Self-adaptive defibrillation component control system based on multi-source data analysis

Abstract

The invention discloses a self-adaptive defibrillation assembly control system based on multi-source data analysis, and relates to the technical field of intelligent rescue, eight independent switchable electrodes of a multi-electrode array module are arranged on the surface of the trunk of a patient in pairs, impedance synchronous measurement in multiple space directions is carried out in cooperation with an impedance measurement module, and a three-dimensional impedance field is constructed; calculating a space impedance gradient field, comprehensively evaluating the space coordinate of the heart center of the patient, determining the main axis direction of the heart, and calculating a myocardial state index; the electrode optimization module is used for evaluating the multi-electrode array and screening out an optimal electrode combination so as to execute defibrillation operation; according to the whole system, the multi-frequency-band impedance measuring device of the multi-electrode array and the electrophysiological sensing and collecting device are utilized, a comprehensive scoring function is combined, accurate control and optimization of the defibrillation process of a patient are achieved, the defibrillation effect is improved, and the tissue damage risk is reduced.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of intelligent rescue, and in particular relates to an adaptive defibrillation assembly control system based on multi-source data analysis. BACKGROUND

[0002] With the continuous progress of medical technology, the treatment means for heart disease is increasingly rich. In the field of cardiac first aid, defibrillation technology plays a crucial role.

[0003] The existing defibrillation technology generally relies on fixed electrode configuration and standardized operation process, such as using front-lateral or front-post two electrode plates, adjusting defibrillation energy based on overall thoracic impedance estimation. This method has a series of significant shortcomings in clinical application. First, the fixation of electrode position ignores the differences in individual patient's anatomical structure, such as the diversity of heart position, thoracic shape and tissue composition, which may result in that the defibrillation current path cannot optimally cover the key areas of the heart, thereby reducing the success rate of defibrillation and forcing the operator to increase the energy output, increasing the risk of tissue damage to the patient. Second, only single or limited impedance measurement is performed, which cannot capture the spatial distribution changes of thoracic impedance, so it is difficult to accurately reflect the electrical physiological characteristics of the heart region, causing the mismatch between energy delivery and individual needs, and part of the myocardium may not be effectively defibrillated due to insufficient current, while other areas may cause burns or myocardial damage due to excessive exposure to current. In addition, the existing technology lacks accurate positioning of the real-time position and direction of the heart, and relies on the operator's experience to place the electrode, which is easily affected by subjective factors, and the error is greater in special body types such as obese or thoracic deformity patients, which may cause the current to deviate from the heart axis, reducing the efficiency of the electric shock. At the same time, the state of the myocardium such as edema, ischemia or fibrosis is not included in the defibrillation decision-making process, which may cause ineffective electric shock or aggravate myocardial damage. The existing fixed electrode combination cannot dynamically evaluate the current density distribution, and skin burns or deep tissue necrosis often occur due to local current concentration. In order to provide a corresponding mechanism to optimize electrode selection to minimize these risks, finally, the traditional defibrillation has limitations in energy transmission efficiency. Due to insufficient current coverage and impedance mismatch, high energy output may still fail to restore the rhythm, prolonging the rescue time. These drawbacks collectively result in the standardization but lack of adaptability of the defibrillation process, which is unstable in complex clinical scenarios.

[0004] In order to solve the above problems, the present application provides an adaptive defibrillation assembly control system based on multi-source data analysis. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides an adaptive defibrillation assembly control system based on multi-source data analysis, which solves the problems of insufficient individual adaptation, inaccurate positioning and poor risk control of traditional defibrillation.

[0006] The object of the present application can be achieved by the following technical solutions:

[0007] An adaptive defibrillator control system based on multi-source data analysis, the system comprising:

[0008] The multi-electrode array module includes eight independent switchable electrodes, which are arranged in pairs on the surface of the patient's torso.

[0009] The impedance measurement module performs simultaneous measurement of bioimpedance values ​​in multiple spatial directions based on a multi-electrode array, constructing a three-dimensional impedance field that reflects the impedance distribution inside the patient's trunk.

[0010] Based on the bioimpedance value of the three-dimensional impedance field, the spatial impedance gradient field is determined, and the spatial coordinates of the patient's heart center are comprehensively evaluated.

[0011] Determine the main axis direction of the heart and calculate the myocardial state index by combining the bioimpedance value in the three-dimensional impedance field;

[0012] The electrode optimization module evaluates the multi-electrode array, determines the current coverage, energy transfer efficiency, and tissue damage risk of each electrode combination in the cardiac region, selects the optimal electrode combination based on a comprehensive scoring function, and performs defibrillation on the patient based on the optimal electrode combination.

[0013] As a further embodiment of the present invention, in the multi-electrode array module, eight independent switchable electrodes are respectively arranged in the right subclavian, left subclavian, left parasternal, right midaxillary line, left subcostal, right subcostal, left back, and right back, and are sequentially summarized as multi-electrode array E={E1,E2,E3,E4,E5,E6,E7,E8}.

[0014] The eight independent switchable electrodes are electrodes with the same interface specifications and switchable modes. Each electrode integrates a multi-band impedance measurement device and an electrophysiological sensing acquisition device, and the electrodes can be grouped and connected in parallel.

[0015] As a further aspect of the present invention, the specific method for constructing a three-dimensional impedance field reflecting the impedance distribution within the patient's torso in the impedance measurement module is as follows:

[0016] The multi-electrode array E is divided into three spatial directions: front-back, left-right, and diagonal.

