainly in the three dimensions of whether the potential appears, the potential amplitude and the potential frequency. The respiratory state has diversity, including but not limited to calm inhalation state, calm exhalation state, deep breathing / movement breathing state, active exhalation state (such as forced cough, holding breath) and the like. In different respiratory states, the potential performance has certainty, and has short-term discrete performance and long-term linear clustering performance in a statistical sense. Through linear processing of the potential performance signal, observation data with qualitative accuracy and quantitative effectiveness can be obtained.

[0035] Step 120: Time sequence correlation of the quantification parameters in each respiratory state with the potential performance to form potential correlation parameters.

[0036] Based on the time sequence and the existing quantification parameters, the potential correlation parameters with the quantification dimension of the existing quantification parameters are formed. The numerical dimensions of different quantification parameters are different, and the normalization of the potential performance to the numerical dimension of the vectorization parameter forms the potential correlation parameters associated with the determined quantification parameters. That is, the potential correlation parameters with the quantification dimension adapted to the determined quantification parameters can be formed.

[0037] The respiratory compensation monitoring method of the embodiment of the present application establishes the qualitative correlation of the existing quantification parameters for the respiratory state and the potential correlation parameters, forms the mapping quantification mechanism of the existing quantification parameters and the diaphragm potential rule, and provides a new dimension measurement method for the respiratory state change process.

[0038] As Figure 1 shown in the embodiment of the present application, step 200 comprises: Step 210: Obtain time sequence ventilation data of the patient in the exhalation phase, and the ventilation data includes exhalation flow, exhalation flow rate, exhalation frequency, exhalation pressure and pressure.

[0039] The ventilation data of the patient in the exhalation phase is obtained by mature sensor technology. Compared with the patient in the inhalation phase, the data acquisition sensor in the exhalation phase can be reliably fixed on the frame of the mask, such as the frame of the nose bridge position of the open mask, directly facing the ventilation channel. The ventilation data of the patient in the exhalation phase needs to implant the sensor into the ventilation channel.

[0040] The respiratory compensation monitoring method of the embodiment of the present application utilizes the frame stability of the open oxygen inhalation mask to obtain various ventilation data with quantifiable reference, forming the measurement reference of phase, depth, rhythm and the time sequence reference.

[0041] As shown in the embodiment of the present application, step 300 comprises: Figure 1 Step 310: Establishing the time sequence segment of the patient ventilation according to the potential correlation parameter and the expiratory phase ventilation parameter.

[0042] According to the patient course, the continuous time sequence segment is established, forming the time sequence interval and the data interval of the data comparison. In the embodiment of the present application, the time sequence segment can be the same timing length. It can also be the timing length set according to the patient expiratory phase and inspiratory phase. It can also be the dynamic length set according to the patient course. It can also be the longer length formed by the continuation of adjacent time sequence segments. According to the time sequence segment, the comparison reference of the potential correlation parameter and the expiratory phase ventilation data is established.

[0043] In the embodiment of the present application, the time sequence segment of the entire expiratory phase and inspiratory phase is reconstructed according to the time sequence segment of the expiratory phase, forming the comparison reference of the potential correlation parameter and other quantifiable parameters.

[0044] Step 320: Quantifying the data variation trend of the potential correlation parameter and the data variation trend of the expiratory phase ventilation parameter in the time sequence segment.

[0045] Based on the time sequence segment, the variation trend of the potential correlation parameter and different quantifiable parameters (in the embodiment, the expiratory phase ventilation data) is quantified. The linear fitting of the variation trend of each parameter in the time sequence segment is formed, including the trend direction, the trend rate, the related trend deviation point, the related trend intersection point and other quantifiable data.

[0046] Step 330: Comparing and evaluating the linear convergence feature of the biological energy and the ventilation kinetic energy and the course feature of the respiratory state per unit time according to the variation trend.

[0047] In the embodiment of the present application, the potential correlation parameter data is taken as the quantifiable data of the biological energy measurement, and the expiratory phase ventilation parameter data is taken as the quantifiable data of the ventilation kinetic energy measurement, and then the evaluation result of the linear (trend) convergence feature is formed through the linear fitting of the quantifiable data. The linear (trend) convergence feature can be evaluated according to the linear (trend) convergence feature of a single time sequence segment, or can be evaluated according to the linear (trend) convergence feature change of continuous time sequence segments. Then, the evaluation result of the compensation / decompensation course feature of the stage respiratory state is formed according to the law of conservation of energy.

