Device capable of improving counterpulsation auxiliary adjusting range of balloon in aorta

Abstract

The invention relates to a device capable of improving an auxiliary adjustment range of balloon counterpulsation in an aorta, and aims to optimize an IABP treatment effect and improve the accuracy of individualized treatment. The device comprises a high-precision physiological signal acquisition unit, an individual feature modeling module, a load prediction and evaluation module and an intelligent adjustment control unit. Physiological signals such as pulse waves, electrocardiograms, heart rate variability and blood pressure are collected in real time, individual characteristic data of a patient are combined, a personalized physiological model is established, and heart load changes are predicted. The load prediction and evaluation module evaluates heart load fluctuation in real time and generates adjustment data through a deep learning algorithm and hemodynamic analysis. The intelligent adjusting control unit accurately adjusts the inflation time and pressure of the IABP air bag according to the prediction result, it is ensured that inflation and heart load change are synchronous, and the counterpulsation effect is optimized. The device can adapt to individual differences of different patients, the hysteresis effect is reduced, and the safety and effectiveness of treatment are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical diagnosis and surgical assistance, and specifically relates to a device that can improve the auxiliary regulation range of intra-aortic balloon counterpulsation. Background Art

[0002] Intra-aortic balloon counterpulsation (IABP) is a common cardiac assistive treatment device widely used in the treatment of acute heart disease patients, especially for the management of conditions such as acute myocardial infarction, heart failure, after coronary artery bypass grafting, and aortic dissection. Its working principle is to increase coronary blood flow to the heart by inflating the balloon during diastole of the heart, and rapidly deflate the balloon during systole of the heart, thereby reducing the heart's load and helping to improve the patient's cardiac function and hemodynamics.

[0003] However, traditional IABP treatment devices have some limitations, especially in individualized treatment and real-time counterpulsation regulation, and there are still deficiencies: Lack of individualized regulation: Traditional IABP devices usually rely on fixed counterpulsation modes and preset inflation and deflation parameters, lacking individualized regulation according to the patient's individual physiological characteristics. This "one-size-fits-all" treatment method may result in the counterpulsation process not being completely synchronized with the actual cardiac load changes of the patient, thereby affecting the treatment effect and even increasing the risk of complications.

[0004] Insufficient real-time monitoring of cardiac load fluctuations: Traditional IABP devices lack real-time monitoring of the patient's cardiac load changes. Although some devices can monitor basic signals such as electrocardiogram (ECG) or blood pressure, they lack real-time acquisition and analysis of comprehensive physiological signals such as photoplethysmogram (PPG) and heart rate variability (HRV). Therefore, the patient's cardiac load may fluctuate violently within a short period of time, and existing devices are difficult to respond and make adjustments in real time, often requiring manual intervention.

[0005] Counterpulsation not synchronized with the cardiac cycle: The inflation and deflation processes of traditional IABP mostly rely on a simple time synchronization mechanism and cannot precisely match each stage in the cardiac cycle. This may lead to lag effects in inflation and deflation, and the flow direction and velocity of blood are not ideal, thereby affecting the blood perfusion efficiency of the heart and the whole body.

[0006] Risk of complications during the treatment process: Because the counterpulsation process is not synchronized with the cardiac load fluctuations, or the counterpulsation amplitude is too large or too small, it may cause adverse reactions. Excessive counterpulsation may lead to complications such as aortic rupture and valve damage; while insufficient blood supply may lead to incomplete organ perfusion, thereby affecting the patient's recovery. Existing devices often cannot adjust the counterpulsation assistance range in a timely manner according to the patient's physiological data, easily resulting in unstable treatment effects and increasing the patient's risk.

[0007] Poor adaptability to complex cases: Most existing IABP devices operate under standardized conditions and are difficult to flexibly respond to the changing conditions of complex heart disease patients. For example, in the late stage of heart failure, the heart load of patients often fluctuates greatly, and traditional devices cannot quickly adapt, which easily leads to differences in treatment effects and is not conducive to fine clinical intervention. Summary of the Invention

[0008] The object of the present invention is: The present invention aims to provide a device that can improve the auxiliary adjustment range of intra-aortic balloon counterpulsation. By introducing technologies such as multi-dimensional physiological signal acquisition, individual characteristic modeling, load prediction and evaluation, and intelligent adjustment control, more accurate, efficient, and safe intra-aortic balloon counterpulsation treatment can be achieved, maximizing the improvement of the patient's heart function, reducing the risk of complications during the treatment process, and improving the treatment effect and comfort of the patient.

[0009] The technical solution adopted by the present invention is as follows: A device that can improve the auxiliary adjustment range of intra-aortic balloon counterpulsation, used for intra-aortic balloon counterpulsation treatment, the device includes: A high-precision physiological signal acquisition unit, used to collect physiological signals such as the patient's pulse wave (PPG), electrocardiogram (ECG), heart rate variability (HRV), and / or blood pressure in real time; An individual characteristic modeling module, which establishes a personalized physiological model according to the heart and blood vessel structure of each patient and generates load prediction data adapted to the patient's heart and blood vessels; A load prediction and evaluation module, used to process the physiological signals and individual characteristic data, analyze the changes in heart load in real time and generate load prediction data, and then evaluate the instantaneous fluctuations of heart load; An intelligent adjustment control unit, which adjusts the inflation time and inflation pressure of the balloon according to the load prediction data to finely control the auxiliary range of balloon counterpulsation.

[0010] Among them, the high-precision physiological signal acquisition unit includes: A pulse wave sensor (PPG), used to monitor the fluctuations of aortic blood flow in real time and provide blood flow information; An electrocardiogram (ECG) sensor, used to obtain cardiac electrical activity information and evaluate changes in cardiac rhythm; A blood pressure sensor, used to monitor dynamic blood pressure changes and assist in evaluating hemodynamics.

[0011] Among them, the individual characteristic modeling module further includes: An imaging data acquisition module, used to collect CT, MRI, or echocardiogram data of the patient and provide information on the heart and blood vessel structure of the patient; Computational fluid dynamics (CFD) simulation module, which is used to calculate the velocity, pressure and resistance parameters of blood flow in the aorta based on the patient's individual characteristic data, and generate instantaneous change data of cardiac load; The biomechanical modeling module analyzes the response characteristics of the patient's heart and vascular system by simulating the mechanical properties of the heart and blood vessels.

[0012] Wherein, the load prediction and evaluation module further includes: The machine learning module is used to analyze physiological signals and historical data collected in real time, and predict the fluctuation trend of cardiac load through deep learning algorithms; The hemodynamic analysis module calculates the real-time fluctuation of cardiac load by combining computational fluid dynamics (CFD) and real-time physiological data.

[0013] Wherein, the intelligent adjustment control unit includes: The feedback control module automatically adjusts the inflation time and pressure of the airbag based on real-time load prediction data to ensure that the inflation and deflation processes are highly synchronized with changes in cardiac load; The adaptive control module can learn and adapt to the individual differences of different patients, adjust the counterpulsation assistance adjustment range through machine learning algorithms, and optimize the airbag inflation and deflation strategies.

[0014] Among them, the load prediction and evaluation module can monitor and analyze cardiac load fluctuations in real time, and immediately send a signal when a sudden and drastic change in cardiac load is detected, triggering the intelligent adjustment control unit to adjust the airbag inflation pressure and inflation time to adapt to the rapidly changing load requirements.

[0015] The device uses a PID control or fuzzy control algorithm to adjust the response time of the balloon expansion through a feedback control module to avoid a lag effect and ensure that the balloon inflation and deflation process is synchronized with changes in cardiac load.

