Closed-loop external chest compression control method and system based on impedance cardiogram

Abstract

The invention discloses a closed-loop external chest compression control method and system based on an impedance cardiogram, the system comprises an impedance cardiogram module, a decision module and a compression module, the impedance cardiogram module is used for obtaining an impedance cardiogram signal and calculated end-expiratory carbon dioxide, and the compression module is used for determining the end-expiratory carbon dioxide; calculating a difference value between the end-expiratory carbon dioxide and an initial end-expiratory carbon dioxide threshold value or a difference value between the cardiac impedance signal and an initial cardiac impedance signal threshold value; the decision module is used for receiving a difference value between the obtained end-expiratory carbon dioxide and an end-expiratory carbon dioxide threshold value or a difference value between the cardiac impedance signal and a cardiac impedance signal threshold value, and generating pressing depth and frequency parameters according to a control rule; and the pressing module executes pressing according to the pressing parameters generated by the decision model. The invention further discloses a control method. By the adoption of the method, the obtained physiological quality parameters can be utilized in real time for autonomous decision optimization pressing, effective pressing is achieved, and the cardio-pulmonary resuscitation quality is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cardiopulmonary resuscitation, and in particular to a closed-loop chest compression control method and system based on impedance cardiogram. Background Art

[0002] Cardiac arrest refers to the critical symptoms of the heart stopping beating effectively, blood circulation suspending, and loss of breathing and consciousness. After cardiac arrest, the blood oxygen supply to organs and tissues throughout the body is interrupted. If not treated in time, it is very easy to cause irreversible necrosis of the body, which seriously threatens human life and health.

[0003] Cardiopulmonary resuscitation is the most direct and effective way to save people from cardiac arrest. Cardiopulmonary resuscitation can restore blood and oxygen delivery throughout the body and correct ECG rhythm through compression, ventilation, defibrillation and other methods. Chest compression is the core of cardiopulmonary resuscitation. It is an effective way to provide blood oxygen perfusion to organs during cardiac arrest and plays a key role in the success of cardiopulmonary resuscitation. Manual cardiopulmonary resuscitation is highly dependent on human experience and skills. Problems with compression depth, frequency, position and other aspects often lead to poor compression quality. In addition, as the compression is carried out, physical energy is consumed, which will also affect the compression quality. At present, mechanical chest compression equipment has been promoted in the field of cardiopulmonary resuscitation, solving the problems of compression and physical energy consumption.

[0004] Current chest compression equipment can only set compression parameters based on the compression depth and frequency recommended by the operator according to the guidelines, and perform compressions under this fixed compression parameter without taking individual differences into account. Since the main purpose of chest compression is to provide blood perfusion to important organs such as the heart and brain, evaluating the blood perfusion effect is the core indicator of compression quality, so feedback of hemodynamic information during chest compression is very necessary.

[0005] It has been shown that end-tidal carbon dioxide has a strong correlation with cardiac output, and blood perfusion during compression can be fed back by monitoring end-tidal carbon dioxide. Conventional monitoring of end-tidal carbon dioxide uses infrared sensors, but this monitoring method is easily interfered by airway condensation and ventilator ventilation; and it is difficult to establish advanced airway support in time in an out-of-hospital environment, resulting in the inability to effectively monitor end-tidal carbon dioxide.

[0006] Impedance cardiography is a clinical non-invasive hemodynamic monitoring method that can monitor hemodynamic information in intensive care units, operating rooms, etc. Conventional impedance cardiography is mainly used for patients without cardiac arrest. It can non-invasively evaluate hemodynamic information such as cardiac output and cardiac index, and has great potential in evaluating patients' cardiac function. Impedance cardiography can be used to evaluate the quality of chest compressions.

