A method and system for respiratory compensation monitoring

Abstract

The application provides a respiratory compensation monitoring method and system, and solves the technical problem of the lack of early identification ability of compensation phenomena in the existing respiratory monitoring process. The method comprises the following steps: associating a quantitative parameter of a respiratory state with respiratory muscle potential information to form a potential correlation parameter of the respiratory state; collecting real-time ventilation data of a patient; comparing the linear trend of the real-time ventilation parameter and the potential correlation parameter to form an evaluation of the respiratory state; and forming a respiratory compensation prediction according to the evaluation result. The association of the respiratory muscle potential information and the ventilation volume flow forms a quantitative mechanism of energy conversion in the respiratory process. The linear trend of the potential information and the ventilation volume of the patient during the course of the disease 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.

Description

Technical Field

[0001] This invention relates to the field of vital sign monitoring technology, specifically to a respiratory compensation monitoring method and system. Background Technology

[0002] The timing, depth, and rhythm of respiration in critically ill patients often change as the disease progresses. Changes in respiratory patterns are closely related to the body's oxygenation, ventilation, and acid-base balance. Current technologies mostly rely on peripheral blood oxygenation sampling and blood gas analysis reports to assess these indicators. However, since these assessments target post-metabolic signs, there is inevitably a feedback delay, hindering timely intervention for metabolic acid-base imbalances in patients.

[0003] The patient's respiratory compensation / decompensation phenomenon can be understood from the perspective of energy conservation. During inspiration, respiratory muscle contraction (mainly relying on the diaphragm) consumes bioenergy, elevating the ribs and lowering the diaphragm, causing the thoracic cavity to expand and create negative pressure (-5 to -8). At this point, bioenergy is converted into elastic potential energy of the chest wall connective tissue. During expiration, the respiratory muscles relax, the thoracic cavity elastically recoils, releasing potential energy and driving gas to be exhaled at an average flow rate of 0.5–1.0 L / s, converting potential energy into gas kinetic energy. The expansion of the thoracic cavity and the roughness of the airway surface naturally result in energy loss, generally following the principle of bioenergy > thoracic cavity potential energy > gas kinetic energy. In the energy conversion process, the healthier the organism, the less energy is lost. Under pathological conditions, energy loss increases, and the more severe the condition, the greater the loss. Under the pathophysiology of disease, the respiratory muscles increase their work to offset the energy loss during energy conversion and meet the body's needs. When the extra work of the respiratory muscles can meet the body's needs, it is the respiratory compensation stage. When the extra work of the respiratory muscles cannot meet the body's needs, it is the respiratory decompensation stage. For example, when the respiratory muscles are fatigued (such as in COPD), the absolute or relative value of bioenergy output decreases, failing to meet the kinetic energy required to maintain ventilation → effective ventilation decreases → respiratory failure. This can be understood as a correlation between the activity level of respiratory muscles and respiratory compensation / decompensation. In the energy conversion chain described above, airway resistance is a key variable determining energy demand. According to the fluid dynamics formula, the kinetic energy of a gas is E = 1 / 2 * m * v² (where m is mass and v is velocity). To maintain a constant ventilation rate (volume / time), the gas velocity must double when the cross-sectional area of ​​the airflow channel is halved. At this point, the kinetic energy required to maintain ventilation becomes four times the original (E ∝ v²), meaning the work required by the respiratory muscles also increases dramatically.

[0004] Existing evidence from the validation of neurally regulated ventilation (NAVA) and the electrophysiological evidence of respiratory compensation mechanisms indicates that the diaphragmatic potential of the respiratory muscles (mainly the diaphragm) is directly and synchronously causally related to the respiratory state. The presence, strength, and frequency of the diaphragmatic potential directly determine the phase (inhalation / exhalation), depth, and rhythm of respiration. The two are completely synchronized and linked, and their causal relationship has high universality under both physiological and pathological conditions.

