A closed-loop chest compression control method and system based on thoracic impedance cardiography

Abstract

This invention discloses a closed-loop chest compression control method and system based on impedance cardiography. The system includes an impedance cardiography module, a decision module, and a compression module. The impedance cardiography module acquires the impedance cardiography signal and calculates the end-tidal carbon dioxide (UTC) value, and calculates the difference between the UTC value and an initial UTC threshold, or the difference between the impedance cardiography signal and an initial impedance cardiography signal threshold. The decision module receives the acquired difference between the UTC value and the UTC threshold, or the difference between the impedance cardiography signal and the impedance cardiography signal threshold, and generates compression depth and frequency parameters according to control rules. The compression module executes compressions according to the compression parameters generated by the decision model. A control method is also disclosed. This invention, using the above method, can autonomously optimize compressions in real time using acquired physiological quality parameters, achieving effective compressions and greatly improving the quality of cardiopulmonary resuscitation (CPR).

Description

Technical Field

[0001] This invention relates to the field of cardiopulmonary resuscitation technology, and in particular to a closed-loop chest compression control method and system based on impedance cardiography. Background Technology

[0002] Cardiac arrest refers to the cessation of effective heartbeat, cessation of blood circulation, and the accompanying critical symptoms of respiration and loss of consciousness. After cardiac arrest, the blood and oxygen supply to all organs and tissues is interrupted, and if treatment is not timely, it can easily lead to irreversible necrosis of the body, seriously threatening human life and health.

[0003] Cardiopulmonary resuscitation (CPR) is the most direct and effective method for saving lives in cases of cardiac arrest. CPR restores the delivery of blood and oxygen throughout the body and corrects cardiac rhythm through chest compressions, ventilation, and defibrillation. Chest compressions are the core of CPR, an effective method for providing oxygenated blood to organs during cardiac arrest, and play a crucial role in the success of CPR. However, manual CPR relies heavily on the user's experience and skill. Poor compression quality is often caused by issues with compression depth, frequency, and placement, and the physical exertion from compressions further affects the quality. Currently, mechanical chest compression devices are widely used in CPR, solving the problems of compression depth and physical exertion.

[0004] Current chest compression devices only allow operators to set compression parameters based on guidelines-recommended depth and frequency, and perform compressions within these fixed parameters, without considering individual differences. Since the primary purpose of chest compressions is to provide blood perfusion to vital organs such as the heart and brain, evaluating the effectiveness of blood perfusion is a core indicator of compression quality; therefore, feedback of hemodynamic information during chest compressions is essential.

[0005] It has been proven that end-tidal carbon dioxide (ETC) is strongly correlated with cardiac output, and this can be monitored by monitoring ETC and blood perfusion during chest compressions. Routine monitoring of ETC uses infrared sensors; however, this method is highly susceptible to interference from airway condensation and mechanical ventilation. Furthermore, in out-of-hospital settings, it is difficult to establish advanced airway support in a timely manner, making effective monitoring of ETC impossible.

[0006] Impedance cardiography (ETC) is a non-invasive hemodynamic monitoring method used in clinical settings such as intensive care units and operating rooms to monitor hemodynamic information. Routine ETC is primarily used for patients not experiencing cardiac arrest, providing non-invasive assessment of hemodynamic information such as cardiac output and cardiac indexes, and holds significant potential for evaluating cardiac function. In chest compressions, ETC can be used to evaluate the quality of compressions.

[0007] Therefore, there is an urgent need to design a closed-loop chest compression control method based on impedance cardiography, which monitors end-tidal carbon dioxide based on impedance cardiography. Summary of the Invention

[0008] The purpose of this invention is to provide a closed-loop chest compression control method and system based on impedance cardiography, which can make autonomous decisions to optimize compressions in real time using acquired physiological quality parameters, thereby achieving effective compressions and greatly improving the quality of cardiopulmonary resuscitation.