[0007] Therefore, it is urgent to design a closed-loop chest compression control method based on impedance cardiography and monitor end-tidal carbon dioxide based on impedance cardiography. Summary of the invention

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

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

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

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

[0012] S3, calculating 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, constructing a decision model, taking a certain difference value obtained in step S3 as an input of the decision model, generating a new compression depth and frequency according to the control rule, and performing compression according to the new compression depth and frequency;

[0014] S5, reacquiring the cardiac impedance signal and calculating the difference, and when the end-tidal carbon dioxide or cardiac impedance signal reaches the initial threshold, increasing the end-tidal carbon dioxide or cardiac impedance signal threshold;

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

[0016] Preferably, the step of estimating end-tidal carbon dioxide by using the cardiac impedance signal comprises:

[0017] S11. construct a crowd test set, and obtain end-tidal carbon dioxide and cardiac impedance signals of the crowd in the crowd test set during chest compression;

[0018] S12, analyzing the correlation between end-tidal carbon dioxide and cardiac impedance signals, constructing a correlation relationship expression, and marking each person's correlation relationship expression through personalized parameters to form a correlation relationship expression library containing markings;

[0019] S13. Based on the real-time acquired cardiac impedance signal, a corresponding correlation relationship expression is obtained through the individualized parameters of the subject, and end-tidal carbon dioxide is estimated according to the correlation relationship expression.

[0020] Preferably, the cardiac impedance signal includes two forms, one is a cardiac impedance signal including a basic impedance signal and an impedance variation, and the other is a cardiac impedance signal including only the impedance variation.

[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, in step S5, the end-tidal carbon dioxide threshold or the cardiac impedance signal threshold is increased for at least one time.

[0024] A closed-loop chest compression control system based on an impedance cardiogram comprises 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.

[0025] Preferably, the cardiac impedance module is used to obtain 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.

[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 rule;

[0027] The compression module performs compression according to the compression parameters generated by the decision model, and displays the cardiac impedance signal, end-tidal carbon dioxide, compression depth, and compression frequency.

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

[0029] (1) The use of impedance cardiography to obtain end-tidal carbon dioxide avoids the problems of the existing chest compression lacking non-invasive and convenient hemodynamic information feedback and the use of infrared sensors to monitor end-tidal carbon dioxide, thus improving the quality and effect of compression;

[0030] (2) By obtaining physiological information about blood perfusion during chest compression, the quality and effect of the current chest compression can be determined. The end-tidal carbon dioxide or the cardiac impedance signal obtained based on the impedance cardiogram can form a closed-loop chest compression to optimize the compression quality and effect and improve the success rate of cardiopulmonary resuscitation.

[0031] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

[0035] Figure 4 A schematic diagram of a control method using a model-free adaptive control algorithm according to an embodiment of the present invention;

[0036] Figure 5 Schematic diagram of a system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0038] Example 1

[0039] The present invention discloses a closed-loop chest compression control method based on impedance cardiogram, which controls chest compression by end-tidal carbon dioxide or cardiac impedance signal. The following are the steps of controlling by end-tidal carbon dioxide, with reference to Figure 1-4 , specifically including:

[0040] S1. Construct a crowd test set and obtain the end-tidal carbon dioxide and cardiac impedance signals of the crowd in the crowd test set during chest compression. The cardiac impedance signal includes two types, impedance signal (ΔZ) and impedance differential signal (dz / dt), namely impedance graph and impedance differential graph.

[0041] In this embodiment, the cardiac impedance signal includes two application forms, one is a cardiac impedance signal including basic impedance and impedance variation, and the other is a cardiac impedance signal including only impedance variation. Regardless of which cardiac impedance signal is used, the correlation and correlation relationship expression between the cardiac impedance signal and end-tidal carbon dioxide can be analyzed, and finally a correlation relationship expression library that can be put into application can be obtained.

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

[0043] S2. Analyze the correlation between end-tidal carbon dioxide and cardiac impedance signals, construct correlation relationship expressions, and mark each person's correlation relationship expressions through personalized parameters (height and weight), and establish a correlation relationship expression library with height and weight as index tags.

[0044] If the test set is not completely collected, then collect the remaining end-tidal carbon dioxide and cardiac impedance signals, and complete the operation according to the above steps. After the test set is collected, the initial correlation relationship expression library is obtained, and the index label is checked for vacancies. Generally, if the test set is small, there will be vacant values. Use interpolation and other methods to fill in the vacant values ​​and obtain a complete correlation relationship expression library.