[0017] The bioimpedance values ​​Z measured by the electrode combinations [E1,E6], [E2,E5], [E3,E8], and [E4,E7] were obtained. 1-6 Z 2-5 Z 3-8 Z 4-7 This forms the forward and backward impedance vector Z_AP=[Z 1-6 Z 2-5 Z 3-8 Z 4-7];

[0018] The bioimpedance values ​​Z measured by the electrode combinations [E1,E2], [E3,E4], [E5,E6], and [E7,E8] were obtained. 1-2 Z 3-4 Z 5-6 Z 7-8 This forms the impedance vector Z_LR=[Z] in the left and right directions. 1-2 Z 3-4 Z 5-6 Z 7-8 ];

[0019] The bioimpedance values ​​Z measured by the electrode combinations [E1,E5], [E2,E6], [E3,E7], and [E4,E8] were obtained. 1-5 Z 2-6 Z 3-7 Z 4-8 The diagonal impedance vector Z_DIAG = [Z 1-5 Z 2-6 Z 3-7 Z 4-8 ];

[0020] The bioimpedance values ​​in each impedance vector Z_AP, Z_LR and Z_DIAG are connected based on the corresponding electrode combinations and mapped to the three-dimensional space formed by the patient's torso.

[0021] A three-dimensional impedance field (DIF) is generated based on bioimpedance values ​​combined with three-dimensional space.

[0022] As a further aspect of the present invention, in the impedance measurement module, the specific method for determining the spatial impedance gradient field based on the bioimpedance value of the three-dimensional impedance field is as follows:

[0023] The first electrode E1 in the multi-electrode array E is defined as the origin of the three-dimensional spatial coordinate system in the three-dimensional impedance field DIF. The spatial coordinates of electrode E1 are denoted as P1(a1,b1,c1). Similarly, the spatial coordinates of the remaining electrodes are determined and denoted as P1(a1,b1,c1), P2(a2,b2,c2),...,P8(a8,b8,c8).

[0024] Based on the three-dimensional impedance field DIF and the spatial coordinates of the electrode combinations constituting each impedance vector in three-dimensional space, the spatial impedance gradients in the left-right direction, front-back direction and diagonal direction are calculated respectively.

[0025] The spatial impedance gradient ΔZ_x in the left and right directions is obtained by calculating the difference in bioimpedance values ​​corresponding to the left and right positional relationships in the impedance vector Z_LR in the left and right directions, and dividing it by the spatial distance of the corresponding electrode assembly in the three-dimensional impedance field DIF.

[0026] Similarly, determine the spatial impedance gradient ΔZ_y in the front-to-back direction and the spatial impedance gradient ΔZ_z in the diagonal direction;

[0027] The spatial impedance gradient field SGF, which characterizes the changes in the impedance within the patient's trunk, is constructed by spatial interpolation based on the bioimpedance values ​​and spatial positional relationships between the spatial impedance gradients ΔZ_x, ΔZ_y, and ΔZ_z.

[0028] As a further aspect of the present invention, the specific method for comprehensively evaluating the spatial coordinates of the patient's heart center in the impedance measurement module is as follows:

[0029] Based on the spatial impedance gradient field SGF, the local spatial impedance gradient vector ΔZi associated with any electrode Ei is obtained. The spatial coordinates P_heart of the patient's heart center are calculated using P_heart=(Σ(|ΔZi|×Pi)) / (Σ|ΔZi|), where i is the counting index, and the value range is 1 to 8.

[0030] |ΔZi| represents the magnitude of the local spatial impedance gradient vector ΔZi at the i-th electrode, and Σ represents the summation.

[0031] As a further aspect of the present invention, the specific method for determining the principal axis direction of the heart and calculating the myocardial state index by combining the bioimpedance value in the three-dimensional impedance field in the impedance measurement module is as follows:

[0032] Based on the spatial coordinates P_heart, an ellipsoid fitting algorithm is used to determine the best-fitting ellipsoid that characterizes the geometric features of the heart.

[0033] The orientation matrix of the best-fit ellipsoid is obtained and eigenvalue decomposition is performed. The resulting three mutually orthogonal eigenvectors are defined as the three principal axes of the patient's heart.

[0034] The lengths of the three principal axes are determined by combining the three principal axis directions with the best-fit ellipsoid and labeled as R1, R2 and R3. The heart volume is calculated using the three principal axis lengths and denoted as V_heart.

[0035] Based on the heart volume V_heart and the impedance change rate parameter ΔZ_rate extracted from the three-dimensional impedance field DIF, the myocardial state index M_mou is calculated using M_mou=k1×V_heart+k2×ΔZ_rate, where k1 and k2 are empirical constants obtained from clinical data calibration.

[0036] As a further aspect of the present invention, in the impedance measurement module, the impedance change rate parameter ΔZ_rate is determined by using ΔZ_rate=σ_Z / μ_Z, where σ_Z is the standard deviation of the bioimpedance value at the spatial coordinates of each point in the best-fit ellipsoid, and μ_Z is the average value of the bioimpedance value at the spatial coordinates of each point in the best-fit ellipsoid. The bioimpedance value at the spatial coordinates of each point in the best-fit ellipsoid is directly obtained through the three-dimensional impedance field DIF.