[0009] To achieve the above objectives, the present invention provides a closed-loop chest compression control method based on impedance cardiography, comprising the following steps:

[0010] S1. Real-time acquisition of cardiac impedance signals during chest compressions, and estimation of end-tidal carbon dioxide through cardiac impedance signals;

[0011] S2, preset the initial end-tidal carbon dioxide threshold or cardiac impedance signal threshold;

[0012] S3. Calculate the difference between the end-tidal carbon dioxide obtained in step S1 and the initial end-tidal carbon dioxide threshold, or the difference between the cardiac impedance signal and the initial cardiac impedance signal threshold.

[0013] S4. Construct a decision model, take a certain difference obtained in step S3 as the input of the decision model, generate a new pressing depth and frequency according to the control rules, and press according to the new pressing depth and frequency.

[0014] S5. Reacquire the cardiac impedance signal and calculate the difference. When the end-tidal carbon dioxide or cardiac impedance signal reaches the initial threshold, increase the threshold of the end-tidal carbon dioxide or cardiac impedance signal.

[0015] S6. Use the difference calculation method and decision model to generate new compression depth and frequency parameters and execute compression according to the parameters.

[0016] Preferably, the step of estimating end-tidal carbon dioxide using cardiac impedance signals includes:

[0017] S11. Construct a population test set and obtain the end-tidal carbon dioxide and cardiac impedance signals of the population in the population test set during chest compressions.

[0018] S12. Analyze the correlation between end-tidal carbon dioxide and cardiac impedance signals, construct a correlation relationship expression, and label the correlation relationship expression for each person through personalized parameters to form a library of labeled correlation relationship expressions.

[0019] S13. Based on the real-time acquired cardiac impedance signal, obtain the corresponding correlation expression through the subject's personalized parameters, and estimate the end-tidal carbon dioxide based on the correlation expression.

[0020] Preferably, the cardiac impedance signal includes two forms: one is a cardiac impedance signal that includes a baseline impedance signal and an impedance change, and the other is a cardiac impedance signal that only includes an impedance change.

[0021] Preferably, the personalized parameters include height and weight.

[0022] Preferably, the decision model in step S4 is constructed using a fuzzy control algorithm or a model-free adaptive control algorithm.

[0023] Preferably, the number of times the end-tidal carbon dioxide threshold or cardiac impedance signal threshold is increased in step S5 is no less than once.

[0024] A closed-loop chest compression control system based on impedance cardiography includes an impedance cardiography module, a decision module, and a compression module. The impedance cardiography module is electrically connected to the decision module, and the compression module is electrically connected to the decision module.

[0025] Preferably, the cardiac impedance module is used to acquire cardiac impedance signals and calculate end-tidal carbon dioxide, and calculate the difference between end-tidal carbon dioxide and an initial end-tidal carbon dioxide threshold or the difference between the cardiac impedance signal and an initial cardiac impedance signal threshold.

[0026] The decision module is used to receive the difference between the acquired end-tidal carbon dioxide and the end-tidal carbon dioxide threshold or the difference between the cardiac impedance signal and the cardiac impedance signal threshold, and generate compression depth and frequency parameters according to the control rules.

[0027] The compression module performs compressions based on the compression parameters generated by the decision model and displays cardiac impedance signals, end-tidal carbon dioxide, compression depth, and compression frequency.

[0028] Therefore, the present invention employs the above-described closed-loop chest compression control method and system based on impedance cardiography, which has the following beneficial effects:

[0029] (1) Using impedance cardiography to obtain end-tidal carbon dioxide avoids the shortcomings of existing chest compressions, such as the lack of non-invasive and convenient hemodynamic information feedback and the use of infrared sensors to monitor end-tidal carbon dioxide, thus improving the quality and effectiveness of compression.

[0030] (2) By obtaining physiological information on blood perfusion during chest compressions, the quality and effect of chest compressions can be known. Closed-loop chest compressions can be constructed by using end-tidal carbon dioxide or cardiac impedance signals obtained from impedance cardiograms to optimize compression quality and effect and improve cardiopulmonary resuscitation success rate.