[0005] By utilizing the qualitative correlation between respiratory muscle potential signals and respiratory work and respiratory airflow rate, a new monitoring process for respiratory compensation phenomena can be established. Summary of the Invention

[0006] In view of the above problems, embodiments of the present invention provide a respiratory compensation monitoring method and system to solve the technical problem that existing respiratory monitoring processes lack the ability to identify compensation phenomena in the early stages.

[0007] The respiratory compensation monitoring method of this invention includes: 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.

[0008] In one embodiment of the present invention, the potential correlation 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.

[0009] In one embodiment of the present invention, the collection of the patient's real-time ventilation data 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.

[0010] In one embodiment of the present invention, the assessment of the formation of 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.

[0011] In one embodiment of the present invention, the formation of respiratory compensation prediction 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.

[0012] The respiratory compensation monitoring system of this invention includes: 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.

[0013] In one embodiment of the present invention, 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.

[0014] In one embodiment of the present invention, 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.

[0015] In one embodiment of the present invention, 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.

[0016] In one embodiment of the present invention, 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.

[0017] The respiratory compensation monitoring method and system of this invention establishes a quantitative mechanism for energy conversion during respiration by correlating respiratory muscle potential information with ventilation volume and flow rate. By utilizing the linear trend of potential information and ventilation volume during the patient's disease course, the likelihood of respiratory decompensation in the patient is qualitatively assessed, forming an auxiliary reference factor for diagnosing metabolic disorders. This method has auxiliary application value in the research and development of sports, health rehabilitation, and metabolic therapy equipment. Attached Figure Description

[0018] Figure 1 The diagram shown is a flowchart of a respiratory compensation monitoring method according to an embodiment of the present invention.

[0019] Figure 2 The diagram shown is a schematic diagram of the architecture of a respiratory compensation monitoring system according to an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer and more understandable, the invention will be further described below in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0021] An embodiment of the present invention provides a respiratory compensation monitoring method as follows: Figure 1 As shown. In Figure 1 In this embodiment, the following are included: Step 100: Correlate the quantitative parameters of respiratory state with respiratory muscle potential information to form potential-correlated parameters of respiratory state.

[0022] Those skilled in the art will understand that respiratory status has temporal quantitative parameters that accompany the phase, depth, and rhythm of breathing. These quantitative parameters include, but are not limited to, ventilation flow, pressure, and velocity, and the temporal data of these quantitative parameters can form a trend curve for quantifying a certain dimension of the respiratory status.

[0023] In one embodiment of the present invention, in the coordinate space of the trend curve, the horizontal axis can be a time node or a period. The vertical axis can be the range of numerical distribution of the time-series data of the quantified parameters. The vertical axis can be the range of numerical differences in the time-series data of the quantified parameters. The vertical axis can be the trend of change between the numerical values ​​of the time-series data of each quantified parameter (e.g., increasing or decreasing). The vertical axis can be the normalized numerical range of the physical quantity values ​​of the time-series data of each quantified parameter.

[0024] By establishing a data correlation between respiratory muscle potential information and existing quantitative parameters via a time axis, potential-correlated parameters corresponding to each quantitative parameter and the current respiratory state are formed. That is, while quantifying the respiratory state through quantitative parameters such as ventilation flow rate, ventilation pressure, and ventilation velocity, the respiratory muscle work state is quantified through potential-correlated parameters, and the respiratory muscle work state is mapped to the bioenergy consumption state.

[0025] The correlation between potential correlation parameters and quantification parameters is affected by the differences in physical quantities. The correlation between them partially reflects quantitative relationships and partially reflects qualitative relationships. By comparing potential correlation parameters with other quantification parameters, a linear trend between the parameters can be observed.

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

[0027] Given the difficulty in sensor setup and interference elimination, ventilation measurement is preferably performed by converting thoracic potential energy into exhaled gas kinetic energy. For example, by using an open-face oxygen mask, the sensor can be fixed in the mask frame to collect exhaled airflow, and then time-series data of quantitative parameters of respiratory status such as respiratory rate and respiratory depth can be obtained based on the ventilation flow rate.

[0028] Step 300: Assess respiratory status by comparing the linear trends of real-time ventilation parameters and potential correlation parameters.