[0037] As a further aspect of the present invention, the specific method for selecting the optimal electrode combination based on the comprehensive scoring function in the electrode optimization module is as follows:

[0038] Determine all electrode combinations in the multi-electrode array E, denoted as u, and randomly arrange them into an electrode combination sequence ZH1, ZH2, ..., ZHu;

[0039] The comprehensive score Sc_o of any electrode combination ZHo is calculated using the comprehensive scoring function Sc_o=α×E_eff_o+β×(1-SH_o / SH_max)+γ×(1-D_o / D_max), where o is the counting index, ranging from 1 to u; E_eff_o is the energy transmission efficiency of electrode combination ZHo; SH_o is the tissue damage risk of electrode combination ZHo; SH_max is the preset tissue damage risk threshold; D_o is the spatial distance between the two electrodes in electrode combination ZHo in the three-dimensional impedance field DIF; D_max is the preset maximum spatial distance threshold; and α, β, and γ are preset weighting coefficients, and α+β+γ=1.

[0040] E_eff_o is the energy transfer efficiency of the electrode combination ZHo, E_eff_o=C_o / (G_o×D_o), where C_o is the current coverage of the electrode combination ZHo. It is determined by applying input electric power between the electrode combination ZHo through electromagnetic field simulation based on the three-dimensional impedance field DIF and the human tissue conductivity model, so that a current density field DL is generated in the entire three-dimensional impedance field DIF. The square of the current density field in the best-fit ellipsoid is divided by the human tissue conductivity and then integrated. The integral result is then multiplied by the electrode spacing D_o.

[0041] G_o is the input electrical power applied to the electrode assembly ZHo in the electromagnetic field simulation, and it is a known value;

[0042] Tissue damage risk SH_o = max(DL) / SH_safe, where max(DL) is the global maximum value in the current density field DL, and SH_safe is the safe current density threshold preset by the operator based on electrophysiological safety standards and clinical experience;

[0043] The optimal electrode combination is selected based on a comprehensive score of the electrode combination.

[0044] As a further aspect of the present invention, in the electrode optimization module, the electrode combination ZHo with the largest comprehensive scoring function value is extracted from the electrode combination sequence ZH1, ZH2, ..., ZHu and marked as the optimal electrode combination;

[0045] The system obtains the preset input electrical power for the patient from the operator, adjusts the electrodes in the optimal electrode combination to be the discharge electrodes, and uses the input electrical power as the discharge power to perform the defibrillation operation.

[0046] The beneficial effects of this invention are:

[0047] (1) This invention achieves precise adaptive control of the defibrillation process by constructing a multi-electrode array and a three-dimensional impedance field; it uses 8 switchable electrodes to perform multi-directional impedance synchronous measurement, dynamically constructs a three-dimensional impedance field of the human body and calculates the spatial gradient, thereby accurately locating the spatial coordinates and main axis of the heart, breaking through the limitation of fixed positions of traditional defibrillation electrodes; on this basis, it uses an optimization algorithm to evaluate the current coverage, transmission efficiency and damage risk of each electrode combination in real time, automatically selects the optimal solution to perform defibrillation, significantly improves the targeting and treatment efficiency of electric shock energy, and greatly reduces the risk of tissue damage caused by improper electrode placement or energy dispersion, thus achieving safer and more intelligent personalized defibrillation treatment;

[0048] (2) This invention achieves multi-dimensional and high-precision dynamic assessment of the patient's cardiac structure and function by constructing a multi-electrode array to synchronously measure the three-dimensional impedance field and further generating a spatial impedance gradient field. By converting discrete impedance measurements into a continuous spatial three-dimensional field and using the gradient field to accurately locate the heart center and main axis direction, the limitations of traditional single-point or two-dimensional measurements are overcome. Furthermore, by combining geometric fitting and impedance change rate parameters to calculate the myocardial state index, it can non-invasively and comprehensively reflect the changes in the morphology and physiological state of the heart, thereby improving the accuracy and efficiency of defibrillation.

[0049] (3) This invention realizes intelligent screening of defibrillation electrode combinations by constructing a comprehensive scoring function, and optimizes the treatment effect and safety in a quantitative way. By comprehensively considering energy transmission efficiency, tissue damage risk and electrode spatial distance, the current distribution is accurately simulated by electromagnetic field simulation, thereby ensuring efficient defibrillation energy transmission while actively suppressing the risk of tissue damage. By replacing empirical selection with automated calculation, the scientificity and repeatability of electrode configuration are significantly improved. It can quickly lock the global optimal electrode combination under the individualized three-dimensional impedance field model, and finally achieve safe and efficient electric shock defibrillation treatment at the preset power. Attached Figure Description

[0050] The invention will now be further described with reference to the accompanying drawings.

[0051] Figure 1 This is a schematic diagram of the system described in this invention;

[0052] Figure 2 This is a flowchart illustrating the method described in Embodiment 3 of the present invention. Detailed Implementation

[0053] 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 embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] like Figure 1 As shown, this application provides an adaptive defibrillation component control system based on multi-source data analysis;

[0055] As an embodiment 1 of this application, it specifically includes:

[0056] The multi-electrode array module includes eight independent switchable electrodes, which are arranged in pairs on the surface of the patient's torso.

[0057] The impedance measurement module performs simultaneous measurement of bioimpedance values ​​in multiple spatial directions based on a multi-electrode array, constructing a three-dimensional impedance field that reflects the impedance distribution inside the patient's trunk.

[0058] Based on the bioimpedance value of the three-dimensional impedance field, the spatial impedance gradient field is determined, and the spatial coordinates of the patient's heart center are comprehensively evaluated.

[0059] Determine the main axis direction of the heart and calculate the myocardial state index by combining the bioimpedance value in the three-dimensional impedance field;

[0060] The electrode optimization module evaluates the multi-electrode array, determines the current coverage, energy transfer efficiency, and tissue damage risk of each electrode combination in the cardiac region, selects the optimal electrode combination based on a comprehensive scoring function, and performs defibrillation on the patient based on the optimal electrode combination.