[0031] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0032] Figure 1 This is a flowchart of a method according to an embodiment of the present invention;

[0033] Figure 2 This is a triangular membership function diagram of the deviation e in an embodiment of the present invention;

[0034] Figure 3 This is a triangular membership function diagram of the control quantity u in an embodiment of the present invention;

[0035] Figure 4 This is a schematic diagram illustrating the control method using a model-free adaptive control algorithm in an embodiment of the present invention.

[0036] Figure 5 This is a system schematic diagram according to an embodiment of the present invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0038] Example 1

[0039] This invention discloses a closed-loop chest compression control method based on impedance cardiography, which controls chest compressions using end-expiratory carbon dioxide or cardiac impedance signals. The following are the steps for control using end-expiratory carbon dioxide, please refer to... Figure 1-4 Specifically, it includes:

[0040] S1. Construct a population test set and obtain end-tidal carbon dioxide and cardiac impedance signals from the population in the population test set during chest compressions. The cardiac impedance signal includes two types: impedance signal (ΔZ) and impedance differential signal (dz / dt), i.e., impedance diagram and impedance differential diagram.

[0041] In this embodiment, the cardiac impedance signal includes two application forms: one is a cardiac impedance signal that includes both baseline impedance and impedance change, and the other is a cardiac impedance signal that only includes impedance change. Regardless of which type of cardiac impedance signal is used, the correlation between the cardiac impedance signal and end-tidal carbon dioxide and the correlation expression can be analyzed, ultimately resulting in a library of correlation expression expressions that can be applied.

[0042] It should be noted that in order to make the correlation expression applicable to different individuals, a test group (which can be called the test set) needs to be selected. The constructed test set should not be too small and should be able to cover the range of individual differences well. Taking height and weight as two important individualized parameters, the height h range is 140cm-190cm and the weight w range is 40kg-90kg, so as to cover most adults. The end-tidal carbon dioxide and cardiac impedance signals of each subject in the test set are collected during chest compressions.

[0043] S2. Analyze the correlation between end-tidal carbon dioxide and cardiac impedance signals, construct a correlation expression, and label the correlation expression for each person using personalized parameters (height and weight), and establish a correlation expression library indexed by height and weight.

[0044] If the test set is not completely collected, continue collecting the remaining end-tidal carbon dioxide and cardiac impedance signals, completing the operation as described above. After the test set is collected, an initial correlation expression library is obtained. Check for any missing index labels. Generally, missing values ​​will exist when the test set is too small. Use interpolation or other methods to fill in the missing values ​​to obtain a complete correlation expression library.

[0045] S3. Real-time acquisition of cardiac impedance signals during chest compressions; for the chest compression subject, measurement of height and weight data; obtaining the corresponding correlation expression using these two data as index labels; and estimating end-tidal carbon dioxide based on the correlation expression.

[0046] S4, preset the initial end-tidal carbon dioxide threshold.

[0047] S5. Calculate the difference between the end-tidal carbon dioxide obtained in step S3 and the initial end-tidal carbon dioxide threshold.

[0048] S6. Construct a decision model, using the difference obtained in step S5 as the input to the decision model, generate new pressing depth and frequency according to the control rules, and press according to the new pressing depth and frequency.

[0049] It should be noted that the pressing parameters should be controlled within a certain range, such as a pressing depth of 5-6cm and a pressing frequency of 100-120 times / minute.

[0050] S7. Acquire a new impedance signal and calculate the difference. Determine if the difference is zero or small enough. If the difference is zero or small enough, it is determined that the threshold has been reached. After the end-expiratory carbon dioxide reaches the initial threshold, increase the end-expiratory carbon dioxide or cardiac impedance signal threshold. Otherwise, if the threshold has not been reached, input the difference into the decision model to obtain new compression parameters and perform compression.

[0051] S8. If the end-tidal carbon dioxide threshold is updated, perform the same operation as described above, using the difference to generate new compression depth and frequency parameters using a decision model, and perform compressions according to the parameters. If the threshold is reached, maintain compressions with the current compression parameters; otherwise, repeat the above steps.