[0029] Real-time ventilation data quantifies the ventilatory kinetic energy consumed by resistance during the functional conversion of respiratory muscles, mapping the final ventilatory effect of the respiratory muscles' work. Meanwhile, potential correlation parameters primarily form clear qualitative and quantitative indicators of respiratory muscle work. By comparing the linear trends of real-time ventilation data and potential correlation parameters within a patient's respiratory cycle, a data assessment of various respiratory states can be formed.

[0030] Step 400: Develop a respiratory compensation prediction based on the assessment results.

[0031] Those skilled in the art will understand that respiratory decompensation implies the possibility of metabolic acidosis, metabolic alkalosis, or blood oxygenation imbalance. Mapping the assessment results of different respiratory states to the probability of respiratory compensation / decompensation can provide a clinically meaningful early prediction of the condition.

[0032] The respiratory compensation monitoring method of this invention establishes a quantitative mechanism for energy conversion during respiration by correlating respiratory muscle potential information with ventilation volume and flow rate. By utilizing the linear trend of potential information and ventilation volume during the patient's disease course, the likelihood of respiratory decompensation in the patient is qualitatively assessed, forming an auxiliary reference factor for diagnosing metabolic disorders. This method has auxiliary application value in the research and development of sports, health rehabilitation, and metabolic therapy equipment.

[0033] like Figure 1 As shown, in one embodiment of the present invention, step 100 includes: Step 110: Obtain the electrical potential of the respiratory muscles under various respiratory states.

[0034] The presence, strength, and frequency of diaphragmatic potentials directly affect the phase (inspiration / expiration), depth, and rhythm of respiration. Diaphragmatic potentials adjust with changes in respiratory state, primarily manifested in three dimensions: the presence of potentials, their amplitude, and their frequency. Respiratory states are diverse, including but not limited to quiet inspiration, quiet expiration, deep breathing / exercise breathing, and active expiration (such as forceful coughing or breath-holding). Under different respiratory states, the potential manifestations are deterministic, exhibiting short-term discreteness and long-term linear clustering in a statistically significant manner. Linear processing of the potential manifestation signals can yield qualitatively accurate and quantitatively effective observational data.

[0035] Step 120: Correlate the quantitative parameters of each respiratory state with the potential performance over time to form potential correlation parameters.

[0036] By associating temporal relationships with existing quantization parameters, potential-related parameters are formed that correspond to the quantization dimensions of the existing quantization parameters. Different quantization parameters have different numerical dimensions; by normalizing the numerical dimensions of the vectorized parameters based on potential representation, potential-related parameters are formed that are associated with the determined quantization parameters. In other words, a potential-related parameter with a matching quantization dimension can be formed for each determined quantization parameter.

[0037] The respiratory compensation monitoring method of this invention establishes a qualitative correlation between the existing quantitative parameters and potential correlation parameters of the respiratory state, forming a mapping and quantification mechanism between the existing quantitative parameters and the respiratory muscle potential law, and providing a new dimension of measurement method for the respiratory state change process.

[0038] like Figure 1 As shown, in one embodiment of the present invention, step 200 includes: Step 210: Obtain the patient's expiratory phase time-sequential ventilation data, including expiratory flow rate, expiratory flow rate, expiratory frequency, expiratory pressure, and pressure.

[0039] The patient's expiratory ventilation data is obtained using mature sensor technology. Compared to the patient's inspiratory phase, the sensor for acquiring data during the expiratory phase can be reliably mounted on the mask frame, such as the nasal bridge frame of an open-face mask, directly facing the ventilation channel. However, the patient's expiratory ventilation data requires the sensor to be implanted into the ventilation channel.

[0040] The respiratory compensation monitoring method of this invention utilizes the frame stability of an open-type oxygen mask to obtain various ventilation data with quantifiable benchmarks, forming measurement benchmarks and time-series benchmarks for phase, depth, and rhythm.

[0041] like Figure 1 As shown, in one embodiment of the present invention, step 300 includes: Step 310: Establish a time sequence segment during patient ventilation based on the potential correlation parameters and expiratory ventilation parameters.