[0061] Example 2

[0062] Adaptive defibrillation component control system based on multi-source data analysis, such as Figure 1 As shown, this system includes the following:

[0063] This system integrates an adaptive defibrillation component control system based on multi-source data analysis into existing semi-automatic external defibrillator products. It supports adult or pediatric mode discharge and meets the defibrillation treatment requirements for patients with transthoracic bioimpedance values ​​of 20-180Ω. In addition to the multi-electrode array module, impedance measurement module, and electrode optimization module mentioned in this system, it also integrates a terminal processing module to handle the calculation, analysis, and data processing steps involved in each module and to cache various types of data.

[0064] It should be noted that the use case of this system is special, so the terminal processing module has high computing power requirements and needs to complete the computing and processing of each module in the system in a short period of time.

[0065] Traditional AEDs and semi-automatic and manual defibrillators have fixed electrode positions, such as anterior-lateral or anterior-posterior, and the energy output is preset. This approach does not take into account the significant anatomical differences between patients, such as body size, heart position, and physiological state. This may result in the defibrillation current not passing through the heart via the optimal path, thereby reducing the success rate or increasing the risk of myocardial damage. This system uses a multi-electrode array and real-time multi-source data to dynamically construct a model of the patient's trunk and heart, and intelligently selects the best electrode pairs and energy delivery strategy accordingly to achieve individualized and precise defibrillation.

[0066] First, the multi-electrode array module integrates eight independent switchable electrodes, which are respectively located in the right subclavian, left subclavian, left parasternal, right midaxillary, left subcostal, right subcostal, left back, and right back, and are collectively referred to as the multi-electrode array E={E1,E2,E3,E4,E5,E6,E7,E8}. All eight independent switchable electrodes are of the same interface specification and are switchable mode electrodes (suitable for adults and children). Each electrode integrates a multi-band impedance measurement device and an electrophysiological sensing acquisition device (for measuring relevant patient parameters), and any two or more electrodes can be grouped and connected in parallel.

[0067] Next, in the impedance measurement module, the patient's bioimpedance values ​​are simultaneously measured from different spatial directions based on any two of the eight independent switchable electrodes in the multi-electrode array and multiple electrode combinations.

[0068] By fusing the bioimpedance values ​​from multiple points and directions, a three-dimensional distribution map can be formed that reflects the conductivity differences of different tissues inside the patient's trunk (because different tissues have different conductivity). This three-dimensional distribution map is then marked as the three-dimensional impedance field of the impedance distribution inside the patient's trunk.

[0069] It is important to note that the process of constructing the three-dimensional distribution map requires the integration of medical imaging technology. In this solution, electrical impedance tomography (EIT) technology is referenced for implementation. The resulting three-dimensional impedance field does not require extensive determination of various organs within the patient's torso; it only needs to simulate the outer contour of the torso. Therefore, the required computing power is greatly reduced, thereby improving the overall implementation efficiency of the solution.

[0070] Then, by analyzing the degree of change in bioimpedance values ​​within the three-dimensional impedance field, a spatial impedance gradient field is constructed. Based on the spatial impedance gradient field, the precise location of the heart can be inferred, i.e., the spatial coordinates of the center of the patient's heart in the three-dimensional impedance field. This is because the blood-filled ventricles have significantly different impedances from the surrounding lung tissue, and the heart is an approximately ellipsoid, making it easier to distinguish from other organs.

[0071] By combining the spatial coordinates of the heart center, an ellipsoid fitting algorithm is used to determine the best-fitting ellipsoid that characterizes the geometric features of the heart. Based on the best-fitting ellipsoid, the three principal axis directions of the heart are determined. The myocardial state index is calculated by combining the bioimpedance values ​​of the region in the three-dimensional impedance field in which the heart is located.

[0072] Finally, in the electrode optimization module, the multi-electrode array is evaluated, and the evaluation dimensions include the following:

[0073] Current coverage: This indicates whether the electric field generated by the electrode combination can adequately cover the defined cardiac area, preventing the current from bypassing the heart and thus affecting the defibrillation effect;

[0074] Energy transfer efficiency: This indicates whether the overall bioimpedance of the current path output by the electrode assembly is low. If the impedance is too high, energy will be lost, resulting in insufficient energy actually reaching the heart.

[0075] Tissue damage risk: This indicates whether the current output from the electrode assembly is too concentrated through a non-cardiac tissue, such as the diaphragm, potentially causing burns or functional impairment.

[0076] Next, the above assessment dimensions are input into a pre-built comprehensive scoring function to screen out the optimal electrode combination, and defibrillation is performed on the patient based on the optimal electrode combination. It should be noted that the actual need for defibrillation is determined by the operator or medical staff, so the operator or medical staff need to have a certain level of professionalism and medical (use) qualifications.

[0077] Example 3

[0078] This embodiment further discloses a method for monitoring a patient's heart based on multi-electrode bioimpedance measurement, building upon Embodiment 2. Figure 2 As shown, it specifically includes the following:

[0079] First, based on the descriptions in Embodiments 1 and 2, the multi-electrode array E is divided into three spatial directions: front-back, left-right, and diagonal, specifically as follows:

[0080] Anterior-posterior direction: Electrode combinations [E1,E6], [E2,E5], [E3,E8], [E4,E7], and the bioimpedance values ​​measured by the anterior-posterior direction electrode combinations are expressed as: Z 1-6 Z 2-5 Z 3-8 Z 4-7 And by Z 1-6 Z 2-5 Z 3-8 Z 4-7 The forward and backward impedance vector Z_AP = [Z 1-6 Z 2-5 Z 3-8 Z 4-7 ].