[0052] The above describes only one hypothetical scenario; other situations may arise during actual implementation. One such scenario is that the end-tidal carbon dioxide (ETC) calculated using cardiac impedance signals is less than the initial ETC threshold. The decision model will eventually bring the calculated ETC to the initial threshold, and also eventually bring the calculated ETC to the updated threshold. When the calculated ETC during the initial compression phase exceeds the initial threshold, it should be updated to a larger, more appropriate threshold promptly. The threshold update is not limited to once and can be performed multiple times as needed, but should not be excessive. There may also be situations where the threshold cannot be reached for an extended period, such as if it is not reached after more than 5 minutes. In this case, first, confirm whether the end-tidal carbon dioxide calculated by the cardiac impedance signal is at or above the end-tidal carbon dioxide range (10 mmHg-20 mmHg) for high-quality chest compressions. If it is at or above this range, the decision model can be terminated and compressions can be performed using the current compression parameters. If it is below this range, it indicates that there are some problems or limitations with the chest compression recipient or the closed-loop chest compression system. In this case, the decision model should also be terminated and compressions should be performed using the compression parameters recommended by the American Heart Association.

[0053] The decision model is constructed using fuzzy control algorithms or model-free adaptive control algorithms.

[0054] The control situation using the fuzzy control algorithm is as follows.

[0055] First, the difference *e* between end-tidal carbon dioxide and the preset initial end-tidal carbon dioxide threshold is obtained. The deviation *e* is then divided into four fuzzy sets: negative large (NB), negative medium (NM), negative small (NS), and zero (O); the fuzzy subsets are -6, -4, -2, and 0. The triangular membership function representation is shown in the appendix. Figure 2 The control quantity u represents the pressing depth, which is divided into four fuzzy sets: zero (O), small (PS), medium (PM), and large (PB); the fuzzy subsets are 0, 2, 4, and 6. The triangular membership function is used for representation (see appendix). Figure 3 .

[0056] The fuzzy rules are designed as follows:

[0057] If e is negative (NBe), then u is positive (PBu);

[0058] If e is negative (NMe), then u is positive (PMu);

[0059] If e is negative (NSe), then u is positive (PSu);

[0060] If e is zero (Oe), then u is zero (Ou).

[0061] Each fuzzy rule can give a fuzzy relation Ri (i = 1, 2, 3, 4), and the total number of fuzzy relations is:

[0062] R = R1∪R2∪R3∪R4(1)

[0063] Where R1=NBe∩PBu, R2=NMe∩PMu, R3=NSe∩PSu, R4=Oe∩Ou; ∪ represents the union, which takes the larger value for two values; ∩ represents the intersection, which takes the smaller value for two values.

[0064] The output of the fuzzy controller is a synthesis of the deviation e and the fuzzy relation, as shown in the formula: in, This is a fuzzy synthesis operation.

[0065] Finally, the center of gravity method is used for deblurring to obtain the pressing depth at the next moment. The formula for the center of gravity method is as follows:

[0066]

[0067] After the above fuzzification, fuzzy decision-making and defuzzification, the difference e between the end-expiratory carbon dioxide and the preset initial end-expiratory carbon dioxide threshold is used as the input of fuzzy control, and the corresponding compression depth will be output, thus realizing closed-loop compression.

[0068] It's important to note that the fuzzy set established in the fuzzy control algorithm is not a symmetric structure, and this is further reflected in the design of the fuzzy control rules. The reason for this is that when the end-expiratory carbon dioxide level is below the preset initial end-expiratory carbon dioxide threshold, the difference *e* will inevitably be less than zero, in which case the compression depth needs to be increased. If the difference *e* is greater than or equal to zero, it indicates that the end-expiratory carbon dioxide level has reached the preset initial end-expiratory carbon dioxide threshold, in which case the compression depth does not need to be decreased. Alternatively, the change in compression depth can also be used as an output in the fuzzy control algorithm, and the establishment process is similar to the one described above.

[0069] The control situation using a model-free adaptive control algorithm is as follows:

[0070] Model-free adaptive control (MFAC) is a control design method for nonlinear systems. The idea is to establish an equivalent dynamic linear data model of the nonlinear system at each operating point, estimate the pseudo-partial derivatives (PPD) or pseudo-gradients (PG) of the system online using the I / O data of the controlled system, and then design a weighted one-step forward controller to achieve data-driven control.