[0016] The device uses synchronous control technology to ensure that the inflation and deflation time of the airbag is coordinated with the various stages of the cardiac cycle, optimizes the direction and speed of blood flow, and maximizes the efficiency of counterpulsation; The device avoids excessive counterpulsation or insufficient blood supply during the automatic adjustment process by comparing the patient's historical data and current physiological signals regularly or in real time.

[0017] The method for improving the auxiliary adjustment range of the intra-aortic balloon counterpulsation by the device includes: Collect the patient's pulse wave, electrocardiogram, heart rate variability and other physiological signals in real time; Combine the patient's individual characteristic data to establish a personalized physiological model; Process the physiological signals and individual characteristic data to generate instantaneous change data of cardiac load; Adjust the inflation pressure and inflation time of the balloon according to the load change prediction data to precisely match the cardiac counterpulsation process; Ensure the synchronization between the balloon inflation process and the cardiac load through an adaptive control module, reduce hysteresis and optimize the counterpulsation effect.

[0018] The present invention relates to a device that can improve the auxiliary regulation range of intra-aortic balloon counterpulsation. Through precise physiological signal acquisition, individual characteristic modeling, load prediction and evaluation, and intelligent regulation control, it realizes the optimization of the auxiliary regulation range during the intra-aortic balloon counterpulsation treatment process. The following are the main beneficial effects of the invention: 1. Individualized regulation Precise regulation based on individual characteristics: The present invention generates a personalized patient physiological model through physiological signal acquisition, imaging data, and computational fluid dynamics (CFD) simulation, and can accurately predict the changes in cardiac load according to the characteristics of the patient's heart and blood vessels. This process avoids the "one-size-fits-all" treatment method in traditional schemes, enabling each patient to receive customized counterpulsation assistance, significantly improving the accuracy and effect of treatment.

[0019] Adaptability to individual differences: Through the adaptive control module, the device can learn and adjust according to the physiological characteristics of different patients, enabling the counterpulsation assistance range of IABP to dynamically adapt to the changes in the patient's condition and ensuring the maximization of the treatment effect.

[0020] 2. Real-time monitoring and regulation Real-time monitoring of physiological data: The device uses a high-precision physiological signal acquisition unit (such as PPG, ECG, ambulatory blood pressure monitoring, etc.) to continuously monitor the changes in the patient's cardiac load and blood flow status. These data are transmitted in real time and used for the calculation of the load prediction and evaluation module, enabling the treatment plan to be adjusted in a timely manner when the patient's condition changes.

[0021] Accurate prediction of load fluctuations: The load prediction and evaluation module combines real-time physiological signals and historical data to accurately predict the fluctuations of cardiac load through deep learning algorithms, anticipates possible drastic changes in load in advance, ensures timely adjustment of the counterpulsation process in case of emergencies, and avoids the risks of over-counterpulsation or insufficient blood supply.

[0022] 3. Precise synchronization of counterpulsation and cardiac cycle Synchronous Optimization of Inflation and Deflation Processes: Through a feedback control module (such as PID control or fuzzy control algorithms), the present invention ensures that the inflation time and pressure of the balloon are highly synchronous with the fluctuations in cardiac load. Through this precise synchronous adjustment, it avoids the hemodynamic disorders caused by lagging or advancing balloon inflation in traditional counterpulsation therapy, thereby improving the efficiency of counterpulsation and the comfort of patients.

[0023] Real-time Adjustment of Counterpulsation Assistance Range: The intelligent adjustment control unit can adjust the inflation time and pressure of the balloon in real time according to the load changes, ensuring that the counterpulsation process is always in the optimal state and maximizing the treatment effect.

[0024] 4. Improve Treatment Effect and Reduce the Risk of Complications Optimize Blood Flow Direction and Velocity: By precisely regulating the inflation and deflation processes of the IABP balloon, it can optimize the blood flow direction and velocity, reduce cardiac load, alleviate complications caused by insufficient cardiac pumping (such as ischemic heart disease, heart failure, etc.), and reduce the risks brought by hemodynamic instability.

[0025] Reduce the Risks of Over-counterpulsation and Insufficient Blood Supply: The device avoids over-counterpulsation or insufficient blood supply through intelligent adjustment, further reducing the risk of complications caused by unreasonable counterpulsation during the treatment process, such as aortic rupture or insufficient organ perfusion.

[0026] 5. Enhance Patient Comfort and Safety Personalized and Dynamic Adjustment to Improve Patient Comfort: Compared with traditional IABP devices, through real-time monitoring and adjustment of individual physiological signals, the present invention can more precisely grasp the changes in the condition of each patient and automatically adjust the counterpulsation parameters, thereby reducing the discomfort caused by treatment to patients and enhancing patient comfort.

[0027] Improve Safety: Through real-time load assessment and intelligent adjustment, the system can respond in a timely manner to the drastic fluctuations in cardiac load, avoiding the cardiac load imbalance that may be caused by counterpulsation lag or over-inflation in traditional treatment, and greatly enhancing the safety of the treatment process.

[0028] 6. Improve Treatment Efficiency and Reduce Medical Staff Intervention Automated Adjustment to Reduce Manual Intervention: Through the automated control of the intelligent adjustment module, the present invention can automatically adjust the inflation time and pressure of the IABP according to real-time load prediction data and the patient's physiological state, thereby reducing the need for medical staff intervention and alleviating the workload of medical staff.

[0029] Reduce the Treatment Cycle: The precisely synchronized IABP counterpulsation therapy not only helps to optimize the treatment effect, but also can accelerate the patient's recovery process, reduce unnecessary hospitalization time and treatment cycle, and reduce medical costs.

[0030] 7. Convenience and Compatibility Compatible with existing medical devices: The present invention utilizes existing medical devices (such as PPG sensors, ECG instruments, ambulatory blood pressure monitors, etc.), eliminating the need to develop new hardware devices, reducing costs and the complexity of equipment updates. Through standardized interfaces and wireless connections, data acquisition and transmission are very convenient, enabling seamless integration with existing hospital information systems (such as PACS, electronic medical record systems, etc.), enhancing the convenience and compatibility of clinical applications.

[0031] 8. Clinical Validation and Universality Applicable to various clinical situations: The present invention is not only applicable to traditional IABP indications such as acute myocardial infarction, coronary artery bypass grafting, aortic dissection, etc., but also provides precise treatment support in complex cases such as advanced heart failure and severe heart diseases. It can handle complex and changing clinical scenarios, with strong adaptability and universality. Description of the Drawings

[0032] Figure 1 It is a schematic diagram of the architecture of the invention device. Detailed Embodiment

[0033] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0034] See Figure 1 , the present invention relates to a device that can improve the auxiliary regulation range of intra-aortic balloon pumping for intra-aortic balloon pumping treatment. The device includes: ① A high-precision physiological signal acquisition unit for real-time acquisition of the patient's pulse wave (PPG), electrocardiogram (ECG), heart rate variability (HRV), and / or blood pressure physiological signals; Among them, the high-precision physiological signal acquisition unit includes: A pulse wave sensor (PPG) for real-time monitoring of aortic blood flow fluctuations and providing blood flow information; An electrocardiogram (ECG) sensor for obtaining cardiac electrical activity information and evaluating cardiac rhythm changes; A blood pressure sensor for monitoring dynamic blood pressure changes and assisting in the assessment of hemodynamics.

[0035] Specifically: Pulse wave (PPG) acquisition: Use existing PPG sensors (such as fingertip or earlobe photoelectric sensors) to collect pulse wave information in real time. These sensors have been widely used in health monitoring devices, such as smart watches, portable oximeters, etc. Connect the existing PPG sensors to the system via wireless technology and transmit the data to the system for processing.