[0081] Left-right direction: Electrode combinations [E1,E2], [E3,E4], [E5,E6], [E7,E8]. The bioimpedance values ​​measured by the electrode combinations in the left-right direction are expressed as: Z 1-6 Z 2-5 Z 3-8 Z 4-7 And by Z 1-6 Z 2-5 Z 3-8 Z 4-7 The impedance vector Z_LR is formed in the left and right directions. 1-2 Z 3-4 Z 5-6 Z 7-8 ].

[0082] Diagonal direction: Electrode combinations [E1,E5], [E2,E6], [E3,E7], [E4,E8]. The bioimpedance values ​​measured by the diagonal electrode combinations are expressed as: Z 1-5 Z 2-6 Z 3-7 Z 4-8 And by Z 1-5 Z 2-6 Z 3-7 Z 4-8 The diagonal impedance vector Z_DIAG = [Z 1-5 Z 2-6 Z 3-7 Z 4-8 ].

[0083] Next, the impedance vector in the forward and backward directions Z_AP=[Z 1-6 Z 2-5Z 3-8 Z 4-7 ]、Left and right direction impedance vector Z_LR=[Z 1-2 Z 3-4 Z 5-6 Z 7-8 And the diagonal impedance vector Z_DIAG=[Z 1-5 Z 2-6 Z 3-7 Z 4-8 By connecting the positions of each component in the three-dimensional space formed by the components within the patient's torso, a three-dimensional impedance field, denoted as DIF, is constructed and associated with the three-dimensional space corresponding to the patient's torso. In this way, the bioimpedance value of any spatial coordinate within the three-dimensional space (three-dimensional impedance field DIF) corresponding to the patient's torso can be determined. The construction process of the three-dimensional impedance field DIF is essentially a spatial discrete interpolation process, which can be directly implemented using existing technologies. Therefore, this solution will not elaborate further.

[0084] Next, based on the three-dimensional impedance field DIF, the rate of change and direction of change of bioimpedance values ​​in space are analyzed to more sensitively reflect the dynamic changes of local electrical properties caused by physiological conditions such as heartbeat.

[0085] First, the first electrode E1 in the multi-electrode array E is defined as the origin of the three-dimensional spatial coordinate system in the three-dimensional impedance field DIF, and the spatial coordinates of electrode E1 are marked as P1(a1,b1,c1). Thus, the spatial coordinates of all electrodes are determined by combining the three-dimensional impedance field DIF and the spatial coordinates of electrode E1 P1(a1,b1,c1), and are represented according to the arrangement order of the electrodes in the multi-electrode array E as P1(a1,b1,c1), P2(a2,b2,c2),...,P8(a8,b8,c8).

[0086] Next, using the bioimpedance value at any spatial coordinate in the three-dimensional impedance field DIF and the forward and backward impedance vector Z_AP=[Z 1-6 Z 2-5 Z 3-8 Z 4-7 ]、Left and right direction impedance vector Z_LR=[Z 1-2 Z 3-4 Z 5-6 Z 7-8 ], Diagonal impedance vector Z_DIAG=[Z 1-5 Z 2-6 Z 3-7 Z 4-8The spatial impedance gradient of the three-dimensional impedance field DIF in the left-right, front-back and diagonal directions is calculated based on the spatial coordinates of each electrode in the multi-electrode array E. The calculation method is as follows: based on the difference in bioimpedance measured by a pair of electrodes, the change in bioimpedance per unit length is calculated by dividing the difference between the two electrodes by the spatial distance between them in the three-dimensional impedance field DIF.

[0087] For example, the spatial impedance gradient ΔZ_x in the left and right directions is obtained by calculating the difference in bioimpedance values ​​corresponding to the left and right positional relationships in the impedance vector Z_LR in the left and right directions, and dividing it by the spatial distance of the corresponding electrode assembly in the three-dimensional impedance field DIF.

[0088] Similarly, determine the spatial impedance gradient ΔZ_y in the front-to-back direction and the spatial impedance gradient ΔZ_z in the diagonal direction.

[0089] Then, spatial interpolation is performed on the spatial impedance gradients ΔZ_x, ΔZ_y, and ΔZ_z along all measurement paths to obtain the spatial impedance gradient field, denoted as SGF, which characterizes the changes in impedance within the patient's trunk.

[0090] Next, the geometric center of the heart is located in the spatial impedance gradient field SGF and used as the origin for subsequent cardiac analysis.

[0091] Based on the spatial impedance gradient field SGF, the local spatial impedance gradient vector ΔZi associated with any electrode Ei in the multi-electrode array E is obtained. It should be noted that the impedance gradient around the heart changes most significantly due to the heartbeat (when the heart stops beating, defibrillation cannot be performed, but CPR is required). Therefore, the larger the value of the impedance gradient amplitude |ΔZi| at a certain point, the closer that point is to the patient's heart.

[0092] Using the magnitude of the local spatial impedance gradient vector at each electrode as the weight of the electrode coordinate, a weighted average is taken of all electrode coordinates to obtain the spatial coordinates of the heart center, expressed as: P_heart=(Σ(|ΔZi|×Pi)) / (Σ|ΔZi|). The spatial coordinates P_heart of the patient's heart center are calculated, where i is the counting index, with a value ranging from 1 to 8.

[0093] After determining the spatial coordinates P_heart of the heart center, the direction of the heart's principal axis is determined, and the myocardial state index is calculated by combining the bioimpedance values ​​in the three-dimensional impedance field.

[0094] First, using the spatial coordinates P_heart of the heart center as the center, the heart is fitted with an ellipsoid fitting algorithm to obtain an optimal fitted ellipsoid that characterizes the geometric features of the heart.