[0042] Based on the patient's disease progression, continuous time-series segments are established to form time-series intervals and data intervals for data comparison. In one embodiment of the invention, the time-series segments can be of the same duration, or they can be time-series segments set according to the patient's expiratory and inspiratory phases, or they can be dynamic durations set according to the patient's disease progression, or they can be longer durations composed of consecutive adjacent time-series segments. A benchmark for comparing potential correlation parameters with expiratory ventilation data is established based on the time-series segments.

[0043] In one embodiment of the present invention, the entire expiratory and inspiratory time segments are reconstructed based on the time segments of the expiratory phase to form a benchmark for comparing the potential correlation parameter with other quantification parameters.

[0044] Step 320: Quantify the data trends of potential correlation parameters and expiratory ventilation parameters within the time segment.

[0045] Based on the time-series segment quantification of potential correlation parameters and the changing trends of different quantification parameters (in this embodiment, expiratory ventilation data), a linear fit is formed for the changing trends of each parameter within the time-series segment, including quantitative data such as trend direction, trend rate, relevant trend divergence points, and relevant trend intersection points.

[0046] Step 330: Based on the changing trends, compare and evaluate the linear convergence characteristics of bioenergy and ventilatory kinetic energy and the disease progression characteristics of respiratory status per unit time.

[0047] In one embodiment of the present invention, potential correlation parameter data is used as quantitative data for measuring bioenergy, and expiratory ventilation parameter data is used as quantitative data for measuring ventilatory kinetic energy. Then, an evaluation result of linear (trend) convergence characteristics is formed through linear fitting of the quantitative data. The linear (trend) convergence characteristics can be evaluated based on the linear (trend) convergence characteristics of a single time segment, or based on changes in the linear (trend) convergence characteristics of continuous time segments. Finally, based on the law of energy conservation, an evaluation result of the compensatory / decompensated disease course characteristics of the staged respiratory state is formed.

[0048] In one embodiment of the present invention, the rules for comparing and evaluating change trends are as follows: Comparison parameters Trend Performance 1 Trend Performance 2 Trend Performance 3 Trend Performance 4 Trend Performance 5 Trend Performance 6 Exhalation → ↗ High energy level → High energy level → ↘ → Potential correlation → ↗ → ↘ ↘ ↗ Basic breathing state smooth Compensation aggravated Stable compensation Compensatory breathing relief Improvement Uncompensated 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 this embodiment, the following are included: The parameter establishment device 10 is used to associate the quantitative parameters of the respiratory state with the respiratory muscle potential information to form the potential-related parameters of the respiratory state. Data acquisition device 20 is used to collect real-time ventilation data of patients; The parameter comparison device 30 is used to form an assessment of respiratory status based on the linear trend comparison of real-time ventilation parameters and potential-related parameters. State prediction device 40 is used to form a respiratory compensation prediction based on the assessment results.

[0057] Figure 2 As shown, in one embodiment of the present invention, the parameter establishment device 10 includes: The potential receiving module 11 is used to acquire the potential performance of the respiratory muscles under various respiratory states. The parameter formation module 12 is used to correlate the quantitative parameters of each respiratory state with the potential performance in a time sequence to form potential-related parameters.

[0058] Figure 2 As shown, in one embodiment of the present invention, the real-time data acquisition device 20 includes: The kinetic energy acquisition module 21 is used to acquire the patient's expiratory phase temporal ventilation data, which includes expiratory flow rate, expiratory flow rate, expiratory frequency, expiratory pressure and pressure.

[0059] Figure 2 As shown, in one embodiment of the present invention, the parameter comparison device 30 includes: The baseline establishment module 31 is used to establish a time sequence segment during patient ventilation based on potential correlation parameters and expiratory ventilation parameters. Data processing module 32 is used to quantify the data change trends of potential correlation parameters and expiratory ventilation parameters within a time segment; The trend quantification module 33 is used to compare and evaluate the linear convergence characteristics of bioenergy and ventilatory kinetic energy and the disease course characteristics of respiratory status per unit time based on the trend of change.