[0071] A single-input single-output (SISO) discrete-time system can be represented as:

[0072] y(k+1)=f(y(k),···,y(kn y ),u(k),···,u(kn u ))(3)

[0073] Where y(k)∈R and u(k)∈R represent the system's output and input at time k, respectively, and n y n u They are two unknown positive integers; It is an unknown nonlinear function.

[0074] Assume 1 that, except at finite time points, f(···) with respect to the (n)th time point y The partial derivatives of the +2) variables are continuous.

[0075] Assume that, except at finite time points, the system satisfies the generalized Lipschitz, i.e., for any k1≠k2, k1,k2≥0 and u(k1)≠u(k2), we have

[0076] y(k1+1)-y(k2+1)|≤bu(k1)-u(k2)| (4)

[0077] Where y(k) i +1)=f(y(k i ),···,y(k i -n y ),u(k i ),···,u(k i -n u ), i = 1, 2; b > 0 is a constant.

[0078] From a practical perspective, the above assumptions about the controlled object are reasonable and acceptable. Assumption 1 is a typical constraint on general nonlinear systems in control system design, implying that the system operation is smooth and stable; Assumption 2 is a restriction on the upper bound of the system's output rate of change, meaning that from an energy perspective, a bounded change in input energy should produce a bounded change in output energy within the system.

[0079] Theorem 1: When a nonlinear system satisfies Assumptions 1 and 2, and |Δu(k)|≠0 holds for all times k, there must exist a time-varying parameter φ called PPD. c (k)∈R, such that the nonlinear system is transformed into the following compact form dynamic linearization (CFDL) data model:

[0080] Δy(k+1)=φ c (k)Δu(k) (5)

[0081] Among them, Δy(k+1)=y(k+1)-y(k), Δu(k+1)=u(k+1)-u(k), φ c (k)∈R is the pseudo-partial derivative of the nonlinear system, and φ c (k) is bounded for any time k.

[0082] Use the following control input criterion function:

[0083] J(u(k))=|y * (k+1)-y(k+1)| 2 +λ|u(k)-u(k-1)| 2 (6)

[0084] Where λ > 0 is a weighting factor used to limit the variation of the control input, y * (k+1) represents the desired output signal.

[0085] Substituting equation (5) into equation (6), differentiating u(k) with respect to zero, we obtain the following control algorithm.

[0086]

[0087] Where ρ∈(0,1] is the step size factor, its inclusion makes the control algorithm more general. To implement the control algorithm, it is necessary to know PPDφ. c (k), since the system's mathematical model is unknown, it is necessary to estimate φ using I / O data. c (k). Use the following PPD estimation criterion function:

[0088]

[0089] Where μ > 0 is the weighting factor, and for equation (8) with respect to φ c (k) To find the extreme value, the algorithm for estimating PPD is as follows:

[0090]

[0091] Where η∈(0,1] is the step size factor, the purpose of which is to make the algorithm more flexible and general. For PPDφ c The estimated value of (k).

[0092] if Or |Δu(k-1)|≤ε or So

[0093]

[0094] The closed-loop chest compression system satisfies two assumptions of the model-free adaptive control algorithm (CFDL) data model. First, the closed-loop compression system is a continuously operating system, and its dynamics satisfy a certain smoothness. Second, when the input is within an allowable range, the output change caused by a bounded change in that input must also be bounded. Given the expected end-tidal carbon dioxide (y) of closed-loop chest compressions... * d (k+1), therefore, the closed-loop chest compression model-free adaptive control scheme is designed as follows. The algorithm reset mechanism (12) is proposed to improve the ability of the PPD estimation algorithm (11) to track time-varying parameters. The expression is:

[0095]

[0096] if Or |Δu(k-1)|≤ε or So

[0097]

[0098] Where λ>0, μ>0, ρ∈(-,1], η∈(0,1]; ε is a sufficiently small positive number; yes initial value, Let represent the PPD estimate, η∈(0,1] be the step size factor, which aims to make the algorithm more flexible and general, μ>0 be the weight factor, and Δ u is u(k)-u(k-1), Δy is y(k)-y(k-1).