[0095] Obtain the orientation matrix of the best-fit ellipsoid, where the orientation matrix is ​​composed of the coupling relationship of the bioimpedance values ​​at the spatial coordinates of each point in the best-fit ellipsoid in three spatial dimensions, i.e., the covariance matrix of the impedance gradient vector.

[0096] Then, the eigenvalue decomposition of the direction matrix is ​​performed to obtain three mutually orthogonal eigenvectors. The directions of the three mutually orthogonal eigenvectors are defined as the three principal axis directions of the patient's heart.

[0097] The lengths of the best-fit ellipsoid along the three principal axes are determined based on the three principal axis directions and the best-fit ellipsoid. These three principal axis lengths are labeled R1, R2, and R3 respectively. The heart volume is calculated using these three principal axis lengths and denoted as V_heart. The heart volume calculation is based on the ellipsoid volume formula, and all three principal axis lengths refer to the total axis length.

[0098] Finally, the impedance change rate parameter of the heart volume V_heart region is extracted from the three-dimensional impedance field DIF and labeled as ΔZ_rate;

[0099] The impedance change rate parameter ΔZ_rate is determined by using ΔZ_rate=σ_Z / μ_Z, where σ_Z is the standard deviation of the bioimpedance value at the spatial coordinates of each point in the best-fit ellipsoid, reflecting the synchronicity of electrical activity, and μ_Z is the average value of the bioimpedance value at the spatial coordinates of each point in the best-fit ellipsoid, reflecting the average conductivity of the tissue.

[0100] A normal heart exhibits synchronized contraction, synchronized impedance changes, small σ_Z, and small ΔZ_rate.

[0101] Heart failure: asynchronous contraction, dispersed impedance changes, large σ_Z, large ΔZ_rate;

[0102] Cardiac arrhythmia: Disorders of electrical activity, with a significant increase in σ_Z and ΔZ_rate.

[0103] Among them, the bioimpedance values ​​at the spatial coordinates of each point in the best-fit ellipsoid are directly obtained through the three-dimensional impedance field DIF, and the impedance change rate parameter ΔZ_rate reflects the relative fluctuation of the bioimpedance values ​​in the patient's cardiac region.

[0104] The myocardial state index M_mou of the patient in the current state is calculated by using M_mou=k1×V_heart+k2×ΔZ_rate, where k1 and k2 are empirical constants calibrated by the operator in combination with clinical data. k1 is obtained by regression of the actual volume measured by echocardiography or MRI, and k2 is obtained by regression of electrocardiogram synchronicity indicators, such as QRS width.

[0105] The myocardial state index M_mou essentially integrates information about a patient's cardiac structure and electrical function (bioimpedance value). It is a patient-specific parameter, and the myocardial state index M_mou is not the same for different patients.

[0106] Example 4

[0107] This embodiment, based on embodiment 3, further discloses a method for selecting the optimal electrode combination scheme from various electrode combinations and performing defibrillation operations through a comprehensive scoring function, specifically including the following:

[0108] The multi-electrode array E contains multiple electrodes, and these electrodes have the potential to discharge in various combinations. Different combinations will produce different electric field distributions, resulting in different defibrillation effects and safety.

[0109] Based on the above, this plan designs a screening method that simultaneously satisfies both defibrillation effectiveness and safety, as detailed below:

[0110] First, based on the multi-electrode array E, the electrodes are combined in pairs to obtain several electrode combinations. The total number of all electrode combinations is counted and denoted as u. The u electrode combinations are arranged in a random manner and denoted as the electrode combination sequence ZH1, ZH2, ..., ZHu.

[0111] Example processing is performed on any electrode combination ZHo from the electrode combination sequence ZH1, ZH2, ..., ZHu, where o is the counting index, with a value range from 1 to u;

[0112] The comprehensive score Sc_o of electrode assembly ZHo is calculated using the pre-constructed comprehensive scoring function Sc_o=α×E_eff_o+β×(1-SH_o / SH_max)+γ×(1-D_o / D_max);

[0113] It should be noted that E_eff_o is the energy transfer efficiency of the electrode assembly ZHo, specifically expressed as: E_eff_o=C_o / (G_o×D_o). The energy transfer efficiency E_eff_o measures how much of the input electrical power can be effectively converted into electrical energy acting on the patient's heart area. The higher the value of the energy transfer efficiency E_eff_o, the better, indicating that the electrode assembly ZHo is more effective in performing defibrillation on the patient.

[0114] In the energy transfer efficiency calculation formula, C_o represents the current coverage of the electrode assembly ZHo. This is achieved by applying input electrical power between the electrode assembly ZHo through electromagnetic field simulation using a three-dimensional impedance field DIF and a human tissue conductivity model. This generates a current density field DL within the entire three-dimensional impedance field DIF. The square of the current density field within the best-fit ellipsoid is then divided by the human tissue conductivity, and the result is multiplied by the electrode spacing D_o. The specific process can be summarized as follows:

[0115] First, in the three-dimensional impedance field DIF, a known input power G_o is applied to the electrode assembly ZHo, which is generally preset by the operator or input by the medical staff on site;

[0116] Next, electromagnetic field simulation is performed using the human tissue conductivity model combined with the three-dimensional impedance field DIF until a current density field DL is generated within the entire three-dimensional impedance field DIF. The current density field DL clarifies the magnitude and direction of the current at each spatial coordinate within the three-dimensional impedance field DIF.