[0060] Figure 2 As shown, in one embodiment of the present invention, the state prediction device 40 includes: The process prediction module 41 is used to predict the development process of respiratory compensation based on the duration of linear convergence characteristics. The disease course prediction module 42 is used to predict the urgency of respiratory decompensation based on the continuous trend of linear convergence characteristics.

[0061] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the 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; 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-related parameters; Collect the patient's real-time ventilation parameters; the collected real-time ventilation parameters include: Acquire the patient's expiratory phase time-sequential ventilation parameters, including expiratory flow rate, expiratory flow rate, expiratory frequency, expiratory pressure, and pressure. The assessment results of compensatory and decompensated respiratory states are formed by comparing the data trends of real-time ventilation parameters with the data trends of potential correlation parameters; the assessment results of compensatory and decompensated respiratory states formed by comparing the data trends of real-time ventilation parameters with the data trends of potential correlation parameters include: Establish time-series segments of patient ventilation based on potential correlation parameters and real-time ventilation parameters; Quantify the data trends of potential correlation parameters and real-time ventilation parameters within the time segment; The assessment results of compensatory and decompensatory respiratory states are formed based on the data trends of potential correlation parameters and real-time ventilation parameters. The assessment rules include: When the data trends of the potential correlation parameters are stable and the data trends of the real-time ventilation parameters are stable, the respiratory status is assessed as stable. When both the potential correlation parameter and the real-time ventilation parameter show an increasing trend, the assessment is that respiratory compensation has worsened. When the data trends of potential correlation parameters and real-time ventilation parameters are both decreasing, it is assessed that respiratory compensation has improved. When the trend of the potential correlation parameter is increasing and the trend of the real-time ventilation parameter is stable, it is assessed as respiratory decompensation. A respiratory compensation prediction is generated based on the assessment results; the generation of the respiratory compensation prediction based on the assessment results includes: The development process of respiratory compensation can be predicted based on the synchronicity of the changes in the data trends of potential correlation parameters and real-time ventilation parameters within the duration of continuous time segments. The urgency of respiratory decompensation is predicted based on the rate of change of the data trends of potential correlation parameters and real-time ventilation parameters over a duration consisting of consecutive time segments.

2. 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. 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-related parameters; Data acquisition device, used to collect real-time ventilation parameters of the patient; the real-time ventilation parameters of the patient include: Acquire the patient's expiratory phase time-sequential ventilation parameters, including expiratory flow rate, expiratory flow rate, expiratory frequency, expiratory pressure, and pressure. A parameter comparison device is used to form an assessment result of compensatory and decompensated respiratory status by comparing the data change trends of real-time ventilation parameters with the data change trends of potential-related parameters; the assessment result of compensatory and decompensated respiratory status formed by comparing the data change trends of real-time ventilation parameters with the data change trends of potential-related parameters includes: Establish time-series segments of patient ventilation based on potential correlation parameters and real-time ventilation parameters; Quantify the data trends of potential correlation parameters and real-time ventilation parameters within the time segment; The assessment results of compensatory and decompensatory respiratory states are formed based on the data trends of potential correlation parameters and real-time ventilation parameters. The assessment rules include: When the data trends of the potential correlation parameters are stable and the data trends of the real-time ventilation parameters are stable, the respiratory status is assessed as stable. When both the potential correlation parameter and the real-time ventilation parameter show an increasing trend, the assessment is that respiratory compensation has worsened. When the data trends of potential correlation parameters and real-time ventilation parameters are both decreasing, it is assessed that respiratory compensation has improved. When the trend of the potential correlation parameter is increasing and the trend of the real-time ventilation parameter is stable, it is assessed as respiratory decompensation. A state prediction device is used to generate a respiratory compensation prediction based on the assessment results; the generation of the respiratory compensation prediction based on the assessment results includes: The development process of respiratory compensation can be predicted based on the synchronicity of the changes in the data trends of potential correlation parameters and real-time ventilation parameters within the duration of continuous time segments. The urgency of respiratory decompensation is predicted based on the rate of change of the data trends of potential correlation parameters and real-time ventilation parameters over a duration consisting of consecutive time segments.

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