[0045] S3. Collect cardiac impedance signals during chest compression in real time, measure height and weight data of the chest compression subjects, use the two as index labels to obtain corresponding correlation expressions, and estimate end-tidal carbon dioxide based on the correlation expressions.

[0046] S4. Preset 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, use the difference obtained in step S5 as the input of the decision model, generate a new compression depth and frequency according to the control rule, and perform compression according to the new compression depth and frequency.

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

[0050] S7. Reacquire the new impedance signal and calculate the difference to determine whether the difference is zero or small enough. If the difference is zero or small enough, it is determined that the threshold is reached. When the end-tidal carbon dioxide reaches the initial threshold, the end-tidal carbon dioxide or cardiac impedance signal threshold is increased. Otherwise, the threshold is not reached, and the difference is input into the decision model to obtain new compression parameters and perform compression.

[0051] S8. When the end-tidal carbon dioxide threshold is updated, the same operation as above is performed, and the difference is used to adopt the decision model to generate new compression depth and frequency parameters and perform compression according to the parameters. If the threshold is reached, the compression is maintained with the current compression parameters; otherwise, the above steps are repeated.

[0052] The above is just a description of an expected situation, and other situations will be encountered during actual implementation. In this case, the end-tidal carbon dioxide calculated by the cardiac impedance signal is less than the initial end-tidal carbon dioxide threshold, and the decision model can eventually make the collected and calculated end-tidal carbon dioxide reach the initial threshold, and the decision model can also eventually make the collected and calculated end-tidal carbon dioxide reach the updated threshold. When the end-tidal carbon dioxide collected and calculated during the initial compression phase is greater than the initial threshold, it should be updated to a larger appropriate threshold in a timely manner; the updated threshold is not limited to once, and can be updated multiple times according to the situation, but not too much. It is also possible that the threshold cannot be reached for a long time, for example, the threshold is not reached for more than 5 minutes. At this time, first, confirm whether the end-tidal carbon dioxide currently calculated by the cardiac impedance signal is at or above the end-tidal carbon dioxide range of high-quality chest compression (10mmHg-20mmHg). If it is at or above this range, the decision model can be terminated and compressions can be performed with the current compression parameters. If it is lower than this range, it means that there are some problems or constraints with the chest compression object or the closed-loop chest compression system, and the decision model should also be terminated and converted to compressions with the compression parameters recommended by the American Heart Association.

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

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

[0055] First, the difference e between the end-tidal carbon dioxide and the preset initial end-tidal carbon dioxide threshold is obtained, and the deviation e is 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 triangle membership function is shown in the attached figure. Figure 2 The control quantity u is the pressing depth, which is divided into four fuzzy sets: zero (O), positive small (PS), positive medium (PM), and positive large (PB); the fuzzy subsets are 0, 2, 4, and 6. The triangle membership function is shown in the attached figure. Figure 3 .

[0056] The designed fuzzy rules are as follows:

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

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

[0059] If e is negative and small (NSe), then u is positive and small (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 fuzzy relation is

[0062] R=R1∪R2∪R3∪R4(1)

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

[0064] The output of the fuzzy controller is the synthesis of the deviation e and the fuzzy relationship, and the formula is: in, It is a fuzzy synthesis operation.

[0065] Finally, the centroid method is used to defuzzify and obtain the pressing depth at the next moment. The formula of the centroid method is as follows:

[0066]

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

[0068] It should be noted that the fuzzy set established in the fuzzy control algorithm is not a symmetrical structure, and is further reflected in the design of the fuzzy control rules. The reason for this is that when the end-tidal carbon dioxide is lower than the preset initial end-tidal carbon dioxide threshold, the difference e must be less than zero. In this case, the compression depth needs to be increased; if the difference e is greater than or equal to zero, it means that the end-tidal carbon dioxide has reached the preset initial end-tidal carbon dioxide threshold, and in this case, the compression depth does not need to be reduced. In addition, 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 above process.

[0069] The control situation using the 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 a dynamic linear data model equivalent to the nonlinear system at each operating point, use the I / O data of the controlled system to online estimate the pseudo partial derivative (PPD) or pseudo gradient (PG) of the 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, u(k)∈R represent the output and input of the system at time k, respectively. y , n u are two unknown positive integers; is an unknown nonlinear function.