[0117] Then, the current density field within the best-fit ellipsoid (i.e., the patient's heart region) is determined in the three-dimensional impedance field DIF, and the evaluation is focused on this key region.

[0118] Within this critical region, the square of the current density field is divided by the conductivity of human tissue to calculate the Joule thermal power generated in this region, which is the energy actually used for electrical stimulation and defibrillation.

[0119] Then, the result of the integration is multiplied by the electrode spacing D_o and divided by the input power G_o to finally obtain the energy transfer efficiency E_eff_o. Therefore, the evaluation result of the energy transfer efficiency E_eff_o is independent of the input power and the electrode geometry, and is a standardized efficiency index.

[0120] SH_o represents the tissue damage risk of the electrode combination ZHo, expressed as: SH_o = max(DL) / SH_safe, where max(DL) is the global maximum value in the current density field DL, and SH_safe is the safe current density threshold preset by the operator based on electrophysiological safety standards and clinical experience. The larger the ratio of max(DL) to SH_safe, the closer the peak current is to or exceeds the safe limit, and the higher the risk.

[0121] SH_max is the preset tissue damage risk threshold for operators. When SH_o approaches SH_max, this score approaches 0, thereby lowering the total score and preventing high-risk use cases.

[0122] D_o is the spatial distance between the two electrodes in the electrode combination ZHo in the three-dimensional impedance field DIF, D_max is the preset maximum spatial distance threshold, which makes the defibrillation operation select a more compact electrode combination with more concentrated energy, α, β, γ are the weighting coefficients preset by the operator based on clinical experience, and α+β+γ=1.

[0123] Finally, the optimal electrode combination was selected based on the comprehensive score of all electrode combinations.

[0124] Then, extract the electrode combination ZHo with the largest comprehensive scoring function value from the electrode combination sequence ZH1, ZH2, ..., ZHu, and mark it as the optimal electrode combination;

[0125] Finally, the system obtains the input electrical power preset by the operator or medical staff for the patient, automatically adjusts the electrodes in the optimal electrode combination to be the discharge electrodes, and de-energizes the other electrodes. The input electrical power is used as the discharge power of the discharge electrodes to perform defibrillation on the patient.

[0126] All data in the formulas described above have been calculated with dimensions removed. Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0127] The above description is merely an example and illustration of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0128] It should be stated that all user data collected in this application was collected with the user's consent and authorization. Furthermore, the uses of user data are legal and compliant, and the use and processing of user data comply with the relevant laws, regulations, and standards of the relevant regions.

Claims

1. An adaptive defibrillation assembly control system based on multi-source data analysis, characterized in that, The system comprises: a multi-electrode array module comprising 8 independently switchable electrodes and arranged in pairs on the surface of the patient's torso; an impedance measurement module for synchronously measuring bioimpedance values in multiple spatial directions based on the multi-electrode array, and constructing a three-dimensional impedance field reflecting the internal impedance distribution of the patient's torso; based on the bioimpedance values of the three-dimensional impedance field, determining a spatial impedance gradient field, and comprehensively evaluating the spatial coordinates of the patient's heart center; determining the main axis direction of the heart, and calculating the myocardial state index based on the bioimpedance values in the three-dimensional impedance field; an electrode optimization module for evaluating the multi-electrode array, determining the current coverage, energy transmission efficiency and tissue damage risk of each electrode combination on the heart region, screening the optimal electrode combination based on a comprehensive scoring function, and performing defibrillation operation on the patient based on the optimal electrode combination.

2. The system of claim 1, wherein, In the multi-electrode array module, the 8 independently switchable electrodes are arranged at right subclavian, left subclavian, left parasternal, right midaxillary line, left subcostal, right subcostal, left back and right back, respectively, and are sequentially summarized as a multi-electrode array E={E1, E2, E3, E4, E5, E6, E7, E8}. The 8 independently switchable electrodes are of the same interface specification and switchable mode, and each electrode integrates a multi-band impedance measurement device and an electrophysiological sensing acquisition device, and the electrodes can be grouped in parallel.

3. The system of claim 2, wherein, In the impedance measurement module, the specific way to construct the three-dimensional impedance field reflecting the internal impedance distribution of the patient's torso is as follows: Divide the multi-electrode array E into three spatial directions of front-back direction, left-right direction and diagonal direction; Obtaining the bioimpedance values Z measured by the electrode combinations [E1, E6], [E2, E5], [E3, E8], [E4, E7] 1-6 2-5 3-8 4-7 1-6 2-5 3-8 4-7 ]​​​​​​​ Obtaining the bioimpedance values Z measured by the electrode combinations [E1, E2], [E3, E4], [E5, E6], [E7, E8] 1-2 3-4 5-6 7-8 , forming a left-right direction impedance vector Z LR = [Z 1-2 3-4 5-6 7-8 ]​​​​​​ The bioimpedance values Z measured by the electrode combinations [E1, E5], [E2, E6], [E3, E7], [E4, E8] are acquired 1-5 2-6 3-7 4-8 The diagonal direction impedance vector Z_DIAG = [Z 1-5 2-6 3-7 4-8 ] is formed.​​​​​​ Connect the bioimpedance values in each impedance vector Z_AP, Z_LR and Z_DIAG based on the corresponding electrode combination, and map them to the three-dimensional space formed by the patient's torso; Generate a three-dimensional impedance field DIF based on the bioimpedance values and the three-dimensional space.