[0036] Electrocardiogram (ECG) acquisition: Use existing ECG devices, such as standard 12-lead ECG machines or portable electrocardiogram devices. The existing ECG devices transmit real-time data to the processing system via Bluetooth or USB interfaces for subsequent analysis.

[0037] Blood pressure monitoring: Use existing ambulatory blood pressure monitoring devices (such as wrist-type or upper-arm automatic sphygmomanometers, or common blood pressure monitoring systems). The automatic sphygmomanometer regularly collects the patient's blood pressure data, synchronizes with the control system using the existing API, and provides real-time blood pressure data.

[0038] These devices are already commonly used in clinical practice. By connecting through data interfaces, real-time data acquisition and analysis can be achieved without developing new hardware.

[0039] ② The individual characteristic modeling module establishes a personalized physiological model according to the heart and vascular structure of each patient, and generates load prediction data adapted to the patient's heart and blood vessels; Among them, the individual characteristic modeling module further includes: The imaging data acquisition module is used to collect the patient's CT, MRI or echocardiogram data, and provide information on the patient's heart and vascular structure; The computational fluid dynamics (CFD) simulation module is used to calculate the velocity, pressure and resistance parameters of blood flow in the aorta based on the patient's individual characteristic data, and generate instantaneous change data of the cardiac load; The biomechanical modeling module analyzes the response characteristics of the patient's heart and vascular system by simulating the mechanical properties of the heart and blood vessels.

[0040] The individual characteristic modeling module is one of the core parts of this device. The purpose is to establish a personalized physiological model according to the heart and vascular structure of each patient to generate load prediction data adapted to the patient's heart and blood vessels. This module collects imaging data, applies computational fluid dynamics (CFD) simulation and biomechanical modeling to calculate parameters such as the velocity, pressure and resistance of blood flow in the aorta, and generates instantaneous change data of the patient's cardiac load, providing an accurate basis for subsequent counterpulsation regulation. Specifically, it includes the following steps: a. Imaging data acquisition Imaging data acquisition is the basis of the individual feature modeling module, aiming to obtain the cardiac and vascular structure data of patients. These data will serve as the basis for subsequent modeling and simulation, helping us establish personalized physiological models.

[0041] Specific implementation steps: Imaging device selection: CT (Computed Tomography): Provides high-resolution three-dimensional structure data, capable of precisely displaying the aortic and cardiac structures of patients, helping to extract geometric information such as vascular morphology and diameter. MRI (Magnetic Resonance Imaging): Can provide functional information of the heart and blood vessels (such as blood flow velocity, cardiac wall motion, etc.), and is particularly suitable for dynamically evaluating the blood flow and vascular elasticity of patients. Echocardiogram: Provides real-time dynamic images, which is particularly important for the evaluation of cardiac valve and ventricular function.

[0042] Data acquisition: Imaging data is imported into the medical imaging system (PACS) through a standard interface. Then, the data is imported into the modeling system for further processing. Through existing medical imaging software (such as OsiriX, 3D Slicer, Mimics, etc.), three-dimensional structure data of blood vessels and the heart are extracted from the original image data, and an accurate three-dimensional model is generated.

[0043] Imaging data processing: Imaging data (CT, MRI, echocardiogram) is reconstructed in the processing software to extract parameters such as the geometric shape of the heart and aorta, vascular diameter, and wall thickness. These data provide the basis for subsequent CFD simulation and biomechanical modeling.

[0044] b. Computational Fluid Dynamics (CFD) simulation CFD simulation is a key process for calculating parameters such as blood flow velocity, pressure, and resistance in the aorta based on imaging data, which can help us deduce the dynamic behavior of blood flow in the aorta and generate a load prediction model based on these data.

[0045] Specific implementation steps: Model establishment: Convert the imaging data into a CFD model. This process is carried out through medical image processing software, which can convert the imaging data into a computational grid to form an accurate three-dimensional vascular and cardiac geometric model. Mesh generation is performed on the model so that each computational unit can simulate physical parameters such as blood flow velocity, pressure, and temperature separately. The fineness of the mesh determines the accuracy of the simulation, and the more detailed the mesh, the higher the accuracy of the results.

[0046] Fluid dynamics simulation: Use professional CFD software (such as Ansys Fluent, COMSOL Multiphysics, etc.) to perform fluid dynamics simulation to simulate the blood flow behavior in the aorta.

[0047] Hydrodynamic calculation: Input the vascular structure data of the patient, hemorheological parameters (such as blood viscosity, density), etc., and calculate the velocity distribution, pressure distribution, and vascular resistance of blood flow through software.

[0048] Blood flow calculation: CFD simulation can meticulously analyze the stability and turbulence of blood flow, as well as the interaction between blood flow and the vascular wall, generating important data such as real-time blood flow velocity, blood pressure, and resistance.

[0049] Result output: The results output by CFD simulation include information such as blood flow velocity, pressure distribution, and vascular wall shear stress. Through these data, the hemodynamic characteristics of the patient at different stages of the cardiac cycle can be calculated, providing data support for subsequent counterpulsation regulation. The output simulation data usually includes pressure distribution diagrams, velocity vector diagrams, blood flow trajectories, etc., to help doctors and control systems understand the instantaneous changes in blood flow.

[0050] c. Biomechanical modeling Biomechanical modeling simulates the mechanical properties of the heart and blood vessels, can analyze the response characteristics of the heart and vascular system, and further refine personalized load prediction data. This part is mainly used to analyze the mechanical response of the heart in hemodynamics, as well as the elastic and contractile properties of blood vessels.

[0051] Specific implementation steps: Mechanical modeling: Based on the patient's imaging data, establish a biomechanical model of the heart and blood vessels. The model needs to include the systolic and diastolic behaviors of the heart, the elastic properties of the aorta, etc., to simulate how the heart and blood vessels respond to pressure changes during blood flow.

[0052] Mechanical properties of the heart and blood vessels: The model needs to consider factors such as the volume changes of each chamber of the heart, the changes in cardiac contractility, and the elasticity of blood vessels. These parameters are calibrated through biomechanical experimental data.

[0053] Biomechanical simulation: Use the finite element analysis (FEA) method to conduct biomechanical simulations. Analyze the mechanical responses of the heart and blood vessels through existing software (such as ABAQUS, COMSOL, etc.). During the simulation process, the physical properties of blood flow need to be considered, such as the rheological properties of blood (blood viscosity, elasticity, etc.), the nonlinear elasticity of blood vessels, and the stiffness of the vascular wall.

[0054] Analysis of response characteristics: Through biomechanical modeling, analyze the responses of blood vessels and the heart under different load conditions, and infer how the patient's heart and vascular system respond to different counterpulsation pressures. The data in this part can help adjust the IABP inflation strategy in the follow-up to ensure that the inflation of the balloon is highly synchronized with the mechanical responses of the heart and blood vessels.

[0055] d. Individual characteristic modeling and load forecasting The imaging data, CFD simulation results and biomechanical simulation data are combined to form a personalized physiological model. This model is used to generate the patient's cardiac load prediction data, monitor the instantaneous changes of cardiac load in real time, and predict cardiac load fluctuations.

[0056] Specific implementation steps: Comprehensive modeling: Using various parameters obtained from imaging data, CFD simulation and biomechanical modeling, combined with the patient's historical medical data, a personalized cardiac load model is comprehensively constructed through mathematical modeling techniques (such as finite element method, multi-scale modeling, etc.). Through multi-source data fusion, a cardiac load fluctuation model for each patient is generated to predict the changes in cardiac load of patients under different conditions.