[0099] The MFAC controller's input is the difference between end-expiratory carbon dioxide and a preset initial end-expiratory carbon dioxide threshold. The MFAC controller's output is the compression depth. The chest compression system's input is the compression depth output by the controller, and its output is end-expiratory carbon dioxide (collected by sensors and fed back to the controller input). The scheme is as follows: Figure 4 As shown.

[0100] The closed-loop chest compression decision-making and execution steps under this scheme are as follows:

[0101] 1) Parameter initialization. Set initial values ​​for the pressing depth and pseudo-partial derivative, and set appropriate values ​​for λ, μ, ρ, η, etc. in the control system.

[0102] 2) Give the system's expected end-tidal carbon dioxide (y). * d (k+1) collects the input and output data of the control system.

[0103] 3) Substitute the value obtained from 2) into equation (11) and perform the MFAC algorithm to obtain the PPD estimate. If the reset mechanism of equation (12) is satisfied, a reset operation is required.

[0104] 4) The estimated value obtained in 3) Substituting into equation (13), the pressing depth u(k) at time k is calculated.

[0105] 5) Collect the end-tidal carbon dioxide y(k+1) of the compression system.

[0106] 6) Determine whether the end-tidal carbon dioxide y(k+1) has reached the desired end-tidal carbon dioxide y * d (k+1), if not finished, return to 2) repeat.

[0107] This invention establishes a correlation expression between cardiac impedance signals and end-tidal carbon dioxide, enabling cardiac impedance signals to provide feedback on the quality of chest compressions. Specifically, there is a correspondence between the waveform (or value) of the cardiac impedance card and the waveform (or value) of end-tidal carbon dioxide; a higher end-tidal carbon dioxide value corresponds to a higher cardiac impedance value. Therefore, using cardiac impedance signals as closed-loop parameters can also achieve closed-loop compressions. Specific steps include:

[0108] S1. Real-time acquisition of cardiac impedance signals during chest compressions.

[0109] S2, Preset initial cardiac impedance signal threshold.

[0110] S3. Calculate the difference between the cardiac impedance signal and the initial cardiac impedance signal threshold.

[0111] S4. Construct a decision model, using the difference obtained in step S3 as the input to the decision model, generate new pressing depth and frequency according to the control rules, and press according to the new pressing depth and frequency.

[0112] S5. Reacquire the cardiac impedance signal and calculate the difference. Once the cardiac impedance signal reaches the initial threshold, increase the cardiac impedance signal threshold.

[0113] S6. Use the difference calculation method and decision model to generate new compression depth and frequency parameters and perform compression according to the parameters.

[0114] The decision-making algorithm is constructed using fuzzy control or model-free adaptive control. The construction process is similar to that of control using end-tidal carbon dioxide, and will not be described in detail here.

[0115] Example 2

[0116] The present invention also discloses a closed-loop chest compression control system based on impedance cardiography, including an impedance cardiography module 51, a decision module 52 and a compression module 53. The impedance cardiography module 51 is electrically connected to the decision module 52, and the compression module 53 is electrically connected to the decision module 52.

[0117] The cardiac impedance module 51 includes a new impedance acquisition 511 and an end-tidal carbon dioxide calculation 512, which is used to acquire the cardiac impedance signal and the calculated end-tidal carbon dioxide, and calculate the difference between the end-tidal carbon dioxide and the initial end-tidal carbon dioxide threshold or the difference between the cardiac impedance signal and the initial cardiac impedance signal threshold. Then, the difference between the cardiac impedance signal or the end-tidal carbon dioxide is input to the decision module 52.

[0118] Acquiring cardiac impedance signals involves applying excitation to the human body and acquiring the signal. The excitation signal is a sine wave or square wave and is a high-frequency, low-amplitude current. The acquisition part acquires the modulated cardiac impedance signal, which can be obtained by demodulation.