[0074] Assumption 1 Except for a finite number of time points, f(···) is y +2) The partial derivatives of the variables are continuous.

[0075] Assumption 2 Except for a finite number of time points, the system satisfies the generalized Lipschitz 1 ≠k 2 ,k 1 ,k 2 ≥0 and u(k 1 )≠u(k 2 )have

[0076] y(k 1 +1)-y(k 2 +1)|≤bu(k 1 )-u(k 2 )| (4)

[0077] Among them, 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 point of view, the above assumptions about the control object are reasonable and acceptable. Assumption 1 is a typical constraint condition for general nonlinear systems in control system design, which means that the system operation process is stable and smooth; Assumption 2 is a restriction on the upper limit of the system output change rate. From an energy perspective, bounded input energy changes should produce bounded output energy changes in the system.

[0079] Theorem 1: When a nonlinear system satisfies Assumptions 1 and 2 and |Δu(k)|≠0 for all times k, there must exist a time-varying parameter φ called PPD. c (k)∈R, so that the nonlinear system is transformed into the following compact 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 criteria 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 weight factor used to limit the change of control input, y * (k+1) is the desired output signal.

[0085] Substituting equation (5) into equation (6), taking the derivative of u(k) and setting it equal to zero, we can obtain the following control algorithm:

[0086]

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

[0088]

[0089] Where μ>0 is the weight factor, for equation (8) c (k) Find the extreme value, and the PPD estimation algorithm can be obtained 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. PPDφ c (k) is the estimated value.

[0092] if or |Δu(k-1)|≤ε or So

[0093]

[0094] The closed-loop chest compression system satisfies two assumptions of the CFDL data model. First, the closed-loop compression system is a continuously running system, and its dynamic system satisfies certain smoothness. Second, when the input is within the allowed range, the output change caused by the bounded change of the input must also be bounded. Given the expected end-tidal carbon dioxide y for closed-loop chest compression * 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, and the expression is:

[0095]

[0096] if or |Δu(k-1)|≤ε or So

[0097]

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

[0099] The input of the MFAC controller is the difference between the end-tidal carbon dioxide and the preset initial end-tidal carbon dioxide threshold. The output of the MFAC controller is the compression depth. The input of the chest compression system is the compression depth output by the controller. The output of the chest compression system is the end-tidal carbon dioxide (collected by the sensor and fed back to the controller input). The scheme is as follows: Figure 4 shown.

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

[0101] 1) Parameter initialization: Set the 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 expected end-tidal carbon dioxide y of the system * d (k+1), collect the input and output data of the control system.

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

[0104] 4) Substitute the estimated value calculated in 3) Substituting into equation (13), the compression 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) reaches the expected end-tidal carbon dioxide y * d (k+1), if not finished, return to 2) and repeat.

[0107] The method of the present invention establishes a correlation expression between the cardiac impedance signal and the end-tidal carbon dioxide, so that the cardiac impedance signal has the role and function of feeding back the quality of chest compression. Specifically, the waveform (or value) of the impedance cardiogram and the waveform (or value) of the end-tidal carbon dioxide have a corresponding relationship. A higher end-tidal carbon dioxide value corresponds to a higher cardiac impedance value. Therefore, using the cardiac impedance signal as a closed-loop parameter can also achieve closed-loop compression. The specific steps include:

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

[0109] S2. Preset the 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, use the difference obtained in step S3 as the input of the decision model, generate a new compression depth and frequency according to the control rule, and perform compression according to the new compression depth and frequency.

[0112] S5. Reacquire the cardiac impedance signal and calculate the difference. When 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 algorithm is constructed using a fuzzy control algorithm or a model-free adaptive control algorithm. The construction process is similar to the control process through end-tidal carbon dioxide, and will not be introduced in detail here.

[0115] Example 2

[0116] The present invention also discloses a closed-loop chest compression control system based on an impedance cardiogram, including an impedance cardiogram module 51, a decision module 52 and a compression module 53, wherein the impedance cardiogram 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, including a new impedance acquisition 511 and an end-tidal carbon dioxide calculation 512, is used to acquire a cardiac impedance signal and calculated end-tidal carbon dioxide, and calculate the difference between the 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, and 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 includes applying excitation to the human body and collecting signals. The excitation signal is a sine wave or a square wave, and is a high-frequency, low-amplitude current. The collecting part collects the modulated cardiac impedance signal, which can be obtained by demodulation.