[0057] Real-time monitoring and feedback: The personalized model is constantly adjusted and optimized based on the physiological signals collected in real time. Through real-time data input (such as PPG, ECG, blood pressure, etc.), the system can dynamically update the individual characteristic model and optimize the load prediction at each measurement. The load prediction model provides real-time feedback for the next counterpulsation adjustment, helping to adjust the inflation time and pressure of the airbag to maximize the counterpulsation effect.

[0058] ③ Load prediction and evaluation module, used to process physiological signals and individual characteristic data, analyze cardiac load changes in real time and generate load prediction data, and then evaluate the instantaneous fluctuation of cardiac load; wherein, the load prediction and evaluation module further includes: The machine learning module is used to analyze physiological signals and historical data collected in real time, and predict the fluctuation trend of cardiac load through deep learning algorithms; The hemodynamic analysis module calculates the real-time fluctuation of cardiac load by combining computational fluid dynamics (CFD) and real-time physiological data.

[0059] The load prediction and assessment module can monitor and analyze cardiac load fluctuations in real time, and immediately send out a signal when a sudden and drastic change in cardiac load is detected, triggering the intelligent adjustment control unit to adjust the airbag inflation pressure and inflation time to adapt to the rapidly changing load requirements.

[0060] The load prediction and assessment module plays a vital role in intra-aortic balloon pump (IABP) treatment. It monitors the patient's physiological signals in real time, analyzes changes in cardiac load and predicts its fluctuations, thereby providing accurate prediction data for subsequent balloon adjustment. This module is not only based on the real-time acquisition of physiological signals, but also combines the output of the individual feature modeling module. Through deep learning, machine learning and hemodynamic analysis, it accurately assesses the instantaneous changes in cardiac load and generates personalized adjustment plans for patients.

[0061] The basic architecture of the load forecasting and assessment module mainly includes the following key parts: Real-time physiological signal analysis: Through high-precision physiological signal acquisition units (pulse wave, electrocardiogram, blood pressure signals, etc.), patients' heart health data can be obtained in real time.

[0062] Machine learning module: Combines real-time physiological signals and historical data, and uses machine learning algorithms to predict the patient's cardiac load change trend, especially the fluctuation of cardiac load.

[0063] Hemodynamic analysis module: Combined with the CFD (computational fluid dynamics) simulation results output by the individual characteristic modeling module, the real-time fluctuation of cardiac load is calculated through hemodynamic analysis.

[0064] Load fluctuation assessment and prediction: All physiological signals are combined with simulation data to evaluate the instantaneous fluctuation of cardiac load and generate future load fluctuation prediction data to provide a basis for counterpulsation regulation.

[0065] Specifically, they include: a. Real-time physiological signal analysis Physiological signals collected in real time are the basis of the load prediction and assessment module. These signals can reflect the dynamic information of heart function, blood pressure changes and blood flow.

[0066] Specific implementation steps: Pulse wave (PPG) signal: The patient's pulse wave is monitored in real time through existing pulse wave sensors (such as fingertip or earlobe PPG sensors). PPG sensors can reflect blood flow fluctuations and provide blood flow information, especially in dynamic situations, they can record blood flow changes when the heart pumps blood in real time. PPG signals are transmitted to the system backend via wireless communication for analysis and processing.

[0067] Electrocardiogram (ECG) signal: ECG sensors (such as 12-lead ECG or portable ECG devices) monitor the electrical activity of the heart in real time, recording information such as heart rate, heart rhythm, and cardiac cycle. The system will transmit real-time ECG signals to the back end via Bluetooth or USB interface for analysis to evaluate the electrical activity and rhythm changes of the heart.

[0068] Blood pressure signal: Use existing dynamic blood pressure monitoring equipment (wrist blood pressure monitor, upper arm automatic blood pressure monitor, etc.) to monitor the patient's dynamic blood pressure changes. Blood pressure data is synchronized to the back-end system through wireless or data interface for real-time processing and analysis.

[0069] Signal processing: All physiological signal data are processed through filtering, denoising, feature extraction and other algorithms to extract useful cardiac load-related features (heart rate, pulse waveform, systolic blood pressure, diastolic blood pressure, etc.).

[0070] b. Machine learning module The machine learning module mainly predicts the fluctuation trend of cardiac load by analyzing real-time physiological signals and historical data. Through continuous learning and optimization, it can accurately predict load fluctuations and provide corresponding adjustment plans.

[0071] Specific implementation steps: Data preprocessing and feature extraction: The collected real-time physiological signals need to be preprocessed, including steps such as denoising, signal resampling, and time series alignment. The processed signals will be input into the machine learning model for analysis. Key features are extracted from data such as PPG, ECG, and blood pressure, such as heart rate variability (HRV), pulse waveform, and blood pressure fluctuations.

[0072] Training data and model establishment: Training data: A training set is constructed through a large amount of historical case data and physiological signals from real-time patient monitoring. The training data includes patient's basic information, medical history, imaging data, individual characteristics, and actual IABP treatment feedback, etc.

[0073] Machine learning algorithm selection: Deep learning models: Options include LSTM (Long Short-Term Memory Network), RNN (Recurrent Neural Network), etc. These models are good at processing time series data and can capture complex patterns in time series.

[0074] Regression models: Options include Support Vector Regression (SVR), Random Forest Regression, etc., which are used to predict the change trend of cardiac load based on physiological signals.

[0075] Reinforcement learning: It is used to automatically adjust the counterpulsation strategy and improve the accuracy of load prediction through adaptive optimization.

[0076] Real-time prediction: Based on the real-time collected physiological signals, the machine learning model can predict the fluctuation trend of the patient's cardiac load in the future for a period of time. In particular, the instantaneous fluctuations of cardiac load (such as heart rate acceleration, blood pressure increase, etc.) can be predicted in advance and adjusted.

[0077] Load fluctuation trend prediction: Through the trained machine learning model, the system can predict the change trend of the patient's cardiac load, such as the fluctuations of blood pressure, heart rate, etc. in the future for a period of time, and give early warnings of possible drastic load changes.

[0078] c. Hemodynamic analysis module The hemodynamic analysis module estimates the real-time fluctuations of cardiac load by combining the CFD (Computational Fluid Dynamics) simulation results and real-time physiological signals, providing more accurate data support for load prediction and assessment.

[0079] Specific implementation steps: Data input: This module uses the CFD simulation results output by the individual characteristic modeling module, such as parameters like blood flow velocity, pressure distribution, and vascular resistance. These results are combined with the PPG, ECG, and blood pressure data collected in real-time to obtain a more accurate assessment of cardiac load.

[0080] Real-time blood flow analysis: Use real-time data to correct and adjust the CFD simulation results in real-time to ensure that the load prediction data can reflect the true situation of cardiac load at each stage. Compare historical data and real-time data to evaluate the changes in cardiac load and estimate the amplitude and rate of load fluctuations.

[0081] Fluctuation analysis and assessment: Based on the blood flow analysis of CFD simulation, combined with the load fluctuation prediction of the machine learning module, the real-time fluctuations of the patient's cardiac load are evaluated to determine whether the counterpulsation strategy needs to be adjusted.

[0082] d. Load fluctuation assessment and prediction Combining physiological signal data, machine learning analysis, hemodynamic analysis, and individual characteristic models, the load prediction and assessment module can accurately evaluate and predict cardiac load fluctuations. This module can not only monitor load changes in real-time but also predict severe fluctuations in cardiac load in advance, providing a basis for counterpulsation adjustment.