4. The system of claim 3, wherein, In the impedance measurement module, based on the bioimpedance values of the three-dimensional impedance field, the specific way to determine the spatial impedance gradient field is as follows: Define the first electrode E1 in the multi-electrode array E as the coordinate origin of the three-dimensional coordinate system in the three-dimensional impedance field DIF, and denote the spatial coordinates of the electrode E1 as P1(a1, b1, c1). Similarly, determine the spatial coordinates of the other electrodes, which are sequentially represented as: P1(a1, b1, c1), P2(a2, b2, c2),..., P8(a8, b8, c8); Based on the three-dimensional impedance field DIF and the spatial coordinates of the electrode combinations constituting each impedance vector in the three-dimensional space, calculate the spatial impedance gradients in the left-right direction, front-back direction and diagonal direction, respectively; Among them, the spatial impedance gradient ΔZ_x in the left-right direction is obtained by calculating the difference between the bioimpedance values corresponding to the left-right position relationship in the left-right direction impedance vector Z_LR, and dividing by the spatial distance of the corresponding electrode combination in the three-dimensional impedance field DIF; Similarly, determine the spatial impedance gradient ΔZ_y in the front-back direction and the spatial impedance gradient ΔZ_z in the diagonal direction; The spatial impedance gradient field SGF representing the impedance change in the patient's torso is constructed by spatial interpolation based on the bioimpedance values and spatial position relationship between the spatial impedance gradients ΔZ_x, ΔZ_y and ΔZ_z.

5. The system of claim 4, wherein, The specific way of comprehensively evaluating the spatial coordinates of the patient's heart center in the impedance measurement module is: Based on the spatial impedance gradient field SGF, a local spatial impedance gradient vector ΔZi associated with any one electrode Ei is obtained, and the spatial coordinates P_heart of the patient's heart center are calculated using P_heart=(Σ(|ΔZi|×Pi)) / (Σ|ΔZi|), where i is a count index, and the value range is 1 to 8; |ΔZi| represents the length of the local spatial impedance gradient vector ΔZi at the i-th electrode, and Σ represents summation.

6. The system of claim 5, wherein, The specific way of determining the principal axis direction of the heart in the impedance measurement module is: Based on the spatial coordinates P_heart, an optimal fitting ellipsoid representing the geometric characteristics of the heart is determined using an ellipsoid fitting algorithm; The direction matrix of the optimal fitting ellipsoid is obtained and eigenvalue decomposition is performed to obtain three mutually orthogonal eigenvectors, which are defined as the three principal axis directions of the patient's heart, respectively Based on the three principal axis directions, the lengths of the three principal axes are determined based on the optimal fitting ellipsoid and are marked as R1, R2, and R3. The volume of the heart is calculated based on the three principal axis lengths and is denoted as V_heart. Based on the volume of the heart V_heart and the impedance change rate parameter ΔZ_rate extracted from the three-dimensional impedance field DIF, the myocardial state index M_mou is calculated using M_mou=k1×V_heart+k2×ΔZ_rate, where k1 and k2 are empirical constants obtained through clinical data calibration.

7. The system of claim 6, wherein, The impedance change rate parameter ΔZ_rate is determined by using ΔZ_rate=σ_Z / μ_Z, where σ_Z is the standard deviation of the bioimpedance value at the spatial coordinates of each point in the optimal fitting ellipsoid, and μ_Z is the mean value of the bioimpedance value at the spatial coordinates of each point in the optimal fitting ellipsoid. The bioimpedance value at the spatial coordinates of each point in the optimal fitting ellipsoid is directly obtained from the three-dimensional impedance field DIF.

8. The system of claim 7, wherein, The specific way of screening the optimal electrode combination based on the comprehensive score function in the electrode optimization module is: All electrode combinations in the multi-electrode array E are determined, and the total number is denoted as u. They are randomly arranged as electrode combination sequences ZH1, ZH2,..., ZHu; The comprehensive score Sc_o of any one electrode combination ZHo is calculated using Sc_o=α×E_eff_o+β×(1-SH_o / SH_max)+γ×(1-D_o / D_max), where o is a count index, the value range is 1 to u, E_eff_o is the energy transmission efficiency of the electrode combination ZHo, SH_o is the tissue damage risk of the electrode combination ZHo, SH_max is the preset tissue damage risk threshold, D_o is the spatial distance between two electrodes in the electrode combination ZHo in the three-dimensional impedance field DIF, D_max is the preset maximum spatial distance threshold, α, β, γ are preset weight coefficients, and α+β+γ=1; E_eff_o is the energy transmission efficiency of the electrode combination ZHo, E_eff_o = C_o / (G_o x D_o), wherein C_o is the current coverage of the electrode combination ZHo, which is determined by the three-dimensional impedance field DIF and the human tissue conductivity model by applying input electric power between the electrode combination ZHo to generate a current density field DL in the entire three-dimensional impedance field DIF, then integrating the square of the current density field in the best-fitting ellipsoid divided by the human tissue conductivity, and then multiplying the integral result by the electrode spacing D_o; G_o is the input electric power applied to the electrode combination ZHo in the electromagnetic field simulation, which is a known value; The tissue damage risk SH_o = max(DL) / SH_safe, wherein max(DL) is the global maximum value in the current density field DL, and SH_safe is the safe current density threshold value preset by the operator based on electrophysiological safety standards and clinical experience; Based on the comprehensive score of the electrode combination, the optimal electrode combination is screened.

9. The system of claim 8, wherein, In the electrode optimization module, the electrode combination ZHo with the maximum value of the comprehensive score function is extracted from the electrode combination sequence ZH1, ZH2,..., ZHu, and is marked as the optimal electrode combination; The input electric power preset by the operator for the patient is obtained, the electrodes in the optimal electrode combination are controlled as discharge electrodes, the input electric power is used as the discharge power, and the defibrillation operation is performed.

Images