[0048] In the embodiment of the present application, the comparison and evaluation rules of the variation trend are as follows: Contrast parameters Trend 1 Trend 2 Trend 3 Trend 4 Trend 5 Trend 6 Expiratory phase ventilation → ↗ High energy level High energy level ↘ → Potential correlation → ↗ → ↘ ↘ ↗ Basic respiratory status Stable Compensation aggravation Compensation stable Compensation respiratory relief Improvement De-compensation ​Where: → represents stability or maintenance, ↗ represents increase, and ↘ represents decrease.

[0049] The respiratory compensation monitoring method of this invention establishes a state quantification mechanism for the conversion of bioenergy into aerodynamic energy by analyzing the correlation of parameter data change trends. Through a non-destructive physical quantity measurement process, it identifies and quantifies detailed differences in respiratory status during the course of the disease, enabling real-time observation of the respiratory process.

[0050] like Figure 1 As shown, in one embodiment of the present invention, step 400 includes: Step 410: Predict the development process of respiratory compensation based on the duration of linear convergence characteristics.

[0051] By quantifying the synchronicity of changes in linear convergent features within a continuous time segment, the subsequent development of respiratory compensation can be quantified, providing reference information before patients experience decompensation.

[0052] Step 420: Predict the urgency of respiratory decompensation based on the persistent trend of linear convergence characteristics.

[0053] By analyzing the rate of change of the linear trend of parameters within the duration of continuous time segments, the urgency of decompensation can be further determined, providing reference information for patients before decompensation worsens.

[0054] The respiratory compensation monitoring method of this invention uses energy form mapping data that conforms to the law of energy conservation to predict the inducing factors before the occurrence of decompensation and the aggravation of decompensation, thus avoiding the untimely treatment after the formation of delayed diagnostic results.

[0055] An embodiment of the present invention provides a respiratory compensation monitoring system, comprising: The memory is used to store the program code in the process of the respiratory compensation monitoring method described in the above embodiments; The processor is used to execute the program code in the respiratory compensation monitoring method of the above embodiments during the processing. The processor can be a DSP (Digital Signal Processor), an FPGA (Field-Programmable Gate Array), an MCU (Microcontroller Unit) system board, a SoC (System on a Chip) system board, or a PLC (Programmable Logic Controller) minimum system including I / O.

[0056] An embodiment of the present invention is a respiratory compensation monitoring system, such as Figure 2 As shown, in Figure 2In some embodiments, the present application comprises: a parameter establishing device 10 for associating the quantified parameters of respiratory states with the electrical potential information of respiratory muscles to form potential-associated parameters of respiratory states; a data collecting device 20 for collecting real-time ventilation data of a patient; a parameter comparing device 30 for comparing the data linear trends of real-time ventilation parameters and potential-associated parameters to form an evaluation of respiratory states; a state predicting device 40 for forming a respiratory compensation prediction according to the evaluation results.

[0057] Figure 2 In some embodiments of the present application, the parameter establishing device 10 comprises: a potential receiving module 11 for obtaining the electrical potential performance of respiratory muscles in each respiratory state; a parameter forming module 12 for time-series associating the quantified parameters of each respiratory state with the electrical potential performance to form potential-associated parameters.

[0058] Figure 2 In some embodiments of the present application, the data real-time collecting device 20 comprises: a kinetic energy collecting module 21 for obtaining time-series ventilation data of a patient in the expiratory phase, including expiratory flow, expiratory flow rate, expiratory frequency, expiratory pressure, and pressure.

[0059] Figure 2 In some embodiments of the present application, the parameter comparing device 30 comprises: a reference establishing module 31 for establishing time-series segments of patient ventilation according to the potential-associated parameters and expiratory phase ventilation parameters; a data processing module 32 for quantifying the data variation trends of potential-associated parameters and the data variation trends of expiratory phase ventilation parameters within the time-series segments; a trend quantifying module 33 for comparing and evaluating the linear convergence characteristics of biological energy and ventilation kinetic energy and the disease course characteristics of respiratory states per unit time according to the variation trends.