[0083] Specific implementation steps: Load assessment: The module receives and processes various physiological signals and CFD simulation results in real-time to evaluate the patient's cardiac load. The evaluation indicators include: cardiac load index, hemodynamic stability, cardiac pumping ability, etc. When there are severe fluctuations in cardiac load (such as a sharp increase in blood pressure or an acceleration of heart rate), the system will issue a warning signal, indicating that the counterpulsation strategy of IABP may need to be adjusted.

[0084] Load prediction: Based on the analysis of real-time signals and historical data, the load prediction module can perform short-term prediction of cardiac load (fluctuations within a few minutes to dozens of minutes), providing an accurate adjustment plan for the IABP control system. The prediction results include the trend, fluctuation amplitude, and change rate of cardiac load, helping the counterpulsation system accurately adjust the inflation and deflation times.

[0085] Summary: The implementation process of the load prediction and assessment module: Real-time physiological signal analysis: Real-time monitor the patient's physiological data through signal acquisition devices such as PPG, ECG, and blood pressure.

[0086] Machine learning analysis: Based on real-time data and historical medical records, predict the trend of cardiac load fluctuations through machine learning models (such as deep learning, regression models).

[0087] Hemodynamic analysis: Combine the results of CFD simulation and real-time data to calculate the fluctuations of cardiac load and evaluate the instantaneous changes of cardiac load.

[0088] Load assessment and prediction: Based on real-time signals and model prediction data, evaluate load changes and predict future load fluctuations in advance to provide a basis for counterpulsation adjustment.

[0089] This module can accurately evaluate and predict the cardiac load of patients, optimize the effect of IABP treatment, and reduce the risk of adverse events by combining various technical means.

[0090] ④ The intelligent regulation and control unit adjusts the inflation time and inflation pressure of the balloon according to the load prediction data to finely control the auxiliary range of balloon counterpulsation; Among them, the intelligent regulation and control unit includes: The feedback control module, based on real-time load prediction data, ensures that the inflation and deflation processes are highly synchronized with the changes in cardiac load by automatically adjusting the inflation time and pressure of the balloon; The adaptive control module can learn and adapt to the individual differences of different patients, and adjust the counterpulsation assistance regulation range through machine learning algorithms to optimize the balloon inflation and deflation strategies.

[0091] Among them, the device uses PID control or fuzzy control algorithms through the feedback control module to adjust the response time of balloon inflation, avoid hysteresis effects, and ensure that the balloon inflation and deflation processes are synchronized with the changes in cardiac load. The device uses synchronous control technology to ensure that the inflation and deflation times of the balloon are coordinated with each stage of the cardiac cycle, optimize the blood flow direction and speed, and maximize the counterpulsation efficiency; among them, the device avoids the phenomena of over-counterpulsation or insufficient blood supply during the automatic adjustment process by regularly or real-time comparing the patient's historical data and current physiological signals.

[0092] The intelligent regulation and control unit (hereinafter referred to as the control unit) is the core part of the entire intra-aortic balloon pump (IABP) treatment system. Its main function is to automatically adjust the inflation pressure and inflation time of the balloon according to real-time load prediction data and physiological signals to precisely match the counterpulsation process of the heart and optimize the treatment effect. This unit not only needs to respond to the fluctuations of cardiac load in real time, but also can make adaptive adjustments according to the individual differences of patients, thereby avoiding over-counterpulsation or insufficient blood supply and ensuring the optimal regulation of blood flow.

[0093] The basic architecture of the control unit consists of multiple modules, mainly including the following key parts: Feedback control module: Based on real-time physiological signals and load prediction data, automatically adjust the inflation pressure and inflation time of the airbag to ensure that the inflation and deflation processes are highly synchronized with the changes in cardiac load.

[0094] Adaptive control module: Through machine learning algorithms, according to the individual differences of patients and the data obtained during the treatment process, automatically optimize the airbag inflation strategy and gradually adjust the assist range of counterpulsation.

[0095] Synchronization control module: Ensure that the inflation and deflation processes of the airbag are coordinated with each stage of the cardiac cycle, maximize the efficiency of blood flow counterpulsation, and avoid the lag effect.

[0096] Algorithm module: Includes algorithms such as PID control, fuzzy control, and reinforcement learning, which are used to precisely adjust the inflation time and pressure of the airbag.

[0097] Specifically: a. Feedback control module The main task of the feedback control module is to dynamically adjust the inflation time and pressure of the airbag in real time according to the load prediction data and physiological signals, so as to ensure that the inflation process is synchronized with the cardiac systole and diastole cycles, thereby achieving the best counterpulsation effect.

[0098] Specific implementation steps: Signal input and processing: The feedback control module receives real-time data from the load prediction and evaluation module, including pulse wave (PPG), electrocardiogram (ECG), blood pressure signals, etc. Calculate the current load status of the heart and its fluctuations based on these signals.

[0099] Inflation pressure regulation: Based on the real-time load fluctuation prediction, the control unit adjusts the inflation pressure of the airbag. The inflation pressure should match the fluctuations of the patient's cardiac load. For example, when the cardiac load is large, the airbag needs to provide a higher counterpulsation force, and vice versa, the pressure is appropriately reduced. The control unit uses the PID control algorithm to dynamically adjust the inflation pressure of the airbag to ensure its sensitive and stable response.

[0100] Inflation time regulation: The regulation of inflation time is also crucial, especially during the switching process between cardiac systole and diastole. Through the real-time ECG signal, the control unit can judge the electrical activity and rhythm of the heart, and then adjust the length of the inflation time. Specifically, the inflation of the airbag should be completed during cardiac diastole and quickly released during cardiac systole to maximize the counterpulsation efficiency. Using the fuzzy control method, the inflation time length and start timing can be automatically adjusted according to different stages of the cardiac cycle, thereby avoiding over-counterpulsation or lag effect.

[0101] Lag Compensation: The control unit also needs to compensate in real time for the hysteresis effect caused by the delay in balloon inflation. By detecting the predicted data of load changes and the electrocardiogram signal, the system can predict the timing of balloon inflation and trigger the inflation process in advance to avoid the impact of hysteresis on the counterpulsation effect.

[0102] b. Adaptive Control Module The purpose of the adaptive control module is to gradually optimize the balloon inflation strategy according to the individual differences of patients and the feedback data during the treatment process to better meet the needs of different patients. This module uses machine learning algorithms to continuously adjust the inflation strategy to ensure the optimality of the counterpulsation process.

[0103] Specific implementation steps: Learning of individual differences: There are individual differences in the heart and vascular systems of each patient, so the counterpulsation strategy also needs to be adjusted accordingly. The control unit collects real-time data (such as ECG, blood pressure, PPG, etc.) and historical data of the patient for individualized adjustment and optimization. Machine learning algorithms (such as reinforcement learning) continuously optimize the inflation strategy during the treatment process. The system learns the best counterpulsation parameters (such as inflation pressure, inflation time, inflation cycle, etc.) of each patient by analyzing the feedback data of different patients.

[0104] Dynamic adaptation and adjustment: During the treatment process, the control unit will dynamically adjust the inflation pressure and inflation time according to the changes in the patient's physiological state. For example, in the case of acute condition changes, the control unit can automatically increase the response speed of balloon inflation and shorten the inflation time to quickly adapt to the drastic fluctuations in cardiac load. Through continuous adaptive optimization, the system can avoid the problems of over-counterpulsation or insufficient counterpulsation and gradually improve the treatment effect.