[0119] The end-tidal carbon dioxide calculation calculates the end-tidal carbon dioxide during chest compression based on the cardiac impedance signal and the correlation relationship expression, which can actually be a microprocessor (it can also be a CPU or a computer application, etc.).

[0120] The decision module 52 includes a model input 521, a decision process 522 and a model output 523. The decision module 52 receives the difference between the 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 as a model input, performs a decision operation through the decision process, and outputs the result to the model.

[0121] The compression module 53 includes a compression execution 531 and a compression quality display 532, which are used to perform compression on the chest compression object according to the output compression parameters. At the same time, the compression quality displays the 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 adopts the above-mentioned closed-loop chest compression control method and system based on impedance cardiogram, which can use the acquired physiological quality parameters in real time to make autonomous decisions and optimize compression, achieve effective compression, and greatly improve the quality of cardiopulmonary resuscitation.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A closed-loop chest compression control method based on impedance cardiogram, characterized in that the steps include: S1, real-time acquisition of cardiac impedance signals during chest compression, and estimation of end-tidal carbon dioxide through cardiac impedance signals; S2, preset initial end-tidal carbon dioxide threshold or cardiac impedance signal threshold; S3, calculating 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; S4, constructing a decision model, taking a certain difference value obtained in step S3 as an input of the decision model, generating a new compression depth and frequency according to the control rule, and performing compression according to the new compression depth and frequency; S5, reacquiring the cardiac impedance signal and calculating the difference, and when the end-tidal carbon dioxide or cardiac impedance signal reaches the initial threshold, increasing the end-tidal carbon dioxide or cardiac impedance signal threshold; S6. Use the difference calculation method and decision model to generate new compression depth and frequency parameters and perform compression according to the parameters.

2. A closed-loop chest compression control method based on impedance cardiogram according to claim 1, characterized in that: The step of estimating end-tidal carbon dioxide by using the cardiac impedance signal comprises: S11. construct a crowd test set, and obtain end-tidal carbon dioxide and cardiac impedance signals of the crowd in the crowd test set during chest compression; S12, analyzing the correlation between end-tidal carbon dioxide and cardiac impedance signals, constructing a correlation relationship expression, and marking each person's correlation relationship expression through personalized parameters to form a correlation relationship expression library containing markings; S13. Based on the real-time acquired cardiac impedance signal, a corresponding correlation relationship expression is obtained through the individualized parameters of the subject, and end-tidal carbon dioxide is estimated according to the correlation relationship expression.

3. The closed-loop chest compression control method based on impedance cardiogram according to claim 1, characterized in that: The cardiac impedance signal includes two forms, one is a cardiac impedance signal including a basic impedance signal and an impedance variation, and the other is a cardiac impedance signal including only the impedance variation.

4. The closed-loop chest compression control method based on impedance cardiogram according to claim 2, characterized in that: The personalized parameters include height and weight.

5. The closed-loop chest compression control method and system based on impedance cardiogram according to claim 1, characterized in that: In step S4, the decision model is constructed using a fuzzy control algorithm or a model-free adaptive control algorithm.

6. The closed-loop chest compression control method based on impedance cardiogram according to claim 1, characterized in that: In step S5, the end-tidal carbon dioxide threshold or the cardiac impedance signal threshold is increased for at least one time.

7. A system using a closed-loop chest compression control method based on impedance cardiogram as claimed in any one of claims 1 to 6, characterized in that: It includes a cardiac impedance module, a decision module and a pressing module. The cardiac impedance module is electrically connected to the decision module, and the pressing module is electrically connected to the decision module.

8. A closed-loop chest compression control system based on impedance cardiogram according to claim 7, characterized in that: The cardiac impedance module is used to obtain 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; 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 rule; The compression module performs compression according to the compression parameters generated by the decision model, and displays the cardiac impedance signal, end-tidal carbon dioxide, compression depth, and compression frequency.

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