[0119] End-tidal carbon dioxide calculation is performed based on cardiac impedance signals and correlation expressions to calculate end-tidal carbon dioxide during chest compressions. In practice, this can be done using a microprocessor (or a CPU or computer application, etc.).

[0120] The decision module 52 includes model input 521, decision process 522, and model output 523. The decision module 52 receives the difference between end-tidal carbon dioxide and end-tidal carbon dioxide threshold or the difference between cardiac impedance signal and cardiac impedance signal threshold, and uses it as model input. It performs decision calculations through the decision process and outputs the results to the model.

[0121] The compression module 53 includes compression execution 531 and compression quality display 532, which is used to perform chest compressions on the target according to the output compression parameters, and at the same time displays relevant parameters of the current compression quality, such as cardiac impedance waveform, end-tidal carbon dioxide waveform, compression depth, etc.

[0122] Therefore, the present invention employs the above-mentioned closed-loop chest compression control method and system based on cardiac impedance cardiography, which can make autonomous decisions to optimize compressions in real time using the acquired physiological quality parameters, thereby achieving effective compressions and greatly improving the quality of cardiopulmonary resuscitation.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A closed-loop chest compression control system based on impedance cardiography, characterized in that: It includes a cardiac impedance module, a decision module, and a compression module, wherein the cardiac impedance module is electrically connected to the decision module, and the compression module is electrically connected to the decision module; The closed-loop chest compression control method of the control system includes: S1. Real-time acquisition of cardiac impedance signals during chest compressions, and estimation of end-tidal carbon dioxide using the cardiac impedance signals; wherein, the step of estimating end-tidal carbon dioxide using cardiac impedance signals includes: S11. Construct a population test set and obtain the end-tidal carbon dioxide and cardiac impedance signals of the population in the population test set during chest compressions. S12. Analyze the correlation between end-tidal carbon dioxide and cardiac impedance signals, construct a correlation relationship expression, and label the correlation relationship expression for each person through personalized parameters to form a library of labeled correlation relationship expressions. S13. Based on the real-time acquired cardiac impedance signal, obtain the corresponding correlation expression through the subject's personalized parameters, and estimate the end-tidal carbon dioxide based on the correlation expression. S2, Preset initial end-tidal carbon dioxide threshold; S3. Calculate the difference between the end-tidal carbon dioxide obtained in step S1 and the initial end-tidal carbon dioxide threshold. S4. Construct a decision model, using the difference obtained in step S3 as the input to the decision model, generate new pressing depth and frequency according to the control rules, and perform pressing according to the new pressing depth and frequency; wherein, the decision model is constructed using a model-free adaptive control algorithm; S5. Reacquire the cardiac impedance signal and calculate the difference. When the end-tidal carbon dioxide reaches the initial end-tidal carbon dioxide threshold, increase the end-tidal carbon dioxide threshold. S6. Use the difference calculation method and decision model to generate two new parameters, compression depth and frequency, and execute compression according to the parameters.

2. The closed-loop chest compression control system based on impedance cardiography according to claim 1, characterized in that: The cardiac impedance signal includes two forms: one is a cardiac impedance signal that includes a baseline impedance signal and an impedance change, and the other is a cardiac impedance signal that only includes an impedance change.

3. A closed-loop chest compression control system based on impedance cardiography according to claim 2, characterized in that: The personalized parameters include height and weight.

4. A closed-loop chest compression control system based on impedance cardiography according to claim 1, characterized in that: In step S5, the end-tidal carbon dioxide threshold is raised at least once.

5. A closed-loop chest compression control system based on impedance cardiography according to claim 1, characterized in that: The cardiac impedance module is used to acquire cardiac impedance signals and calculate end-tidal carbon dioxide, and to calculate the difference between end-tidal carbon dioxide and the initial end-tidal carbon dioxide threshold. The decision module is used to receive the difference between the acquired end-tidal carbon dioxide and the initial end-tidal carbon dioxide threshold, and generate compression depth and frequency parameters according to the control rules. The compression module performs compressions based on the compression parameters generated by the decision model and displays cardiac impedance signals, end-tidal carbon dioxide, compression depth, and compression frequency.

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