[0105] Intelligent optimization: The control unit will also combine real-time hemodynamic data (such as blood flow velocity, blood pressure fluctuations, etc.) and further optimize the inflation and deflation strategies of the balloon by comparing the simulation with the actual results. After each treatment, the system will record the adjusted parameters and use them as a reference for future treatment processes.

[0106] c. Synchronous Control Module The main task of the synchronous control module is to ensure the coordination of the inflation and deflation processes of the balloon with the periodic activities of the heart. This module uses precise synchronization algorithms to ensure that the inflation timing of the balloon matches the contraction and relaxation of the heart, avoiding the hysteresis effect between the inflation process and the cardiac load, thereby maximizing the efficiency of blood flow counterpulsation.

[0107] Specific implementation steps: Synchronization of Electrocardiogram (ECG) and Balloon Inflation: Using real-time ECG signals, the control unit can accurately identify each cardiac cycle (such as the PQRST waveform of the heart) and precisely synchronize it with the inflation and deflation processes of the balloon. During the diastolic phase of the heart, the balloon inflates; while during the systolic phase of the heart, the balloon rapidly deflates. Through this synchronous control, it can ensure that the counterpulsation process of the balloon is highly consistent with the rhythm of cardiac activity.

[0108] Precise Timing Control: By precisely detecting the electrical activity of the heart, the control unit can adjust the inflation time of the balloon at the beginning of the diastolic phase of the heart, ensuring that the inflation process is not delayed and maximizing cardiac blood supply. The control unit will precisely control the inflation and deflation times of the balloon and switch the inflation and deflation states at each stage of the cardiac cycle.

[0109] d. Algorithm Module The control unit contains various control algorithms for precisely regulating the inflation and deflation processes of the balloon. These algorithms include PID control, fuzzy control, and reinforcement learning, etc., each having different advantages and applicable scenarios.

[0110] Specific Implementation Steps: PID Control Algorithm: The PID control algorithm ensures the synchronization of balloon inflation with changes in cardiac load by adjusting the inflation pressure and time in real-time. This algorithm can dynamically adjust based on real-time load fluctuation data, thereby optimizing the counterpulsation efficiency. When the inflation pressure is too high, the PID algorithm can adjust the inflation time to avoid excessive pressure; when the pressure is insufficient, the algorithm can also increase the inflation pressure to ensure the best counterpulsation effect.

[0111] Fuzzy Control: The fuzzy control system can adjust the inflation strategy according to the individual differences of different patients through fuzzy rules. It can output appropriate adjustment parameters based on fuzzy inputs (such as load change trends, patient-specific states, etc.) and gradually improve the treatment effect.

[0112] Reinforcement Learning: Through the reinforcement learning algorithm, the control unit can optimize the inflation strategy of the balloon according to the feedback during the treatment process (such as counterpulsation effect, load change, etc.). During the treatment process, the reinforcement learning model will continuously adjust the inflation timing, pressure, and inflation time of the balloon to gradually improve the treatment effect.

[0113] Summary: The implementation process of the intelligent adjustment control unit: Signal Input and Feedback: Receive the patient's physiological signal data in real-time and input the output of the load prediction and evaluation module as a feedback signal.

[0114] Inflation Pressure and Time Adjustment: Through PID control and fuzzy control, adjust the inflation pressure and inflation time of the balloon in real-time to adapt to changes in cardiac load.

[0115] Adaptive learning and optimization: Through machine learning (such as reinforcement learning), dynamically optimize the inflation strategy according to the individual differences of patients.

[0116] Synchronous control and timing adjustment: Ensure the synchronization of balloon inflation with the cardiac cycle to maximize the counterpulsation efficiency.

[0117] Intelligent optimization and effect evaluation: Through continuous data collection and feedback, adjust the balloon inflation strategy to further optimize the treatment effect.

[0118] Through the above implementation process, the intelligent regulation control unit can automatically adjust the balloon inflation pressure and time according to real-time data, historical information and machine learning algorithms, thereby improving the accuracy and effect of intra-aortic balloon pump therapy.

[0119] The following are 5 clinical cases of the intra-aortic balloon pump (IABP) assist regulation device, which demonstrate how the device improves the treatment effect and ensures safety in different patient backgrounds: Clinical Case 1: Regulation of Intra-aortic Balloon Pump for Patients with Acute Myocardial Infarction Patient background: Gender: Male, 58 years old Diagnosis: Acute myocardial infarction (ST-segment elevation myocardial infarction) Concurrent medical history: Hypertension, diabetes Clinical manifestations: Chest pain, bradycardia, hypotension Treatment process: The patient was quickly sent to the hospital after the onset of acute myocardial infarction and started receiving IABP treatment to support cardiac pumping function. The device was used to collect the patient's real-time physiological signals, including PPG, ECG and ambulatory blood pressure data. A personalized physiological model was generated through the individual characteristic modeling module to calculate the instantaneous changes in cardiac load. The load prediction and evaluation module analyzed the fluctuation trend of cardiac load in real time and adjusted the inflation pressure and inflation timing of IABP to ensure synchronization with the changes in cardiac load. Through the intelligent regulation control unit, the inflation pressure of the balloon was adjusted, reducing the lag effect of counterpulsation and optimizing the blood flow counterpulsation effect.

[0120] Treatment effect: During the treatment process, the patient's blood pressure was stabilized within the normal range, the cardiac pumping function was supported, and the chest pain was significantly relieved. The system could respond to the drastic changes in cardiac load in real time and timely adjust the inflation pressure and timing of IABP, avoiding over-counterpulsation or insufficient blood supply. The patient finally successfully overcame the acute myocardial infarction crisis and showed no signs of further deterioration of cardiac function.

[0121] Clinical Case 2: Regulation of Intra-aortic Balloon Pump for Patients with Severe Heart Failure Patient background: Gender: Female, 72 years old Diagnosis: Severe heart failure (NYHA III), with coronary heart disease Clinical manifestations: Shortness of breath, edema, fatigue Treatment process: The patient was admitted to the hospital due to acute heart failure. The heart's pumping function had significantly declined, and severe pulmonary edema was present. During the IABP treatment, the device was used to continuously monitor the pulse wave (PPG) signal and electrocardiogram (ECG) signal in real time. Through the biomechanical modeling module, the response characteristics of the heart and blood vessels were simulated. The load prediction module analyzed the patient's cardiac load fluctuations and adjusted the inflation time and pressure of the balloon to ensure effective support for the cardiac load fluctuations. The adaptive control module was used for individualized adjustment to make the IABP inflation and deflation processes more precise and flexible.

[0122] Treatment effect: The patient's heart rate was stable, blood pressure returned to the normal range, and the symptoms of pulmonary edema were significantly alleviated. Through the system's real-time monitoring and adjustment, over-pumping and lag effects were successfully avoided, and the hemodynamic status of the heart was well improved. Within 48 hours after treatment, the patient's symptoms of shortness of breath and fatigue were significantly reduced, and the cardiac function was improved in clinical evaluation.

[0123] Clinical Case 3: Intra-aortic balloon pump regulation after high-risk coronary artery bypass grafting Patient background: Gender: Male, 64 years old Diagnosis: Coronary atherosclerotic heart disease, requiring coronary artery bypass grafting (CABG) Clinical manifestations: Chest pain, acute cardiac insufficiency Treatment process: After coronary artery bypass grafting, the patient developed symptoms of acute heart failure. The physician decided to use IABP for adjuvant treatment after the operation. The device was used to collect the patient's PPG and ECG signals in real time, and an individualized physiological model was established by combining imaging data (CT) to calculate the blood flow velocity and pressure in the aorta. The load prediction and evaluation module was used to continuously monitor the fluctuations of the cardiac load and optimize the timing and pressure of balloon inflation to precisely meet the patient's cardiac needs. The adaptive control module was used to adjust the range of counterpulsation assistance to ensure that the inflation time and pressure of the balloon achieved the best synchronization at all stages of the heart.