[0060] Figure 2 In some embodiments of the present application, the state predicting device 40 comprises: a progress predicting module 41 for predicting the development progress of respiratory compensation according to the duration of linear convergence characteristics; a disease course predicting module 42 for predicting the urgency of respiratory decompensation according to the sustained trend of linear convergence characteristics.

[0061] The above merely describes preferred specific embodiments of the present application, but the protection scope of the present application is not limited thereto, any changes or replacements within the technical scope disclosed by the present application, which can be easily thought by those skilled in the art, should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for monitoring respiratory compensation, characterized in that, include: The quantitative parameters of respiratory state are correlated with respiratory muscle potential information to form potential correlation parameters of respiratory state; Collect real-time ventilation data from patients; An assessment of respiratory status is formed by comparing the linear trends of real-time ventilation parameters and potential correlation parameters. A respiratory compensation prediction is generated based on the assessment results.

2. The respiratory compensation monitoring method as described in claim 1, characterized in that, The potential-related parameters that form the respiratory state include: Acquire the electrical potential of the respiratory muscles under various respiratory states; The quantitative parameters under each respiratory state are correlated with the potential performance over time to form potential-correlated parameters.

3. The respiratory compensation monitoring method as described in claim 1, characterized in that, The real-time ventilation data collected from the patient includes: Acquire temporal ventilation data of the patient during the expiratory phase, including expiratory flow rate, expiratory flow rate, expiratory frequency, expiratory pressure, and expiratory pressure.

4. The respiratory compensation monitoring method as described in claim 1, characterized in that, The assessment that establishes the respiratory state includes: Establish time segments of patient ventilation based on potential correlation parameters and expiratory ventilation parameters; Quantitative analysis of the data trends of potential correlation parameters and expiratory ventilation parameters within time segments; The linear convergence of bioenergy and ventilatory kinetic energy and the pathological characteristics of respiratory status per unit time were evaluated by comparing and contrasting the changing trends.

5. The respiratory compensation monitoring method as described in claim 1, characterized in that, The prediction of respiratory compensation includes: Predict the development process of respiratory compensation based on the duration of linear convergence characteristics. The urgency of respiratory decompensation can be predicted based on the persistent trend of linear convergence characteristics.

6. A respiratory compensation monitoring system, characterized in that, include: A parameter establishment device is used to correlate quantitative parameters of respiratory state with respiratory muscle potential information to form potential-correlated parameters of respiratory state. Data acquisition device, used to collect real-time ventilation data of patients; A parameter comparison device is used to assess respiratory status by comparing the linear trends of real-time ventilation parameters and potential-related parameters. A condition prediction device is used to generate respiratory compensation predictions based on assessment results.

7. The respiratory compensation monitoring system as described in claim 6, characterized in that, The parameter establishment device includes: The potential receiving module is used to acquire the potential performance of the respiratory muscles under various respiratory states. The parameter formation module is used to correlate the quantitative parameters of each respiratory state with the potential performance in a time sequence to form potential-correlated parameters.

8. The respiratory compensation monitoring system as described in claim 6, characterized in that, The real-time data acquisition device includes: The kinetic energy acquisition module is used to acquire the patient's temporal ventilation data during the expiratory phase. The ventilation data includes expiratory flow rate, expiratory flow rate, expiratory frequency, expiratory pressure, and pressure intensity.

9. The respiratory compensation monitoring system as described in claim 6, characterized in that, The parameter comparison device includes: The baseline establishment module is used to establish a time sequence segment of patient ventilation based on potential correlation parameters and expiratory ventilation parameters. The data processing module is used to quantify the data change trends of potential correlation parameters and expiratory ventilation parameters within a time segment. The trend quantification module is used to compare and evaluate the linear convergence characteristics of bioenergy and ventilatory kinetic energy and the disease progression characteristics of respiratory status per unit time based on the trend of change.

10. The respiratory compensation monitoring system as described in claim 6, characterized in that, The state prediction device includes: The process prediction module is used to predict the development process of respiratory compensation based on the duration of linear convergence characteristics. The disease course prediction module is used to predict the urgency of respiratory decompensation based on the continuous trend of linear convergence characteristics.

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