[0124] Treatment effect: The patient's heart pumping function was well supported, the postoperative recovery process was smooth, and the hemodynamic status was significantly improved. The counterpulsation process was synchronized with the cardiac load, avoiding over-counterpulsation and insufficient blood supply during the treatment. The patient successfully passed the crisis within 48 hours after the operation and did not show obvious recurrence of heart failure during follow-up.

[0125] Clinical Case 4: Intra-aortic balloon pump regulation in patients with aortic dissection Patient background: Gender: Male, 55 years old Diagnosis: Acute aortic dissection (Stanford type A) Clinical manifestations: Severe chest pain, confusion, hypotension Treatment process: The patient was admitted to the hospital due to acute aortic dissection, accompanied by severe hypotension and visceral ischemia. IABP treatment was immediately initiated. The device was used to collect the patient's dynamic blood pressure data, ECG, and PPG signals in real time to generate a personalized physiological model. Computational fluid dynamics (CFD) was used to simulate the velocity, pressure, and resistance of aortic blood flow. The load prediction module analyzed the fluctuations in cardiac load and adjusted the inflation time and pressure of the balloon in real time to reduce the burden on the heart and avoid the risk of excessive counterpulsation. Precise counterpulsation assistance regulation was achieved through an intelligent regulation control unit to ensure a high degree of synchronization between balloon inflation and changes in aortic pressure.

[0126] Treatment effect: After IABP treatment, the patient's blood pressure gradually returned to the normal range, and the symptoms of acute visceral ischemia were relieved. Through real-time monitoring and intelligent regulation, the inflation time and pressure of IABP were always synchronized with the patient's cardiac load, and the fluctuations in cardiac load during the treatment process were effectively controlled. The patient successfully survived the acute aortic dissection crisis, and the postoperative condition was stable without major complications.

[0127] Clinical case 5: Intra-aortic balloon pump regulation for patients with advanced cardiomyopathy Patient background: Gender: Female, 70 years old Diagnosis: Advanced dilated cardiomyopathy, heart failure with functional congestive heart failure Clinical manifestations: Edema, dyspnea, fatigue Treatment process: The patient was hospitalized due to severe heart failure caused by advanced cardiomyopathy, with a significant decline in cardiac pumping function. Urgent treatment with IABP was required. The device was used to collect real-time PPG, ECG, and dynamic blood pressure signals, and a biomechanical modeling module was used to evaluate the response characteristics of the patient's heart and blood vessels. The load prediction module predicted the instantaneous changes in cardiac load based on the real-time collected data and adjusted the inflation pressure and time of IABP in real time to precisely control the counterpulsation process and avoid excessive counterpulsation. The IABP assistance range was adjusted through an adaptive control module to ensure precise support for the cardiac load during the treatment process.

[0128] Therapeutic effect: During the treatment process, the symptoms of dyspnea and edema in the patient were significantly alleviated, and the cardiac pumping function was effectively supported. The IABP auxiliary regulation device ensures the optimized regulation of the patient's cardiac load through the precisely synchronized inflation and deflation processes, and there is no phenomenon of insufficient or excessive counterpulsation during the treatment process. The patient successfully passed the crisis within 4 days after treatment, with stable hemodynamics and obvious improvement in heart failure symptoms.

[0129] The above 5 clinical cases demonstrate how this device optimizes the IABP auxiliary treatment effect in patients with different etiologies and disease stages through real-time monitoring and intelligent regulation, ensuring that the cardiac load regulation during the treatment process reaches the best state, improving the treatment effect of patients and reducing potential risks.

[0130] Specifically, the method for the device to achieve an improved range of intra-aortic balloon counterpulsation assistance regulation includes: S1. Real-time collection of the patient's physiological signals First of all, the device needs to collect various physiological signals of the patient in real time to provide raw data for subsequent analysis and control. These physiological signals include physiological data such as pulse wave (PPG), electrocardiogram (ECG), heart rate variability (HRV), and blood pressure.

[0131] Specific steps: Pulse wave (PPG) collection: Use existing optoelectronic sensors (such as fingertip or earlobe PPG sensors) to collect pulse wave information. The PPG sensor monitors the minute optical changes caused by blood flow and reflects the fluctuations of blood flow in real time. Transmit the PPG data to the system for real-time transmission and processing through wireless technology.

[0132] Electrocardiogram (ECG) collection: Use existing 12-lead ECG devices or portable ECG devices to collect the electrical activity information of the heart in real time. These devices transmit the ECG signals to the control system through Bluetooth or USB interfaces for subsequent analysis.

[0133] Blood pressure collection: Use existing automatic blood pressure monitoring devices (such as wrist or upper arm automatic sphygmomanometers) to collect the dynamic blood pressure data of the patient in real time. The blood pressure data is transmitted to the system through the interface for comprehensive analysis with other physiological signals.

[0134] Data fusion and synchronization: Various physiological signals are transmitted to the control system in real time and are fused through the data interface. These signals will provide basic data for subsequent individual characteristic modeling and load prediction.

[0135] S2. Establish a personalized physiological model by combining the patient's individual characteristic data To achieve personalized adjustment according to the characteristics of the patient's heart and blood vessel structure, the device needs to collect the patient's individual characteristic data through imaging devices (such as CT, MRI, echocardiogram, etc.), establish a personalized physiological model, and calculate the patient's heart load and blood flow conditions.

[0136] Specific steps: Imaging data acquisition: Use imaging devices such as CT, MRI, or echocardiogram to obtain the heart and blood vessel structure information of the patient. The image data will be stored through a Picture Archiving and Communication System (PACS) and imported into the system.

[0137] Three-dimensional modeling and parameter extraction: Use professional medical image processing software to perform three-dimensional modeling on the image data and extract the geometric parameters of the heart and blood vessels. These parameters will be used for Computational Fluid Dynamics (CFD) simulation to calculate the velocity, pressure, and resistance of blood flow in the aorta.

[0138] CFD and biomechanical simulation: Based on the patient's three-dimensional heart and blood vessel model, perform hemodynamic simulation through CFD software to calculate parameters such as blood flow velocity, pressure, and resistance in the aorta. Generate personalized heart load prediction data based on these data. At the same time, through biomechanical modeling, analyze the mechanical properties of the heart and blood vessels to further refine the load prediction data and ensure the consistency between the simulation and the actual physiological state.

[0139] S3. Process physiological signals and individual characteristic data to generate load prediction data The purpose of this step is to generate instantaneous change data of the heart load by combining the real-time collected physiological signals and the patient's individual characteristic data. These data will provide a basis for prediction and decision-making for subsequent intelligent adjustment control.

[0140] Specific steps: Physiological signal processing: Synchronously process the real-time collected data such as photoplethysmogram (PPG), electrocardiogram (ECG), and blood pressure, and analyze the instantaneous changes in the heart load. By analyzing the characteristics such as the QRS complex and ST segment changes in the electrocardiogram waveform, calculate the heart load status.

[0141] Load fluctuation trend prediction: Use machine learning algorithms (such as support vector machines, regression models, or deep learning, etc.) to process the real-time signals and analyze them in combination with historical data to predict the load fluctuation trend of the heart. The load fluctuation data will be used as the input data for the intelligent adjustment control unit to adjust the inflation pressure and time of the balloon in real time.

[0142] Generate load change data: Based on the real-time collected heart rate variability (HRV) data and blood pressure fluctuations, generate instantaneous change data of cardiac load. These data will be used to accurately predict the fluctuation trend of cardiac load and ensure a high degree of matching between the counterpulsation assistance process and the heart's needs.

[0143] S4. Adjust the inflation pressure and inflation time of the balloon According to the generated load prediction data, the control system will adjust the inflation pressure and inflation time of the balloon in real time to achieve the best effect of intra-aortic balloon counterpulsation. This process is completed through the feedback control module and the adaptive control module in the intelligent regulation control unit.

[0144] Specific steps: Real-time load adjustment: After receiving the load prediction data, the control unit starts to adjust the inflation pressure of the balloon. Through the PID control algorithm, the inflation pressure is adjusted in real time according to the change of cardiac load to ensure a high degree of synchronization between the inflation and deflation processes of the balloon and the cardiac load fluctuation.

[0145] Inflation timing and pressure regulation: The control unit identifies the systolic and diastolic phases of the heart according to the real-time ECG signal and adjusts the inflation timing of the balloon. Inflate during diastole and deflate during systole. Through this precise control of the inflation timing, the efficiency of blood counterpulsation is maximized.

[0146] Optimize the counterpulsation effect: According to the individual differences of patients and the feedback data collected during the treatment process, the system will continuously optimize the inflation pressure and time to meet the needs of different patients. The adaptive control module optimizes the inflation strategy of the balloon through machine learning to avoid over-counterpulsation or insufficient blood flow supply.

[0147] S5. Achieve intelligent adaptive regulation and optimize the counterpulsation assistance effect During the treatment process, as the patient's physiological state changes, the device will perform adaptive regulation through real-time feedback and machine learning algorithms to ensure the best treatment effect.

[0148] Specific steps: Reinforcement learning optimization regulation: Through the reinforcement learning algorithm, the system can continuously optimize the inflation and deflation strategies of the balloon according to the feedback results of each treatment (such as counterpulsation effect, blood flow fluctuation, etc.). The feedback after each treatment will be used as learning data to continuously improve the accuracy of regulation.

[0149] Individualized adjustment: According to the individual differences of patients and the feedback during the treatment process, the device can automatically adjust the counterpulsation assistance range during each treatment. For example, for patients with poor heart function, the system can appropriately increase the counterpulsation intensity, while for patients with low cardiac load, appropriately reduce the counterpulsation intensity to ensure the safety and effectiveness of the treatment.

[0150] Reducing hysteresis effect and optimizing synchronization: The device monitors the changes in physiological signals in real time, predicts the fluctuations in cardiac load in advance, and reduces the hysteresis effect. The synchronization control module ensures that the inflation process of the balloon is highly synchronized with each stage of the cardiac cycle, thus maximizing the counterpulsation effect.

[0151] Through the above steps, the device can collect the patient's physiological signals and imaging data in real time, generate personalized cardiac load prediction data, and adjust the inflation pressure and time of the balloon according to the real-time changes. This process not only optimizes the effect of counterpulsation assistance, but also enables the device to adjust according to the individual differences of different patients through machine learning and adaptive regulation functions, maximizing the treatment effect, ensuring safety, and reducing the risks of over-counterpulsation or insufficient blood supply.

[0152] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A device for improving the auxiliary regulation range of intra-aortic balloon counterpulsation, which is used for intra-aortic balloon counterpulsation treatment, and is characterized in that, The device comprises: High-precision physiological signal acquisition unit, used to collect the patient's pulse wave, electrocardiogram, heart rate variability and / or blood pressure physiological signals in real time; Individual characteristic modeling module, which builds a personalized physiological model based on the heart and vascular structure of each patient, and generates load prediction data that is suitable for the patient's heart and blood vessels; A load prediction and evaluation module, used to process the physiological signals and individual characteristic data, analyze the changes in cardiac load in real time and generate load prediction data, and then evaluate the instantaneous fluctuation of cardiac load; The intelligent adjustment control unit adjusts the inflation time and inflation pressure of the airbag according to the load prediction data to finely control the auxiliary range of the airbag counterpulsation.

2. The device according to claim 1, wherein, The high-precision physiological signal acquisition unit comprises: Pulse wave sensor, used to monitor aortic blood flow fluctuations in real time and provide blood flow information; Electrocardiogram (ECG) sensors, which are used to obtain information about the heart's electrical activity and assess changes in heart rhythm; Blood pressure sensor, used to monitor dynamic blood pressure changes and assist in the assessment of hemodynamics.

3. The device according to claim 1, characterized in that, The individual feature modeling module further comprises: Imaging data acquisition module, used to collect CT, MRI or echocardiography data of patients and provide information on the patient's heart and blood vessel structure; The computational fluid dynamics simulation module is used to calculate the velocity, pressure and resistance parameters of blood flow in the aorta based on the individual characteristic data of the patient, and generate instantaneous change data of cardiac load; The biomechanical modeling module analyzes the response characteristics of the patient's heart and vascular system by simulating the mechanical properties of the heart and blood vessels.

4. The device according to claim 1, wherein The load forecasting and evaluation module further includes: The machine learning module is used to analyze physiological signals and historical data collected in real time, and predict the fluctuation trend of cardiac load through deep learning algorithms; The hemodynamic analysis module calculates the real-time fluctuation of cardiac load by combining computational fluid dynamics and real-time physiological data.

5. The device according to claim 1, characterized in that, The intelligent adjustment control unit comprises: The feedback control module automatically adjusts the inflation time and pressure of the airbag based on real-time load prediction data to ensure that the inflation and deflation processes are highly synchronized with changes in cardiac load; The adaptive control module can learn and adapt to the individual differences of different patients, adjust the counterpulsation assistance adjustment range through machine learning algorithms, and optimize the airbag inflation and deflation strategies.

6. The device according to claim 1, characterized in that, The load prediction and assessment module can monitor and analyze cardiac load fluctuations in real time, and immediately send out a signal when a sudden and drastic change in cardiac load is detected, triggering the intelligent adjustment control unit to adjust the airbag inflation pressure and inflation time to adapt to the rapidly changing load requirements.

7. The device according to claim 1, characterized in that, The device uses a PID control or fuzzy control algorithm to adjust the response time of the balloon expansion through a feedback control module to avoid a lag effect and ensure that the balloon inflation and deflation process is synchronized with changes in cardiac load.

8. The device according to claim 1, characterized in that, The device uses synchronous control technology to ensure that the inflation and deflation time of the airbag is coordinated with the various stages of the cardiac cycle, optimizes the direction and speed of blood flow, and maximizes the efficiency of counterpulsation.

9. The device according to claim 1, wherein, The device avoids excessive counterpulsation or insufficient blood supply during the automatic adjustment process by comparing the patient's historical data and current physiological signals regularly or in real time.

10. A method for realizing a method for improving the auxiliary adjustment range of intra-aortic balloon counterpulsation according to the device described in any one of claims 1 to 9, characterized in that, The method includes: Real-time collecting physiological signals of the patient's pulse wave, electrocardiogram and heart rate variability; Combining the patient's individual characteristic data to establish a personalized physiological model; Processing the physiological signals and individual characteristic data to generate instantaneous change data of the cardiac load; Adjusting the inflation pressure and inflation time of the balloon according to the load change prediction data to precisely match the cardiac counterpulsation process; Ensuring the synchronization of the balloon inflation process with the cardiac load through an adaptive control module to reduce lag and optimize the counterpulsation effect.

Images